Fundamentals and Applications of Fourier Transform Mass Spectrometry [1 ed.] 9780128140130

Table of contents :
Cover......Page 1
FUNDAMENTALS
AND
APPLICATIONS OF
FOURIER
TRANSFORM MASS
SPECTROMETRY
......Page 3
Copyright......Page 4
Contributors......Page 5
Foreword......Page 9
Preface......Page 11
Acknowledgments......Page 14
Section A: Historical chapters
......Page 15
1
Historical developments in Fourier transform ion cyclotron resonance mass spectrometry......Page 16
Introduction......Page 17
1965......Page 18
1967......Page 19
1970......Page 20
1971......Page 21
1973......Page 22
1974......Page 23
1976......Page 24
1980......Page 25
Developments from 1981 to 1990......Page 26
Developments from 1991 to 2000......Page 28
Developments from 2001......Page 30
Conclusion......Page 35
References......Page 36
Section B: Fundamental/technology chapters
......Page 47
2
Fundamentals of Orbitrap analyzer......Page 48
Principles of operation......Page 49
Non-ideal orbital traps and their calibration......Page 54
Fourier transform methods......Page 59
Autocorrelation methods......Page 62
Maximum likelihood parameter estimators......Page 63
Deconvolution method......Page 64
Evolution of the Orbitrap platform and selected applications......Page 65
References......Page 68
3
Fundamentals, strengths, and future directions for Fourier transform ion cyclotron resonance mass spectrometry......Page 73
FT-ICR fundamentals......Page 77
Significant recent developments in FT-ICR......Page 82
References......Page 91
Ion motion in the electromagnetic field......Page 99
Ion motion in traps with quadrupolar type field distribution......Page 101
The detection of induced signal by cylindrical geometry electrodes......Page 102
Harmonics and multiple electrode detection......Page 104
The influence of inharmonicity of electrostatic field and inhomogeneity of the magnetic field on ion motion synchronizatio .........Page 106
Ion traps with dynamic harmonization......Page 110
Coalecsence......Page 113
Conclusion......Page 118
References......Page 119
ICR and Orbitrap FTMS: A preamble......Page 122
Mass spectra processing: From a single to a summed (averaged) mass spectrum......Page 124
Representation of mass spectra in full and reduced profile modes......Page 127
FTMS resolution performance: Orbitrap and ICR......Page 130
Conclusions......Page 137
Acknowledgments......Page 138
References......Page 139
Introduction......Page 142
Noise and de-noising in FTMS......Page 143
Correct assignment of chemically relevant peaks in FTMS......Page 150
Magnitude mode detection in FT-ICR-MS......Page 153
Absorption mode detection in FT-ICR-MS......Page 155
Non-Fourier transform techniques......Page 159
Apodization......Page 166
Calibration......Page 168
The physics behind the need of additional terms in the calibration equation......Page 170
Further developed external calibration equations......Page 171
Beat patterns in time-domain FTMS transients [121]......Page 178
FT artifacts in FTMS and their implications on data processing......Page 180
Batch processing of FTMS mass spectra......Page 181
Automation of FTMS instruments......Page 183
References......Page 186
Introduction......Page 195
Contemporary FT-ICR mass spectrometers and tandem mass spectrometry......Page 197
Mass spectrometry in the second dimension......Page 201
Interpretation of a 2D mass spectrum......Page 205
Noise in 2D-MS......Page 209
Resolving power and mass accuracy......Page 212
Alternative 2D-MS......Page 214
MSn/2D-MS......Page 216
Data acquisition and processing......Page 217
Data analysis......Page 222
Applications of 2D-MS......Page 226
Conclusion......Page 233
Glossary......Page 235
References......Page 236
Introduction......Page 241
Background......Page 242
TIMS analyzer......Page 243
OSA-TIMS......Page 245
G-TIMS (linear, non-linear targeting and nonlinear stepping)......Page 246
2D-TIMS-FT-ICR MS plots and Ko determination......Page 247
TIMS-TOF MS vs. TIMS-FT-ICR MS......Page 250
Conclusions......Page 251
References......Page 253
Introduction......Page 260
Common preservation methods for cancer tissues......Page 263
MALDI-FT-ICR MSI in metabolomic-based cancer research......Page 266
Data processing in high-resolution MALDI MSI......Page 273
Discovery of diagnostic markers and tissue-based disease classification by mass spectrometry imaging......Page 276
Inter- and intratumoral heterogeneity at metabolite levels......Page 277
Therapy response prediction and prognosis......Page 279
Conclusion......Page 280
References......Page 281
Introduction......Page 287
General concepts of laser-matter interaction......Page 289
Standing of the laser fluence and laser irradiance......Page 293
Simulation of laser-matter interaction and influence of the electrons......Page 294
Principles of matrix-assisted laser desorption/ionization......Page 296
The laser induced acoustic desorption (LIAD) technique......Page 300
Laser gas phase ion dissociation......Page 302
First instruments fitted with internal ion sources......Page 303
Instruments fitted with external ion sources......Page 305
Laser-based ionization techniques at atmospheric pressure......Page 306
Mass spectrometry imaging (MSI or IMS)......Page 308
Some applications of laser ionization coupled to FT-ICR MS......Page 312
Study of inorganic cluster ions......Page 313
Study of gas phase thermochemistry of ions and cluster ions in the FT ICR cell......Page 315
Organic compounds......Page 316
Petroleomics (petroleum and bio-oils)......Page 317
Environmental organic contaminants......Page 318
Conclusions......Page 319
References......Page 320
Further reading......Page 328
Section C: Applications chapters
......Page 329
Introduction......Page 330
Sample preparation......Page 333
Data acquisition......Page 334
Data preprocessing......Page 335
Statistical analyses......Page 336
Metabolite identification......Page 337
The input of Fourier mass spectrometry to metabolite detection and quantification......Page 338
LC/HRMS-based metabolomics......Page 339
High-throughput metabolomics......Page 340
Towards high-throughput LC/HRMS-MS metabolomics......Page 342
Automatic peak detection, alignment and integration of features......Page 343
Correction of analytical drifts and batch to batch variations......Page 344
The input of Fourier transform mass spectrometry to metabolome annotation and metabolite identification......Page 345
Acknowledgment......Page 352
References......Page 353
Introduction to metabolomics......Page 362
Primary analysis: Annotation, identification, knowns and unknowns......Page 366
Secondary analysis: Pathways and fluxes......Page 367
Mass differences in instrumental quality parameters......Page 369
Compositional space......Page 376
Traditional means of UHR-MS data visualization and interpretation......Page 378
Mass difference networks in the visualization and primary analysis of UHR-MS data......Page 383
MDiN's for dereplication......Page 385
MDiN's for third and fourth level identification......Page 387
MDiN strategies for second level identification......Page 389
MDiN strategies for contextualization of 4th level identification......Page 390
Classical pathway mapping......Page 392
Genome scale metabolic models and flux analyzes......Page 393
Mass difference networks and genome scale models......Page 396
Mass difference enrichment analysis (MDEA)......Page 398
Conclusion......Page 401
References......Page 402
General introduction......Page 411
Drinking water and the discovery of new disinfection by-products......Page 412
DOM precursors of DBPs......Page 413
DBPs in drinking water......Page 415
DBPs in hydraulic fracturing fluids......Page 416
The composition of effluent organic matter (EfOM)......Page 417
Hydraulic fracturing organic matter......Page 418
Non-targeted approaches in characterizing pollutants......Page 419
Contaminants in surface and groundwater......Page 421
Conclusions and suggestions for future work......Page 422
References......Page 423
Introduction......Page 428
FTMS basics......Page 429
FTMS for mass fingerprinting of peptides......Page 431
Fourier transform tandem mass spectrometry......Page 434
Collisional activation......Page 436
Electron-based dissociation......Page 442
Photoactivation......Page 449
Combining different activation methods......Page 450
Conclusion......Page 461
References......Page 462
Introduction......Page 472
Illicit abused drugs......Page 474
Marijuana......Page 476
Cocaine......Page 479
Hallucinogens......Page 482
New psychoactive substances......Page 484
Foods and beverages falsification......Page 489
Evidence analysis......Page 497
Conclusions......Page 504
References......Page 505
Introduction......Page 512
Ionization methods......Page 513
Electrospray ionization......Page 514
Atmospheric pressure chemical ionization (APCI)......Page 515
Atmospheric pressure photoionization (APPI)......Page 516
Matrix-assisted laser desorption/ionization (MALDI)......Page 517
High-resolution mass analyzers applied to petroleomics......Page 518
FT-ICR......Page 519
Orbitrap......Page 524
Conclusion......Page 527
References......Page 528
Introduction: the importance of proteins in a multi-omics context......Page 532
Analytical strategies: from top-down to bottom-up and vice versa......Page 534
Sequencing and activation methods......Page 537
Bioinformatics......Page 543
Separative techniques......Page 544
LC-MS hyphenation......Page 545
Scan modes, targeted analysis and data independent analysis......Page 546
Quantification......Page 549
Top-down proteomics......Page 552
Purification and separation of proteins......Page 553
Specificity of MS analysis......Page 555
Epilogue......Page 557
References......Page 558
Further reading......Page 570
Introduction......Page 571
Hydrogen and rare gases......Page 572
Carbon, silicon, and germanium......Page 574
Nitrogen, phosphorus, and arsenic......Page 576
Oxygen, sulfur, selenium, and tellurium......Page 584
Fluorine and chlorine......Page 586
References......Page 587
Introduction......Page 594
Pressure dependent peak broadening......Page 595
Black body induced radiative dissociation (BIRD)......Page 596
In chemistry......Page 600
In nuclear physics......Page 605
Cryo spectroscopy by X-rays: magnetic moments......Page 607
Cryo kinetics of trapped ions......Page 609
Cryo spectroscopy in the IR: molecular vibrations......Page 612
The future-a short outlook......Page 613
Conclusions......Page 617
References......Page 618
Further reading......Page 622
An introduction to glycan biology......Page 623
Introduction......Page 627
Ionization......Page 628
Structural characterization......Page 629
Electron activation......Page 634
Liquid chromatography (LC)......Page 638
Capillary electrophoresis (CE)......Page 639
Ion mobility (IM)......Page 640
Challenges in automation......Page 641
Automated software suites......Page 643
Conclusion......Page 644
References......Page 645
Further reading......Page 649
Introduction......Page 650
High-resolution mass spectrometry in foodomics......Page 651
Data management in FT-ICR-MS......Page 654
New insights in food processing-the Maillard reaction......Page 655
Wine......Page 658
Spirits......Page 664
Aspects of nutritive quality of foods: vitamins......Page 666
Conclusions......Page 670
References......Page 671
Introduction......Page 677
Pyrolysis bio-oil......Page 680
Liquefaction bio-oil......Page 682
Upgrading treatments......Page 683
Sample preparation......Page 684
Targeted analytical methods......Page 685
Introduction......Page 686
Analyses of bio-oils by ESI-FTMS......Page 687
Influence of dopants added to the bio-oil solution......Page 694
ESI-FTMS analysis of bio-oils from various feedstock......Page 696
Water-soluble and water-insoluble fractions......Page 699
Other fractionation processes......Page 701
ESI FT-MS analysis for optimizing the production and upgrading processes of bio-oils......Page 702
Comments on the ESI FT-MS analysis of bio-oil......Page 705
Analyses of bio-oils by APPI, APCI, and LDI-FTMS-Additional insights to ESI-FTMS analyses......Page 706
Concluding remarks on the characterization of bio-oil by FT-MS......Page 717
References......Page 719
A......Page 732
C......Page 733
D......Page 735
E......Page 736
F......Page 737
H......Page 739
I......Page 740
K......Page 741
M......Page 742
N......Page 744
O......Page 745
P......Page 746
R......Page 747
S......Page 748
T......Page 749
U......Page 750
Z......Page 751
Back Cover......Page 752

Citation preview

FUNDAMENTALS AND APPLICATIONS OF FOURIER TRANSFORM MASS SPECTROMETRY

FUNDAMENTALS AND APPLICATIONS OF FOURIER TRANSFORM MASS SPECTROMETRY Edited by

BASEM KANAWATI Analytical Biogeochemistry, Helmholtz Zentrum M€ unchen, Neuherberg, Germany

PHILIPPE SCHMITT-KOPPLIN Analytical Biogeochemistry, Helmholtz Zentrum M€ unchen, Neuherberg, Germany Analytical Food Chemistry, Technical University of Munich, Freising, Germany

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States © 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-814013-0 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Susan Dennis Acquisition Editor: Kathryn Eryilmaz Editorial Project Manager: Redding Morse Production Project Manager: Divya KrishnaKumar Cover Designer: Miles Hitchen Typeset by SPi Global, India

Contributors Jeffery N. Agar Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, United States Deborah V.A. de Aguiar Chemistry Institute, Federal University of Goia´s, Goi^ania, Brazil Michaela Aichler Research Unit Analytical Pathology, Helmholtz Zentrum M€ unchen, German Research Center for Environmental Health, Neuherberg, Germany Herve Alexandre UMRPAM University of Burgundy/Agrosup Dijon, Institut Universitaire de la Vigne et du Vin, Jules Guyot, Dijon, France Joa˜o Francisco Allochio Filho Laboratory of Petroleology and Forensic Chemistry, Department of Chemistry, Federal University of Espı´rito Santo, Vito´ria, Brazil I. Jonathan Amster Department of Chemistry, University of Georgia, Athens, GA, United States Frederic Aubriet Laboratory of Chemistry and Physics—Multi-Scale Approach of Complex Systems, FR 2843 Jean Barriol Institut, FR 3624 French High Field FT-ICR Mass Spectrometry Network, Lorraine University, ICPM, Metz, France Konstantin Ayzikov Thermo Fisher Scientific, Bremen, Germany Vincent Carre Laboratory of Chemistry and Physics—Multi-Scale Approach of Complex Systems, FR 2843 Jean Barriol Institut, FR 3624 French High Field FT-ICR Mass Spectrometry Network, Lorraine University, ICPM, Metz, France Sebastian Dillinger Fachbereich Chemie and Forschungszentrum OPTIMAS, Technical University Kaiserslautern, Kaiserslautern, Germany Jiana Duan Department of Chemistry, University of Georgia, Athens, GA, United States Michael L. Easterling Bruker Daltonics Inc., Billerica, MA, United States Franc¸ ois Fenaille Pharmacology and Immunoanalysis Unit (SPI), CEA, INRA, Paris-Saclay University, Gif-sur-Yvette, France

xiii

xiv

Contributors

Francisco Fernandez-Lima Department of Chemistry and Biochemistry; Biomolecular Sciences Institute, Florida International University, Miami, FL, United States Federico Floris Department of Chemistry, University of Warwick, Coventry, United Kingdom Lena Gmelch Comprehensive Foodomics Platform, Analytical Food Chemistry, Technical University Munich, Freising, Germany Michael Gonsior University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory, Solomons, MD, United States Marina Gotthardt Comprehensive Foodomics Platform, Analytical Food Chemistry, Technical University Munich, Freising, Germany Regis D. Gougeon UMRPAM University of Burgundy/Agrosup Dijon, Institut Universitaire de la Vigne et du Vin, Jules Guyot, Dijon, France Dmitry Grinfeld Thermo Fisher Scientific, Bremen, Germany Daniel Hemmler Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M€ unchen, Neuherberg; Comprehensive Foodomics Platform, Analytical Food Chemistry, Technical University Munich, Freising, Germany Jasmine Hertzog Laboratory of Chemistry and Physics—Multi-Scale Approach of Complex Systems, FR 2843 Jean Barriol Institut, FR 3624 French High Field FT-ICR Mass Spectrometry Network, Lorraine University, ICPM, Metz, France; Comprehensive Foodomics Platform, Analytical Food Chemistry, Technical University Munich, Freising; Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M€ unchen, Neuherberg, Germany Christophe Junot Pharmacology and Immunoanalysis Unit (SPI), CEA, INRA, Paris-Saclay University, Gif-sur-Yvette, France Basem Kanawati Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M€ unchen, Neuherberg, Germany Yury I. Kostyukevich Skolkovo Institute of Science and Technology Novaya St. Russian Federation, Russia Sergey V. Kovalev Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia Anton N. Kozhinov Spectroswiss, EPFL Innovation Park, Lausanne, Switzerland

Contributors

xv

Lisa Kreutzer Research Unit Analytical Pathology; Research Unit of Radiation Cytogenetics & Research Unit Analytical Pathology, Helmholtz Zentrum M€ unchen, German Research Center for Environmental Health, Neuherberg, Germany Valdemar Lacerda, Jr. Laboratory of Petroleology and Forensic Chemistry, Department of Chemistry, Federal University of Espı´rito Santo, Vito´ria, Brazil Albert T. Lebedev Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia Youzhong Liu UMRPAM University of Burgundy/Agrosup Dijon, Institut Universitaire de la Vigne et du Vin, Jules Guyot, Dijon, France Alexander Makarov Thermo Fisher Scientific, Bremen, Germany Franco Moritz Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M€ unchen, Neuherberg, Germany Konstantin O. Nagornov Spectroswiss, EPFL Innovation Park, Lausanne, Switzerland Gereon Niedner-Schatteburg Fachbereich Chemie and Forschungszentrum OPTIMAS, Technical University Kaiserslautern, Kaiserslautern, Germany Eugene N. Nikolaev Skolkovo Institute of Science and Technology Novaya St. Russian Federation, Russia Peter B. O’Connor Department of Chemistry, University of Warwick, Coventry, United Kingdom Igor Pereira Chemistry Institute, Federal University of Goia´s, Goi^ania, Brazil Wanderson Roma˜o Laboratory of Petroleology and Forensic Chemistry, Department of Chemistry, Federal University of Espı´rito Santo, Vito´ria; Federal Institute of Espı´rito Santo, Vila Velha; National Institute of Forensic Science and Technology (INCT Forensic), Vito´ria, Brazil Chloe Roullier-Gall UMRPAM University of Burgundy/Agrosup Dijon, Institut Universitaire de la Vigne et du Vin, Jules Guyot, Dijon, France Michael Rychlik Comprehensive Foodomics Platform, Analytical Food Chemistry, Technical University Munich, Freising, Germany; Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland, Brisbane, Australia

xvi

Contributors

Philippe Schmitt-Kopplin Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M€ unchen, Neuherberg; Comprehensive Foodomics Platform, Analytical Food Chemistry, Technical University Munich, Freising, Germany J€ org-Peter Schnitzler Helmholtz Zentrum M€ unchen, Institute of Biochemical Plant Pathology, Research Unit Environmental Simulation, Neuherberg, Germany Yury O. Tsybin Spectroswiss, EPFL Innovation Park, Lausanne, Switzerland Gessica Vasconselos Chemistry Institute, Federal University of Goia´s, Goi^ania, Brazil Boniek G. Vaz Chemistry Institute, Federal University of Goia´s, Goi^ania, Brazil Joelle Vinh Laboratory of Biological Mass Spectrometry and Proteomics, CNRS USR 3149, ESPCI Paris, PSL University, Paris; TGE FT-ICR CNRS, France Gleb Vladimirov Skolkovo Institute of Science and Technology Novaya St. Russian Federation, Russia Axel Karl Walch Research Unit Analytical Pathology, Helmholtz Zentrum M€ unchen, German Research Center for Environmental Health, Neuherberg, Germany Karl Peter Wanczek Institute of Inorganic and Physical Chemistry, University of Bremen, Bremen, Germany

Foreword This book provides a wide-ranging description of the current state of the art in Fourier transform mass spectrometry. As described in several chapters, beginning in late 1973 with the first FT ion cyclotron resonance mass spectrum (showing just a single CH+4 peak), the field has since exploded, with installation of nearly 1000 FT-ICR MS plus several thousand orbitrap instruments. FT mass analyzers offer the highest mass resolution (e.g., baseline separation of ions differing in mass by less than the mass of an electron) and mass accuracy (mass error as low as a few ppb). Those figures of merit have recently been improved by increased magnetic field strength (FT-ICR) or smaller-size ion trap (orbitrap), as well as phase correction to double the mass resolving power (FT-ICR and orbitrap), and frequency-multiple detection for enhancement of FT-ICR mass resolving power by factors of 2–4. Conversely, for a given mass resolving power, data acquisition can be speeded up to achieve LC-MS without compromising chromatographic resolution. Instrumentation improvements in ion injection, ion trapping (dynamically harmonized ICR cell; “nadel” ICR cell, Cassini quadrupolar cell), excitation, and detection continue to be guided by ion trajectory simulations. Multifaceted applications have been enabled by coupling FT mass analyzers with gas- and liquid-chromatography (collected fractions or on-line), trapped ion mobility source, various ionization methods (e.g., electron impact, laser desorption/ionization, MALDI, electrospray, photoionization), various ion fragmentation methods (ion-neutral collisions, IR and UV photodissociation, electron capture, electron transfer, surface-induced dissociation). FTMS has had particularly high impact in proteomics (MS/MS identification of protein amino acid sequences, including posttranslational (chemical) modifications not accessible from corresponding DNA base sequences, and petroleomics (prediction of the properties and behavior of petroleum and its products based on detailed organic compositional analysis), and is expanding into various other “-omics” fields (food, geochemistry, environment, forensics, carbohydrates, lipids, metabolism, etc.) and in molecular imaging in biochemistry and medicine. The chapters summarize the actual state of the art in the field as for example two-dimensional FT-ICR MS/MS, which yields a single 2D spectrum

xvii

xviii

Foreword

that connects each precursor ion with all of its product ions. Other chapters describe gas-phase ion-molecule chemistry and low-temperature inorganic clusters. In summary, FTMS instruments now extend to virtually the full range of mass analysis applications, thereby setting new “gold standards” which serve to guide subsequence “targeted” analysis with lower-resolution instruments. Alan G. Marshall Robert O. Lawton Professor of Chemistry & Biochemistry Founding Director and Chief Scientist, Ion Cyclotron Resonance Program, National High Magnetic Field Laboratory Florida State University Tallahassee, FL, United States

Preface The world of mass spectrometry is expanding rapidly in this century thanks to many advances in physics, electronics, vacuum technology and mathematical considerations, especially in the field of statistical data analysis and engineering. Therefore a strong need emerged to write this book, which addresses many aspects of technical fundamentals in the physics of mass spectrometric FTMS analyzers and ion beam guides as well as diverse applications in many fields of natural and life science. Here we give a brief account on the diverse topics which are included in this comprehensive book: Chapter 1 addresses the historical developments in the well-known Fourier Transform Ion Cyclotron Resonance Mass Spectrometry FT-ICR-MS. Chapter 1 does not only list the main historical developments of the FT-ICR but also it dates back to the invention of ICR, prior to implementation of the effective Fourier Transform (FT) algorithm to speed up the whole ICR experimental setup, by enabling the great technological transfer from magnetic field scan to the rapid frequency chirp “all ions excitation” for ICR detection. Chapter 2 addresses technical fundamentals in the efficient Orbitrap mass analyzer, showing its physical principle of operation, dealing with nonideal orbitrap geometries and showing their possible calibration, and giving some advances in signal processing which are steadily growing in the recent time. The reader will find applications to this great mass analyzer not only in Chapter 2 but also in Chapters 5, 11, 15, 16, 17 and 21, which discuss advanced fundamentals, metabolomics, forensics, petroleomics, proteomics and foodomics, respectively. Advances in the new Paracell ICR mass analyzer can be found in Chapters 3 and 4. In this respect, the reader will especially get advanced knowledge on the physical developments of the Paracell, showing also with sophisticated ion trajectory simulations on supercomputers with more sophisticated ion trajectory simulators beyond SIMION the challenges that could be overcome in ICR to reduce space-charge effects and coalescences and also enhance both resolution and acquisition time. Chapter 5 discusses mass resolving power characteristics in both Orbitrap and ICR mass analyzers for achieving ultra-high resolution spectra in a broad mass range. This discussion is assisted by robust and clear mathematical

xix

xx

Preface

equations to facilitate understanding the differences in the resolution behavior as a function of m/z. This chapter also addresses data reduction approaches and also shows the possible conversion from raw (full) to line shape (reduced profile) mass spectra with their pros and cons. Extensive data processing strategies are discussed in Chapter 6, where magnitude and absorption detection modes in FT-ICR are discussed in great detail as well as many non-Fourier Transform techniques. Calibration, apodization, denoising and batch processing as well as isotopic beat patterns are also treated. Chapter 6 represents also different successful strategies and also discusses advanced ICR control units for FT-ICR-MS automation to give the reader some hands-on experience for achieving a full FTMS acquisitiontime investment. Chapter 7 is dedicated for explaining all the fundamentals and also showing many successful applications in the field of two dimensional FT-ICR-MS. Data acquisition and processing as well as important denoising algorithms are also discussed there which are specific to 2D-FT-ICR-MS. Coupling of FTMS mass analyzers to ion mobility cells and to chromatographic systems is discussed in Chapters 8 and 11, respectively. Chapter 9 shows many applications of FT-ICR-MS to medical imaging of cancer biological tissues and Chapter 10 shows diverse coupling of laser sources for ionization to FT-ICR-MS with many discussed inorganic applications. Many metabolomics applications can be found in both Chapter 11 for Orbitrap and Chapter 12 for FT-ICR-MS. The latter chapter shows also the importance of mass difference networks for revealing not only the compositional elemental space in complex mixtures but also going in deep detail beyond it (see details in Chapter 12). Environmental applications related to aquatic and sediment chemistry can be found in Chapter 13, whereas deep discussions for peptide sequencing can be found in both Chapters 14 and 17. Forensic FTMS applications are given in Chapter 15, while many applications in the field of petroleomics exist in Chapter 16. Many challenges in proteomics and proteoforms are greatly discussed in Chapter 19, which shows the transition from the comprehensive description of exhaustive proteome to functional proteomics by the use of FTMS. Both Chapters 18 and 19 shows diverse FT-ICR-MS applications into the gas phase ion chemistry, done in several ICR cells, which serve as electronic reagent glass (electronic reactor). Key physical chemistry thermodynamic and kinetic data could be achieved in the past 40 years with FT-ICR-MS and they are discussed for the case of inorganic elements and their substances as a review in Chapter 18, focusing on nonmetal applications, while Chapter 19 discusses FT-ICR-MS

Preface

xxi

investigations of several inorganic metal cluster ions and also show the advantage of cold ICR cell relative to traditional ambient temperature operation mode. The last three chapters, shows deep FT-ICR-MS investigations in glycomics, foodomics and bio-oil analysis. With comprehensive treatments of these divers topics of FTMS applications, the reader will get thorough up to date knowledge (some of which are very practical), which can help the reader to further run and overcome some scientific challenges while running further FTMS applications. Despite the complex physical and mathematical fundamentals of the FTMS mass analyzers, every effort has been done from the editorial side to make every chapter as clear as possible and therefore accessible to a large audience of scientific readers. We hope that the reader will enjoy also reading these very interesting chapters. The editors

Acknowledgments The editors thank all book chapter contributors for their great efforts and time spent to add up to date scientific knowledge from their great handson expertise. We enjoyed cooperating together with many authors, who provided excellent reviews and many explanations to our scientific queries to make this book as clear as possible. Basem Kanawati greatly thanks professor Karl Pater Wanczek for his valuable comments and assistance, while preparing this book. He is the first scientist, who introduced me in the past to advanced ICR-MS techniques, with its diverse fundamental physical and chemical elements, ranging from experimental physics studies and ion trajectory simulations in several ICR cell designs to applications of this technique in studying many gas phase ion-molecule reactions and ion energetics. The editors also thank all Elsevier’s team members for witnessing and greatly assisting the development of this extensive book. With their help, we could all contribute to make the production of this comprehensive book a reality.

xxiii

CHAPTER 1

Historical developments in Fourier transform ion cyclotron resonance mass spectrometry Karl Peter Wanczek*, Basem Kanawati† *

Institute of Inorganic and Physical Chemistry, University of Bremen, Bremen, Germany Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M€ unchen, Neuherberg, Germany

†

Contents Introduction Developments 1965 1966 1967 1968 1969 Developments 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 Developments Developments Developments Conclusion References

4 5 5 6 6 7 7 7 7 8 9 9 10 11 11 12 12 12 12 13 15 17 22 23

before 1970

from 1970 to 1980

from 1981 to 1990 from 1991 to 2000 from 2001

Fundamentals and Applications of Fourier Transform Mass Spectrometry https://doi.org/10.1016/B978-0-12-814013-0.00001-6

© 2019 Elsevier Inc. All rights reserved.

4

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Introduction This review covers instrumentation and theory as well as applications, which open new fields in scientific research. Therefore, the first decades will occupy more space than the more recent years. The material is presented in a chronological sequence of events to facilitate a historical point of view through the discussion. The review is not intended to be comprehensive, but a personal view of the authors of the most important developments in the field. Four different ion traps are known: – Penning trap [1–3] (ion trapping by DC electrical fields and a homogeneous magnetic field), “ICR cells” – Paul trap [4, 5] (ion trapping by RF electrical fields), – Kingdon trap [6] (ion trapping by DC fields) and – Combined trap [7] (a combination of the Paul and Penning trapping principles). Penning traps are covered as far as they are employed in ion cyclotron resonance (ICR) mass spectrometry and Fourier transform ion cyclotron—FT ICR—mass spectrometry. Also covered are ICR drift cells employed in the early times of the ICR technique. In the ICR drift cell ions drift on cycloidal paths along the longitudinal axis, guided by magnetic and electric fields (see text below). In physics, precision measurements of atomic masses and fundamental constants are performed with Penning traps. With ICR, elementary steps of chemical reactions and ion-molecule reactions, which normally occur at near-thermal energies, are studied. After the invention of the Fourier transform technique, analytical applications dominate. The ICR method developed independently from the physical applications until early 1980s (cf. Ref. [8]: In this ICR volume, for the first time, physical applications of Penning traps are described by G. Gr€aff from the Institute of Physics, University of Mainz, in the chapter: “precision determination of cyclotron frequencies of free electrons and ions,” pp. 318–325). Several publications of general interest in ICR have been published. The first book was written by Lehman and Bursey [9] in 1976. The book by Marshall and Verdun [10] presents a detailed general introduction. Early reviews in 1971 by Gray [11], by Beauchamp [12], by Futrell [13] and in 1973 by Hartmann et al. [14] covered the new methodology comprehensively. Several reviews [15–18] of early ICR development were published, and more recently [19, 20] Hartmann and Wanczek [8, 21] edited two

Historical developments in FT ICR mass spectrometry

5

volumes in Lecture Notes in Chemistry on ICR Spectrometry in 1978 and 1982 which covered many aspects of the development of the field. A recent contribution to the history of mass spectrometry is in “The Encyclopedia of Mass Spectrometry,” Vol. 9, Part A [22]. The volume contains a history of ICR by C.L. Wilkins [23] with many details.

Developments before 1970 In ICR, the ions were trapped in or drifted through an ICR cell at reduced pressure and detected with the aid of image current [24], induced in the cell plates by a coherent ion motion. The coherent motion is generated by excitation of one of the characteristic ion frequencies. The method has many similarities with NMR spectroscopy. The ions are present in the ICR cell after detection. In general, only ions of one charge polarity can be trapped, negative ions or positive ions. The Omegatron of Sommer et al. [25] employed cyclotron resonance excitation and charge detection of ions. The Omegatron was utilized widely. Several improvements were introduced [26]. By the Hipple group a further instrument was described, which employed the ion cyclotron motion for focusing: The trochoidal mass Spectrometer [27]. Goudsmit [28] described a Time-of-Flight mass spectrometer where the trajectory of the ions is inside a magnetic field. A very advanced instrument, the Mass “Synchrometer,” was built by Smith and Damm [29]. High harmonics of the ion cyclotron frequency were employed for mass determination with hitherto unparalleled accuracy. Coggeshall [30] first described the path of ions and electrons in nonuniform crossed electric and magnetic fields. In 1962, Graham et al. [31, 32] described an ICR instrument and discussed the determination of collision cross sections. A detailed description of the ICR spectrometer followed [33].

1965 Llewellyn [34] of Varian Ass. filed a patent of an ICR spectrometer with drift ICR cell and ion resonance detection with the aid of a marginal oscillator (Pound box [35, 36]): “A spectrometer is described which employs ion cyclotron resonance and energy absorption in mass analysis. In an evacuated envelope ions are formed in the first of two regions which are subjected to static magnetic and electric fields disposed at right angles to each other and to the common axis of the two regions. The ions are caused by the interaction of the fields to move with cycloidal motion into the

6

Fundamentals and Applications of Fourier Transform Mass Spectrometry

second region which is additionally subjected to an oscillating electric field in the same direction as the static field. The ions in resonance with the oscillating field absorb energy therefrom and separate from the nonresonant ions. The energy absorbed by the resonant ions is then detected as a measure of the resonant ions.” (Abstract of patent)

Furthermore, this ICR cell had a third region for total ion current measurement. The spectrometer was produced and named “Syrotron.” The instrument had a small mass range, below m/z ¼ 200 and a resolution of m/Δm  1000. Because the early marginal oscillators operated at constant frequency, the magnetic field had to be scanned for a mass spectrum. The scan was slow. However, the instrument allowed to investigate ionmolecule reactions, ion chemical reaction mechanisms at low pressure under single collision conditions at (near)-thermal energies. The new technique was easy to operate. As a consequence of this great development, a new field of chemistry was opened. The first prototype instrument went to the Baldeschwieler group at Stanford University, a chemistry group. Many fundamental publications followed by this group, by former members of the group who went to other places, and by “newcomers.” Most of these publications opened new research fields for the first time.

1966 The Baldeschwieler group published a report on ion cyclotron double resonance for the study of the mechanisms of ion-molecule reactions with the aid of kinetic energy dependence [37]. Ions are resonantly irradiated to increase their kinetic energy. The mechanism of the ion-molecule reactions of a mixture of CD4 and N2 was investigated. After electron impact ionization, the mixture contained a small proportion of ions. Therefore, in the ICR cell, all ion-molecule reactions are of pseudo-first order.

1967 A theory of collision-broadened ICR spectra was developed by Beauchamp [38]. The Baldeschwieler [39] group published a detailed investigation of a complicated ion chemical reaction system, generated from chloroethylene. The enormous potential of the ICR method was shown for the first time. Dunbar [40] studied the energy dependence of ion-molecule reactions. A description of the instrument was given by Baldeschwieler et al. [41]. The authors stated: “the characteristics used in the observation and measurement of ion molecule reactions are then: 1. The ion lifetimes extend to 100 ms. 2. The path lengths of the ions extend to 100 m.

Historical developments in FT ICR mass spectrometry

7

3. The electric fields can be as small as 10 mV/cm. 4. Ions of specific charge to mass ratio can be accelerated by the application of resonant radio frequency fields.” (p.113).

1968 New groups appeared. They all employ more or less laboratory-rebuilt, Syrotron ICR spectrometers. Bowers and Elleman from JET Propulsion Laboratory and Beauchamp, now at California Institute of Technology, studied the ion-molecule reactions of olefins [42]. Brauman and Blair [43], at Stanford University, directly measured gas-phase acidities with proton transfer reactions and double resonance experiments. Henis and coworkers from Monsanto Company investigated the ion chemistry of methanol [44], and described a detection scheme with electron energy modulation [45]. Kaplan [46], later at the University of Cincinnati, identified collision-induced fragmentation with ion cyclotron double resonance ICDR. King and Elleman [47] from Jet Propulsion Laboratory studied ion-molecule reactions of hexafluoroethane.

1969 Clow and Futrell [48] from the University of Utah, observed charge exchange by ICDR. Huntress and Beauchamp [49] adapted the ICR technique for Penning ionization. Benzene ionization by metastable N2 was investigated. Isotopic exchange reactions of CH4-D2 and CD4-H2 mixtures and the mechanism of self-induced labeling of methane by Tritium were investigated by Inoue and Wexler [50] of Argonne National Laboratory. O’Malley and Jennings [51] investigated the ion chemistry of acetylene. Jennings and coworkers at the University of Sheffield, England were the first research group outside the United States. The publications refs. [49] and [51] appeared in the just established new “Journal of Mass Spectrometry and Ion Physics.”

Developments from 1970 to 1980 From 1970 on besides many new research areas and new ICR groups, several major improvements of the ICR technique were achieved.

1970 McIver [52] introduced the trapped ion analyzer ICR cell. Contrary to the ICR drift cell, this cell has only a single region. The cell has trapping plates

8

Fundamentals and Applications of Fourier Transform Mass Spectrometry

on both ends to trap the ions. Now the ions are generated, trapped, reacted, and detected in a single cell by a pulse scheme. This trapped ion ICR cell is a simplified Penning trap with planar electrodes. Compared to the drift cell, sensitivity and trapping time were greatly increased. The new cell therefore allowed to study ion-molecule reactions of high kinetic order, a capability provided by no other technique in the field. Bursey et al. [53], University of North Carolina, first realized the analytical potential of ICR spectrometry. They described acetylation as a soft chemical ionization technique. The chemical ionization (CI) agent was generated by ion-molecule reactions in the ICR cell in large yields. Clow and Futrell [54] from the University of Utah started a detailed study of the kinetic energy dependence of ion-molecule reaction rates. They utilized an ICR drift cell with four sections to reduce electric field penetration and distortion. The Djerassi group [55], from Stanford University, applied drift cell ICR technique to study in great detail the structure of the C3H6O+ ion formed from aliphatic ketones in the double McLafferty rearrangement. Two detailed studies of rate constants determination [56] and double resonance signal interpretation [57] were published by the Jenning’s group. From the dependence of half-width and half-height of power absorption lines, Henis and Mabie [58] studied in detail the lifetime of negative ions. An extremely high lifetime of 500 μs was measured for the SF 6 ion. Lebert [59], from the Hartmann group, Johann-Wolfgang-Goethe University of Frankfurt/Main, published a short account on ICR. Marshall and Butrill [60], Stanford University, presented a general method to calculate ion-molecule reactions rate constants from ICR spectra. Bowers et al. [61], University of California at Santa Barbara, described an accurate method for relative proton affinity determination with the aid of equilibrium reactions. The relative proton affinities of trimethylamine, pyrrolidine, azetidine, and piperidine were determined with an accuracy of 0.2 kcal/mol or lower.

1971 Dunbar [62], Case Western Reserve University, showed that with the ICR technique ions can be photodissociated mass-selectively. The ions CH3Cl+ and N2O+ were investigated. Forster and Beauchamp [63] studied the ion chemistry of iron pentacarbonyl in detail and established ICR in the field of transition metal complexes. Gross and McLafferty [64], Cornell University, identified C3H+6 structural isomers with the aid of characteristic

Historical developments in FT ICR mass spectrometry

9

ion-molecule reactions. Huntress [65], Jet Propulsion Laboratory, obtained information about ion power absorption from the complete solution of the equation of motion and measured momentum transfer rate constants for several ions. Marshall [66], at the University of British Columbia, developed a comprehensive theory for ICR absorption line shapes for reactive and unreactive ion species. McIver and Dunbar [67] described pulsed ion cyclotron double resonance. Smith, University of Utah, and Kevan [68], Wayne State University, determined total charge transfer cross sections. Smyth et al. [69] used a continuously tunable laser to determine photodetachment cross  sections of PH 2 and NH2 .

1972 Lieder et al. [70] described a method for determination of ion-molecule collision frequencies from their kinetic energy dependence by phase coherent pulsed ICR. Several new ICR groups appeared in the literature: From the Universite de Paris-Sud, Centre d’Orsey, Marx and Mauclaire [71] described positive and negative ion-molecule reactions in ammonia. McAllister [72], C.S.I.R.O., investigated electron impact excitation spectra. Tse [73], University of Hong Kong, studied the reactivity of the CHO+ ion by ion cyclotron double resonance spectrometry. Tsuboi et al. [74], University of Electro-Communications, described the construction of an ICR spectrometer similar to the Varian instrument. Riveros and Tiedemann [75], University of Sao Paulo, described the gas phase protonation site of formamide. McMahon and Beauchamp [76] closed the rear of the source region of a drift ICR cell to operate it in a trapping mode. The ions are trapped in the source region of the cell and drifted to the analyzer region for detection. Sharp et al. [77] developed a detailed theory of trapped ion motion for the trapped ion ICR cell, recently developed by McIver. Smith and Futrell [78, 79] presented a new tandem mass spectrometer with a Dempster mass spectrometer as 1st stage and an ICR spectrometer as 2nd stage.

1973 A new company, Dynaspec [80], offered an ICR mass Spectrometer, ICR 9 R, based on the Varian instrument. Huntress and Simms [81] constructed an ICR detector based on a Q-meter with greatly extended frequency range, even electrons could be detected. McIver [82], now in University of

10

Fundamentals and Applications of Fourier Transform Mass Spectrometry

California at Irvine, presented a solid-state marginal oscillator for pulsed ICR spectroscopy. Anicich and Bowers [83] described an approximate, relatively simple method to determine absolute ion-molecule rate constants from ICR drift cell measurements. H+3 ions formed in the reaction of H+2 + H2 are highly excited. Bowers et al. [84] showed the influence of excitation/de-excitation of these ions on the product distribution of reactions with CH3NH2, CH3OH and CH3SH and compared the results with quasi equilibrium theory. The group has great interest in theory of ion-molecule interactions and published a series of papers. Ion-polar molecule collisions are investigated [85–87] Interest in this field is very general: van der Hart [88], University of Leiden, proposed to use total absorption intensities for rate constant determination. Buttrill [89] investigated the temperature dependence of momentum transfer collision rate constants. Gas phase ion-molecule reactions of highly delocalized anions are remarkably slow. Brauman et al. [90] showed that this type of ion-molecule reaction is accessible to catalysis. The group of Dunbar [91] performed fundamental studies in ion photon interaction. Jaffe et al. [92], The Weizmann Institute of Science at Rehovot, studied ionmolecule reactions in ionized nitrogen. Nibbering [93], University of Amsterdam, employed a direct insertion probe to study isomeric ions.

1974 Comisarow and Marshall [94] introduced the Fourier transform technique into ICR. The patent granted to the authors was assigned to Nicolet Technology Corporation [95]. The method operates with a fixed frequency pulse which excites a mass range of ions followed by broad band detection, digitization of the time-domain transient response, and digital Fourier transformation. The same authors described frequency sweep excitation [96] and selective-phase FT ICR [97]: In-phase (dispersion), 90° out-of-phase (absorption), and absolute-value (square root of the sum of the squares of the absorption and dispersion) ion cyclotron resonance spectra were produced. These inventions changed the whole ICR technique. It soon was applied by most research groups. An ICR spectrum could now be recorded by a fast frequency sweep. Slow magnetic field scan was no longer necessary. Sensitivity, mass range, and resolution were dramatically extended. McIver and Baranyi [98] reached a mass resolution of m/Δm ¼ 5700 for the N+2 /CO+ doublet with the trapped ion cell and advanced marginal oscillator detection. Marx et al. [99] mounted an ICR cell and an electron spin

Historical developments in FT ICR mass spectrometry

11

resonance (ESR) cavity in the same magnetic field, optical detection of ions was also possible. After electron impact ionization of ammonia, NH2 radicals and excited radicals could be detected. The group of Dunbar [100], Case Western Reserve University, analyzed complex ion-molecule reaction pathways by single and double resonance experiments. It was shown that the main effect of double resonance irradiation is ion ejection. The group [101] investigated photodissociation spectra in great detail. In cooperation with theoretical chemists, Hehre et al. [102] confirmed the Baker-Nathan order of alkyl substituent effects: (Me > Et > i-Pr > t-Bu). Eyler [103], now at National Bureau of Standards, employed an intracavity laser technique to enhance the yield of laser-induced ionic processes, in this case photodetachment of OH– ions. Eyler et al. [104], all National Bureau of Standards, analyzed the cleavage of carbonyl bond in the reactions with carbon halide ions. Bowie and Williams [105], University of Adelaide, studied the ion chemistry of organic cyanides.

1975 Comisarow and Marshall [106] investigated the mass resolution dependence from several experimental parameters. At a time-domain transient acquisition time of 102.4 ms a resolution of m/Δm ¼ 25,600 at m/z 28 was reached. Lieder and Brauman [107] were able to detect neutral products of ion-molecule reactions with a static gas sample technique on the basis of long trapping times in the trapped ion ICR cell. McIver et al. [108] proposed a chemical ionization method for analysis of samples with vapor pressures in the 1010 Torr range. Viehland and Mason, Brown University at Providence, and Whealton [109], University of Colorado, developed a rigorous kinetic theory for collision broadened ICR lines, which applies for all ion-neutral intermolecular potentials and mass ratios.

1976 Aoyagi et al. [110], Hokkaido University at Sapporo, studied the mechanism of C3H+3 reactions in benzene ion chemistry. Defrees et al. [111] described a method to obtain electronic absorption spectra of ions, if the ions show different reactivities in ground and excited states. Freiser and Beauchamp [112] studied the electron impact dissociation of trapped ions. Atkins and Clugston [113], University of Oxford, developed a quantum mechanical approach for the calculation of ion motion and instantaneous power absorption. Barker and Ridge [114], University of Delaware,

12

Fundamentals and Applications of Fourier Transform Mass Spectrometry

proposed a statistical theory to yield quantitative values for ion-polar neutral momentum transfer collision frequencies. Comisarow and Marshall [115] derived the fundamental equations for line width and resolution in FT ICR.

1977 Hunter and McIver [116] developed the rapid scan ICR technique: At constant magnetic field, ions trapped in the trapped ion cell were excited by a rapid frequency scanning RF signal and the transient signal was recorded. Due to the rapid scan rate, the detected signal was greatly distorted, but cross-correlation with the undisturbed signal of a single mass can be used to recover the true mass spectrum. Bowers et al. [117] described a temperature dependent ICR cell for the temperature range from 80 K to 450 K for the study of reactive and momentum transfer rate constants. Hartmann and Chung [118] employed a minimized wave packet approach for a quantumtheoretical treatment of ion motion in ICR cells.

1978 McIver [119] described a pulsed ICR spectrometer, based on his trapped ion ICR cell. Comisarow et al. [120] incorporated ion cyclotron double resonance in the Fourier transform spectrometer. The same author [121] developed the rotating monopole signal model for ICR. Aoyagi [122] of Jeol studied quasipeaks in ion cyclotron double resonance with the Jeol ICR spectrometer JIC-3B, equipped with a three sections drift cell.

1979 Cody and Freiser [123] introduced electron impact excitation for the dissociation of ions (EIEIO). A theory of signal-to-noise ratio was given by Marshall [124] for a wide range of experimental conditions. Hartmann et al. [125] presented the Schr€ odinger equation equivalent to the Langevin equation to describe collisionally damped ion motion in an ICR cell. Dunbar [126] explored coupling of ion cyclotron motion with ion spin via the relativistic Hamiltonian, and with ion rotation, via its equivalence to the Stark effect.

1980 Allemann and Kellerhals, Spectrospin AG, and Wanczek [127], University of Bremen, developed a new Fourier transform ICR spectrometer with a superconducting magnet with 4.7 T field strength. The magnet had a vertical room temperature bore and solenoidal field geometry. Extremely high

Historical developments in FT ICR mass spectrometry

13

resolution of m/Δm ¼ 1.5  106 at m/z ¼ 166 and trapping times of >12 h were achieved. This initiated a new generation of ICR spectrometers, operating at high magnetic fields. The spectrometer was produced by Spectrospin (Bruker) and named CMS 47. With a trapped cell, White et al. [128] showed great resolution and signal-to-noise improvement, both of which could be increased simultaneously. A cylindrical trapped ion ICR cell for an electromagnet was described by Lee et al. [129]. Dunbar et al. [130] invented simultaneous two photon irradiation of gas-phase ions in the infrared and visible wavelength range. This had implications for the nature of multiphoton events and relaxation processes in gas-phase species. Amano [131] developed a theory of image current detection from first principles for the marginal oscillator and Fourier transform techniques. Two further publications were dealing with detection theory: McIver et al. [132] developed a complete line shape theory for broadband detection. Marshall and Roe [133] investigated response to frequency-sweep excitation. Hartmann et al. [134] applied the minimized wave packet to calculate the shift of ion cyclotron resonance frequency due to coupling with ion rotation. A small shift of 106 was predicted.

Developments from 1981 to 1990 In a trapped ion ICR cell, generation, trapping, reaction and detection of ions are performed “tandem-in-time.” This presents difficulties, especially if one wants to obtain high mass resolution at low pressure, which is essential for analytical applications. The major methodical development in the 1980s was to solve this problem. Two methods were introduced: The dual cell and the external ion source outside the room temperature bore of the superconducting magnet. The dual cell configuration of Littlejohn and Ghaderi [135] of Nicolet Instrument Corporation unites two trapped ion ICR cells with a common trapping electrode in the homogeneous region of the superconducting magnet. The common trapping electrode has a pinhole, so that the cells can be pumped differentially, and a pressure gradient can be maintained. For example, ions can be generated, reacted and trapped in the cell with higher pressure, then transferred through the pinhole into the lower pressure cell for high resolution detection. The method was described by Cody et al. [136] in detail. The transfer of ions from one cell to the other was studied by Honovich and Markey [137]. Mauclaire and Marx [138] introduced the tricyclotron with three cascaded ICR cells connected by ion funnels and separately pumped.

14

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Kofel et al. [139] developed an ICR spectrometer, with the ion source located outside the room temperature bore of the superconducting magnet. The ions are transferred with an electrostatic lens system from the ion source to the ICR cell. Ion source and ICR cell are differentially pumped. The access to the ion source is greatly improved. This is important for application of the recently developed soft ionization methods, electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI). The ICR cell can easily be operated at very low pressure, necessary for high resolution. Shortly later, Meek and Stockton [140], American Cyanamid Company, patented a somewhat different instrument with external ion source. Utilizing the same instrumental design, Kofel and McMahon [141] connected a high pressure ion source to an ICR spectrometer for the study of extensively solvated ions. Maruyama et al. [142], developed a small supersonic cluster source, which could directly be attached to an FT ICR spectrometer. Spectra of large carbon and germanium cluster ions were obtained. McIver et al. [143] coupled a quadrupole mass spectrometer with an ICR instrument. The ions are generated in the ion source of the quadrupole. An advanced design of this instrument was later marketed by Ionspec company, founded by McIver. Kofel et al. [144] introduced an “ICR-ICR” instrument, an ICR spectrometer with a trapped ion source in the fringing field of an unshielded supercon magnet (0.15 T fringing field). The authors showed theoretically, that in this way the sensitivity for a coupling with a gas chromatograph can be greatly increased. Four configurations were compared: GC capillary with split, introduced directly into a single ICR cell; dual cell with split; external ion source with continuous extraction; and external trap with discontinuous extraction. The relative sensitivities were: 1:70:15:2500. Kemper and Bowers [145] published an improved tandem spectrometer, related to the design of Smith and Futrell. The spectrometer included two major changes: a velocity filter at the entrance of the ICR cell, to remove translationally hot ions; and the use of a second ICR cell as an ion source. Kofel et al. [146] introduced the time-of-flight ICR spectrometer. The ion flight time between the external ion source and the ICR cell is mass dependent and can be employed for mass-selective storage of ions in the ICR cell. Dunbar and Weddle [147] described a different ICR time-offlight method. After switching off the trapping potentials of the ICR cell for a selected time, the ions fly freely along the magnetic field lines. The portion of ions with sufficient kinetic energy to reach the trapping plates is measured.

Historical developments in FT ICR mass spectrometry

15

Harmonics and multiples of the ICR frequencies appear in the ICR signals and are employed to enhance resolution by several authors. Pan et al. [148] alter a capacitance bridge detector for detection of the second harmonic. Nikolaev et al. [149] utilized a multielectrode ICR cell for detection of multiples of the cyclotron frequency and observed the expected increased resolution. Schweikhard [150] added interesting comments to the publication of Nikolaev et al. Gabrielse et al. [151] invented the cylindrical multiple sections openendcap ICR cell. Wang et al. [152] extended the dynamic range with the introduction of stored waveform inverse Fourier transform (SWIFT) excitation. With the SWIFT technique ions can be excited very selectively. McIver et al. [153] employed an impulse excitation scheme to improve uniform ion excitation over a wide mass range. Pf€andler et al. [154] introduced two dimensional FT ICR mass spectrometry which allows to study a great number of ion-molecule reactions simultaneously. Williams et al. [155] performed remeasurement of the same ion population in the ICR cell. With continuous ion production by 252Cf plasma desorption a signal enhancement of a factor of 100 was obtained with 1000 remeasurements. Three theoretical works: Laukien [156], investigated the shifts in ion frequencies and the spectral broadening caused by magnetic field gradients. The theory of space charge shifts of ICR frequencies was derived by Jeffries et al. [157]. Hartmann et al. [158] extended the method of quantum mechanical treatment of classically behaving ion motion with minimalized wave packets to the investigation of the dependence of the ion cyclotron frequency upon electric field inhomogeneities.

Developments from 1991 to 2000 During this decade, the ICR spectrometry develops again to more sophisticated instruments and applications: New instrumentation, higher magnetic fields, improved ion transfer from the external source to the ICR cell, and new ICR cells. New companies appear with new instruments. Schlereth et al. [159] obtained a patent for a Fourier transform molecular spectrometer, assigned to Leybold Inficon. Thermo instruments acquired Extrel FTMS in 1996. (Nicolet FTMS was acquired by Extrel some years ago). In 2000, Siemens AG, presented the Quantra Process ICR [160]. Shi et al. [161] described an FT ICR mass spectrometer with a 25 T resistive magnet. Bamberg and Wanczek [162] developed an ICR spectrometer with a large supersonic beam external ion source and an advanced lens set for

16

Fundamentals and Applications of Fourier Transform Mass Spectrometry

ion transfer for the study of van der Waals cluster ions. Reents et al. [163] constructed an external source FTICR spectrometer with a split pair magnet. Reagent gases and spectroscopic probes can be introduced into the ICR cell through the four ports of the split-pair magnet. Gorshkov et al. [164] designed a dual ICR trap instrument. The first trap operated at higher pressure for selective ion accumulation. The second trap operated at low pressure for mass spectra acquisition. Shaffer et al. [165] applied a novel ion funnel for the focusing of ions. The funnel can be incorporated in tandem mass spectrometers or external ion source ICR spectrometers. Guan and Marshall [166] reviewed design principles of a number of ICR cells. A Kingdon trap in a magnetic field as an ICR cell is presented by Gillig et al. [167]. For the elimination of ion ejection along the magnetic field axis, Caravatti and Allemann [168] constructed the “infinity cell.” The trapping electrodes are segmented and covered with the excitation rf frequency, so that the ICR acts like an infinitively long cell. Harmonics of the ion frequencies arise from nonlinearity phenomena. Nikolaev et al. [169] and Knobeler and Wanczek [170] analyzed the harmonics in detail with a multi electrode ICR cell. Signal detection at harmonics or multiples can be utilized to increase mass resolution. A combined trap operation mode is described by two groups [171, 172] (for another application cf. next paragraph). An RF frequency was applied to the ICR cell, which improved ion manipulation and trapping at much higher pressure. ICR cells trap only one polarity of ions. It is possible however, to change the construction of the ICR cell to trap both ion polarities at different sites of the cell and to unite the ions later. In this way ion-ion reactions can be studied. Several groups have described methods to achieve this. Gorshkov et al. [173] applied an RF voltage to the end caps of the ICR cell. The ion motion in z direction is now governed by the Mathieu equation like in a Paul trap. Positive and negative ions could be trapped, the magnetron motion is eliminated. The “nested” trap produces a bipolar trapping potential with several extrema for trapping of positive and negative ions. Wanczek and coworkers developed three different cylindrical ICR cells for nested trapping: A cell with additional meshed grids in front of the trapping electrodes [174], a cell with segmented trapping electrodes [175], and an ICR cell with electrodes of different diameters: a central segmented electrode for ion trapping and detection and two planar trapping electrodes with a center bore, to which two small tube electrodes are fitted [176] (see next section). With the cell with segmented trapping electrodes, ion-ion reactions of Xe and SF6 were investigated. In a cylindrical open endcap multi electrode ICR cell,

Historical developments in FT ICR mass spectrometry

17

Vartanian and Laude [177] studied the ion chemistry of dichloromethane positive and negative ions. An advanced design of a cylindrical open endcap ICR cell was employed by Hall and Gabrielse [178] for proton cooling with electrons. Three theoretical papers: A comprehensive theory of ICR signal for all trap geometries [179], description of ion motion in an ICR cell with classical canonical formalism [180], and a perturbation theory of trapped ion motion in nonquadrupolar electrostatic fields [181].

Developments from 2001 In this period too, the development of the FT ICR method, instrumentation, and application have made several important advances. The infrared absorption spectroscopy of trapped ions is a most important tool for ion structure elucidation. The method was limited by the low concentration of trapped ions. The combination of an ICR instrument with a free electron laser (FEL) [182] realized this spectroscopy by infrared multiple photon dissociation (IRMPD). The FEL provides continuously tunable infrared radiation at high intensity over a large frequency range. In 2018, Jasikova et al. [183] published a review dealing with the application of free electron lasers for IRMPD. Several ion classes were presented, ranging from ions derived from small molecules to those derived from biomolecules. The University of Virginia [184], in cooperation with Thermo Electron Corp., introduced in 2004 a linear quadrupole ion trap, coupled to a FT MS, a versatile instrument with two ion traps, operating with nano-flow HPLC and providing a resolution of 100,000 at a scan rate of 1 Hz, designed for analytical applications. MS/MS experiments could be performed at a scanning rate of 4 Hz. They showed the capability of this instrument to perform sequence analysis of peptides at very low concentrations and high mass accuracy, due to the integration of automatic gain control in this instrument. Applications in the field of histone H3 posttranslational modifications were also provided using this system. An improved version with two linear ion traps was developed at the University of Washington [185] in cooperation with (now) Thermo Scientific, a tandem triple trap instrument. The new instrument incorporates a LTQ-Velos mass spectrometer, which has two linear RF ion traps: High pressure trap (HPT) and low pressure trap (LPT), instead of only one trap in the previous version. With this developed instrument, a MS/MS acquisition rate of 10 Hz is possible. It provides also an order of magnitude increase in ion accumulation efficiency.

18

Fundamentals and Applications of Fourier Transform Mass Spectrometry

O’Connor [186] discussed considerations, such as liquid helium boiloff, metal mechanical stress, pumping speed, Johnson noise, when constructing a FT ICR spectrometer in the 4.2K vertical cold bore of a superconducting magnet. He mentioned that if the ion optics and vacuum chamber surfaces are cooled down to 4.2K, they become cryopumping and this helps to decrease the base pressure and increase the pumping efficiency in a narrow magnet bore. The first signals were obtained in 2007 from a cryogenic FT-ICR-MS, coupled to an actively-shielded 14 T vertical magnet [187]. The built system has a helium boiloff rate of 5 L/day. Marshall and coworkers [188], National High Magnetic Field Laboratory, in cooperation with Korean Basic Science Institute, studied in 2006 the requirements for designing of a 21 T superconducting magnet for an FT ICR mass spectrometer. They suggested minimization of the axial distance from the end of the cryostat to central field of the magnet and studied the fringe field, suggesting active shielding instead of passive shielding. The same group studied also considerations for the axial orientation of the horizontal magnet and field inhomogeneity as well as refrigeration requirements. Hendrickson et al. [189] reported a successfully achieved 21 T magnet design with negligible liquid helium consumption, due to helium liquification functionality, which is achieved by two-stage cryocoolers and lambda refrigerator. The 21 T magnet has a sufficient magnetic field homogeneity for the implemented ICR cell dimension. The new FT-ICR-MS instrument was finished in 2015. They observed ions in the whole mass range m/z (2002000). The instrument includes three linear quadrupoles and an RF ion injection optics, and a novel dynamically harmonized ICR cell. With four stages of pumping a base pressure at the site of the ICR cell of 1010 Torr could be achieved. A mass resolution was reported of 150,000 for BSA protein (66 kDa) for 350 ms transient length and a resolution of 2 million for 12 s transient length. The instrument is capable of achieving a resolution of 300,000 at m/z 400 in short (760 ms) transients. Two-dimensional ICR mass spectrometry was introduced by Pf€andler et al. [154]. It is a very powerful method for correlation of parent and product ions. Van Agthoven et al. [190] re-introduced this method with the implementation of IRMPD as a fragmentation tool. They implemented a pulse program for 2D-FTMS, where two encoding pulses separated by a variable delay time, are used followed by IRMPD pulse and subsequent ion excitation and detection events. They presented the additional advantages of the use of IRMPD as a fragmentation mode instead of classical

Historical developments in FT ICR mass spectrometry

19

gas assisted collision induced dissociation (CID). The improved computer technology enabled analytical applications. A recent review [191] exists showing the capabilities and some challenges of 2D-FTMS as well as analytical applications in regard to fragmentation mapping of peptide ions. van Agthoven et al. [192] optimized the discrete pulse sequence of the IRMPD-2D-FTMS to significantly decrease the adverse effects of harmonic signals, which accompany 2D-FTMS detection, by adjusting the experimental parameters of the above mentioned encoding pulses and obtained enhanced mass spectra with higher S/N rations even without the use of de-noising algorithms. Sehgal et al. recently published a theory of spiraling ions for 2D FT-ICR [193]. Scintillation noise was challenging in 2D-FTMS and van Agthoven presented effective scintillation noise reduction in 2D-FT-ICR-MS by the use of Cadzow data processing approach [194]. Several groups studied absorption mode spectra, because resolving power and signal-to-noise can be improved. Qi et al. [195] described the application of a phase correction scheme to generate absorption mode spectra. Xian et al. [196] applied baseline correction. The use of the “Paracell” [197] improved the generation of the spectra, because damping of the transient is greatly reduced. The peak had classic ‘sinc’ function line shape, the absorption-mode improved the resolving power by almost a factor of 2, baseline noise was reduced by 21/2. The improvement of ICR cells for ion trapping, excitation and detection had a great interest. Kanawati and Wanczek [198] developed a new open cylindrical ICR cell, which has two types of trapping electrodes with different diameters. They illustrated in their design that simultaneous trapping and excitation of both ion polarities are possible. Detection of both ion polarities is feasible “inside” the detection segment of the cell. Both authors ran extensive investigations on this cell to study long range gas phase ion-ion interactions as well as axial ion dynamics upon application of pulsed RF square wave form [199]. Nagornov et al. [200] developed the “Nadel” ICR cell with a pair of 90° standard excitation electrodes and a pair of narrow aperture detection electrodes, which could be used to operate the FT ICR spectrometer at the unperturbed cyclotron frequency [201]. Compared with the open cylindrical ICR “Ultra Cell,” they showed that the sensitivity of that cell is not reduced when flat detection electrodes (instead of the standard wide aperture detection electrodes) are used. They reasoned this due to lower capacitance of the narrow aperture detection electrodes and also due to shorter distance between the ion’s postexcitation cyclotron radius and the flat detection

20

Fundamentals and Applications of Fourier Transform Mass Spectrometry

electrodes in the new Nadel ICR design. The same group presented several analytical applications of the Nadel ICR cell concerning crude oil analysis and also peptide ion detection. Bruker [202], in cooperation with Russian Academy of Sciences, presented a new FT ICR mass spectrometer with quadrupolar detection which operates at the double cyclotron frequency, developed from the solariX XR FT ICR MS instrument with the Paracell. At the same scan time, the resolution is doubled, or at the same resolution, the scan time is halved. Nikolaev et al. [203] obtained with their new ICR cell (Paracell), which is equipped with dynamic harmonization a greatly increased resolution. At m/z ¼ 609, from reserpine, 24,000,000 resolving power (FWHM) was measured in narrow band mode. In broad band mode, a resolution of 1,200,000 was obtained for the bovine serum albumin (BSA) in the charge state 49 + (BSA with 49 protons). The Paracell has specially shaped electrodes: four full leaf and four divided leaf electrodes, as well as eight inverse leaf electrodes. With this ICR cell, Jertz et al. [204] performed a detailed investigation of the magnetron motion. It is well-known that both classical closed and open cylindrical ICR cells produce nonideal electric potential distribution along the z axis, which deviates to a significant extent from the desired ideal quadrupolar electric potential distribution. Therefore a strong need for cell electrode compensation exists. Here, we give several attempts in this direction. After the successful studies of Gabrielse and his co-workers in this field [151, 205, 206], Tolmachev et al. [207] added two pairs of cylindrical potential compensation electrodes to an open cylindrical ICR cell and minimization of spatial deviations of radial electric field over cell radius was discussed. They obtained a spatially independent (constant) cyclotron frequency of studied peptide ions. In another study, Tolmachev et al. [208] developed also a different concept of a harmonized ICR. The open cylindrical ICR cell is coaxially embedded into external shim electrodes. The ICR cell has cut-outs in the excite-detect plates to allow penetration of the imposed harmonic trapping potential. Kaiser et al. designed and studied voltage compensated ICR multisegmented open cylindrical ICR cell for complex mixture chemical analysis [209] to obtain an ideal 3D axial quadrupolar potential inside the seven segmented cell. They could preserve ion cloud coherence inside the newly developed cell by a factor of 2 relative to older open cylindrical ICR cells, thus enabling extension of the recorded time-domain transient for ultrahigh resolution experiments of complex chemical mixtures, where differentiation between isobaric ionic species is needed.

Historical developments in FT ICR mass spectrometry

21

Ostrander et al. [210] developed a central trapping ring electrode for space limited ICR cell arrangements, reducing cell electrode configuration and the involved electrical wiring of the ICR cell. With this cell design, no need for additional trapping electrodes, since the same set of electrodes, which are utilized for excitation and detection can be used also for ion trapping by applying a DC offset on those central excitation and detection ring segments during ion trapping, RF dipolar excitation and also during the time of recording of the whole transient. The same group could show that, their new design could overcome the problems of electric fields interferences between a central trapping electrode and the excitation electric fields in the older Vartanian’s single trapping electrode design [211], where this trapping electrode was positioned inside the excitation and detection electrodes. Ostrander et al. also showed that the axial electric potential configuration in the Vartanian’s single trapping electrode design has a significant well depth, thus increasing trapped ion capacity [212]. Weisbrod et al. [213] introduced a closed cylindrical ICR cell with segmented terminal trapping electrodes. Postexcitation modulation of the electric trapping potentials applied to these segmented electrodes significantly reduces the radial electric field variations along >50% of the axial cell length, when compared to a simple closed cell geometry with unsegmented end cap electrodes, when ions are previously excited to 38% of the maximal cell radius. With this approach, significant extension of the time domain transient recording could be achieved. The same group tested this new ICR cell with mellitin polypeptide ions and they could observe a resolution increase from 7000 to 421,000 for [M + 4H]4+ ions along 26 s time-domain transient length, presenting a noticeably enhanced mass resolving power as well as enhanced signal-to-noise. Misharin and Zubarev [214] developed a new “O-trap” for improved ion detection. An additional, internal coaxial cylinder is added, around which ions with excited cyclotron orbits rotate. The O-trap can provide optimized sinusoidal wave shapes in the produced time-domain transients by the use of harmonizing electrodes, which significantly reduces parasitic harmonics and enables detection at a multiple of the cyclotron frequencies of all excited ions in the spatial region between the inner and the outer cylinders. The radial electric field component, which is caused by trapping electric potential, during detection is much more homogeneous and smaller when compared with a classical open cylindrical ICR cell geometry. The same group found that, the trapping field does not penetrate significantly

22

Fundamentals and Applications of Fourier Transform Mass Spectrometry

into the O-trap and this enables design of shorter ICR cells for better magnetic field homogeneity. Furthermore, higher sensitivity and ion trapping capacity were reported with this design. Brustkern et al. [215] electrically compensated to eighth order a cylindrical closed ICR cell with the aid of three compensation electrodes between central and trapping electrodes. Kim et al. [216] constructed a new “infinity” hybrid cylindrical ICR cell, with capacitive coupling between compensation and excitation electrodes and improved specifications. Lobodin et al. [217] employed a seven section open cylindrical ICR cell for tandem in time charge reversal. The cell traps positive and negative ions simultaneously. The charge reversal is activated by high energy electrons or photons. Parallel spectral acquisition improves spectral acquisition time. For this purpose, several miniature ICR cells [218] can be operated simultaneously. Park et al. [219] employed two orthogonal ICR cells in the same magnet for parallel data acquisition. A second arrangement with three to five ICR cells was investigated by the group [220]. Balaj et al. [221] developed a temperature controlled ICR cell for the study of low temperature black body infrared radiative dissociation (BIRD). The cell is surrounded with cooled walls, and can reach a well-defined radiation temperature down to 160K. A linear Arrhenius plot of the Ag+(H2O)6 ion could be generated.

Conclusion FT ICR mass spectrometry is an elaborate method with very broad applications. The authors enjoyed sketching the development of several new ICR cells, presenting both instrumental developments and ion manipulation possibilities though diverse pulse programs. Through all the years the method grows continuously, new instrumentation generates new applications. Sometimes surprisingly new instrumental and applicative advances appeared. The method has not lost its dynamic development over all the years and will continue in the same manner, due to the knowledge, activity and wealth of imagination of many active research groups in several fields of science (physics, chemistry, biology, informatics, electronics). All those scientists indeed contribute to more sophisticated ICR instrumentation, to improved cell geometries and operation modes, to advanced data acquisition and processing and to more elaborated spectra interpretation. We believe that this technique continues its success.

Historical developments in FT ICR mass spectrometry

23

References [1] H.G. Dehmelt, Radiofrequency spectroscopy of stored ions. I: storage, Adv. At. Mol. Phys. 3 (1967) 53–72. [2] H.G. Dehmelt, Radiofrequency spectroscopy of stored ions. II: spectroscopy, Adv. At. Mol. Phys. 5 (1969) 109–154. [3] F.M. Penning, Die Glimmentladung bei niedrigem Druck zwischen koaxialen Zylindern in einem axialen Magnetfeld, Physica 3 (1936) 873–894. [4] W. Paul, H. Steinwedel, DE Patent 944,900, Verfahren zur Trennung, bzw. Zum getrennten Nachweis von Ionen verschiedener spezifischer Ladung, patented June 7, 1956. [5] W. Paul, Electromagnetic traps for charged and neutral particles, (nobel lecture), Rev. Mod. Phys. 62 (1990) 531–540. [6] K.H. Kingdon, Electron Discharge Device, US Patent 1,579,117, patented March 30, 1928. [7] G.-Z. Li, G. Werth, The combined Trap and some possible applications, Phys. Scr. 46 (1992) 587–592. [8] H. Hartmann, K.P. Wanczek (Eds.), in: Ion cyclotron resonance spectrometry II, Lecture Notes Chemistry, vol. 31, 1982. [9] T.A. Lehman, M.M. Bursey, Ion Cyclotron Resonance Spectrometry, WileyInterscience, New York, 1976. [10] A.G. Marshall, F.R. Verdun, Fourier Transforms in NMR, Optical, and Mass Spectrometry, Elsevier, New York, 1990. [11] G.A. Gray, Ion cyclotron resonance, Adv. Chem. Phys. 19 (1971) 141–207. [12] J.L. Beauchamp, Ion cyclotron resonance spectroscopy, Annu. Rev. Phys. Chem. 22 (1971) 527–561. [13] J.H. Futrell, Ion cyclotron resonance mass spectroscopy, Dyn. Mass Spectrom. 2 (1971) 97–134. [14] H. Hartmann, K.-H. Lebert, K.P. Wanczek, Ion cyclotron resonance spectroscopy, Top. Curr. Chem. 43 (1973) 57–115. [15] R.C. Dunbar, Ion cyclotron resonance and Fourier transform mass spectrometry, Tech. Chem. (N. Y.) 6 (1986) 903–950. [16] K.P. Wanczek, Ion cyclotron resonance spectrometry: a review on instrumentation and theory, Dyn. Mass Spectrom. 6 (1982) 14–32. [17] K.P. Wanczek, ICR spectrometry—a review of new developments in theory, instrumentation, and applications. I. 1983–1986, Int. J. Mass Spectrom. Ion Process. 95 (1989) 1–38. [18] A.G. Marshall, C.L. Hendrickson, G.S. Jackson, Fourier transform ion cyclotron resonance mass spectrometry: a primer, Mass Spectrom. Rev. 17 (1998) 1–35. [19] A.G. Marshall, P. Armentrout (Ed.), in: The Encylopedia of Mass Spectrometry, vol. 1, Elsevier, Amsterdam, 2003, pp. 131–143. [20] E.N. Nikolaev, Y.I. Kostyukevich, G.N. Vladimirov, Fourier transform ion cyclotron resonance (FT ICR) mass spectrometry: theory and simulations, Mass Spectrom. Rev. 35 (2016) 219–258. [21] H. Hartmann, K.P. Wanczek (Eds.), in: Ion cyclotron resonance spectrometry, Lecture Notes Chemistry, vol. 7, 1978. [22] K.A. Nier, A.L. Yergey, P.J. Gale (Eds.), Historial Perspectives, Part A: The Development of Mass Spectrometry, Elsevier, Amsterdam, 2016 (Cf. also: Vol. 9, Part B, Notable people in Mass Spectrometry.). [23] C.L. Wilkins, History of Ion Cyclotron Resonance (ICR) and Fourier Transform (FTICR) Mass Spectrometry, 2016, pp. 61–67. [24] W. Schockley, Currents to conductors induced by a moving charge, J. Appl. Phys. 9 (1938) 635–636.

24

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[25] H. Sommer, H.A. Thomas, J.A. Hipple, The measurement of e/m by cyclotron resonance, Phys. Rev. 82 (1951) 697–702. [26] A. Klopfer, W. Schmidt, An Omegatron mass spectrometer and its characteristics, Vacuum 10 (1960) 363–372. [27] W. Bleakney, J.A. Hipple Jr., A new mass spectrometer with improved focusing properties, Phys. Rev. 53 (1938) 521–529. [28] S.A. Goudsmit, A time-of-flight mass spectrometer, Phys. Rev. 74 (1949) 622–623. [29] L.G. Smith, C.C. Damm, Mass Synchrometer doublet measurements at masses 28 and 30, Phys. Rev. 90 (1953) 324–325. [30] N.D. Coggeshall, The paths of ions and electrons in non-uniform crossed electric and magnetic fields, Phys. Rev. 70 (1946) 270–280. [31] J.R. Graham, D.P. Malone, D.C. Wobschall, Ion cyclotron resonance in weakly ionized gases, Bull. Am. Phys. Soc. 7 (1962) 69. [32] D.C. Wobschall, J.R. Graham, D.P. Malone, Ion cyclotron resonance and the determination of collision cross sections, Phys. Rev. 131 (1963) 1565–1571. [33] D. Wobschall, Ion cyclotron resonance spectrometer, Rev. Sci. Instrum. 36 (1965) 466–475. [34] P.M. Llewellyn, Ion Cyclotron Resonance Mass Spectrometer Having Means for Detecting the Energy Absorbed by Resonant Ions, US Patent 3,390,265, filed May 17, (1965). [35] R.V. Pound, W.D. Knight, A radiofrequency spectrograph and simple magnetic field meter, Rev. Sci. Instrum. 21 (1950) 219–225. [36] F.N.H. Robinson, Nuclear resonance absorption circuit, J. Sci. Instrum. 36 (1959) 481–487. [37] L.R. Anders, J.L. Beauchamp, R.C. Dunbar, J.D. Baldeschwieler, Ion cyclotron double resonance, J. Chem. Phys. 45 (1966) 1062–1063. [38] J.L. Beauchamp, Theory of collision-broadened ion cyclotron resonance spectra, J. Chem. Phys. 46 (1967) 1231–1243. [39] J.L. Beauchamp, L.R. Anders, J.D. Baldeschwieler, The study of ion-molecule reactions in chloroethylene by ion cyclotron resonance spectroscopy, J. Am. Chem. Soc. 89 (1967) 4569–4577. [40] R.C. Dunbar, Energy dependence of ion-molecule reactions, J. Chem. Phys. 44 (1967) 5445–5446. [41] J.D. Baldeschwieler, H.P. Benz, P.M. Llewellyn, Ion-molecule reactions in an ion cyclotron resonance mass spectrometer, Adv. Mass Spectrom. 4 (1968) 113–120. [42] M.T. Bowers, D.D. Elleman, J.L. Beauchamp, Ion cyclotron resonance of olefins. I. A study of the ion-molecule reactions in electron-impacted ethylene, J. Phys. Chem. 72 (1968) 3599–3612. [43] J.I. Brauman, L.K. Blair, Gas-phase acidities of alcohols. Effects of alkyl groups, J. Am. Chem. Soc. 90 (1968) 6561–6562. [44] J.M.S. Henis, An ion cyclotron resonance study of ion-molecule reactions in methanol, J. Am. Chem. Soc. 90 (1968) 844–851. [45] J.M.S. Henis, W. Frasure, Ion cyclotron resonance detection using electron energy modulation, Rev. Sci. Instrum. 39 (1968) 1772–1774. [46] F. Kaplan, Identification of collision-induced fragmentation pathways by ion cyclotron double resonance, J. Am. Chem. Soc. 90 (1968) 4483–4485. [47] J. King, D.D. Elleman, Ion cyclotron resonance study of the secondary ion-molecule reactions in hexafluoroethane, J. Chem. Phys. 48 (1968) 412–414. [48] R.P. Clow, J.H. Futrell, Observation of charge exchange by ion cyclotron double resonance, J. Chem. Phys. 50 (1969) 5041–5042. [49] W.T. Huntress, J.L. Beauchamp, Application of ion cyclotron resonance to the study of ionizing reactions of metastable molecules, Int. J. Mass Spectrom. Ion Phys. 3 (1969) 149–152.

Historical developments in FT ICR mass spectrometry

25

[50] M. Inoue, S. Wexler, Isotopic exchange in CH4–D2 and CD4–H2 mixtures studied by ion cyclotron resonance spectroscopy. The mechanism of self-induced labelling of CH4 by tritium, J. Am. Chem. Soc. 91 (1969) 5730–5740. [51] R.M. O’Malley, K.R. Jennings, Ion cyclotron resonance mass spectrometry of acetylene, Int. J. Mass Spectrom. Ion Phys. 2 (1969) 257–263. [52] R.T. McIver, A trapped ion analyzer cell for ion cyclotron resonance spectroscopy, Rev. Sci. Instrum. 41 (1970) 555–558. [53] M.M. Bursey, T.A. Elwood, M.K. Hoffman, T.A. Lehman, J.M. Tesarek, Analytical ion cyclotron resonance spectrometry, acetylation as a chemical ionization technique, Anal. Chem. 42 (1970) 1370–1374. [54] R.P. Clow, J.H. Futrell, Ion cyclotron resonance study of the kinetic energy dependence of ion-molecule reaction rates. I. Methane, hydrogen and rare gas-hydrogen systems, Int. J. Mass Spectrom. Ion Phys. 4 (1970) 165–179. [55] G. Eadon, J. Diekman, C. Djerassi, Application of ion cyclotron resonance to the structure elucidation of the C3H6O+ ion formed in the double McLafferty rearrangement, J. Am. Chem. Soc. 92 (1970) 6205–6212. [56] G.C. Goode, R.M. O’Malley, A.J. Ferrer-Correia, R.I. Massey, K.R. Jennings, J. H. Futrell, P.M. Llewellyn, Rate constants for ion-molecule reactions determined by ICR mass spectrometry, Int. J. Mass Spectrom. Ion Phys. 5 (1970) 393–405. [57] G.C. Goode, A.J. Ferrer-Correia, K.R. Jennings, The interpretation of double resonance signals in ion cyclotron resonance mass spectrometry, Int. J. Mass Spectrom. Ion Phys. 5 (1970) 229–240. [58] J.M.S. Henis, C.A. Mabie, Determinization of autoionization lifetimes by ion cyclotron resonance linewidth, J. Chem. Phys. 53 (1970) 2999–3013. [59] K.-H. Lebert, Ionen-Cyklotronresonanz zur Untersuchung von Ion-Molek€ ulReaktionen, Messtechnik 78 (1970) 109–115. [60] A.G. Marshall, S.E. Butrill, Calculation of ion-molecule reaction rates from ion cyclotron resonance spectra: methyl fluoride, J. Chem. Phys. 52 (1970) 2752–2759. [61] M.T. Bowers, D.H. Aue, H.M. Webb, R.T. McIver, Equilibrium constants for gas-phase ionic reactions. Accurate determination of relative proton affinities, J. Am. Chem. Soc. 93 (1971) 4314–4315. [62] R.C. Dunbar, Photodissociation of CH3Cl+ and N2O+ cations, J. Am. Chem. Soc. 93 (1971) 4354–4358. [63] M.S. Foster, J.L. Beauchamp, Potential of ion cyclotron resonance spectroscopy for the study of the intrinsic properties and reactivity of transition metal complexes in the gas phase. Ion-molecule reactions of Ironpentacarbonyl, J. Am. Chem. Soc. 93 (1971) 4924–4926. [64] M.L. Gross, F.W. McLafferty, Identification of C3H+6 structural isomers by ion cyclotron resonance spectroscopy, J. Am. Chem. Soc. 93 (1971) 1267–1268. [65] W.T. Huntress, Ion cyclotron resonance power absorption: collision frequencies for CO+2 , N+2 , H+3 ions in their parent gases, J. Chem. Phys. 55 (1971) 2146–2155. [66] A.G. Marshall, Theory for ion cyclotron resonance absorption line shapes, J. Chem. Phys. 55 (1971) 1343–1354. [67] R.T. McIver, R.C. Dunbar, Pulsed ion cyclotron double resonance for the study of ion-molecule reactions, Int. J. Mass Spectrom. Ion Phys. 7 (1971) 471–483. [68] D.L. Smith, L. Kevan, Total charge-transfer cross sections in molecular systems, J. Am. Chem. Soc. 93 (1971) 2113–2117. [69] K.C. Smyth, R.T. McIver, J.I. Brauman, R.W. Wallace, Photodetachment of negative ions using a continuously tunable laser and an ion cyclotron resonance spectrometer, J. Chem. Phys. 54 (1971) 2758–2759. [70] C.A. Lieder, R.W. Wien, R.T. McIver, Ion-molecule collision frequencies in gases determined by phase coherent pulsed ICR, J. Chem. Phys. 56 (1972) 5184–5185.

26

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[71] R. Marx, G. Mauclaire, Ion cyclotron resonance study of positive and negative ionmolecule reactions in ammonia, Int. J. Mass Spectrom. Ion Phys. 10 (1972) 213–226. [72] T. McAllister, Electron impact excitation spectra in an ion cyclotron resonance mass spectrometer, J. Chem. Phys. 57 (1972) 3353–3355. [73] R.S. Tse, On the reactivity, state and heat of formation of the CHO+ ion, Int. J. Mass Spectrom. Ion Phys. 9 (1972) 351–353. [74] M. Tsuboi, S. Saida, M. Inoue, A. Amemiga, Construction of ion cyclotron resonance mass spectrometer, Mass Spectrom. Jpn. 20 (1972) 173–184. [75] J.M. Riveros, P.W. Tidemann, Gas phase kinetic protonation site of formamide, An. Acad. Brasil. Cienc. 44 (1972) 413–417. [76] T.B. McMahon, J.L. Beauchamp, A versatile trapped ion cell for ion cyclotron resonance spectroscopy, Rev. Sci. Instrum. 43 (1972) 509–512. [77] T.E. Sharp, J.R. Eyler, E. Li, Trapped ion motion in ion cyclotron resonance spectroscopy, Int. J. Mass Spectrom. Ion Phys. 9 (1972) 421–439. [78] D.L. Smith, J.H. Futrell, A Dempster-ion cyclotron resonance tandem mass spectrometer, in: Proc. 20th Annu. Conf. Mass Spectrom. Allied Topics, Dallas, Texas, 1972, pp. 405–406. Paper S 12. [79] The detailed description is published in: D.L. Smith, J.H. Futrell, A new tandem mass spectrometer for the study of ion-molecule reactions, Int. J. Mass Spectrom. Ion Phys. 14 (1974) 171–181. [80] Dynaspec Inc., cf. Anal. Chem. New Products, vol. 45, 1973. 933A. [81] W.T. Huntress, W.T. Simms, A new ion and electron detector for ion cyclotron resonance spectroscopy, Rev. Sci. Instrum. 44 (1973) 1274–1277. [82] R.T. McIver, A solid-state marginal oscillator for pulsed ion cyclotron resonance spectroscopy, Rev. Sci. Instrum. 44 (1973) 1071–1074. [83] V.G. Anicich, M.T. Bowers, Absolute ion-molecule rate constants from drift cell ion cyclotron resonance spectroscopy, Int. J. Mass Spectrom. Ion Phys. 11 (1973) 329–344. [84] M.T. Bowers, W.J. Chesnavich, W.T. Huntress, Deactivation of internally excited H+3 ions: comparison of experimental product distributions of reactions of H+3 ions with CH3NH2, CH30H and CH3SH with predictions of quasi equilibrium theory calculations, Int. J. Mass Spectrom. Ion Phys. 12 (1973) 357–382. [85] T. Su, M.T. Bowers, Theory of ion-polar molecule collisions. Comparison with experimental charge transfer reactions of rare gas ions to geometric isomers of difluorobenzene and dichloroethylene, J. Chem. Phys. 58 (1973) 3027–3037. [86] M.T. Bowers, T. Su, V.G. Anicich, Theory of ion-polar molecule collisions. Kinetic energy dependence of ion-polar molecule reactions: CH3OH+ + CH3OH ! CH3OH+2 + CH3O, J. Chem. Phys. 58 (1973) 5175–5176. [87] T. Su, M.T. Bowers, Ion-polar molecule collisions: the effect of ion size on ion-polar molecule rate constants; the parameterization of the average-dipole-orientation theory, Int. J. Mass Spectrom. Ion Phys. 12 (1973) 347–356. [88] W.J. van der Hart, Comment on calculation of rate constants from ion cyclotron resonance spectra, Chem. Phys. Lett. 23 (1973) 93–94. [89] S.E. Buttrill, Temperature dependence of the rates of ion-molecule collisions, J. Chem. Phys. 58 (1973) 656–659. [90] J.I. Brauman, C.A. Lieder, M.J. White, Homogeneous catalysis of a gas-phase ionmolecule reaction, J. Am. Chem. Soc. 95 (1973) 927–928. [91] For example:R.C. Dunbar, J.M. Kramer, ICR study of the angular dependence of photodissociation and the photodissociation transition moment orientation in gaseous CH3Cl+, J. Chem. Phys. 58 (1973) 1266–1267. [92] S. Jaffe, Z. Karpas, F.S. Klein, Ion cyclotron mass spectrometric study of the reaction N+2 + N2 ! N+3 + N, J. Chem. Phys. 58 (1973) 2190–2191.

Historical developments in FT ICR mass spectrometry

27

[93] J. Memel, N.M.M. Nibbering, An investigation by ion cyclotron double resonance of the structural differences of isomeric ions using a direct insertion probe, J. Chem. Soc. Perkin II (1973) 2088–2090. [94] M.B. Comisarow, A.G. Marshall, Fourier transform ion cyclotron resonance spectroscopy, Chem. Phys. Lett. 25 (1974) 282–283. [95] M.B. Comisarow, A.G. Marshall, Fourier Transform Ion Cyclotron Resonance Spectroscopy, Method and Apparatus, US Patent 3,937,955, filed Oct. 15, 1974. [96] M.B. Comisarow, A.G. Marshall, Frequency-sweep Fourier transform ion cyclotron resonance spectroscopy, Chem. Phys. Lett. 26 (1974) 489–490. [97] M.B. Comisarow, A.G. Marshall, Selective-phase ion cyclotron resonance spectroscopy, Canad. J. Chem. 52 (1974) 1997–1999. [98] R.T. McIver, A.D. Baranyi, High resolution ion cyclotron resonance spectroscopy, Int. J. Mass Spectrom. Ion Phys. 14 (1974) 449–458. [99] R. Marx, G. Mauclaire, A. Wallart, A. Deroulede, ICR-ESR and optical spectroscopy study of electron impact excitation and ionization in ammonia, Adv. Mass Spectrom. 6 (1974) 735–742. [100] R.C. Dunbar, M.M. Bursey, D.A. Chatfield, Ion cyclotron single and double resonance signals for a complex reaction scheme, Int. J. Mass Spectrom. Ion Phys. 13 (1974) 195–206. [101] P.P. Dymerski, E.W. Fu, R.C. Dunbar, Ion cyclotron resonance photodissociation spectroscopy of substituted benzenes, J. Am. Chem. Soc. 96 (1974) 4109–4114. [102] W.J. Hehre, R.T. McIver, J.A. Pople, P.V.R. Schleyer, Alkyl substituent effects on the stability of protonated benzene, J. Am. Chem. Soc. 94 (1974) 7162–7163. [103] J.R. Eyler, Intracavity dye laser technique for the study of laser-induced ionic processes, Rev. Sci. Instrum. 45 (1974) 1154–1155. [104] J.R. Eyler, P. Ausloos, S.G. Lias, A novel ion-molecule reaction involving cleavage of the carbonyl bond in ketones and aldehydes, J. Am. Chem. Soc. 96 (1974) 3673–3675. [105] J.H. Bowie, B. Williams, Electron impact studies. LXXXVI. Ion-molecule reactions of alkyl and aryl cyanides, Aust. J. Chem. 27 (1974) 769–775. [106] M.B. Comisarow, A.G. Marshall, Resolution-enhanced Fourier transform ion cyclotron resonance spectroscopy, J. Chem. Phys. 62 (1975) 293–295. [107] C.A. Lieder, J.I. Brauman, A technique for detection of neutral products in gas phase ion-molecule reactions, Int. J. Mass Spectrom. Ion Phys. 16 (1975) 307319. [108] R.T. Mciver, E.B. Ledford, J.S. Miller, Proposed method for mass spectrometric analysis for ultra-low vapor pressure compounds, Anal. Chem. 47 (1975) 692–697. [109] L.A. Viehland, E.A. Mason, J.H. Wealton, Kinetic theory of ion cyclotron resonance collision broadening, J. Chem. Phys. 62 (1975) 4715–4726. [110] K. Aoyagi, Y. Hasuda, M. Irie, K. Hayashi, J. Soiima, Ion cyclotron resonance study of ion-molecule reactions in benzene. Mechanism of C3H+3 formation, Bull. Chem. Soc. Jpn. 49 (1976) 2033–2036. [111] D.J. Defrees, W.J. Hehre, R.T. McIver, Electronic spectra of gas phase ions: an equilibrium perturbation approach, Chem. Phys. Lett. 42 (1976) 564–566. [112] B.S. Freiser, J.L. Beauchamp, Electron impact dissociation cyanobenzene radical cations by ion cyclotron resonance spectroscopy, Chem. Phys. Lett. 42 (1976) 380–382. [113] P.W. Atkins, M.J. Clugston, Linear and non-linear aspects of ion cyclotron resonance, Mol. Phys. 31 (1976) 603–614. [114] R.A. Barker, D.P. Ridge, lon-polar neutral momentum transfer collision frequencies: a theoretical approach, J. Chem. Phys. 64 (1976) 4411–4416. [115] M.B. Comisarow, A.G. Marshall, Theory of Fourier transform ion cyclotron resonance mass spectroscopy. I. Fundamental equations and low-pressure line shape, J. Chem. Phys. 64 (1976) 110–119.

28

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[116] R.L. Hunter, R.T. McIver, Conceptual and experimental basis for rapid scan ion cyclotron resonance spectroscopy, Chem. Phys. Lett. 49 (1977) 577–582. [117] M.T. Bowers, P.V. Neilson, P.R. Kemper, A.G. Wren, Temperature dependence of ion-molecule collision phenomena: an application of ion cyclotron resonance spectroscopy to the determination of reactive and momentum transfer rate constants, Int. J. Mass Spectrom. Ion Phys. 25 (1977) 103–116. [118] H. Hartmann, K.-M. Chung, Quantum-theoreticalt treatment of motions of ions in ion cyclotron resonance cells, Theor. Chim. Acta 45 (1977) 137–145. [119] R.T. McIver, Pulsed ion cyclotron resonance mass spectrometer for studying ion– molecule reactions, Rev. Sci. Instrum. 49 (1978) 111–118. [120] M.B. Comisarow, V. Grassi, G. Parisod, Fourier transform ion cyclotron double resonance, Chem. Phys. Lett. 57 (1978) 413–416. [121] M.B. Comisarow, Signal modeling for ion cyclotron resonance, J. Chem. Phys. 69 (1978) 4097–4104. [122] K. Aoyagi, Study of the quasi-peaks in the ion cyclotron double resonance, Bull. Chem. Soc. Jpn. 51 (1978) 355–359. [123] R.B. Cody, B.S. Freiser, Electron impact excitation of ions from organics: an alternative to collision induced dissociation, Anal. Chem. 51 (1979) 547–551. [124] A.G. Marshall, Theoretical signal-to-noise ratio and mass resolution in Fourier transform ion cyclotron resonance mass spectrometry, Anal. Chem. 51 (1979) 1710–1714. [125] H. Hartmann, K.-M. Chung, D. Schuch, J. Radtke, Collisionally damped ion motion in ICR spectrometry, Theor. Chim. Acta 53 (1979) 203–214. [126] R.C. Dunbar, On the coupling of ion cyclotron motion to ion internal degrees of freedom, Int. J. Mass Spectrom. Ion Phys. 29 (1979) 77–84. [127] M. Allemann, H.P. Kellerhals, K.P. Wanczek, A new Fourier transform mass spectrometer with superconducting magnet, Chem. Phys. Lett. 75 (1980) 328–331. [128] R.L. White, E.B. Ledford, S. Ghaderi, C.L. Wilkins, M.L. Gross, Resolution and signal-to-noise in Fourier transform mass spectrometry, Anal. Chem. 52 (1980) 1525–1527. [129] S.H. Lee, K.P. Wanczek, H. Hartmann, Adv. Mass Spectrom. 8B (1980) 1645–1649. [130] R.C. Dunbar, J.D. Hays, J.P. Honovich, N.B. Lev, Two laser multiphoton dissociation of iodobenzene cation, J. Am. Chem. Soc. 102 (1980) 3950–3951. [131] C. Amano, Detection theory of ion cyclotron resonance phenomena, Int. J. Mass Spectrom. Ion Phys. 35 (1980) 47–57. [132] R.T. McIver, E.B. Ledford, R.L. Hunter, Theory for broadband detection of ion cyclotron resonance signals, J. Chem. Phys. 72 (1980) 2535–2547. [133] A.G. Marshall, D.C. Roe, Theory of ion cyclotron resonance mass spectroscopy: response to frequency-sweep excitation, J. Chem. Phys. 73 (1980) 1581–1590. [134] H. Hartmann, K.-M. Chung, D. Schuch, K.P. Wanczek, On the Coriolis coupling of ion cyclotron motion to ion internal degrees of freedom, Int. J. Mass Spectrom. Ion Phys. 34 (1980) 303–310. [135] D.P. Littlejohn, S. Ghaderi, Mass Spectrometer and Method, US Patent 4,581,533, filed May, 15, 1984. [136] R.B. Cody, J.A. Kinsinger, S. Ghaderi, I.J. Amster, F.W. McLafferty, C.E. Brown, Developments in analytical Fourier transform mass spectrometry, Anal. Chim. Acta 178 (1985) 43–66. [137] J.P. Honovich, S.P. Markey, Characterization of ion trapping motion in the dual cell ion cyclotron resonance spectrometer: experimental and theoretical studies, Int. J. Mass Spectrom. Ion Process. 98 (1990) 51–68. [138] G. Mauclaire, R. Marx, Tricyclotron: a triple cell ICR, Adv. Mass Spectrom. 10B (1985) 913.

Historical developments in FT ICR mass spectrometry

29

[139] P. Kofel, M. Allemann, H.P. Kellerhals, K.P. Wanczek, External generation of ions in ICR spectrometry, Int. J. Mass Spectrom. Ion Process. 65 (1985) 97–103. [140] J.T. Meek, G.W. Stockton, Fourier Transform Ion Cyclotron Resonance Mass Spectrometer With Spatially Separated Sources and Detector, US Patent 4,686,365, filed Dec. 24, 1984. [141] P. Kofel, T.B. McMahon, High pressure external ion source for Fourier transform ion cyclotron resonance spectrometry, Int. J. Mass Spectrom. Ion Process. 98 (1990) 1–24. [142] S. Maruyama, L.R. Anderson, R.E. Smalley, Direct injection supersonic cluster beam source for FT ICR studies of clusters, Rev. Sci. Instrum. 61 (1990) 3686–3693. [143] R.T. McIver, R.L. Hunter, W.D. Bowers, Coupling a quadrupole mass spectrometer and a Fourier transform mass spectrometer, Int. J. Mass Spectrom. Ion Process. 64 (1985) 67–77. [144] P. Kofel, M. Allemann, H.P. Kellerhals, K.P. Wanczek, An external trapped ion source for ICR spectrometry, Int. J. Mass Spectrom. Ion Process. 87 (1989) 237–247. [145] P.R. Kemper, M.T. Bowers, An improved tandem mass spectrometer- ion cyclotron resonance spectrometer, Int. J. Mass Spectrom. Ion Phys. 52 (1983) 1–24. [146] P. Kofel, M. Allemann, H.P. Kellerhals, K.P. Wanczek, Time-of-flight ICR spectrometry, Int. J. Mass Spectrom. Ion. Process. 72 (1986) 53–61. [147] R.C. Dunbar, G.H. Weddle, Ion cyclotron resonance time-of-flight spectroscopy. Kinetic energy of p-iodotuluene photodissociation fragment ions, J. Phys. Chem. 92 (1988) 5706–5709. [148] Y. Pan, D.P. Ridge, J. Wronka, A.L. Rockwood, Resolution improvement through harmonic detection in an ion cyclotron resonance mass spectrometer, Rapid Commun. Mass Spectrom. 1 (1987) 120–121. [149] E.N. Nikolaev, M.V. Gorhkov, A.V. Mordehai, V.L. Talrose, Ion cyclotron resonance signal detection at multiples of the cyclotron frequency, Rapid Commun. Mass Spectrom. 4 (1990) 144–146. [150] L. Schweikhard, Ion cyclotron resonance signal detection at multiples of the cyclotron frequency, Rapid Commun. Mass Spectrom. 4 (1990) 360. [151] G. Gabrielse, L. Haarsma, S.L. Rolston, Open-endcap Penning trap for high precision experiments, Int. J. Mass Spectrom. Ion Process. 88 (1988) 319–332. [152] T.C.L. Wang, T.L. Ricca, A.G. Marshall, Extension of dynamic range in Fourier transform ion cyclotron resonance mass spectrometry via stored waveform inverse Fourier transform excitation, Anal. Chem. 58 (1986) 2935–2938. [153] R.T. McIver, R.L. Hunter, G. Baykut, Impulse excitation for Fourier transform mass spectrometry, Anal. Chem. 61 (1989) 489–491. [154] P. Pf€andler, G. Bodenhausen, J. Rapin, R. Houriet, T. G€aumann, Two-dimensional Fourier transform ion cyclotron resonance mass spectrometry, Chem. Phys. Lett. 138 (1987) 195–200. [155] E.R. Williams, K.D. Henry, F.W. McLafferty, Multiple remeasurements of ions in Fourier transform mass spectrometry, J. Am. Chem. Soc. 112 (1990) 6157–6162. [156] F.H. Laukien, The effects of residual spatial magnetic field gradients on Fourier transform ion cyclotron resonance spectra, Int. J. Mass Spectrom. Ion Process. 73 (1986) 81–107. [157] J.B. Jeffries, S.E. Barlow, G.H. Dunn, Theory of space charge shifts of ion cyclotron resonance frequencies, Int. J. Mass Spectrom. Ion Process. 54 (1983) 169–187. [158] H. Hartmann, K.-M. Chung, G. Baykut, K.P. Wanczek, Dependence of ion cyclotron frequency on electric field inhomogeneity, J. Chem. Phys. 78 (1983) 424–431. [159] F.H. Schlereth, D.P. Littlejohn, J.E. Phillips, Fourier Transform Molecular Spectrometer, US Patent 5,233,190, filed Nov 22, (1991). [160] S. Bichlmeier, A. Kania, FTICR Massenspektrometer, LABO, 2002, pp. 24–27.

30

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[161] S.D.-H. Shi, J.J. Drader, C.L. Hendrickson, A.G. Marshall, Fourier transform ion cyclotron resonance mass spectrometer in a high homogeneity 25 Tesla resistive magnet, J. Am. Soc. Mass Spectrom. 10 (1999) 265–268. [162] M. Bamberg, K.P. Wanczek, A supersonic beam ICR instrument for the study of van der Waals cluster ions, Z. Phys. D 20 (1991) 433–435. [163] W.D.F. Reents, M.L. Mandich, A.M. DeSantolo, External source Fourier transform ion cyclotron resonance mass spectrometry by using a split-pair magnet, Int. J. Mass Spectrom. 177 (1998) 63–81. [164] M.V. Gorshkov, L. Pasa-Tolic, J.E. Bruce, G.A. Anderson, R.D. Smith, A dual-trap design and its applications in electrospray ionization FTICR mass spectrometry, Anal. Chem. 69 (1997) 1307–1314. [165] S.A. Shaffer, K.Q. Tang, G.A. Anderson, D.C. Prior, H.R. Udseth, R.D. Smith, A novel ion funnel for focusing ions at elevated pressures using electrospray ionization mass spectrometry, Rapid Commun. Mass Spectrom. 11 (1997) 1813–1817. [166] S. Guan, A.G. Marshall, Ion traps for Fourier transform ion cyclotron resonance mass spectrometry: principles and design of geometric and electric configurations, Int. J. Mass Spectrom. Ion Process. 146 (147) (1995) 261–296. [167] K.J. Gillig, B.K. Blum, D.H. Russell, Ion motion in a Fourier transform ion cyclotron resonance wire ion guide cell, Int. J. Mass Spectrom. Ion Process. 158 (1996) 129–147. [168] P. Caravatti, M. Allemann, The ‘Infinity Cell’: a new trapped-ion cell with radiofrequency covered trapping electrodes for Fourier transform ion cyclotron resonance mass spectrometry, Org. Mass Spectrom. 26 (1991) 514–518. [169] E.N. Nikolaev, V.S. Rakova, J.H. Futrell, Analysis of harmonics for an elongated FTMS cell with multiple electrode detection, Int. J. Mass Spectrom. Ion Process. 157 (158) (1996) 215–232. [170] M. Knobeler, K.P. Wanczek, Suppression, amplification and application of the third harmonic of the cyclotron frequency in ion cyclotron resonance spectrometry, Int. J. Mass Spectrom. Ion Process. 125 (1993) 127–134. [171] C.B. Jacoby, C.L. Holliman, D.L. Rempel, M.L. Gross, Ion cloud manipulation using the radiofrequency-only-mode as an improvement for high mass detection in Fourier transform mass spectrometry, J. Am. Soc. Mass Spectrom. 4 (1993) 186–189. [172] Y.L. Huang, G.Z. Li, S.H. Guan, A.G. Marshall, A combined linear ion trap for mass spectrometry, J. Am. Soc. Mass Spectrom. 8 (1997) 962–969. [173] M.V. Gorshkov, S. Guan, A.G. Marshall, Dynamic ion trapping for Fourier transform ion cyclotron resonance mass spectrometry: simultaneous positive and negative ion detection, Rapid Commun. Mass Spectrom. 6 (1992) 166–172. [174] Y. Wang, K.P. Wanczek, A new ion cyclotron resonance cell for simultaneous trapping of positive and negative ions, Rev. Sci. Instrum. 64 (1993) 883–889. [175] R. Malek, K.P. Wanczek, FT ICR spectrometry with simultaneous trapping of positive and negative ions, Int. J. Mass Spectrom. Ion Process. 157 (158) (1996) 199–214. [176] B. Kanawati, K.P. Wanczek, Characterization of a new open cylindrical ion cyclotron resonance cell with unusual geometry, Rev. Sci Instrum. 78 (2007)074102. [177] V.H. Vartanian, D.A. Laude, Simultaneous trapping of positive and negative ions using a nested open-ended trapped ion cell in Fourier transform ion cyclotron resonance mass spectrometry, Org. Mass Spectrom. 29 (1994) 692–694. [178] D.S. Hall, G. Gabrielse, Electron cooling of protons in a nested Penning trap, Phys. Rev. Lett. 77 (1996) 1962–1965. [179] P.B. Grosshans, P.J. Shields, A.G. Marshall, Comprehensive theory of the Fourier transform ion cyclotron resonance signal for all ion trap geometries, J. Chem. Phys. 94 (1991) 5341–5352. [180] M. Kretschmar, Excitation of particle motions in a Penning trap: description of the classical canonical formalism, Phys. Scr. 46 (1992) 555–559.

Historical developments in FT ICR mass spectrometry

31

[181] D.W. Mitchell, Theory of trapped ion motion in the non-quadrupolar electrostatic potential of a cubic ion cyclotron resonance cell, Int. J. Mass Spectrom. Ion Process. 142 (1995) 1–22. [182] J.J. Valle, J.R. Eyler, J. Oomens, D.T. Moore, A.F.G. Vandermeer, G. Vonhelden, G. Meijer, C.L. Hendrickson, A.G. Marshall, G.T. Blakney, Free electron laser-Fourier transform ion cyclotron resonance mass spectrometry facility for obtaining infrared multiphoton dissociation spectra of gaseous ions, Rev. Sci. Instrum. 76 (2005)023103. [183] L. Jasikova, J. Roithova´, Infrared multiphoton dissociation spectroscopy with free electron lasers: on the road from small molecules to biomolecules, Chem. Eur. J. (2018) https://doi.org/10.1002/chem.201705692 Accepted Manuscript Online 3 January. [184] J.E.P. Syka, J.A. Marto, D.L. Bai, S. Horning, M.W. Senko, J.C. Schwartz, B. Ueberheide, B. Garcia, S. Busby, T. Muratore, J. Shabanowitz, D.F. Hunt, Novel linear quadrupole ion trap/FT mass spectrometer: performance characterization and use in the comparative analysis of histone H3 post-translational modifications, J. Proteome Res. 3 (2004) 621–626. [185] C.R. Weisbrod, M.R. Hoopmann, M.W. Senko, J.E. Bruce, Performance evaluation of a dual linear ion trap-Fourier transform ion cyclotron resonance mass spectrometer for proteomics research, J. Proteom. 88 (2013) 109–119. [186] P.B. O’Connor, Considerations for design of a Fourier transform mass spectrometer in the 4.2K cold bore of a superconducting magnet, Rapid Commun. Mass Spectrom. 16 (2002) 1160. [187] C. Lin, R. Mathur, K. Aizikov, P.B. O’Connor, First signal on the cryogenic Fouriertransform ion cyclotron resonance mass spectrometer, J. Am. Soc. Mass Spectrom. 18 (2007) 2090–2093. [188] T.A. Painter, W.D. Markiewicz, J.R. Miller, S.T. Bole, I.R. Dixon, K.R. Cantrell, S. J. Kenney, A.J. Trowell, D.L. Kim, B.S. Lee, Y.S. Choi, H.S. Kim, C.L. Hendrickson, A.G. Marshall, Requirements and conceptual super conducting magnet design for a 21 T Fourier transform ion cyclotron resonance mass spectrometer, IEEE Trans. Appl. Supercond. 16 (2006) 945–948. [189] C.L. Hendrickson, J.P. Quinn, N.K. Kaiser, D.F. Smith, G.T. Blakney, T. Chen, A. G. Marshall, C.R. Weisbrod, S.C. Beu, 21 Tesla Fourier transform ion cyclotron resonance mass spectrometer: a national resource for ultrahigh resolution mass analysis, J. Am. Soc. Mass Spectrom. 26 (2015) 1626–1632. [190] M.A. van Agthoven, M.-A. Delsuc, C. Rolando, Two-dimensional FT-ICR/MS with IRMPD as fragmentation mode, Int. J. Mass Spectrom. 306 (2011) 196–203. [191] M.A. van Agthoven, M.-A. Delsuc, G. Bodenhausen, C. Rolando, Towards analytically useful two-dimensional Fourier transform ion cyclotron resonance mass spectrometry, Anal. Bioanal. Chem. 405 (2013) 51–61. [192] M.A. van Agthoven, L. Chiron, M.-A. Coutouly, A.A. Sehgal, P. Pelupessy, M.A. Delsuc, C. Rolando, Optimization of the discrete pulse sequence for twodimensional FT-ICR mass spectrometry using infrared multiphoton dissociation, Int. J. Mass Spectrom. 370 (2014) 114–124. [193] A.A. Sehgal, P. Pelupessy, C. Rolando, G. Bodenhausen, Theory for spiraling ions for 2D FT-ICR and comparison with precessing magnetization vectors in 2D NMR, Phys. Chem. Chem. Phys. 18 (2016) 9167–9175. [194] M.A. van Agthoven, M.-A. Coutouly, C. Rolando, M.-A. Delsuc, Two-dimensional Fourier transform ion cyclotron resonance mass spectrometry: reduction of scintillation noise using Cadzow data processing, Rapid Commun. Mass Spectrom. 25 (2011) 1609–1616. [195] Y. Qi, M.P. Barrow, H. Li, J.E. Meier, S.L. Van Orden, C.J. Thompson, P. B. O’Connor, Absorption-mode: the next generation of Fourier transform mass spectra, Anal. Chem. 84 (2012) 2923–2929.

32

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[196] F. Xian, Y.E. Corilo, C.L. Hendrickson, A.G. Marshall, Baseline correction of absorption-mode Fourier transform ion cyclotron resonance mass spectra, Int. J. Mass Spectrom. 325 (2012) 67–72. [197] Y. Qi, M. Witt, R. Jertz, G. Baykut, M.P. Barrow, E.N. Nikolaev, P.B. O’Connor, Absorption-mode spectra on the dynamically harmonized Fourier transform ion cyclotron resonance cell, Rapid Commun. Mass Spectrom. 26 (2012) 2021–2026. [198] B. Kanawati, K.P. Wanczek, Characterization of a new open cylindrical ICR cell for ion–ion collision studies, Int. J. Mass Spectrom. 269 (2008) 12–23. [199] B. Kanawati, K.P. Wanczek, RF-driven ion–ion long-range interaction in a new open cylindrical ICR cell, Int. J. Mass Spectrom. 274 (2008) 30–47. [200] K.O. Nagornov, A.N. Kozhinov, O.Y. Tsybin, Y.O. Tsybin, Ion trap with narrow aperture detection electrodes for Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Soc. Mass Spectrom. 26 (2015) 741–751. [201] K.O. Nagornov, A.N. Kozhinov, Y.O. Tsybin, Fourier transform ion cyclotron resonance mass spectrometry at the cyclotron frequency, J. Am. Soc. Mass Spectrom. 28 (2017) 768–780. [202] R. Jertz, C. Gosteli, W. Roeck, C. Kriete, M. Witt, J. Friedrich, S.L. VanOrden, C. J. Thompson, C. Berg, J. Fuchser, C. Henkel, M.L. Easterling, E.N. Nikolaev, G. Baykut, 2ω-FT-ICR mass spectrometry with quadrupolar detection, in: 21st Int. Mass Spectrom. Conf. Toronto, Canada, Aug. 20–26, 2016. [203] E.N. Nikolaev, I.A. Boldin, R. Jertz, G. Baykut, Initial experimental characterization of a new ultra-high resolution FTICR Cell with dynamic harmonization, J. Am. Soc. Mass Spectrom. 22 (2011) 1125–1135. [204] R. Jertz, J. Friedrich, C. Kriete, E.N. Nikolaev, G. Baykut, Tracking the magnetron motion in FT-ICR mass spectrometry, J. Am. Soc. Mass Spectrom. 26 (2015) 1349–1366. [205] G. Gabrielse, Relaxation calculation of the electrostatic properties of compensated Penning traps with hyperbolic electrodes, Phys. Rev. A 27 (1983) 2277–2290. [206] G. Gabrielse, F.C. MacKintosh, Cylindrical Penning traps with orthogonalized anharmonicity compensation, Int. J. Mass Spectrom. Ion Process. 57 (1984) 1–17. [207] A.V. Tolmachev, E.W. Robinson, S. Wu, H. Kang, N.M. Lourette, L. Pasa-Tolic, R. D. Smith, Trapped-Ion cell with improved DC potential harmonicity for FT-ICR MS, J. Am. Soc. Mass Spectrom. 19 (2008) 586–597. [208] A.V. Tolmachev, E.W. Robinson, R.D. Smith, F.E. Leach, J.H. Futrell, L. PasaTolic, A conceptual approach for FT-ICR cell harmonization utilizing external shim electrodes, Int. J. Mass Spectrom. 325 (2012) 45–50. [209] N.K. Kaiser, J.J. Savory, A.M. McKenna, J.P. Quinn, C.L. Hendrickson, A. G. Marshall, Electrically compensated Fourier transform ion cyclotron resonance cell for complex mixture mass analysis, Anal. Chem. 83 (2011) 6907–6910. [210] C.M. Ostrander, C.R. Arkin, D. Laude, Central ring Electrode for trapping and excitation/detection in Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Soc. Mass Spectrom. 12 (2001) 30–37. [211] V.H. Vartanian, D.A. Laude, High performance Fourier transform ion cyclotron resonance mass spectrometry via a single trap electrode, J. Am. Soc. Mass Spectrom. 6 (1995) 812–821. [212] C.M. Ostrander, C.R. Arkin, D. Laude, Magnetic field focused ion accumulation for an internal bore liquid chromatography electrospray ionization Fourier transform ion cyclotron resonance mass spectrometer using a central trapping electrode, J Am Soc Mass Spectrom. 11 (2000) 592–595. [213] C.R. Weisbrod, N.K. Kaiser, G.E. Skulason, J.E. Bruce, Trapping ring electrode cell: a FTICR mass spectrometer cell for improved signal-to-noise and resolving power, Anal. Chem. 80 (2008) 6545–6553.

Historical developments in FT ICR mass spectrometry

33

[214] A.S. Misharin, R.A. Zubarev, Coaxial multi-electrode cell (‘O-trap’) for highsensitivity detection at a multiple frequency in Fourier transform ion cyclotron resonance mass spectrometry: main design and modeling results, Rapid Commun. Mass Spectrom. 20 (2006) 3223–3228. [215] A.M. Brustkern, D.L. Rempel, M.L. Gross, An electrically compensated trap designed to eighth order for FT-ICR mass spectrometry, J. Am. Soc. Mass Spectrom. 19 (2008) 1281–1285. [216] S. Kim, M.C. Choi, M. Hur, H.S. Kim, J.S. Yoo, C.L. Hendrickson, A.G. Marshall, The ‘hybrid cell’: a new compensated infinity cell for larger radius ion excitation in Fourier transform ion cyclotron resonance mass spectrometry, Rapid Commun. Mass Spectrom. 22 (2008) 1423–1429. [217] V.V. Lobodin, J.J. Savory, N.K. Kaiser, P.W. Dunk, A.G. Marshall, Charge reversal Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Soc. Mass Spectrom. 24 (2013) 213–221. [218] F. L€ ubkemann, K.P. Wanczek, Miniature ICR cells, Int. J. Mass Spectrom. 281 (2009) 150–156. [219] S.-G. Park, G.A. Anderson, J.E. Bruce, Parallel spectral acquisition with orthogonal ICR cells, J. Am. Soc. Mass Spectrom. 28 (2017) 515–524. [220] S.-G. Park, G.A. Anderson, A.T. Navare, J.E. Bruce, Parallel spectral acquisition with an ion cyclotron resonance cell array, Anal. Chem. 88 (2016) 1162–1168. [221] O.P. Balaj, C.B. Berg, S.J. Reitmeier, V.E. Bondybey, M.K. Beyer, A novel design of a temperature-controlled FT-ICR cell for low-temperature black-body infrared radiative dissociation (BIRD) studies of hydrated ions, Int. J. Mass Spectrom. 279 (2009) 5–9.

CHAPTER 2

Fundamentals of Orbitrap analyzer Alexander Makarov, Dmitry Grinfeld, Konstantin Ayzikov Thermo Fisher Scientific, Bremen, Germany

Contents Principles of operation Non-ideal orbital traps and their calibration Advances in signal processing Fourier transform methods Autocorrelation methods Maximum likelihood parameter estimators Deconvolution method Evolution of the Orbitrap platform and selected applications Acknowledgments References

38 43 48 48 51 52 53 54 57 57

The effect of ion confinement in magnetic fields or radio-frequency fields is widely known and used for decades in many technical applications including, first of all, mass spectrometry. The use of purely electrostatic traps for analytical purposes was still very attractive because of the compact design and their relative simplicity. As it follows from the Earnshaw’s theorem, an electrostatic field doesn’t allow any stable static equilibrium of a charged particle. Nevertheless, a particle may be still confined dynamically thus giving the opportunity to build an electrostatic ion trap with desirable properties, in which the ions would be revolving on orbits. The idea of orbital ion confinement dates back to 1923 when Kingdon placed a negatively biased thin wire into a metal cylinder thus implementing the logarithmic electrostatic potential ∝ ln r [1]. Positive ions were stored in the trap for a long time while revolving around the wire, as the angular momentum conservation didn’t allow them to fall onto the wire. In the course of the several following decades, this principle was often used in ion spectroscopy, but it was not until 1981 that Knight equipped the Kingdon’s trap with a pair of tapered positive electrodes which also restricted the ion motion in the longitudinal direction [2]. The axial quasi-harmonic ion Fundamentals and Applications of Fourier Transform Mass Spectrometry https://doi.org/10.1016/B978-0-12-814013-0.00002-8

© 2019 Elsevier Inc. All rights reserved.

37

38

Fundamentals and Applications of Fourier Transform Mass Spectrometry

oscillations were excited by a resonant waveform applied to the restricting electrodes, and a simple mass analysis was performed as the oscillation frequency was apparently mass dependent. To describe properties of this trap, Knight introduced the quadro-logarithmic approximation for the electrostatic potential
 r2 r 2 2 + C2 (2.1) φðz, r Þ ¼ C1 z  + Rm ln 2 Rm where Rm was a geometrical parameter and C1 and C2 were some constants. An important property of such field distribution would be the ideal isochronism of ion oscillations in the axial direction. The actual field distribution was, however, rather different from that given by Eq. (2.1), which substantially limited the mass resolving quality. From today’s perspective, Knight has demonstrated the importance of precise electric field definition to achieve the oscillation isochronism. The charged particle motion in the quardo-logarithmic field was studied in details by Gall et al. who proposed it for the time-of-flight mass spectrometry [3]. The use of quadro-logarithmic fields for accurate energy analysis was shown in [4]. If the field is defined with the sufficient precision to preserve isochronicity during not one but many oscillations, the list of possible analytical applications may be appended with the Fourier transform mass spectrometry (FT MS). The ideal axial harmonicity of ion oscillations will allow differentiation of the ions with respect to their mass-to-charge ratios with ultimate precision and resolution, which was earlier only achievable with the use of superconductive magnets [5, 6]. This chapter is devoted to the principles of the Orbitrap™ mass spectrometry. The first section introduces basic equations of ion motion in the quadro-logarithmic field and describes the orbital trap’s design. The second section considers effects of small but unavoidable non-idealities of the practical ion trap implementation and discusses methods of aberration compensation. Relevant methods of signal processing are described in the third section. In the fourth section, we consider the evolution of the Orbitrap mass spectrometers and selected mass-spectrometric applications.

Principles of operation The electrostatic field distribution given by Eq. (2.1) eventually satisfies the Laplace equation and, therefore, may be realized with a system of electrodes.

Fundamentals of Orbitrap analyzer

39

Fig. 2.1 The orbital ion trap. (1) The spindle-like inner electrode, (2) the outer electrode, (3) ion orbits, (4) isolating ring separating two sections of the outer electrode, (5) the tangential aperture for ion injection. At the bottom: the axial and radial distributions of the effective potential.

The simplest, but still very accurate, method of field definition employs a pair of axisymmetrical electrodes shaped to follow the equipotential surfaces as shown in Fig. 2.1. The appropriate electrodes have the spindle-like shapes, and their profiles are parametrically expressed as rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r 2  R2 r  Rm2 ln z ðr Þ ¼  (2.2) 2 R where R < Rm is the largest electrode radius (see also [7]). Correspondingly, the inner and the other electrodes are defined by their maximum radii R1 and R2. The outer electrode is grounded and the inner electrode is biased with a negative voltage Vc. Such boundary conditions define the constants C1 and C2, giving
 k0 2 R22  r 2 r 2 (2.3) z + + Rm ln ϕ ¼ Vc ϕ0 ðz, r Þ, ϕ0 ¼  2 2 R2 and k0 ¼ 4/(R21  R22 + 2R2m ln (R2/R1)).

40

Fundamentals and Applications of Fourier Transform Mass Spectrometry

The most important property of the quadro-logarithmic field distribution is that the ion motion equation for the axial coordinate z pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ‥ z + ω2 z ¼ 0, ω ¼ Vc k0 q=m (2.4) is separated from the other equations with respect to the radial coordinate r and the revolving angle ψ. The solution reads z ¼ Z cos ðωt + ζ Þ

(2.5)

where the amplitude Z and the phase ζ are constants of motion. The axial oscillation frequency ω depends solely on the ion’s mass-to-charge ratio m/q, and ions of the same m/q would, therefore, preserve the common phase. The radial motion stability comes from conservation of the rotational momentum K ¼ r 2 ψ_ (per unit mass) defined by injection. As K is a constant of motion, the angular coordinate ψ may be excluded from the equation for r, and the latter acquires the closed form
 q 0 Vc k0 r r2 m K2 ‥ 2 Rm ln +  (2.6) r ¼ Ur , Ur ðr Þ ¼ 2 m Rm 2 q 2r 2 where Ur is the radial potential component which includes the centrifugal term. The radial potential appears to have a stable stationary point rc in which Ur0 ¼ 0 and Ur00 > 0. This radius corresponds to a circular orbit r2 r ¼ rc ðK Þ ¼ m  2

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi!1 2 rm4 2mK 2 K , ψ ¼ ψ0 + 2 t  4 qVc k0 rc

(2.7)

In a general case, however, an ion revolves around the inner electrode on a quasi-elliptic orbit constrained between rmin and rmax both sides from rc. These limits are determined by the equation Ur(rmin) ¼ Ur(rmax) ¼ U∗r , where U∗r is another constant of motion—a conserved radial energy. So, an ion orbit is defined by three conserved values {Z, K, U∗r } and three initial phases. Since Eqs. (2.7) and (2.8) establish the one-to-one correspondence between {K, U∗r } and {rmin, rmax}, one can alternatively use the parameters {Z, rmin, rmax} to identify an orbit. In case that the rectangle Z  z  Z and rmin  r  rmax lies entirely in the space between the electrodes, an ion may oscillate infinitely provided that no interactions with the residual gas and other ions take place. Further details may be found in [7–10]. A modified realization of the quadro-logarithmic field was proposed by Doroshenko and Misharin [11] who extended the outer electrode to the

Fundamentals of Orbitrap analyzer

41

field’s saddle point r ¼ Rm. It is also possible to generate a similar field with a stack of round electrodes as shown in [12]. It should be noted that the quadro-logarithmic field given by Eq. (2.3) is not the only possible class of fields with the axial harmonicity and the radial confinement. Other relevant field distributions may be obtained as superpositions of distributions like Eq. (2.3) with shifted axes of symmetry, and any regular term φ1(x, y) satisfying the 2D Laplace equation may be added. Some sophisticated examples were proposed by K€ oster [13, 14]. Nevertheless, Eq. (2.3) has the crucial advantage of simplicity and, what is more important, the oscillation frequency in this field is immune to almost all sorts of assembly misalignments as follows from the theoretical consideration below. Introducing ions into an orbital trap is hindered by the absence of a direct line of sight to the trapping region, and the only way to bring ions in is through an aperture in the outer electrodes. In the considered design, the ions are injected tangentially into the orbital trap through a narrow slot as shown in Figs. 2.1 and 2.2. Advantageously, this method of injection gives the ions an initial oscillation amplitude Z(i ) approximately equal to the z

FIg. 2.2 Ion squeezing process following the injection and signal detection. BottomLeft: effective radial potential after injection (1) and after the inner electrode voltage is increased by 20% (2). The arrows show corresponding radial oscillation spans. Bottom-Right: ion orbits right after injection (1) and after the adiabatic squeezing (2). First several oscillations are shown (3) that originate from the injection slot (4) located at the radius rinj.

42

Fundamentals and Applications of Fourier Transform Mass Spectrometry

coordinate of the slot, so any other excitation means are redundant. Another essential benefit of the excitation via off-center injection is that the oscillation phases of all ions turn out to be predicable. This property is crucial for enhancing the mass resolving capabilities by advanced signal processing. Keeping an injected ion on a stable orbit is not straightforward though. The difficulty is that the orbital parameters rmax and Z turn out to be too large for a long-term ion’s stability. Indeed, after a number of oscillations, the coordinates r and z should inevitably approach their extreme values together and an ion will definitely hit the outer electrode. Fortunately, this number of oscillations is sufficiently large in practice, providing several microseconds following the injection to avoid a collision, during which the voltage on the central electrode should be gradually increased from Vic to Vc. This process is called ‘squeezing’ as it allows to bring the ions onto smaller orbits located at a safe distance from both outer and inner electrodes. The squeezing time incorporates tens of axial and radial oscillations and thus may be described as an adiabatic process that preserves two action integrals rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Z +Z pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Vc ðtÞkm Iz ¼ Vc ϕ0 ðZ, r Þ  Vc ϕ0 ðz, r Þdz ¼ π Z ðtÞ2 ¼ const (2.8) 2 Z and Z Ir ¼

rmax rmin

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ur∗  Ur ðr Þdr ¼ const

(2.9)

related, correspondingly, to the axial and the radial oscillations in a slowly changing potential well. One can see from Eq. (2.8) that the axial amplitude decreases with the central electrode voltage as Z ¼ Z(i )(Vc(i )/Vc)1/4. The radial amplitude △R ¼ rmax  rmin also decreases, though the dependence is more complicated due to the nonquadratic shape of the radial well Eq. (2.6). More prominent effect on rmax comes from the decrease of the circular orbit radius rc as Eq. (2.7) suggests for conserved rotation momentum K and Vc increasing gradually. The axial frequency ω increases proportionally to Vc 1=2 during squeezing in accordance with Eq. (2.4). Fig. 2.2 shows an example of ion orbits right after injection and when squeezing is completed. The rectangular cross-section defines a hollow cylinder that constrains the “squeezed” orbits and which is safely separated from the outer electrode. This not only prevents a collision but also minimizes the effect of field perturbations that arise from the injection slot.

Fundamentals of Orbitrap analyzer

43

The central electrode voltage is stabilized upon squeezing and the ions further oscillate with precise axial frequencies ω defined by Vc and the mass-to-charge ratios but independent of the orbital parameters. It should be specially noted that the radial oscillations and the rotation are not isochronous and their frequencies substantially depend on the orbital parameters rmin and rmax. As these parameters have an intrinsic spread, each population of same m/q ions effectively dephases in the radial and angular coordinates and assumes the shape of a ring that oscillates in-phase along the z axis. The z-phase evolution is observed in time via the image-current detection. For this purpose, the outer electrode is split into two sections isolated from each other with a quartz ring as shown in Fig. 2.1. When an ion with charge q is inside the orbital trap, an opposite image charge q is induced on the electrodes, making the whole system electrically neutral. Distribution of the mirror charge between the two sections of the outer electrode and the inner electrode depends on the momentary ion’s coordinates and induces a small voltage difference between the sections that depends on the ion’s z coordinate as △V ¼ q f(z)/C, where C is the effective electric capacitance of a section and the function f(z) may be calculated with the use of the electrostatic reciprocity theorem; it appears rather close to a linear dependence. Approximately 45%–50% of the total mirror charge may be detected in this way, the remaining charge is induced on the inner electrode. A differential amplifier picks up the voltage difference between the outer electrode sections. The whole ensemble of trapped ions generates a sum of induced-current signals distributed across a range of frequencies, which can be recovered by the Fourier transform or other processing methods.

Non-ideal orbital traps and their calibration Consider the ion motion in the perturbed potential Vcφ0(z, r) + δφ(z, r, ψ) where the first term is the unperturbed ideal field and the second term is a small perturbation. In order to estimate the influence of all such perturbations on the ion motion, one should use a perturbation technique that provides a proper level of precision. The unperturbed ion motion given by Eq. (2.4) has two integrals—the oscillation amplitude Z and the (initial) phase ζ. The ion motion in the presence of a perturbation δφ may also be described by Eq. (2.4), though the amplitude and the phase must be treated as functions of time in accordance with the method of variations of parameters to solve a perturbed system of linear equations with a generally non-linear perturbation.

44

Fundamentals and Applications of Fourier Transform Mass Spectrometry

The Krylov-Bogolubov-Mitropolsky’s method of perturbation averaging [15, 16] leads to the differential equation for the phase drift 1 ∂H1 q ζ_ ¼ , H1 ðZ Þ ¼ ωmZ ∂Z 2π

Zπ δϕ ðZ cosχ Þdχ

(2.10)

π

where H1(Z) is the Hamiltonian perturbation in the action-angle variables. The integrand δφ is the potential perturbation averaged over the spread of the radial and angular coordinates as 1 δφðzÞ ¼ 2π

Zrmax δφðz, r, ψ Þρðr, rmin , rmax Þdrdψ,

(2.11)

rmin

where ρ is normalized radial weight proportional to the time an ion spends at the radius r. By bringing differentiation with respect to the amplitude Z in Eq. (2.10) under the integral sign and changing the order of integrations, the following expression for the relative frequency error is obtained Z rmax δωðZ, rmin , rmax Þ ζ_ δΩðZ, r Þρðr, rmin , rmax Þdr (2.12) ¼ ¼ ω ω rmin where δωðZ, r, r Þ 1 ¼ 2 ΩðZ, r Þ ¼ ω 4π Vc k0 Z

Z

π

π

Z

π

π

δϕ0 z ðZ cos χ, r, ψ Þ cos χdχdψ (2.13)

is the normalized round-orbit frequency shift. Because of a spread of the orbital parameters in the ion ensemble, the oscillation frequencies in a non-ideal field also vary, and the spread of δω imposes the mass resolution limit of the orbital trap. To estimate the effect, we should consider the function δΩ(Z, r) within the populated domain of amplitudes and radii. Ideally, this function must be constant in this domain, which gives all ions the same frequency shift. Being not necessarily zero, this shift can be easily calibrated out. The field perturbations may be classified into three groups according to their origin. The first class of perturbations is due to assembly inaccuracies that include shifts and tilts of the field-defining electrodes relative to each other. Fortunately, they generate only the first-order spatial harmonics of the field perturbations like δφ ¼ a1 cos ψ + a2 sin ψ. Eq. (2.13) shows that averaging of such perturbations in the [0 … 2π] range of the angular variable

Fundamentals of Orbitrap analyzer

45

ψ annihilates any effect of such perturbations on the ion oscillating frequencies; the only exception is resonances between the frequencies of _ which are to be thoroughly avoided. z-oscillations ω and revolutions ψ, The integral in Eq. (2.13) vanishes for any anti-symmetrical perturbation δφ with respect to the middle plane z ¼ 0, making the oscillation frequency also immune in the first approximation to the axial displacement of the inner electrode with respect to outer electrode. By such considerations, one can see that the only assembly misalignment that contributes significantly to the frequency perturbation is the variation of the separation △zS between the two sections of the outer electrode. The gap variation may be either positive or negative, depending on the thickness of the quartz spacer between the sections. The second class of perturbations is caused by any external field penetrating into the trap’s working volume. This is why the z-extent of the inner and the outer electrodes should be big enough to prevent any fringe-field perturbations. The only unavoidable perturbation of this sort comes from the ion injection slot. The third class of perturbations contains inaccuracies of the electrode shapes. The best modern metal-cutting techniques allow precision on the order of approximately one micron. To compare with the outer radius R2 10 mm, such unavoidable and barely controllable errors may cause the frequency shifts up to δω/ω1/10,000. It means that the mass-resolving power better than 10,000 can only be achieved if such perturbations are properly compensated. Fortunately, compensation of the frequency spread doesn’t require complete elimination of the field perturbation. The only requirement for the function δΩ(Z, r) to be sufficiently flat in the region actually occupied by the ion orbits upon squeezing and defined by the oscillation amplitudes Z and the radii rmin < r < rmax. Moreover, a constant offset of the frequency like δΩ ¼ δΩ0 ¼ const may be easily accounted for by the central electrode voltage Vc adjustment and/or mass calibration with the use of calibrant ions with known mass and charge state. The tuning procedure for the orbital trap consists in compensation of the ion frequency dispersion with the use of two controllable perturbations: the separation △zS of the outer electrode sections and the correcting voltage Vd. Since the oscillation amplitude dispersion is relatively low (practically 0.1 mm), the principal contribution to the frequency spread comes from the radial dependence of the function δΩ(Z, r) at a fixed value oscillation amplitude Z as attained upon squeezing. Fig. 2.3 illustrates the two controllable perturbations and their contributions into the oscillation frequency shift.

46

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Dzs= −0.3 mm

r Vd

1 mm −Vc

(A)

300 mm

140

250

W(Z,r), × 10−6 (ppm)

1

120

W(Z,r), × 10−6 (ppm)

(B)

Z = 5 mm

100 80 60

2

40 20

3

0 −20 −40 −60

(C)

5

6

7

r, mm

6 150 100

8

(D)

50 ppm

50

5

0 −50

4 4

3 ppm

200

4

5

6

7

r, mm

8

Fig. 2.3 (A) Field perturbation distribution caused by the variation of separation between the outer electrode sections, (B) the potential perturbations caused by the field penetration in the ion injection slot and a scratch on the inner electrode. (C) The frequency perturbations due to ΔzS ¼ 1 μm (trace 1) and the correcting voltage settings Vd/Vc ¼ 0.2, 0.12 and 0.05 (traces 2, 3, and 4, correspondingly). (D) The frequency variation due to the scratch 300 μm by 1 μm in the inner electrode if non-compensated (trace 5) and compensated (trace 6).

The equipotentials of the field perturbation caused by varying the separation between the outer electrode sections are shown in Fig. 2.3A. The perturbed field was calculated by the Bruns-Bertein method [17, 18]. It is intuitively clear that a negative variation of the separation △zS < 0 results in a stronger electric field and thus faster ion oscillations. The resulting oscillation frequency shift is shown in Fig. 2.3C with the trace 1. It is of profound importance for our purposes of aberration compensation that this dependence is nonuniform—the ions revolving with higher radii, that is closer to the perturbed outer electrodes are affected to the greater extent. So, introducing a certain amount of perturbation by the electrode separation △zS, either positive or

Fundamentals of Orbitrap analyzer

47

negative, by choosing a quartz spacer of appropriate width may effectively control the gradient of the frequency perturbation ΔrδΩ. It is not, however, sufficient from the practical viewpoint because changing △zS is only possible mechanically and might require trap disassembly. Besides, the frequency aberration to be compensated is not necessarily linearly distributed with the radius. The other compensation parameter is the voltage Vd on the electrode placed over the ion injection slot as schematically shown in Fig. 2.3B. The voltage settings to minimize the field perturbation in the trap’s volume are chosen to generate a zero field gradient discontinuity at the injection slot. It means that Vd constitutes a certain proportion of the central electrode voltage Vc, here Vd/Vc  0.12. Other ratios lead to the field distortion and corresponding radius-dependent field variations as illustrated in Fig. 2.3C with traces 2, 3, and 4. Fig. 2.3D demonstrates the possibility of the frequency dispersion compensation. Suppose, the inner electrode has a circular scratch that is 1 μm deep and 300 μm wide. It generates a frequency deviation δΩ shown with trace 5 in the Z-section. Inside the populated region of radii 5.75  6.75 mm, the maximal difference of oscillation frequencies is ΔΩmax  ΔΩmin  50  106. Nevertheless, an appropriate combination of the outer electrode shift △zS ¼ 0.3 micron and the correcting voltage Vd ¼ 0.05 Vc leads the frequency perturbation with the flatness up to 3  106, which improves the mass resolving power by more than one order of magnitude. More details on the aberration compensation in the orbital trap may be found in [19]. Another class of perturbations is the space-charge repulsion of same or different m/z ions while oscillating in the orbital trap. Fig. 2.4A shows a simulated coulomb energy Ψ (z1, z2) acting between two ring-shaped ion clouds with unit charges located at z ¼ z1 and z1. One can see that the Coulomb 0.10 0.08

0

0.02 4 8 z, mm

0.00

(B)

–6

z1 = 6

–4

0.04

z1 = 3

–8

(A)

z2

z1

z1 = 0

4

0.06

–6 z2, mm

3

6

z1 = –3

6

z1 = –6

Y, 1/mm

r, mm

8

–3

Fig. 2.4 Calculation of the space-charge interaction between two ring-shaped ion clouds. (A) The space-charge field distribution, (B) Coulomb field of the cloud interaction with respect to the screening effect of the ion trap’s electrodes.

48

Fundamentals and Applications of Fourier Transform Mass Spectrometry

potential generated by N105 particles constitutes approximately N qΨ 10 mV which is much smaller than a typical central electrode voltage 3.5–6 kV but still introduces a frequency variation up to several ppm’s (parts per million ¼ 106). Spectrum calibration takes the space-charge correction into account, while the total ion population is precisely controlled by automatic gain control (AGC).

Advances in signal processing As the trapped ions oscillate in an Orbitrap analyzer, they induce the image current on the detection plates of the analyzer. Each ionic species generates a harmonic signal whose amplitude is proportional to the number of charges and the frequency is determined by the mass-to-charge ratio according to Eq. (2.4). The measured signal is, therefore, a superposition of such harmonic components, which in the complex form is Z Sðt Þ ¼ AðωÞexp fiωt + iξ0 ðωÞ  γ ðωÞt g dω, (2.14) where A(ω) is the distribution of the oscillators in frequencies, which is obviously related to the mass spectrum, ξ0(ω) is the oscillation phase, and γ(ω) 0 is the decay rate. The exponential form of the decay is determined by the stochastic nature of the ion collisions with the residual gas, which is the only possible channel an ion may leave the trap while sitting on a stable orbit. The signal amplitude is also known to decrease with time because of the frequency spreads due to ion packet dephasing caused by the field imperfections, including mechanical errors and space-charge effects, which also contributes to the observable spread of. The signal S(t) is measured and digitized during an acquisition time T. The purpose of signal processing consists in recovering an accurate enough approximation for A(ω) from S(t). The mass spectrum is further derived through the relation Eq. (2.4).

Fourier transform methods The traditional approach in the FT MS, as the name suggests, consists in applying the Fourier transform 1 C ðωÞ ¼ T

ZT SðtÞ exp ðiωt Þ dt

(2.15)

0

to the transient measured in the time interval 0  t  T. In the limit of a very long acquisition time T ! ∞ and the zero decay γ ¼ 0, the

Fundamentals of Orbitrap analyzer

49

complex-value spectrum tends to C ! A exp iξ0, and, therefore, its absopffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi lute value jC ðωÞj ¼ Re C 2 + Im C 2 gives a perfect estimate for the ion distribution in frequencies. However, in practice, the acquisition time is finite, which means that the Fourier spectrum is a convolution of the sought distribution A(ω) Z 0 C ðωÞ ¼ Aðω0 Þeiξ0 ðω Þ Ψ ðω0  ωÞdω0 (2.16) with the oscillating complex-value kernel function Ψ ðΔωÞ ¼

exp ðiΔωT  γT Þ  1 , iΔωT  γT

(2.17)

where, for the sake of simplicity, the decay is assumed constant. Recovering A(ω) is an ill-posed problem, and the common practice is still to use the absolute value of C instead of A to obtain the composition of ionic species. This immediately restricts the frequency resolution to the so-called Fourier uncertainty △ω ¼ 2π/T and the mass resolving power is limited by 1 ω T  ωðm=qÞ ¼ (2.18) 2 Δω 4π where ω(m/q) is determined by Eq. (2.4). The maters are further complicated due to the oscillating nature of the kernel Eq. (2.17) which leads to Gibbs oscillations and other undesirable effects such as suppression of neighboring peaks and peak centroiding errors [20]. The problem of Gibbs oscillations can be substantially mitigated by appropriate apodization [21]. Furthermore, capitalizing on the fact that the phases ξ0(ω) in the Orbitrap signals are effectively known for any m/ z the Fourier uncertainty △ω may be improved by the factor of two via projection of the spectrum C onto the known phase, Re (C eiξ0), which constitutes in essence the absorption spectrum method [21] called after the analogues technique in the nuclear magnetic resonance (NMR). The Enhanced Fourier transform (eFT) method specially developed for Orbitrap signal processing [22], uses a special combination of apodized absorption and amplitude spectra to improve resolution and minimize the effects of Gibbs oscillation. The eFT method provides the resolving power almost twice exceeding that of the magnitude spectrum. At the same time, it is almost free of the negative lobes typical of the absorption spectra as Fig. 2.5A illustrates. In practice, the measured spectrum is digitized in a sequence of N equidistant moments of time sn ¼ S(tn), tn ¼ (n/N) T, and the discrete spectrum Rmax ¼

50

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Fig. 2.5 (A) FT spectra of a model single-frequency signal: (1) magnitude-mode spectrum with Hann apodization; (2) absorption-mode; and (3) enhanced FT (eFT). (B) Superposition of FT and ΦSDM spectra of two model peaks separated by (I) 1.2 FT bins and (II) 0.6 FT bin in the frequency domain. (4) The positions of the original model peaks, (5) peak intensities and centroids calculated from (6) frequency distribution histogram j xk j.

ck is calculated by the discrete Fourier transform (DFT) for which Nlog2N effective computational methods are developed [23]. A reliable approximation for the continuous spectrum C(ω) is achieved with the use of zero-padding [21]. However, closely positioned peaks in FT spectra are known to interfere with each other resulting in distorted peak shapes and wrong amplitudes [20]. Fig. 2.5B illustrates this on the model signal consisting of two harmonic components separated by 1.2 FT bins in the frequency domain (Fig. 2.5B.I) and 0.6 FT bin (Fig. 2.5B.II). Although eFT demonstrates superior resolution compared to that of the magnitude mode, both spectra show suppression of intensities when the peaks are resolved partially (Fig. 2.5B.I) or not resolved at all (Fig. 2.5B.II). The centroiding precision of non-baseline resolved peaks is also affected. It should be further noted that the absolute value of Ψ is not integrable and, as a result, the Fourier spectrum C doesn’t inherit the important property of integrability of the frequency distribution A(ω). The integral of C(ω) over a frequency interval doesn’t return a reasonable estimate of the number of ions oscillating in with frequencies in this interval.

Fundamentals of Orbitrap analyzer

51

Autocorrelation methods The said shortcomings of the Fourier analysis of transients encouraged researchers to use other methods to obtain the underlying distribution of ion oscillation frequencies. The first non-FT candidate for the frequency analysis is the method developed by Gaspard de Prony at the end of the eighteenth century. As the ionic species are believed to have discrete masses, the frequency distribution A(ω) may be approximated as a sum of Dirac’s delta-functions and the integral Eq. (2.14) converts into the sum of K harmonics sn ¼

K X k¼1

Ak exp ðiωk tn + iξk  γ k tn Þ ¼

K X k¼1

Xk λnk

(2.19)

where Xk ¼ Ak exp (iξk) are complex amplitudes and λk ¼ exp {(iωk  γ k) T/N} all lie in the unit circle of the complex plane (including its boundary). In case that K < N, the sequences {sm, sm+1, sm+K} are not linearly independent and every transient sample sm+K (0 m < N  K) is effectively a linear combination of K preceding samples sm + K ¼

K1 X

ak sm + k , 0  m < N  K

(2.20)

k¼0

with some unknown coefficients ak, referred to as the the auto-correlation coefficients. The set of λk are eventually the roots of the algebraic equation K1 X

ak λk  λK ¼ 0

(2.21)

k¼0

The frequencies and decay parameters may be further obtained as real and imaginary parts of (N/iT) ln λk, correspondingly, followed by the calculation of the amplitudes. The obvious advantage of the method consists in that the oscillation frequencies may be located closer to each other than the minimal distance Δω ¼ 2π/T dictated by the Fourier uncertainty, and typical interference effects are not expected. The drawback of the auto-correlation method is that the number of sought harmonics K is to be known or, at least, determined beforehand from other considerations. For example, the singular value decomposition may be used to estimate the actual number of ion harmonics and differentiate them from noise [24]. Nevertheless, the method’s applicability to ‘discovery’ modus operandi with no a priori assumptions about the ion species compositions is questionable.

52

Fundamentals and Applications of Fourier Transform Mass Spectrometry

In case that the number of sought harmonics K ¼ N/2, the formula (2.20) defines exactly K linear equations to be resolved in K unknown variables, which constitutes the essence of the filter diagonalization method (FDM). FDM was first introduced for NMR [25] and then adapted for FT MS [26, 27] to overcome the Fourier uncertainty resolution threshold. By claiming exact equalities (2.20), FDM provides no allowance for noise in the transient and is therefore most applicable for the signals with high signalto-noise ratios. Processing noisy spectra can lead to erroneous and sometimes misleading results [28]. In the case that K < N/2, the linear system Eq. (2.20) are over-defined and can be only solved it with certain discrepancies Δm+K (0  m < N  K): Sm + K 

K1 X

ak sm + k ¼ Δm + K ! min

(2.22)

k¼0

So, the system Eq. (2.20) is always resolvable for ak in the sense of the Moore–Penrose pseudo-inversion [29] by claiming the minimum of the discrepancy’s L2 norm. Such procedure constitutes the essence of the linear prediction (LP) method which is widely used in signal processing. The noise allowance in Eq. (2.22) is not quite consistent, however, because the discrepancy is ascribed only to the last elements of the subsets {sm, sm +1, … sm+K}. The noise tolerance is improved if the whole discretized signal is projected onto the linear subspace of K-harmonic noise-free transients is it is done in the modified Prony method [30, 31]. Being more accurate, this approach results in non-linear equations for ak and is, therefore, computationally expensive. The common drawback of the auto-correlation methods is that they require a priori knowledge of the number K of harmonics in a transient. In addition, this number cannot be large because the computational efficiency of solving the algebraic Eq. (2.21) is proportional to K3. So the auto-correlation methods are practically applicable only to sparse spectra of few ionic species or small spectral windows.

Maximum likelihood parameter estimators Another breed of methods, which can be loosely put under the umbrella term of maximum likelihood estimators, fits a model transient like Eq. (2.19) or the Fourier spectrum directly to the measured transient or its spectrum in the sense of a chosen norm. The parameters—frequency, amplitude, phase, and decay—are to be via minimizing the discrepancy.

Fundamentals of Orbitrap analyzer

53

The most common approach, least square fitting, consists in numerical minimizations of the L2 norm of the difference between transients or, equivalently, corresponding complex-value Fourier spectra [32–34]. In case that only a few harmonic components are sought, the method is quite efficient and converges quickly. The maximum likelihood estimators are most effective for the targeted analysis when locations of peaks are approximately known and thus may be used as seed values for the iterative parameter search. Provided with accurate enough initial estimations of frequencies, the method is capable of resolving peaks with separation below the Fourier uncertainty (i.e., less than one FT bin).

Deconvolution method Here recovering the frequency distribution A(ω) from the Fourier spectrum C(ω) is approached as a classical deconvolution problem. The method makes no specific assumptions about either the function A(ω) or the number of species. To allow the super-FT frequency resolution, the sought function A is discretized on a refined frequency grid Ωk ¼ 2πk/PT (0  k < PN) which is P ≫ 1 times denser than that of DFT. Each frequency Ωk is ascribed a complex amplitude xk to be found. The set of such amplitudes serve as an estimate for A(Ωk)eiξ0(Ωk)—the sought distribution with the phase factor. The Eqs. (2.16) and (2.17) are recast as the finite-dimensional minimization problem  2  X k=Pn    Ψ T xk  (2.23) x ¼ argmincn    k 2

Fredholm integral equations of the first kind are generally known to be ill-definite, so are the Eq. (2.16) and its discrete approximation Eq. (2.23), and regularization is required to find a meaningful solution. The knowledge of the oscillation phases gives the clue for the problem’s regularization via imposing the condition on the complex values j arg xk  ξ0(Ωk)j  Δφ/2 where the difference is understood by the modulus of 2π and Δφ ≪ 1. The convex optimization problem Eq. (2.23) with the mentioned conical phase restriction is solved by one of the appropriate methods, for example by the alternative direction method of multipliers (ADMM) [35], and the absolute values of the solution vector’s element give a sought estimate for the frequency distribution Ak ¼ jxk j. The postprocessing consists in finding all local maxima of this distribution and ascribing them to mass peaks. The sums

54

Fundamentals and Applications of Fourier Transform Mass Spectrometry

of Ak in the range of one peak gives its intensity, while the weighted-average frequency characterizes the mass-to-charge ratio. Details of the phased spectrum deconvolution method (ΦSDM) may be found in [36, 37]. Its advantage consists in the super-FT resolving power, which substantially surpasses the Fourier uncertainty limit, and the absence of peak interference artifacts, as shown in Fig. 2.5B. Although ΦSDM has fundamentally the same time complexity with respect to the number N of the transient samples as the conventional FT methods, e.g., eFT, its computational cost considerably (hundreds of times) higher.

Evolution of the Orbitrap platform and selected applications From its inception, the term Fourier transform mass spectrometry was used interchangeably with Fourier transform ion cyclotron resonance mass spectrometry (FT ICR MS) [5, 6]. The shift to biochemical applications with heavy ions and the need for higher mass resolving powers predefined the use of ultra-strong superconductive FT ICR magnets. This made a purely electrostatic FT MS very attractive, as the electric field confines heavy ions with the same efficiency as small ones. Several designs of electrostatic ion traps comprising two coaxial mirrors and pickup electrodes were introduced in the early 2000s [38–40], but were not routinely used as mass-analyzing instruments because of relatively low space-charge capacities and other technical issues. In 1999, at the American Society for Mass Spectrometry (ASMS) conference in Dallas, TX, Orbitrap analyzer was presented for the first time joining the ICRs in the field of FT MS. The first commercial Orbitrap mass spectrometer from Thermo Electron (currently Thermo Fisher Scientific), LTQ Orbitrap instrument, debuted in 2005. Besides the orbital trap as a high-resolution mass analyzer, it comprised a linear ion trap front end. An additional C-shaped quadrupole ion trap, referred to as C-Trap, was introduced to accumulate and cool ions with following pulsed injection into the orbital trap [41]. The Orbitrap family started to expand quickly by incorporating different front-end, mass selection, and ion fragmentation devices. The LTQ Orbitrap XL mass spectrometer introduced in 2007 featured a high-energy collision cell (HCD) for the MS/MS applications [42]. Addition of the electron transfer dissociation (ETD) option qualitatively extended the depth of structural analyses of macromolecular compounds [43]. A stacked ring RF funnel

Fundamentals of Orbitrap analyzer

55

(S-lens) allowed a ten-fold ion transfer efficiency improvement in LTQ Orbitrap Velos (2009), which was also equipped with a dual-pressure linear trap [44]. A high-performance Exactive instrument was introduced in 2009. It incorporated an API source and a stand-alone Orbitrap analyzer [45]. The vacuum in the orbital trap has been improved to 1e-10 Torr and the mass range extended up to m/q 6000. This instrument was followed by the Q Exactive mass spectrometer additionally equipped with a quadrupole mass filter for MS/MS capabilities [46]. The Orbitrap Elite instrument completed the LTQ Orbitrap series in 2011 and featured an orbital trap with a stronger electric field and, consequently, higher oscillation frequencies. The improvement was achieved by decreasing the external electrode’s diameter from 30 mm to 20 mm. Together with a novel signal processing method, eFT, this allowed a significant advance in the mass resolving power which reached 240 K at m/ q ¼ 200 [47]. The Orbitrap Fusion (2013) instrument incorporated a high-frequency orbital trap, QMF, and a dual pressure linear trap. The central electrode voltage elevated up to 5 kV boosted the mass resolving power up to 450 K (and later 500 K) at m/q ¼ 200. This new platform extended already established electron transfer dissociation ETD and HCD capabilities to novel hybrid fragmentation techniques such as electron-transfer and collision induced dissociation ETciD and electron-transfer and higher-energy collision dissociation (EThcD) [48]. A plethora of separation and ionization techniques is currently available for the family of Orbitrap instruments. For instance, such methods of ionization as electrospray (ESI) and matrix-assisted laser desorption and ionization (MALDI) [49–51] as well as atmospheric pressure and ambient approaches such as laserspray ionization (LSI) [52], direct analysis in realtime (DART™, IonSense, Inc., Saugus, MA, USA) [53], atmospheric solids analysis probe (ASAP) [54], atmospheric pressure chemical ionization (APCI) [55], and desorption electrospray ionization (DASI) [56] were reported in the literature in conjunction with the Orbitrap technology. Aside from the liquid chromatography (LC) [57], Orbitrap instruments were successfully coupled to gas chromatography (GC) [58–60], ion mobility spectrometry [61–63], and high-field asymmetric waveform ion mobility spectrometry (FAIMS) [64–66]. Due to its high mass accuracy and resolving power the Orbitrap technology has been rapidly expanding into various areas of analytical science [67].

56

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Specific properties of the technique made it beneficial in a number of biological applications. Being an FT MS instrument-, the mass-resolving power of the Orbitrap analyzers tends to improve with the signal acquisition time. In many applications, such as MS/MS or imaging MS, the acquisition time is a critical performance parameter and is restricted by the experiment’s conditions. The mass resolving power is then determined by the Fourier uncertainty and is proportional to the ion oscillation frequency as Eq. (2.18) suggests. Generally, the resolving power of FT MS degrades with m/q of the ionic species. Purely electrostatic ion traps have, however, an essential advantage to compare with magnetic traps. The oscillation frequency in the Orbitrap analyzers scales as ω ¼ const  (m/q)1/2 compared with the cyclotron frequency dependence ω ¼ const  (m/q)1. The difference in the exponent makes the Orbitrap instruments most suitable for analysis of heavy biological samples, for which its performance may exceed that of FT ICR. The working m/q range up to several thousand is important in a bunch of biochemical studies including the top-down proteomics [68–74], as well as its derivative, the middle-down mass spectrometry [75]. The methodology consists of analyzing intact macromolecules or macromolecular complexes without prior enzymatic degradation, which is followed by successive MSn experiments with complementary fragmentation techniques. Recent studies showed the possibility to inject into the orbital trap and analyze biological particles as heavy as whole viruses [76]. The ion excitation method through injection at a specific distance from the trap’s middle makes the ions with all mass-to-charge ratios oscillate with well definite amplitudes and radial spans. The induced current depends therefore solely on the number of charges in the oscillating ion packets, and the signal has very small dispersion due to different excitation conditions. This means that the Orbitrap technology brings also very high degree of intensity precision to the field of FT MS. This translates into the possibility of quantitative analysis of complex samples, which is of high demand in the biological research. A good example of such approach is the use of the Orbitrap technology in conjunction with the stable isotope labeling protocols like SILAC [77], TMT [78, 79], and NeuCode [80]. The future development of the Orbitrap technology is connected with further mass resolution and sensitivity improvements combined with more precise aberration compensation. The Orbitrap Fusion Lumos, introduced in 2015, has already received the option of one million mass resolving power

Fundamentals of Orbitrap analyzer

57

[81]. Detection of higher oscillation harmonics, e.g. the third harmonic, may also be beneficial. Advanced signal processing promises to overcome the Fourier uncertainty resolution threshold typical of all present FT MS instruments. Coupling the Orbitrap technique with other ion processing methods will boost up the usability of the instruments for a variety of scientific and medical applications.

Acknowledgments This works has been partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686547 (MSMed project).

References [1] K.H. Kingdon, A method for the neutralization of electron space charge by positive ionization at very low gas pressures, Phys. Rev. 21 (1923) 408–418. [2] R.D. Knight, Storage of ions from laser-produced plasmas, Appl. Phys. Lett. 38 (1981) 221–222. [3] L.N. Gall, Y.K. Golikov, M.L. Aleksandrov, Y.E. Pechalina, N.A. Holin, Time-ofFlight Mass Spectrometer, Soviet Union Pat. 1247973, (1986). [4] N.K. Krasnova, Y.K. Golikov, Y.A. Kudinov, S.N. Davydov, An analysing system for concurrent energy and angular distribution measurements of charged particles’ emission, Meas. Sci. Technol. 9 (1998) 1446–1450. [5] M.B. Comisarow, A.G. Marshall, Fourier transform ion cyclotron resonance spectroscopy, Chem. Phys. Lett. 25 (1974) 282–283. [6] A.G. Marshall, C.L. Hendrickson, G.S. Jackson, Fourier transform ion cyclotron resonance mass spectrometry: a primer, Mass Spectrom. Rev. 17 (1998) 1–35. [7] A. Makarov, Electrostatic axially harmonic orbital trapping, a high-performance technique of mass analysis, Anal. Chem. 72 (2000) 1156–1162. [8] A. Makarov, Ideal and quasi-ideal time focusing of charged particles, J. Phys. D. Appl. Phys. 24 (1991) 533–540. [9] A. Makarov, E. Denisov, Dynamics of ions of intact proteins in the Orbitrap mass analyzer, J. Am. Soc. Mass Spectrom. 20 (2009) 1486–1495. [10] R. Zubarev, A. Makarov, Orbitrap mass spectrometry, Anal. Chem. 85 (2013) 5288–5296. [11] V.M. Doroshenko, A. Misharin, Electrostatic Orbital Trap Mass Spectrometer, United States Patent 8796619, 2014. [12] A. Makarov, Multi-Electrode Ion Trap, United States Patent 7767960, 2010. [13] C. K€ oster, Kingdon Ion Trap with Higher-Order Cassini Potentials. United States Patent 8735812, (2014). [14] C. K€ oster, Twin trap or hyphenation of a 3D Paul- and a Cassinian ion trap, J. Am. Soc. Mass Spectrom. 26 (2015) 390–396. [15] N.N. Bogoliubov, Y.A. Mitropolsky, Asymptotic Methods in the Theory of NonLinear Oscillations, Gordon & Breach, New York, 1961. [16] N.M. Krylov, N.N. Bogoliubov, Introduction to Non-Linear Mechanics, 3rd ed., Princeton University Press, Princeton, NJ, 1950.

58

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[17] F. Bertein, Relation entre les defauts des lentilles et la nettete des images, Ann. Radioelectr. 2 (1947) 379. [18] F. Bertein, Relation entre les defauts des lentilles et la nettete des images, Ann. Radioelectr 3 (1948) 49. [19] D. Grinfeld, M. Monastyrskiy, A. Makarov, Control of aberration and space-charge effects in the Orbitrap mass analyzer, Microsc. Microanal. 21 (S4) (2015) 176–181. [20] A.L. Rockwood, J.C. Erve, Mass spectral peak distortion due to Fourier transform signal processing, J. Am. Soc. Mass Spectrom. 25 (2014) 2163–2176. [21] A.G. Marshall, F.R. Verdun, Fourier Transforms in NMR, Optical and Mass Spectrometry, A User’s Handbook, Elsevier, 1990. [22] O. Lange, E. Damoc, A. Wieghaus, A. Makarov, Enhanced Fourier transform for Orbitrap mass spectrometry, Int. J. Mass Spectrom. 369 (2014) 16–22. [23] J.W. Cooley, J.W. Tukey, An algorithm for the machine calculation of complex Fourier series, Math. Comput. 19 (1965) 297–301. [24] M.S. Mazzola, N.H. Younan, R. Soundararajan, S.E. Saddow, Application of the singular valve decomposition–Prony method for analyzing deep-level transient spectroscopy capacitance transients, Rev. Sci. Instrum. 69 (1998) 2459. [25] V.A. Mandelshtam, FDM: the filter diagonalization method for data processing in NMR experiments, Prog. Nucl. Magn. Reson. Spectrosc. 38 (2001) 159–196. [26] K. Aizikov, P.B. O’Connor, Use of the filter Diagonalization method in the study of space charge related frequency modulation in Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Soc. Mass Spectrom. 17 (2006) 836–843. [27] A.N. Kozhinov, Y.O. Tsybin, Filter Diagonalization method-based mass spectrometry for molecular and macromolecular structure analysis, Anal. Chem. 84 (2012) 2850–2856. [28] B.R. Martini, K. Aizikov, V.A. Mandelshtam, The filter Diagonalization method and its assessment for Fourier transform mass spectrometry, Int. J. Mass Spectrom. 373 (2014) 1–14. [29] A. Ben-Israel, T.N.E. Greville, Generalized Inverses, Springer-Verlag, New York, 2003. [30] M.R. Osborne, G.K. Smyth, A modified Prony algorithm for exponential function fitting, SIAM J. Sci. Comput. 16 (1995) 119–138. [31] K. Aizikov, D. Grinfeld, Methods and Apparatus for Obtaining Enhanced Mass Spectrometric Data, European Patent EP2850637, 2018. [32] D.A. Write, R.A. Grothe, Methods of Automated Spectral Peak Detection and Quantification Without User Input, United States Patent US8346487, 2013. [33] R.A. Grothe, Estimation of Ion Cyclotron Resonance Parameters in Fourier Transform Mass Spectrometry, US Patent US8431886, 2013. [34] T. Aushev, A.N. Kozhinov, Y.O. Tsybin, Least-squares fitting of time-domain signals for Fourier transform mass spectrometry, J. Am. Soc. Mass Spectrom. 25 (2014) 1263–1273. [35] S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Lern. 3 (2011) 1–122. [36] D. Grinfeld, K. Aizikov, A. Kreutzman, E. Damoc, A. Makarov, Phase-constrained Spectrum Deconvolution for Fourier transform mass spectrometry, Anal. Chem. 89 (2) (2017) 1202–1211. [37] K. Aizikov, D. Grinfeld, A. Makarov, Method of Producing a Mass Spectrum, United States Patent Application 2016314951(A1), (2016). [38] H.B. Pedersen, D. Strasser, S. Ring, O. Heber, M.L. Rappaport, Y. Rudich, I. Sagi, D. Zajfman, Ion motion synchronization in an ion-trap resonator, Phys. Rev. Lett. 87 (5) 2001, 055001.

Fundamentals of Orbitrap analyzer

59

[39] D. Zajfman, Y. Rudich, I. Sagi, D. Strasser, D.W. Savin, S. Goldberg, M. Rappaport, O. Heber, High resolution mass spectrometry using a linear electrostatic ion beam trap, Int. J. Mass Spectrom. 229 (2003) 55–60. [40] M. Gonin, Electrostatic Ion Trap Mass Spectrometer, United States Patent 6888130, 2005. [41] A. Makarov, E. Denisov, A. Kholomeev, W. Balschun, O. Lange, K. Strupat, S. Horning, Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer, Anal. Chem. 78 (7) (2006) 2113–2120. [42] J.V. Olsen, B. Macek, O. Lange, A. Makarov, S. Horning, M. Mann, Higher-energy C-trap dissociation for peptide modification analysis, Nat. Methods 4 (9) (2007) 709–712. [43] G. McAlister, W. Berggren, J. Griep-Raming, S. Horning, A. Makarov, D. Phanstiel, G. Stafford, D. Swaney, J. Syka, V. Zabrouskov, J. Coon, A proteomics grade Electron transfer dissociation-enabled hybrid linear ion trap-Orbitrap mass spectrometer, J. Proteome Res. 7 (8) (2008) 3127–3136. [44] J.V. Olsen, J.C. Schwartz, J. Griep-Raming, M.L. Nielsen, E. Damoc, E. Denisov, O. Lange, P. Remes, D. Taylor, M. Splendore, E.R. Wouters, M. Senko, A. Makarov, M. Mann, S. Horning, A dual pressure linear ion trap Orbitrap instrument with very high sequencing speed, Mol. Cell. Proteomics 8 (12) (2009) 2759–2769. [45] K.P. Bateman, M. Kellmann, H. Muenster, R. Papp, L. Taylor, Quantitativequalitative data acquisition using a Benchtop Orbitrap mass spectrometer, J. Am. Soc. Mass Spectrom. 20 (2009) 1441–1450. [46] A. Michalski, E. Damoc, J.-P. Hauschild, O. Lange, A. Wieghaus, A. Makarov, N. Nagaraj, J. Cox, M. Mann, S. Horning, Mass spectrometry-based proteomics using Q Exactive, a high-performance Benchtop Quadrupole Orbitrap mass spectrometer, Mol. Cell. Proteomics 10 (2011). M111.011015. [47] A. Michalski, E. Damoc, O. Lange, E. Denisov, D. Nolting, M. Mueller, R. Viner, J. Schwartz, P. Remes, M. Belford, J.J. Dunyach, J. Cox, S. Horning, M. Mann, A. Makarov, Ultra high resolution linear ion trap Orbitrap mass spectrometer (Orbitrap elite) facilitates top down LC MS/MS and versatile peptide fragmentation modes, Mol. Cell. Proteomics 11 (3) (2012). O111.013698. [48] A.M. Brunner, P. L€ ossl, F. Liu, R. Huguet, C. Mullen, M. Yamashita, V. Zabrouskov, A. Makarov, A.F.M. Altelaar, A.J.R. Heck, Benchmarking multiple fragmentation methods on an Orbitrap fusion for top-down Phospho-Proteoform characterization, Anal. Chem. 87 (8) (2015) 4152–4158. [49] K. Strupat, V. Kovtoun, H. Bui, R. Viner, G. Stafford, S. Horning, MALDI produced ions inspected with a linear ion trap-Orbitrap hybrid mass analyzer, J. Am. Soc. Mass Spectrom. 20 (8) (2009) 1451–1463. [50] C. Bingming, B.L. Christopher, L. Li, In situ characterization of proteins using Laserspray ionization on a high-performance MALDI-LTQ-Orbitrap mass spectrometer, J. Am. Soc. Mass Spectrom. 25 (12) (2014) 2177–2180. [51] M. Belov, S. Ellis, M. Dilillo, M. Paine, W. Danielson, G. Anderson, E. de Graaf, G. Eijkel, R. Heeren, L. McDonnell, Design and performance of a novel Interface for combined matrix-assisted laser desorption ionization at elevated pressure and electrospray ionization with Orbitrap mass spectrometry, Anal. Chem. 89 (14) (2017) 7493–7501. [52] S. Trimpin, E.D. Inutan, T.N. Herath, C.N. McEwen, Laserspray ionization, a new atmospheric pressure MALDI method for producing highly charged gas-phase ions of peptides and proteins directly from solid solutions, Mol. Cell. Proteomics 9 (2010) 362–367. [53] E.S. Chernetsova, M. Bromirski, O. Scheibner, G.E. Morlock, DART-Orbitrap MS: a novel mass spectrometric approach for the identification of phenolic compounds in propolis, Anal. Bioanal. Chem. 403 (10) (2012) 2859–2867.

60

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[54] C. McEwen, S. Gutteridge, Analysis of the inhibition of the ergosterol pathway in fungi using the atmospheric solids analysis probe (ASAP) method, J. Am. Soc. Mass Spectrom. 18 (7) (2007) 1274–1278. [55] J. Khandurina, et al., High throughput screening of 1,4-butanediol production in fermentation samples using a LDTD APCI ionization source coupled to a bench top Orbitrap MS, in: 59th ASMS Conference on Mass Spectrometry and Allied Topics [abstract], Denver, USA, 2011. [56] Q. Hu, N. Talaty, R.J. Noll, R.G. Cooks, Desorption electrospray ionization using an Orbitrap mass spectrometer: exact mass measurements on drugs and peptides, Rapid Commun. Mass Spectrom. 20 (22) (2006) 3403–3408. [57] A. Makarov, M. Scigelova, Coupling liquid chromatography to Orbitrap mass spectrometry, J. Chromatogr. A 1217 (25) (2010) 3938–3945. [58] A.C. Peterson, J.-P. Hauschild, S.T. Quarmby, D. Krumwiede, O. Lange, R. A. Lemke, et al., Development of a GC/Quadrupole-Orbitrap mass spectrometer, part I: design and characterization, Anal. Chem. 86 (20) (2014) 10036–10043. [59] A.C. Peterson, A.J. Balloon, M.S. Westphall, J.J. Coon, Development of a GC/Quadrupole-Orbitrap mass spectrometer, part II: new approaches for discovery metabolomics, Anal. Chem. 86 (20) (2014) 10044–10051. [60] H. Kersten, K. Kroll, K. Haberer, K.J. Brockmann, T. Benter, A.C. Peterson, A. Makarov, Design study of an atmospheric pressure photoionization Interface for GC-MS, J. Am. Soc. Mass Spectrom. 27 (4) (2016) 607–614. [61] M. Belov, Method and Apparatus for Coupling Fast Separation and Slow Detection Systems, United States Patent 9269548, 2016. [62] M.E. Belov, W.F. Danielson, D.R. Godlett, Bridging the gap between an ion mobility spectrometer and an Orbitrap, in: ASMS Conference Proceedings, June 4, St. Louis, MO, USA, 2015. [63] Y.M. Ibrahim, S.V.B. Garimella, S.A. Prost, R. Wojcik, R.V. Norheim, E.S. Baker, I. Rusyn, R.D. Smith, Development of an ion mobility spectrometry-Orbitrap mass spectrometry platform, Anal. Chem. 88 (24) (2016) 12152–12160. [64] J. Saba, E. Bonneil, C. Pomies, K. Eng, P. Thibault, Enhanced sensitivity in proteomics experiments using FAIMS coupled with a hybrid linear ion trap/Orbitrap mass spectrometer, J. Proteome Res. 8 (2009) 3355–3366. [65] G. Bridon, E. Bonneil, T. Muratore-Schroeder, O. Caron-Lizotte, P. Thibault, Improvement of phosphoproteome analyses using FAIMS and decision tree fragmentation. Application to the insulin signaling pathway in Drosophila melanogaster S2 cells, J. Proteome Res. 11 (2012) 927–940. [66] K.E. Swearingen, M.R. Hoopmann, R.S. Johnson, S.A. Saleem, J.D. Aitchison, R. L. Moritz, Nanospray FAIMS fractionation provides significant increases in proteome coverage of unfractionated complex protein digests, Mol. Cell. Proteomics 11 (2012). M111.014985. [67] M. Scigelova, A. Makarov, Fundamentals and advances of Orbitrap mass spectrometry, in: Encyclopedia of Analytical Chemistry, Wiley Online Library, 2013, pp. 1–36, https://doi.org/10.1002/9780470027318.a9309. [68] P.V. Bondarenko, T.P. Second, V. Zabrouskov, A. Makarov, Z. Zhang, Mass measurement and top-down HPLC/MS analysis of intact monoclonal antibodies on a hybrid linear Quadrupole ion trap–Orbitrap mass spectrometer, J. Am. Soc. Mass Spectrom. 20 (8) (2009) 1415–1424. [69] J. Zhang, H. Liu, V. Katta, Structural characterization of intact antibodies by highresolution LTQ Orbitrap mass spectrometry, J. Mass Spectrom. 45 (1) (2010) 112–120. [70] R.L. Edwards, P. Griffiths, J. Bunch, H.J. Cooper, Top-down proteomics and direct surface sampling of neonatal dried blood spots: diagnosis of unknown hemoglobin variants, J. Am. Mass Spectrom. 23 (11) (2012) 1921–1930.

Fundamentals of Orbitrap analyzer

61

[71] L. Fornelli, E. Damoc, P.M. Thomas, N.L. Kelleher, K. Aizikov, E. Denisov, A. Makarov, Y.O. Tsybin, Analysis of intact monoclonal antibody IgG1 by electron transfer dissociation Orbitrap FTMS, Mol. Cell. Proteomics 11 (12) (2012) 1758–1767. [72] Y.O. Tsybin, L. Fornelli, C. Stoermer, M. Luebeck, J. Parra, S. Nallet, F.H. Wurm, R. Hartmer, Structural analysis of intact monoclonal antibodies by electron transfer dissociation mass spectrometry, Anal. Chem. 83 (23) (2011) 8919–8927. [73] A. Michalski, E. Damoc, O. Lange, E. Denisov, D. Nolting, M. M€ uller, R. Viner, J. Schwartz, P. Remes, M. Belford, J.J. Dunyach, J. Cox, S. Horning, V. Mann, A. Makarov, Ultra high resolution linear ion trap Orbitrap mass spectrometer (Orbitrap elite) facilitates top down LC MS/MS and versatile peptide fragmentation modes, Mol. Cell. Proteomics 11 (3) (2012) 1–11. [74] D.R. Ahlf, P.D. Compton, J.C. Tran, B.P. Early, P.M. Thomas, N.L. Kelleher, Evaluation of the compact high-field Orbitrap for top-down proteomics of human cells, J. Proteome Res. 11 (2012) 4308–4314. [75] L. Fornelli, D. Ayoub, K. Aizikov, A. Beck, Y.O. Tsybin, Middle-down analysis of monoclonal antibodies with electron transfer dissociation Orbitrap Fourier transform mass spectrometry, Anal. Chem. 86 (6) (2014) 3005–3012. [76] J. Snijder, M. van de Waterbeemd, E. Damoc, E. Denisov, D. Grinfeld, A. Bennett, M. Agbandje-McKenna, A. Makarov, A.J.R. Heck, Defining the stoichiometry and cargo load of viral and bacterial nanoparticles by Orbitrap mass spectrometry, J. Am. Chem. Soc. 136 (20) (2014) 7295–7299. [77] S.J. Deeb, R.C. D’Souza, J. Cox, M. Schmidt-Supprian, M. Mann, Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles, Mol. Cell. Proteomics 11 (5) (2012) 77–89. [78] G.C. Mc’Alister, E.L. Huttlin, W. Haas, L. Ting, M.P. Jedrychowski, J.C. Rogers, K. Kuhn, I. Pike, R.A. Grothe, J.D. Blethrow, S.P. Gygi, Increasing the multiplexing capacity of TMTs using reporter ion Isotopologues with isobaric masses, Anal. Chem. 84 (17) (2012) 7469–7478. [79] M. Bantscheff, M. Boesche, D. Eberhard, T. Matthieson, G. Sweetman, B. Kuster, Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer, Mol. Cell. Proteomics 7 (9) (2008) 1702–1713. [80] A.E. Merrill, A.S. Hebert, M.E. MacGilvray, C.M. Rose, D.J. Bailey, J.C. Bradley, W. W. Wood, M.M. El-Masri, M.S. Westphall, A.P. Gasch, J.J. Coon, NeuCode labels for relative protein quantification, Mol. Cell. Proteomics 13 (9) (2014) 2503–2512. [81] E. Denisov, E. Damoc, O. Lange, A. Makarov, Orbitrap mass spectrometry with resolving powers above 1,000,000, Int. J. Mass Spectrom. 325–327 (2012) 80–85.

CHAPTER 3

Fundamentals, strengths, and future directions for Fourier transform ion cyclotron resonance mass spectrometry Michael L. Easterlinga, Jeffery N. Agarb a

Bruker Daltonics Inc., Billerica, MA, United States Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, United States

b

Contents FT-ICR fundamentals Significant recent developments in FT-ICR References

67 72 81

The behavior of charged particles in magnetic fields has been widely studied in the physical sciences and provided investigators with some of the first clues about the nature of atomic and subatomic species. To resolve discrepancies between the rapidly growing fields of electrodynamics and atomic theory, J.J. Thomson leveraged his intimate understanding of the confluence of energy and magnetism he gained from Helmholtz, Maxwell, and other related researchers of that era. Although the electron’s charge could be assigned with only an electrical field gradient, defining electrons as a common subatomic constituent of constant mass also required an applied magnetic field for mass discrimination [1]. By 1912 Aston had applied this principle of magnetic separation to (positively charged) atoms, resolved the individual isotopes of ionized gases, and proven the existence of stable isotopes. Both Thomson and Aston would receive Nobel prizes for their respective discoveries. These experiments set the stage for applications of magnetic fields in modern mass spectrometry that would create the world’s most robust, stabile, and analytically perceptive instrumentation [2]. One of these techniques, Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), is the focus of this chapter and is described below in terms of history, theory, and recent notable developments. Fundamentals and Applications of Fourier Transform Mass Spectrometry https://doi.org/10.1016/B978-0-12-814013-0.00003-X

© 2019 Elsevier Inc. All rights reserved.

63

64

Fundamentals and Applications of Fourier Transform Mass Spectrometry

In 1929 Earnest Orlando Lawrence, a physicist at the University of California, Berkeley and future Nobel laureate, conceived of the cyclotron as a tool for the acceleration of charged particles to high kinetic energies within a small volume. This extraordinary breakthrough soon enabled >1 MeV acceleration and enabled novel particle physics experiments [3]. Lawrence determined that a particle of a given mass, charge, and kinetic energy component perpendicular to an applied magnetic field, would adopt a circular trajectory exhibiting a characteristic angular velocity as shown in Fig. 3.1. In 1931 he applied these principles to the construction of the first cyclotron. Particles could be accelerated from a given energy state by the application of a pulsed, dipolar electric field applied, in the case of the cyclotron accelerator, by a set of electrodes known as “dees” as a descriptor of their flat curves. Since the dees each occupied π radians of the particles’ path, the polarity of the electrodes could simply be switched with a period equal to half of a particular ion’s cyclotron frequency. This caused the particles to absorb energy and spiral outward, eventually striking a fixed target or collection cup placed at some radius from the magnetic field center. Ions manipulated in this way exhibited two important characteristics. The first was an invariant angular velocity that was independent of both cyclotron radius and kinetic energy, which greatly simplified excitation by allowing a fixed frequency to be applied for the duration of the experiment. Secondly, the spiraling trajectory allowed very long path lengths to be traversed within a relatively small confinement space. The cyclotron was readily adopted by the nuclear physics community, often replacing linear voltage gradient type accelerators that had become unwieldy due to their long lengths. Analytical uses for this resonant technique, however, are not known to be reported during the period 1930–1950 aside from preparative scale purification of the fissionable and non-fissionable isotopes of uranium, which Lawrence provided with a large scale (184 in. magnet) cyclotron for the Manhattan Project during World War II.

Fig. 3.1 Behavior of a charged particle in a magnetic field directed into the plane of the figure. (Reprinted with permission from I.J. Amster, Fourier transform mass spectrometry, J. Mass Spectrom. 31 (1996) 1325–1337.)

Fundamentals and applications of Fourier transform mass spectrometry

65

The first effort to apply the underlying principles of the cyclotron accelerator to analytical measurements was reported in 1949 by Sommer et al. at the National Bureau of Standards [4]. The device, termed the “omegatron” used a multiplicity of opposed, flat electrodes to resonantly excite ions formed by a continuously applied electron beam (positioned between the plates). Upon reaching a sufficient radius, the ions struck a collector attached to one of the electrodes, generating a current that could be amplified and recorded. To prevent ions from drifting along the magnetic field axis, a low amplitude DC field was applied to maintain the ions between the RF electrodes. Although the instrument carried out a one of the original goals, precise measurement of the m/z ratio of a proton, the analytical utility of the method was limited to residual gas detection. Factors contributing to this limitation were the magnetic field strength and homogeneity, space charge effects resulting from the continuously applied electron beam, high pressure resulting in numerous ion neutral collisions as ions progressed towards the collector, and an unreliable detection scheme. This combination of these factors limited the effectiveness of the omegatron such that the upper limit for nominal mass resolution was about 50 Da [5,6]. The largest gains in the analytical utility of the cyclotron method were realized with the introduction of the ion cyclotron resonance (ICR) experiment in 1963 by Wobschall and co-workers [7,8]. This implementation provided a variety of enhancements over the omegatron and introduced key ideas such as temporally spaced experimental events and detection based on interaction with the electric field of gyrating ions, rather than using impact techniques. Instead of using a pair of flat electrodes and guard wires as in the omegatron, the ICR technique developed a clearly defined “cell” used for ion confinement, manipulation, and detection which would become the basis of modern iterations. The Wobschall designed ICR cell, or drift cell, used three sections orthogonally aligned to the magnetic axis to perform different portions of the experiment [9]. Ions were initially formed in the source region, which contained a continuously applied electron beam used for ionization of neutral molecules. After ionization, molecules were influenced by a mass independent drift voltage (DC), which created an ExB force to transport ions at a fixed velocity into the analyzer region for excitation and detection. This was an important advance as the ions could be detected without interference from the electric field generated by the electron beam. In the analyzer region, a fixed RF frequency was applied to opposed electrodes for resonant excitation. The circuit for RF generation was operated at a fixed frequency, requiring a scan of the magnetic field to bring ions into resonance with the driver frequency. Ions

66

Fundamentals and Applications of Fourier Transform Mass Spectrometry

were detected by sensing power loss from a marginal oscillator circuit. After detection, ions traveled to the collector region where the total ion intensity could be measured from the current generated by the ions striking a collector electrode. With the exception of the collector region, a low amplitude DC potential (200–600 mV) was continuously applied to prevent ions from drifting into the cell electrodes along the magnetic axis. One of the most powerful features of the early ICR spectrometer was the ability to perform “double resonance” spectroscopy to examine the relationship between product and reactant ions formed by ion molecule collisions [10–13]. If the product of a reaction was constantly monitored, a variable RF oscillator could be applied in either the source or reactant region. Changes in the product abundance as function of heating the reactant ions by multiple energetic collisions could be used to make inferences concerning the nature of the reaction. This ability, combined with the ion storage time of several milliseconds, affirmed ICR as a powerful tool for both qualitative and quantitative analysis of ion molecule reactions in the gas phase. Furthermore, the flexible ICR experiment was established to be more than a method for simple mass analysis through simple modifications to the experimental pulse sequence. The evolution of ICR from a drift cell to a static, three dimensional trap occurred with the construction of a six electrode ICR cell by McIver in 1970 [14,15]. This cell did not employ a drift voltage as did Wobschall’s multiple compartment scheme. Instead, McIver developed a design in which ion generation, excitation, and detection occurred in the same space. ICR experiments were no longer required to be continuous in nature to avoid problems such as space charge encountered with the omegatron. For example, the electron beam was applied only at the beginning of the experiment and disabled before detection to avoid space charge interference. McIver’s experimental design consisting of a series of events or “pulses” separated in time, is still employed in modern experiments. The emergence of a pulsed experimental setup and the newfound ability to trap ions for hundreds of milliseconds were important milestones leading up to modern ICR methods. Application of the Fast Fourier Transform (FFT) algorithm developed by Cooly and Tukey in 1965 [16–18] to the ICR experiment was the single most significant event in the development of the field. This clever approach, known as Fourier Transform ICR (FT-ICR) was reported in 1974 by Comisarow and Marshall. They used the FFT to decode frequency and magnitude information from currents produced by gyrating ions imaged on a set of trap electrodes shortly after coherent excitation of the cyclotron mode,

Fundamentals and applications of Fourier transform mass spectrometry

67

thus changing the nature of the experimental detection scheme [19,20]. One may question why nearly a decade passed between the appearance of the FFT method and its application to ICR, especially considering the appearance of multichannel FT-NMR and FT-IR shortly after the initial FFT method description. Until the appearance of the trapping cell in 1970, the principal roadblock was the short trap times inherent to the drift cell (2. This way, sharper differentiation between main peaks and surrounding wiggles could be made, programmed and run for achieving very good filtration of each individual mass spectrum from this type of noise. Although it is well known that the measured reduced cyclotron frequency is dependent on the m/z in mathematical power function shape, we showed for the first time that the mass resolving power has also the same relationship with the m/z. We successfully took usage of this power function resolution dependence on m/z to identify a validated statistical normal distribution of all true main signals around a specific power function curve. After determination of 2σ boundaries, we found that all signals, whose resolution values lie in the range of central mean power curve
2σ are true main signals, while those outliers, which lie above the central power curve mean + 2σ represent wiggles. We provided several examples which show the successful implementation of this methodology to recognize and eliminate side lobes from several complex samples such as natural organic matter (NOM) samples as well as plant extracts and lipids samples. Moreover, the same principle also works for peptide and small protein ions, having several multiple charge states and in the extended mass range from 150 to 1500 amu. We explained also that the power function resolution filter can also successfully work based on FWHM information and it can further recognize and eliminate wiggles from doublet peak systems.

Batch processing of FTMS mass spectra All sections discussed so far in this book chapter treated individual mass spectra. However, comparisons between different acquisitions, which represent

Data processing and automation in FTMS

173

several samples of different origins or treatments, are required for sample classification and statistical data analysis. To enable comparison, the m/z vector should be the same for all mass spectra, which are required to be compared with each other. However, the FTMS instrument, depending on its utilized peak picking algorithm generally generates different m/z vectors for different acquired mass spectra, depending on many factors, including the degree of noise, absence of some signals in different mass spectra, presence of some new signals in other acquired mass spectra. Lucio et al. [18] established in 2005 a key tool (Matrix Generator) for mass spectral unification, which generates a matrix consisting of several intensity vectors and one unique m/z vector. Their approach in unification of the mass scale is intensity independent. This great achievement enabled batch processing of mass spectra. A mass tolerance window can be adjusted prior to generation of a matrix representing all the integrated mass spectra of the acquired samples with a unified mass scale vector. A very good value is 1 ppm but lower mass tolerance values can produce more accurate results, provided that peak alignments and calibration of the mass spectra under study are perfect. Therefore, the role of achieving excellent calibration for each individual mass spectrum is again emphasized here for this purpose. Excellent matrix generation with unified m/z vector can thus be achieved with long enough acquired time-domain transients such as 4 or 8 M Words because such long transients enable high resolution which increases the mass measurement accuracy (MMA) and can also enable more accurate peak alignment due to narrow peaks (low full width at half maximum (FWHM) values). Lo´pez-Ferna´ndez et al. [128] emphasized the importance of replicates to study consistency of mass spectrometric measurements. They integrated a percentage of presence parameter for considering a signal to be a true analyte only if it exists in a specific number of replicate measurements. Gavard et al. [125] developed a new algorithm (Themis) for batch processing of FTMS spectra, demonstrating its application in petroleomics. The program examines quality control characteristics of measured replicates, such as signal magnitudes and peak alignments. They showed that when replicates are considered, it is possible also to detect random noise by running reproducibility checks. Thus, no need to set up high intensity thresholds and/or high S/N ratios for exporting mass spectra and therefore more signals can be considered for data analysis. Kazmi et al. [129] adopted a heuristic approach for alignment of high resolution mass spectra of replicates as well as alignment, which is based on complete linkage hierarchical clustering. They proved that both the

174

Fundamentals and Applications of Fourier Transform Mass Spectrometry

heuristic alignment and the alignment, which is based on complete linkage hierarchical clustering give much more accurate alignment results relative to fixed bin and mixed bin algorithms. They also showed the possibility of recognizing non-random noise (noise, originating from electronic spikes for example), which may appear in a single mass spectrum out of many replicates during alignment of the replicates.

Automation of FTMS instruments Direct infusion FTMS delivers immediate mass spectra, which can be consequently analyzed either for targeted or for running non-target data analysis. However, when the number of samples significantly increases, an automation routine is needed. Although Bruker’s Hystar™ program provides full automation capabilities for LC coupling to FT-ICR-MS, direct infusion automations still present a challenge due to some compatibility issues with different types of autosamplers. Moreover, it is important for the user to get updated messages during automation, which inform the user about the activity of sample injections and whether interruption took place in unattended sample measurements. Huang, Siegel, Kruppa and Laukien have succeeded in implementing the Tcl scripting language into XMASS command interpreter, which runs short 512 k acquisitions on Bruker APEX II FT-ICR mass spectrometers [130]. This integration allowed for automated direct infusion acquisitions, analysis and e-mailing of MS reports of identified and/or annotated metabolites in the measured samples. A HPLC coupling and integration could also be achieved though Tcl and XMASS. The main principle, which enabled automation in their system, was actually a TTL pulse, which is triggered from the PC into the mass spectrometer to start a new acquisition. The TTL pulse is normally triggered after a fixed delay time, which allows for the newly injected sample to flow into the electrospray ionization source before a new sample acquisition begins. However, no troubleshooting information was provided in their published work. What happens if the autosampler was not able to inject the newly programmed sample into the mass spectrometer? We had such a problem in the past and this led to empty MS acquisitions, if the autosampler’s PC crashes (for example). Another problem might emerge, if the FTMS’s program crashes without affecting another external (independent) automation program, which continuously triggers the successive TTL pulses, which let the autosampler to continue injecting successive samples into the FTMS. In such case, the autosampler would continue running until injecting the

Data processing and automation in FTMS

175

last programmed sample, without corresponding MS acquisitions of those samples, which have been injected after the FTMS’s software stopped working. To circumvent all the previously mentioned problems, a new automation system was developed by Kanawati in 2013 in Helmholtz Zentrum M€ unchen. The new automation system works on the basis of a bi-directional signal communication between the PC, which runs the FTMS and the autosampler. Not only forward TTL pulses are triggered from the FTMS’s PC to the autosampler but also confirmation TTL pulses are also triggered from the autosampler back to the FTMS’ PC before a true acquisition starts. The latter pulse acts as a confirmation message, indicating that the autosampler has successfully injected the programmed sample and that the sample is currently flowing into the electrospray needle and thus ready for acquisition. This bi-directional messaging between the FTMS’ PC and the autosampler prevents also a one side operation of the autosampler without corresponding acquisitions of the injected samples, if the FTMS’s software hangs out for any reason. This way, no unnecessary sample consumption takes place. Our developed system can run for long acquisitions (4 MW for example) and is compatible with newer systems such as SolariX FTMS. The automation protocol depends on a standalone MATLAB code, which runs on the FTMS’ PC as an external program. The MATLAB program can trigger a TTL pulse into the autosampler for a new sample injection into the FTMS through an FTDI FT232R programmer chip. It can also receive confirmation TTL signals from the autosampler each time, when a new sample is injected successfully into the FTMS and when the sample’s liquid is flowing into the electrospray needle. The mentioned FTDI chip is equipped with UART interface, which can trigger and receive TTL pulses according to the well-known universal asynchronous receiver/transmitter (UART) protocol. An Arduino microcontroller, which also acts as in-system programmer (ISP) can also be used for this purpose. The Tx, Rx and GND connections are utilized for that bi-directional communication mentioned above. For those autosampler systems, that cannot receive and/or transmit TTL pulses, a contact closure to TTL convertor needs to be integrated into the automation hardware for compatibility. Such a device is based on an electromagnetic relay that is powered up by a low energy external power source. When a TTL pulse gets into this convertor, the electromagnet inside the relay turns on so that a contact closure between two pins in the autosampler is generated. In some cases, the incoming TTL pulse may not possess enough power to drive the relay. For this reason, a

176

Fundamentals and Applications of Fourier Transform Mass Spectrometry

couple of transistors should be integrated, which can amplify the electrical power of the TTL pulse, which is coming from the USB interface of the FTMS’s PC so that the functionality of the relay is always guaranteed, even when the communication USB cable is long. The mentioned bi-directional communication system does not only connect FTMS with an autosampler, it also sends notification e-mail for each successfully injected sample for measurement. Thus, the user is always informed about the series of successfully injected (and acquired) samples as a function of time. If this messaging protocol is (for any reason) interrupted, the user can directly know that something wrong happened, which requires manual intervention by the user. This automation system has been running in our high field FTMS laboratory since more than 7 years and it could indeed increase the productivity of the FTMS laboratory, especially when samples are injected in direct infusion mode to full 24 h operation. However, challenges still exist in maintaining suitable ultra-high vacuum (UHV) in the vacuum chamber of the FTMS for successive sample injection and measurements. System bake out is necessary to be performed on a regular basis especially, due to high methanol cleaning solvent flow rate, which is normally injected into the electrospray source after the end of each measured sample for cleaning purposes. Due to the high vapor pressure of methanol as a cleaning organic solvent, the gas load inside the source vacuum chamber of the FTMS is high and this causes a slight but continuous drift in the pressure in the source vacuum chamber. This can successively affect the ICR vacuum region and therefore can reduce the mass resolving power, if the system is not baked out. Utilizing methanol-water (1:1) as a cleaning solvent cannot avoid such mentioned pressure drift in the FTMS. Therefore, time breaks should also be integrated into the program of batch acquisitions to avoid continuous methanol vapor loads into the electrospray source. For LC-FT-ICR-MS automation, the program Hystar™ is being implemented along the last 10 years with annual updates [131]. This program has a robust functionality and measurements in different ionization polarities can be performed in one programmed batch of samples. This enables (for example) performing LC-MS runs for each sample in both positive and negative electrospray ionization mode. Hystar™ is defined as a software for hyphenated experiments and it works not only for FTMS analyzers but also for TOF and ion traps as well as for NMR applications. One of the recent advances in this program lies in its potential capability to control a complete LC-MSSPE-NMR experiment. For SPE-FTMS integration, we previously succeeded in setting up an automated micro-extraction sample preparation

Data processing and automation in FTMS

177

system coupled on-line to FT-ICR-MS. This automated system was successfully used for de-salting and concentration of river and marine dissolved organic matter [132]. By using such system, solvent consumption was significantly reduced. It depends on micro extraction by packed sorbents (MEPS) small cartridges, which can be integrated into the liquid flow of the autosampler. MEPS cartridges possess the same functionality of solid phase extraction (SPE) cartridges but they can treat very small sample volumes in the hundreds of microliter range [133, 134]. Enrichment of chemical signatures could be noticed in the automated MEPS implementation of Suwannee River samples for environmental analysis. Blakney et al. developed a complete unit for FT-ICR-MS in the national high magnetic field laboratory (NHFML) in Florida, USA, which was the first unit to implement peripheral component interconnect (PCI), compact extension for instrumentation (PXI) and PXI express data acquisition hardware since 2004 [135]. This control unit was essential especially for controlling advanced ICR cells, which contain many additional compensation electrodes and also for enabling sophisticated ICR experiments, utilizing SWIFT, frequency chirp and multiple single shot excitation RF signals. Mize et al. [136] developed a data and control system, which utilized fast PXI hardware bus technology and could speed up the data acquisition. It was utilized to control a 7 T FT-ICR-MS coupled to an external ion source. Real time data transfer to show each scan acquired transient is possible in both above mentioned control units.

Acknowledgments The authors thank Christopher Thompson at Bruker Daltonics for providing an up to date version of FTMS-Control program, which enabled discussing important features for data processing in this chapter.

References [1] Y.I. Kostyukevich, G.N. Vladimirov, E.N. Nikolaev, Dynamically harmonized FT-ICR cell with specially shaped electrodes for compensation of inhomogeneity of the magnetic field. Computer simulations of the electric field and ion motion dynamics, J. Am. Soc. Mass Spectrom. 23 (2012) 2198–2207. [2] I.A. Boldin, E.N. Nikolaev, Fourier transform ion cyclotron resonance cell with dynamic harmonization of the electric field in the whole volume by shaping of the excitation and detection electrode assembly, Rapid Commun. Mass Spectrom. 25 (1) (2011) 122–126.

178

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[3] K.O. Zhurov, A.N. Kozhinov, L. Fornelli, Y.O. Tsybin, Distinguishing analyte from noise components in mass spectra of complex samples: where to cut the noise? Anal. Chem. 86 (2014) 3308–3316. [4] M. Mann, O.N. Jensen, Proteomic analysis of post-translational modifications, Nat. Biotechnol. 21 (2003) 255–261. [5] E.S. Witze, W.M. old, K.A. Resing, N.G. Ahn, Mapping protein post-translational modifications with mass spectrometry, Nat. Methods 4 (2007) 798–806. [6] C.L. Gatlin, J.K. Eng, S.T. Cross, J.C. Detter, J.R. Yates III, Automated identification of amino acid sequence variations in proteins by HPLC/microspray tandem mass spectrometry, Anal. Chem. 72 (2000) 757–763. [7] S. Wu, N.M. Lourette, N. Tolic, R. Zhao, E.W. Robinson, A.V. Tolmachev, R.D. Smith, L. Pase-Tolic, An integrated top-down and bottom-up strategy for broadly characterizing protein isoforms and modifications, J. Proteome Res. 8 (2009) 1347–1357. [8] J. Zaia, Mass spectrometry of oligosaccharides, Mass Spectrom. Rev. 23 (2004) 161–227. [9] K. Marino, J. Bones, J.J. Kattla, P.M. Rudd, A systematic approach to protein glycosylation analysis: a path through the maze, Nat. Chem. Biol. 6 (2010) 713–723. [10] L. Wells, K. Vosseller, R.N. Cole, J.M. Cronshaw, M.J. Matunis, G.W. Hart, Mapping sites of O-GlcNAc modification using affinity tags for serine and threonine post-translational modifications, Mol. Cell. Proteomics 10 (2002) 791–804. [11] M. Wuhrer, M.I. Catalina, A.M. Deelder, C.H. Hokke, Glycoproteomics based on tandem mass spectrometry of glycopeptides, J. Chromatogr. B 849 (2007) 115–128. [12] M. Bern, D. Goldberg, W.H. McDonald, J.R. Yates, Automatic quality assessment of peptide tandem mass spectra, Bioinformatics 20 (2004) 49–54. [13] S. Purvine, N. Kolker, E. Kolker, Spectral quality assessment for high-throughput tandem mass spectrometry proteomics, OMICS J. Integr. Biol. 8 (2004) 255–265. [14] J.W.H. Wong, M.J. Sullivan, H.M. Cartwright, G. Cagney, msmsEval: tandem mass spectral quality assignment for high-throughput proteomics, BMC Bioinformatics 8 (2007) 51–61. [15] H. Xu, M.A. Freitas, A dynamic noise level algorithm for spectral screening of peptide MS/MS spectra, BMC Bioinformatics 11 (2010) 436–444. [16] D.M. Horn, A. Zubarev, F.W. McLafferty, Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules, J. Am. Soc. Mass Spectrom. 11 (2000) 320–332. [17] J. Gallia, K. Lavrich, A. Tan-Wilson, P.H. Madden, Filtering of MS/MS data for peptide identification, BMC Genomics 14 (2013) S2. [18] M. Lucio, A. Fekete, M. Frommberger, P. Schmitt-Kopplin, in: F.J. de Bruijn (Ed.), Handbook of Molecular Microbial Ecology I. Metagenomics and Complementary Approaches, John Wiley & Sons, Inc, 2011, pp. 683–695. [19] A.L. Rockwood, P. Haimi, Efficient calculation of accurate masses of isotopic peaks, J. Am. Soc. Mass Spectrom. 17 (3) (2006) 415–419. [20] N. Hertkorn, C. Ruecker, M. Meringer, R. Gugisch, M. Frommberger, E.M. Perdue, M. Witt, P. Schmitt-Kopplin, High-precision frequency measurements: indispensable tools at the core of the molecular-level analysis of complex systems, Anal. Bioanal. Chem. 389 (2007) 1311–1327. [21] E.B. Kujawinski, M.D. Behn, Automated analysis of electrospray ionization Fourier transform ion cyclotron resonance mass spectra of natural organic matter, Anal. Chem. 78 (2006) 4363–4373. [22] E.V. Kunenkov, A.S. Kononikhin, I.V. Perminova, N. Hertkorn, A. Gaspar, P. Schmitt-Kopplin, I.A. Popov, A.V. Garmash, E.N. Nikolaev, Total mass difference

Data processing and automation in FTMS

[23]

[24] [25] [26]

[27] [28] [29] [30] [31]

[32] [33]

[34]

[35] [36]

[37]

[38]

179

statistics algorithm: a new approach to identification of high-mass building blocks in electrospray ionization Fourier transform ion cyclotron mass spectrometry data of natural organic matter, Anal. Chem. 81 (2009) 10106–10115. D. Tziotis, N. Hertkorn, P. Schmitt-Kopplin, Kendrick-analogous network visualisation of ion cyclotron resonance Fourier transform mass spectra: improved options for the assignment of elemental compositions and the classification of organic molecular complexity, Eur. J. Mass Spectrom. 17 (2011) 415–421. L. Pirhaji, P. Milani, M. Leidl, T. Curran, J. Avila-Pacheco, C.B. Clish, F.M. White, A. Saghatelian, E. Fraenkel, Nat. Methods 13 (2016) 770–776. P.J. Roach, J. Laskin, A. Laskin, Higher-order mass defect analysis for mass spectra of complex organic mixtures, Anal. Chem. 83 (2011) 4924–4929. D.P.A. Kilgour, C. Logan Mackay, P.R.R. Langridge-Smith, P.B. O’Connor, Appropriate degree of trust: deriving confidence metrics for automatic peak assignment in high-resolution mass spectrometry, Anal. Chem. 84 (2012) 7431–7435. C.A. Hughey, C.L. Hendrickson, R.P. Rodgers, A.G. Marshall, K. Qian, Kendrick mass defect spectrum: a compact visual analysis for ultrahigh-resolution broadband mass spectra, Anal. Chem. 73 (2001) 4676–4681. T. Kind, O. Fiehn, Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry, BMC Bioinformatics 8 (2007) 105–125. S. Noury, B. Silvi, R.J. Gillespie, Chemical bonding in hypervalent molecules: is the octet rule relevant? Inorg. Chem. 41 (8) (2002) 2164–2172. J.K. Senior, Partitions and their representative graphs, Am. J. Math. 73 (3) (1951) 663–689. B.M. Ruddy, G.T. Blakney, R.P. Rodgers, C.L. Hendrickson, A.G. Marshall, Elemental composition validation from stored waveform inverse Fourier transform (SWIFT) isolation FT-ICR MS isotopic fine structure, J. Am. Soc. Mass Spectrom. 24 (2013) 1608–1611. I. Dubinenkov, R. Flerus, P. Schmitt-Kopplin, G. Kattner, B.P. Koch, Originspecific molecular signatures of dissolved organic matter in the Lena Delta, Biogeochemistry 123 (2015) 1–14. P. Herzsprung, W.V. T€ umpling, N. Hertkorn, M. Harir, K. Friese, P. SchmittKopplin, High-field FTICR-MS data evaluation of natural organic matter: are CHON5S2 molecular class formulas assigned to 13C Isotopic m/z and in reality CHO components? Anal. Chem. 87 (2015) 9563–9566. N. Hertkorn, M. Harir, K.M. Cawley, P. Schmitt-Kopplin, R. Jaffe, Molecular characterization of dissolved organic matter from subtropical wetlands: a comparative study through the analysis of optical properties, NMR and FTICR/MS, Biogeosciences 13 (2016) 2257–2277. E.C. Minor, M.M. Swenson, B.M. Mattsonc, A.R. Oylerb, Structural characterization of dissolved organic matter: a review of current techniques for isolation and analysis, Environ. Sci.: Process. Impacts 16 (2014) 2064–2080. P. Herzsprung, K. Osterloh, W.V. T€ umpling, M. Harir, N. Hertkorn, P. SchmittKopplin, R. Meissner, S. Bernsdorf, K. Friese, Differences in DOM of rewetted and natural peatlands—results from high-field FT-ICR-MS and bulk optical parameters, Sci. Total Environ. 586 (2017) 770–781. P. Herzsprung, W.V. T€ umpling, K. Wendt-Potthoff, N. Hertkorn, M. Harir, P. Schmitt-Kopplin, K. Friese, High field FT-ICR mass spectrometry data sets enlighten qualitative DOM alteration in lake sediment porewater profiles, Org. Geochem. 108 (2017) 51–60. A.G. Marshall, R.P. Rodgers, Petroleomics: the next grand challenge for chemical analysis, Acc. Chem. Res. 37 (2004) 53–59.

180

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[39] R.P. Rodgers, C.L. Hendrickson, M.R. Emmett, A.G. Marshall, M. Greaney, K. Qian, Molecular characterization of petroporphyrins in crude oil by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry, Can. J. Chem. 79 (5–6) (2001) 546–551. [40] M.M. Mapolelo, R.P. Rodgers, G.T. Blakney, A.T. Yen, S. Asomaning, A.G. Marshall, Characterization of naphthenic acids in crude oils and naphthenates by electrospray ionization FT-ICR mass spectrometry, Int. J. Mass Spectrometry 300 (2–3) (2011) 149–157. [41] C.S. Hsu, C.L. Hendrickson, R.P. Rodgers, A.M. McKenna, A.G. Marshall, Petroleomics: advanced molecular probe for petroleum heavy ends, J. Mass Spectrom. 46 (4) (2011) 337–343. [42] V.V. Lobodin, L. Nyadong, B.M. Ruddy, M. Curtis, P.R. Jones, R.P. Rodgers, A.G. Marshall, DART Fourier transform ion cyclotron resonance mass spectrometry for analysis of complex organic mixtures, Int. J. Mass Spectrom. 378 (2015) 186–192. [43] D.W. van Krevelen, Fuel 29 (1950) 269–284. [44] B. Durand, J.C. Monin, in: B. Durand (Ed.), Kerogen: Insoluble Organic Matter from Sedimentary Rocks, Technip, Paris, 1980, pp. 113–142. [45] D.S. Orlov, Humus Acids of Soils, Russian Translation Series 35,A. A. Balkema, Rotterdam, The Netherlands, 1985, pp. 35–70. [46] J.H. Reuter, E.M. Perdue, E.T. Degens, W.E. Krumbein, A.A. Prashnowsky (Eds.), Mitteilungen aus dem Geologisch-Pal€aontologischen Institut der Universit€at Hamburg, Heft 56, Das Institut, Hamburg, 1984, pp. 249–262. [47] L. Sun, E.M. Perdue, J.L. Meyer, J. Weis, Limnol. Oceanogr. 42 (1997) 714–721. [48] N. Hertkorn, M. Frommberger, M. Witt, B.P. Koch, P. Schmitt-Kopplin, E.M. Perdue, Natural organic matter and the event horizon of mass spectrometry, Anal. Chem. 80 (23) (2008) 8908–8919. [49] E.M. Perdue, N.W. Green, Isobaric molecular formulae of C, H, and O: a view from the negative quadrants of van Krevelen space, Anal. Chem. 87 (2015) 5079–5085. [50] N.W. Green, E.M. Perdue, Fast graphically inspired algorithm for assignment of molecular formulae in ultrahigh resolution mass spectrometry, Anal. Chem. 87 (2015) 5086–5094. [51] M.B. Comisarow, A.G. Marshall, Theory of Fourier transform ion cyclotron resonance mass spectroscopy. I. Fundamental equations and low-pressure line shape, J. Chem. Phys. 64 (1976) 110–119. [52] A.G. Marshall, M.B. Comisarow, G. Parisod, Relaxation and spectral line shape in Fourier transform ion resonance spectroscopy, J. Chem. Phys. 71 (1979) 4434–4444. [53] M.B. Comisarow, A.G. Marshall, Selective-phase ion cyclotron resonance spectroscopy, Can. J. Chem. 52 (1974) 1997–1999. [54] K.R. Rao, D.N. Kim, J.J. Hwang, Fast Fourier Transform Algorithms and Applications, Springer, 2008. [55] R. Bracewell, The Fourier Transform and Its Applications, third ed., McGraw-Hill Series in Electrical and Computer Engineering, 1999. [56] A.V. Oppenheim, R.W. Schafer, J.R. Buck, Zeitdiskrete Signalverarbeitung, second ed., Pearson, 2004. [57] V.K. Ingle, J.G. Proakis, Digital Signal Processing using MATLAB, third ed., Cengage Learning Engineering, 2011. [58] A.G. Marshall, Fourier, Hadamard, Hilbert Transforms in Chemistry, Springer Science + Business Media New York, 1982. [59] A.G. Marshall, F.R. Verdun, Fourier Transform in NMR, Optical, and Mass Spectrometry, Elsevier, 1990.

Data processing and automation in FTMS

181

[60] F. Xian, C.L. Henderickson, G.T. Blaknew, S.C. Beu, A.G. Marshall, Automated broadband phase correction of Fourier transform ion cyclotron resonance mass spectra, Anal. Chem. 82 (2010) 8807–8812. [61] Y. Qi, C.J. Thompson, S.L. Van Orden, P.B. O’Connor, Phase correction of Fourier transform ion cyclotron resonance mass spectra using MatLab, J. Am. Soc. Mass Spectrom. 22 (2011) 138–147. [62] Y. Qi, M.P. Barrow, H. Li, J.E. Meier, S.L. Van Orden, C.J. Thompson, P.B. O’Connor, Absorption-mode: the next generation of Fourier transform mass spectra, Anal. Chem. 84 (2012) 2923–2929. [63] Y. Qi, M.P. Barrow, S.L. Van Orden, C.J. Thompson, H. Li, P. Perez-Hurtado, P.B. O’Connor, Variation of the Fourier transform mass spectra phase function with experimental parameters, Anal. Chem. 83 (2011) 8477–8483. [64] E.C. Craig, I. Santos, A.G. Marshall, Dispersion versus absorption (DISPA) method for automatic phasing of Fourier transform ion cyclotron resonance mass spectra, Rapid Commun. Mass Spectrom. 1 (1987) 33. [65] D.P.A. Kilgour, K.O. Nagornov, A.N. Kozhinov, K.O. Zhurov, Y.O. Tsybin, Producing absorption mode Fourier transform ion cyclotron resonance mass spectra with nonquadratic phase correction functions, Rapid Commun. Mass Spectrom. 29 (2015) 087–1093. [66] K. O. Nagornov, A. N. Kozhinov, K. O. Zhurov, Y. O. Tsybin, in Proc. 62nd ASMS Conf. Mass Spectrometry and Allied Topics, Baltimore, 2014. [67] K.O. Nagornov, A.N. Kozhinov, O.Y. Tsybin, Y.O. Tsybin, Ion trap with narrow aperture detection electrodes for Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Soc. Mass Spectrom. 26 (2015) 741–751. [68] S.C. Beu, G.T. Blakney, J.P. Quinn, C.L. Hendrickson, A.G. Marshall, Broadband phase correction of FT-ICR mass spectra via simultaneous excitation and detection, Anal. Chem. 76 (2004) 5756–5761. [69] S. C. Beu, Proceedings of the 46th ASMS Conference on Mass Spectrometry & Allied Topics, Orlando, FL 1998; p 502. [70] S. C. Beu, Proceedings of the 47th ASMS Conference on Mass Spectrometry & Allied Topics, Dallas, TX 1999; CD-ROM. [71] E.G. Schmidt, C.R. Arkin, M.A. Fiorentino, D.A. Laude, Application of simultaneous excitation/detection to generate real-time excitation profiles in Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Soc. Mass Spectrom. 11 (2000) 1009–1015. [72] S. Marple, J. Lawrence, Digital Spectral Analysis, Prentice-Hall, Englewood Cliffs, NJ, 1987. [73] J.F. Hauer, C.J. Demeure, L.L. Scharf, Initial results in Prony analysis of power system response signals, IEEE Trans. Power Syst. 5 (1) (1990) 80–89. [74] J. Zygarlicki, M. Zygarlicka, J. Mroczka, K.J. Latawiec, A reduced Prony’s method in power-quality analysis – parameters selection, IEEE Trans. Power Delivery 25 (2) (2010) 979–986. [75] J. Zygarlicki, J. Mroczka, Variable frequency Prony method in the analysis of electrical power quality, Metrol. Meas. Syst. XIX (1) (2012) 39–48. [76] C. Lancos, Applied Analysis, Sir Isaac Pitman & Sons, 1956. [77] M.H. Kahn, M.S. Mackisack, M.R. Osborne, G.K. Smyth, On the consistency of Prony’s method and related algorithms, J. Comput. Graph. Stat. 1 (4) (1992) 329–349. [78] M.R. Osborne, G.K. Smyth, A modified Prony algorithm for exponential function fitting, SIAM J. Sci. Comput. 16 (1) (1993) 119–138. [79] D. Potts, M. Tasche, Parameter estimation for exponential sums by approximate Prony method, Signal Process. 90 (5) (2010) 1631–1642.

182

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[80] T. Peter, Generalized Prony Method, Der Andere Verlag, 2014. ISBN-10: 3862474852, ISBN-13: 978-3862474851. [81] H. Barkhuijsen, R. De Beer, W.M.M.J. Bovee, D. van Ormondt, Retrieval of frequencies, amplitudes, damping factors, and phases from time-domain signals using a linear least-squares procedure, J. Magn. Reson. 61 (3) (1985) 465–481. [82] T.C. Farrar, J.W. Elling, M.D. Krahling, Application of linear prediction to Fourier transform ion cyclotron resonance signals for accurate relative ion abundance measurements, Anal. Chem. 64 (1992) 2770–2774. [83] S. Guan, A.G. Marshall, Linear prediction Cholesky decomposition vs. Fourier transform spectral analysis for ion cyclotron resonance mass spectrometry, Anal. Chem. 69 (1997) 1156–1162. [84] J.P. Burg, Modern Spectrum Analysis, IEEE Press, New York, 1978. [85] A. Rahbee, High-resolution mass spectrometry using the maximum entropy method, Int. J. Mass Spectrum Ion Process. 72 (1986) 3–13. [86] A. Rahbee, Application of maximum entropy spectral analysis to Fourier transform mass spectrometry, Chem. Phys. Lett. 117 (4) (1985) 352–358. [87] K. Aizikov, P.B. O’Connor, Use of the filter diagonalization method in the study of space charge related frequency modulation in Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Chem. Soc. Mass Spectrom. 17 (2006) 836–843. [88] V.A. Mandelshtam, FDM: the filter diagonalization method for data processing in NMR experiments, Prog. Nucl. Magn. Reson. Spectrosc. 38 (2001) 159–196. [89] A.N. Kozhinov, Y.O. Tsybin, Filter diagonalization method-based mass spectrometry for molecular and macromolecular structure analysis, Anal. Chem. 84 (2012) 2850–2856. [90] B.R. Martini, K. Aizikov, V.A. Mandelshtam, The filter diagonalization method and its assessment for Fourier transform mass spectrometry, Int. J. Mass Spectrom. 373 (2014) 1–14. [91] L. Jiang, M. Moini, Ultramark 1621 as a reference compound for positive and negative ion fast-atom bombardment high-resolution mass spectrometry, J. Am. Chem. Soc. 3 (1992) 842–846. [92] F.E. Leach III, A. Kharchenko, G. Vladimirov, K. Aizikov, P.B. O’Connor, E. Nikolaev, R.M.A. Heeren, I.J. Amster, Analysis of phase dependent frequency shifts in simulated FTMS transients using the filter diagonalization method, Int. J. Mass Spectrom. 325–327 (2012) 19–24. [93] D.W. Mitchell, R.D. Smith, Two dimensional many particle simulation of trapped ions, Int. J. Mass Spectrom. 165 (1997) 271–297. [94] D.W. Mitchell, Realistic simulation of the ion cyclotron resonance mass spectrometer using a distributed three-dimensional particle-in-cell code, J. Am. Soc. Mass Spectrom. 10 (1999) 136–152. [95] E.N. Nikolaev, R.M.A. Heeren, A.M. Popov, A.V. Pozdneev, K.S. Chingin, Realistic modeling of ion cloud motion in a Fourier transform ion cyclotron resonance cell by use of a particle-in-cell approach, Rapid Commun. Mass Spectrom. 21 (2007) 3527–3546. [96] F.E. Leach III, A. Kharchenko, R.M.A. Heeren, E. Nikolaev, I.J. Amster, Comparison of particle-in-cell simulations with experimentally observed frequency shifts between ions of the same mass-to-charge in Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Soc. Mass Spectrom. 21 (2010) 203–208. [97] T. Aushev, A.N. Kozhinov, Y.O. Tsybin, Least-squares fitting of time-domain signals for Fourier transform mass spectrometry, J. Am. Soc. Mass Spectrom. 25 (2014) 1263–1273. [98] D. Grinfeld, K. Aizikov, A. Kreutzmann, E. Damoc, A. Makarov, Phased-constrained spectrum deconvolution method for Fourier transform mass spectrometry, Anal. Chem. 89 (2017) 1202–1211.

Data processing and automation in FTMS

183

[99] Y. Qi, P. O’Connor, Data processing in Fourier transform ion cyclotron resonance mass spectrometry, Mass Spectrom. Rev. 33 (2014) 333–352. [100] Official website of Bruker Daltonics (2017): https://www.bruker.com/de/products/ mass-spectrometry-and-separations/ms-software/compass/overview.html. [101] A.L. Rockwood, J.C.L. Erve, Mass spectral peak distortion due to Fourier transform signal processing, J. Am. Soc. Mass Spectrom. 25 (2014) 2163–2176. [102] Y. Qi, H. Li, R.H. Wills, P. Perez-Hurtado, X. Yu, D.P.A. Kilgour, M.P. Barrow, C. Lin, P.B. O’Connor, Absorption-mode Fourier transform mass spectrometry: the effects of apodization and phasing on modified protein spectra, J. Am. Soc. Mass Spectrom. 24 (2013) 828–834. [103] R.B. Blackman, J.W. Tukey, Particular pairs of windows, in: The Measurement of Power Spectra, From the Point of View of Communications Engineering, Dover, New York, 1959, pp. 95–101. [104] F.J. Harris, On the use of windows for harmonic analysis with the Discrete Fourier Transform, Proc. IEEE 66 (1978) 51–83. [105] Y. Chen, J. Fingler, S.E. Fraser, Multi-shaping technique reduces sidelobe magnitude in optical coherence tomography, Biomed. Opt. Express 8 (11) (2017) 5267–5281. [106] E.B. Ledford, D.L. Rempel, M.L. Gross, Space charge effects in Fourier transform mass spectrometry. Mass calibration, Anal. Chem. 56 (1984) 2744–2748. [107] E.B. Ledford, S. Ghaderi, R.L. White, R.B. Spencer, P.S. Kulkarni, C.L. Wilkins, M.L. Gross, Exact mass measurement by Fourier transform mass spectrometry, Anal. Chem. 52 (1980) 463–468. [108] T.J. Francl, M.G. Sherman, R.L. Hunter, M.J. Locke, W.D. Bowers, R.T. McIver, Experimental determination of the effects of space charge on ion cyclotron resonance frequencies, Int. J. Mass Spectrom. 54 (1983) 189–199. [109] S.D.H. Shi, J.J. Drader, M. Freitas, C.L. Hendrickson, A.G. Marshall, Comparison and interconversion of the two most common frequency-to-mass calibration functions for Fourier transform ion cyclotron resonance mass spectrometry, Int. J. Mass Spectrom. 195 (2000) 591–598. [110] M.L. Easterling, T.H. Mize, I.J. Amster, Routine part-per-million mass accuracy for high-mass ions: space-charge effects in MALDI FT-ICR, Anal. Chem. 71 (1999) 624–632. [111] C. Masselon, A.V. Tolmachev, G.A. Anderson, R. Harkewicz, R.D. Smith, Mass measurement errors caused by “Local” frequency perturbations in FTICR mass spectrometry, J. Am. Soc. Mass Spectrom. 13 (2002) 99–106. [112] D.C. Muddiman, A.L. Oberg, Statistical evaluation of internal and external mass calibration laws utilized in Fourier transform ion cyclotron resonance mass spectrometry, Anal. Chem. 77 (2005) 2406–2414. [113] D.K. Williams, D.C. Muddiman, Parts-per-billion mass measurement accuracy achieved through the combination of multiple linear regression and automatic gain control in a Fourier transform ion cyclotron resonance mass spectrometer, Anal. Chem. 79 (2007) 5058–5063. [114] D.K. Williams, M.A. Chadwick, T.I. Williams, D.C. Muddiman, Calibration laws based on multiple linear regression applied to matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry, J. Mass Spectrom. 43 (2008) 1659–1663. [115] R.L. Wong, I.J. Amster, Sub part-per-million mass accuracy by using stepwiseexternal calibration in Fourier transform ion cyclotron resonance mass spectrometry, J. Am. Soc. Mass Spectrom. 17 (2006) 1681–1691. [116] D.K. Williams, A.L. Kovach, D.C. Muddiman, K.W. Hanck, Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a Fourier transform ion cyclotron resonance mass spectrometer, J. Am. Soc. Mass Spectrom. 20 (2009) 1303–1310.

184

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[117] J.J. Savory, N.K. Kaiser, A.M. McKenna, F. Xian, G.T. Blakney, R.P. Rodgers, C.L. Hendrickson, A.G. Marshall, Parts-per-billion fourier transform ion cyclotron resonance mass measurement accuracy with a “Walking” calibration equation, Anal. Chem. 83 (2011) 1732–1736. [118] A.N. Kozhinov, K.O. Zhurov, Y.O. Tsybin, Iterative method for mass spectra recalibration via empirical estimation of the mass calibration function for Fourier transform mass spectrometry-based petroleomics, Anal. Chem. 85 (2013) 6437–6445. [119] J.W. Flora, J.C. Hannis, D.C. Muddiman, High-mass accuracy of product ions produced by SORI-CID using a dual electrospray ionization source coupled with FTICR mass spectrometry, Anal. Chem. 73 (2001) 1247–1251. [120] D.K. Williams, A.M. Hawkridge, D.C. Muddiman, Sub parts-per-million mass measurement accuracy of intact proteins and product ions achieved using a dual electrospray ionization quadrupole Fourier transform ion cyclotron resonance mass spectrometer, J. Am. Soc. Mass Spectrom. 18 (2007) 1–7. [121] S.A. Hofstadler, J.E. Bruce, A.L. Rockwood, G.A. Anderson, B.E. Winger, R.D. Smith, Isotopic beat patterns in Fourier transform ion cyclotron resonance mass spectrometry: implications for high resolution mass measurements of large biopolymers, Int. J. Mass Spectrom. 132 (1994) 109–127. [122] S.G. Valeja, N.K. Kaiser, F. Xian, C.L. Hendrickson, J.C. Rouse, A.G. Marshall, Unit mass baseline resolution for an intact 148 kDa therapeutic monoclonal antibody by Fourier transform ion cyclotron resonance mass spectrometry, Anal. Chem. 83 (2011) 8391–8395. [123] N.K. Kaiser, J.P. Quinn, G.T. Blakney, C.L. Hendrickson, A.G. Marshall, A novel 9.4 Tesla FTICR mass spectrometer with improved sensitivity, mass resolution, and mass range, J. Am. Soc. Mass Spectrom. 22 (2011) 1343–1351. [124] M.L. Easterling, I.J. Aster, G.J. Van Rooij, R.M.A. Heeren, Isotope beating effects in the analysis of polymer distributions by Fourier transform mass spectrometry, J. Am. Soc. Mass Spectrom. 10 (1999) 1074–1082. [125] R. Gavard, D. Rossel, S.E.F. Spencer, M.P. Barrow, Themis: batch preprocessing for ultrahigh-resolution mass spectra of complex mixtures, Anal. Chem. 89 (2017) 11383–11390. [126] K. Schuhmann, H. Thomas, J.M. Ackerman, K.O. Nagornov, Y.O. Tsybin, A. Schvchenko, Intensity-independent noise filtering in FT MS and FT MS/MS spectra for shotgun lipidomics, Anal. Chem. 89 (2017) 7046–7052. [127] B. Kanawati, T.M. Bader, K.P. Wanczek, Y. Li, P. Schmitt-Kopplin, Fourier transform (FT)-artifacts and power-function resolution filter in Fourier transform mass spectrometry, Rapid Commun. Mass Spectrom. 31 (2017) 1607–1615. [128] H. Lo´pez-Ferna´ndez, H.M. Santos, J.L. Capelo, F. Fdez-Riverola, D. Glez-Pena, M. Reboiro-Jato, Mass-up: an all-in-one open software application for MALDITOF mass spectrometry knowledge discovery, BMC Bioinformatics 16 (2015) 318–330. [129] S.A. Kazmi, S. Ghosh, D.-G. Shin, D.W. Hill, D.F. Grant, Alignment of high resolution mass spectra: development of a heuristic approach for metabolomics, Metabolomics 2 (2006) 75–83. [130] N. Huang, M.M. Siegel, G.H. Gruppa, F.H. Laukien, Automation of a Fourier transform ion cyclotron resonance mass spectrometer for acquisition, analysis, and e-mailing of high-resolution exact-mass electrospray ionization mass spectral data, J. Am. Soc. Mass Spectrom. 10 (1999) 1166–1173. [131] Hystar, Software for Hyphenated Experiments, www.bruker.com/products/mr/ nmr/nmr-software/nmr-software/hystar/overview.html.

Data processing and automation in FTMS

185

[132] G. Morales-Cid, I. Gebefugi, B. Kanawati, M. Harir, N. Hertkorn, R. RosselloMora, P. Schmitt-Kopplin, Automated microextraction sample preparation coupled on-line to FT-ICR-MS: application to desalting and concentration of river and marine dissolved organic matter, Anal. Bioanal. Chem. 395 (2009) 797–807. [133] M. Abdel-Rehim, Recent advances in microextraction by packed sorbent for bioanalysis, J. Chromatogr. A 1217 (2010) 2569–2580. [134] M. Abdel-Rehim, Microextraction by packed sorbent (MEPS): a tutorial, Anal. Chim. Acta 701 (2011) 119–128. [135] G.T. Blakney, C.L. Hendrickson, A.G. Marshall, Predator data station: a fast data acquisition system for advanced FT-ICR MS experiments, Int. J. Mass Spectrom. 306 (2011) 246–252. [136] T.H. Mize, I. Taban, M. Duursma, M. Seynen, M. Konijnenburg, A. Vijftigschild, C.V. Doornik, G.V. Rooij, R.M.A. Heeren, A modular data and control system to improve sensitivity, selectivity, speed of analysis, ease of use, and transient duration in an external source FTICR-MS, Int. J. Mass Spectrom. 235 (3) (2004) 243–253.

CHAPTER 7

Fundamentals of two dimensional Fourier transform mass spectrometry Federico Floris, Peter B. O’Connor

Department of Chemistry, University of Warwick, Coventry, United Kingdom

Contents Introduction Contemporary FT-ICR mass spectrometers and tandem mass spectrometry Mass spectrometry in the second dimension Interpretation of a 2D mass spectrum Noise in 2D-MS Resolving power and mass accuracy Alternative 2D-MS Msn/2D-MS Data acquisition and processing Data analysis Applications of 2D-MS Conclusion Glossary References

187 189 193 197 201 204 206 208 209 214 218 225 227 228

Introduction 2D FT-ICR MS is based on Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), the mass spectrometry technique best known for producing the highest resolving power and mass accuracy among similar analytical platforms. Since its conception by Comisarow et al. [1], FT-ICR MS has been developed for the analysis of substances of all natures, from single compounds to mixtures of substances of increasing complexity, becoming a key platform in the fields of proteomics, glycomics, petroleomics, and polymer analysis among other fields. FT-ICR MS revolves around the motion of ions at the center of a high magnetic field, and their mass measurement based on the correlation Fundamentals and Applications of Fourier Transform Mass Spectrometry https://doi.org/10.1016/B978-0-12-814013-0.00007-7

© 2019 Elsevier Inc. All rights reserved.

187

188

Fundamentals and Applications of Fourier Transform Mass Spectrometry

between the ions’ cyclotron motion and their mass-to-charge ratio (m/z). The technique has been widely explored and described through its development, and details on its principles can be found in different papers, which are reviewed by Amster et al. [2] and by Marshall et al. [3]. In order to obtain more structural information from the molecules under analysis, analytes are often fragmented through a procedure called tandem mass spectrometry (MS/MS or MSn). To perform tandem mass spectrometry, a molecule of interest is isolated from the rest of the mixture, fragmented, and finally detected. The output of MS/MS is a mass spectrum of all the fragments generated by the isolated and fragmented analyte, i.e., the precursor. It is important to isolate a single precursor in order to obtain a mass spectrum containing only its fragments: if more than one precursor is fragmented, it is impossible to correlate the identified fragments to their corresponding precursors. The necessity of a clear isolation before fragmentation might induce the use of separation techniques before introducing the analytes in the mass spectrometer, depending on the complexity of the mixture. Such separations are routinely achieved by hyphenating mass spectrometers with separation systems, such as chromatography (LC-MS or GC-MS), but these techniques add new variables to the experimental setting. Even considering a perfect compatibility of the analytes with the columns and solvent gradients—necessary for separation—and a robust method, online LC-MS/ MS still imposes challenges from a mass spectrometric point of view. These challenges are represented by the slow duty cycle of high resolution FT-ICR MS acquisitions, the low fragmentation efficiencies of some fragmentation techniques such as electron-based dissociations (ExD’s), often requiring more scans than the maximum allowed by an LC separation in order to allow fragment identification, and finally by the resolution obtained by isolating every species introduced in the mass spectrometer after chromatographic separation. However, even when separations are not necessary, or perfectly achieved, it stands to reason that isolating and fragmenting every single peak in a mixture can be extensively sample- and time-consuming. Furthermore, isolation of precursor ions in small m/z windows causes losses in sensitivity, sometimes losing >50% of the ion intensity. The causes for the significant ion losses from narrow quadrupole isolation windows are usually because of the stability and accuracy of the quadrupole isolation device. Quadrupoles typically require extremely high machining accuracy ( 1, is an ion of a salt, does not belong to CHNOPS space, or represents an artifact caused by electrical RF noise in ICR. The second plot for an investigation of UHR-MS data prior to any molecular formula identification is the Kendrick Mass Defect (KMD) plot (Fig. 12.1B). This plot is based on the same physical principles as the j MDj plot, but it uses a transformed mass scale. As lined out by E. Kendrick [74], it was common practice in 1963 to not only express m/z values on the 12 C mass scale, but on the 16O mass scale as well. This practice allowed

Mass differences in mass-spectrometric metabolome analyses

375

spotting series of C and O atoms at hand of integer mass spacings. Researching Naphthalenes, Paraffins and Ketones, Edward Kendrick suggested scaling exact masses by a factor that makes CH2-spacings of homologous series appear on regular spacings of 14 amu instead of 14.01565 amu. He achieved this by dividing each exact mass by the quotient mexact(CH2)/ mnominal(CH2). Hughey et al. [63] multiplied the exact masses by mnominal(CH2)/mexact(CH2) to achieve the same result. The Kendrick mass defect (KMD) is the IUPAC mass defect applied on Kendrick mass scale. Fig. 12.1B shows how plotting HMDB compound classes’ KMDs over Kendrick mass leads to horizontal, equally spaced lines for CH2homologous series, while increasing aliphatic character can be observed along the KMD-sequence 0 ! -0.5 ! 0.5 ! 0. Similar to Fig. 12.1A, we can see that amino acids, peptides, Flavonoid O-Glycosides and Terpene Glycosides do not form extensive CH2-series. Mass defect plots that operate on Δm’s were the only means of visual interpretation of highly complex UHR-MS data until Kim et al. [75] abstracted the van Krevelen plot [76] from geochemical literature in 2003. While not being based on Δm’s explicitly, we introduce the reader to van Krevelen plots (Fig. 12.1C) and mass-edited van Krevelen plots (Fig. 12.1D) as being the third and fourth traditional techniques for UHR-MS data visualization and analysis. Van Krevelen (VK) related techniques require the unequivocal assignment of compositions or molecular formulas to m/z-peaks. Once this task has been performed (e.g., following the seven golden rules [77] and variants of rules formulated therein [78, 79]) for the detected ions’ neutral forms, VK plots are generated by calculating the quotients of H-counts over C-counts and O-counts over C-counts for each molecular formula and plotting those values on the ordinate and abscissa, respectively. A VK-plot of HMDB compound classes (Fig. 12.1C) shows that most compound classes populate fairly conserved regions in H/C-O/C space. Fatty Alcohols, Fatty Acid Esters, Triacylglycerols, Diacylglycerols and Phosphoethanolamines show particularly conserved and highly organized clusters. Phosphoethanolamines differ more strongly from the other classes, as their phosphatidyl-groups carry additional oxygen that is not bound to C-Atoms. A particular feature of VK-plots is their structure: While horizontal lines imply a constant degree of unsaturation, vertical lines imply a constant degree of oxygenation under changing degree of unsaturation. Diagonals that are a linear combination of both lines imply a varying degree of hydration. E.M. Perdue analyzed the compositional changes of these and

376

Fundamentals and Applications of Fourier Transform Mass Spectrometry

other lines on the VK-plot and found that coordinates where extrapolated lines intersect in the negative quadrants of the VK-plot marked the compositions of SMMs [73] (see “Compositional space” section). The relationship of VK-ratios to Δm’s is substantiated in Fig. 12.1D, where exact mass was plotted on the abscissa. All lipid classes displayed on Fig. 12.1C form very distinct VK-patterns, while the clusters of Fatty Acid Esters, Triacylglycerols and Phosphoethanolamines overlap on the scatter plot. Non-lipid classes show weaker VK-patterns and are broadly distributed over the scatter plot. The only non-lipid compounds that seem to follow profound VK-patterns (Fig. 12.1C) are Flavonoid O-gylcosides, which form a cluster with high unsaturation and high content of Oxygen. Terpene Glycosides cluster in a conserved region as well, but they do not seem to adhere to any obvious VK-pattern. Further plots that support the visual interpretation of UHR-MS spectra involve plotting C-count over double bond equivalents (DBE, also Degree of Unsaturation [79]), as often used in petroleomics [80], as well as plotting the aromaticity index [81, 82] or the aromaticity equivalent [83]. Fig. 12.1 shows that MD- and KMD-plots as well as different VK-plots offer visual support in the classification of compound classes detectable by UHR-MS where they follow compositional patterns that can be readily explained by changes in H2, CH2 and O. When molecular complexity does not follow a simple C-H-O-based order, as we can see for Amino Acids, Peptides, Terpenes and Terpene Glycosides in Fig. 12.1, it could be advisable to change Kendrick bases from, e.g., CH2 to CO2 and thus change Δm ¼ 14 amu to Δm ¼ 44 amu. However, no variation of parameters will support visual pattern recognition either once the Kendrick base becomes very large or once a certain number of compositions are co- and overplotted. Over-plotting occurs on a scatter-plot when the number of picture points to be plotted exceeds the number of pixels in the corresponding region of a figure or when it exceeds the resolution of the human eye. KMD plots can be particularly misleading if isobaric series that are separated by SMMs (“Compositional space” section) are being compared across nominal masses, because such subtle differences cannot be resolved on a normal KMD-scale. At this point, it is helpful to employ algorithms that search for exact mass differences directly (without employing the calculation of elemental ratios or KMDs) and create networks of cross-connected homologous series with a user-definable degree of complexity (Fig. 12.2, next section).

Fig. 12.2 Kendrick analogous MDiN-layout of exact monoisotopic masses from the HMDB database. MDiNs in upper half: HMDB-metabolites were connected using the Δm’s 14.015650 amu, 2.015650 amu, 15.994915 amu, 43.989829 amu and 15.010899 amu, for the functionalities CH2, H2, O, CO2 and NH, respectively. MDiNs in lower half: Panels B–E show MDiNs that are generated by combining those MDiNs at the root of each arrow. Panel A shows a zoom-in of a graph component of Gangliosides to visualize the formulaic relationships generated by connecting Ganglioside exact masses with the Δm’s corresponding to CH2 and H2. The lower left panel revisits the color-code of interesting compound classes whose evolution can be followed by moving along the dashed arrows. All MDiNs were laid out in Cytoscape [21].

378

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Mass difference networks in the visualization and primary analysis of UHR-MS data The over-plotting problem as visualized in Fig. 12.1 can be alleviated by searching for mass differences in UHR-MS data directly. The development of Kendrick-analogous networks by Tziotis et al. as published in 2011 [53] was therefore a natural consequence of Kendrick’s initial work. These networks, termed ‘mass difference networks’ (MDiNs) [10] are based on Kendrick’s insights as follows: The molecular formulas of adjacent elements of a homologous series differ by one CH2 unit and these molecular formulas’ corresponding masses differ by 14.01565 amu. In the same manner as each m/z-peak can be assigned to a molecular formula, a given Δm can be assigned to its corresponding formula-difference. This mapping is true for any pair of mono-isotopic peaks. The Δm between any monoisotopic peaks reflects the formulaic difference of their corresponding molecules.

Tziotis et al. [53] showed how purely compositional Δm’s on the basis of C1, C2, C3, H2, H4, H6, N1H1, N2, N3H1, O1, O2, O3 connected the majority of DI-ESI-FT-ICR MS peaks from aerosols just as much as functional Δm’s from hypothesized (bio)chemical reactions. Functional Δm’s can be inserted between CdC or CdH bonds. This second ‘functional’ set of Δm’s reflected CH2 ((de)methylation), H2 (oxidation/reduction), N1H1 ((de) amination), O1 ((de)hydroxylation), O2 (hydro-peroxidation), S ((de)thiolation), SO3 ((de)sulfonation), HPO3 ((de)phosphorylation), etc. The functional list of Δm’s was the actual Kendrick-analogue referred to in their paper’s title. Earlier works of Kujawinski et al. [84] and Kunenkov et al. [85] used mass differences for level 4 identification (as previously described in “Primary analysis: Annotation, identification, knowns and unknowns” section) as well. In fact, a further similar network based molecular formula assignment approach was introduced by Kilgour et al. [52] These authors employed multiple KMDs transformations for mass-pair detection and re-transformed the corresponding KMD-vectors into Δm’s that were matched against an in-house library. A valuable addition to mass-difference based molecular formula assignment was the formulation of an ‘artificial immune system metric’ for the exclusion of incorrect assignments of Δm’s or artifact peaks. Their essential finding was that nodes (m/z peaks) with a low connectivity given a set of Δm’s, tended to be assigned incorrectly. This finding emphasized one advantage of mass difference network (MDiN) approaches: m/z-features with a high connectivity in an MDiN, are convergence points of multiple homologous series. A molecular formula

Mass differences in mass-spectrometric metabolome analyses

379

that is assigned while satisfying the compositional relationships of many homologous series has a higher probability to be in fact correct. The novelty in the work of Tziotis et al. [53] consisted in two insights: (1) it is irrelevant for the molecular formula assignment whether the Δm-composition-pairs reflected true chemical reactions or not, and (2) A graphical representation of the found Δm’s could alleviate the overplotting-problem found on KMD- and VK-plots while maintaining all desired information. Fig. 12.2 shows the sound succession of a stepwise Δm-based crossconnection of multiple homologous series into a Kendrick-analogous functional network of theoretical data derived from the Human Metabolome Database (HMDB) [86]. The MDiNs in the upper half of Fig. 12.2 have molecular formulas/exact masses as nodes and pairs of nodes are connected by an edge assigned using the Δm of that functionality which is highlighted on each panel. The nodes’ coloring scheme is the same as detailed for Fig. 12.1 and the compound classes of gray nodes do not belong to the top 10 most frequent classes. The MDiNs (panels A-E) in the lower half of Fig. 12.2 result from merging the upper-half-MDiNs as indicated by the gray, dashed arrows. Gray arrows pointing at nodes on other dashed arrows signify the fusion of the MDiNs on the arrows’ roots. An example: MDiN (B) results from fusing CH2- and H2-homologous series. MDiN (B) is in turn passed on to MDiN (C) under addition of the MDiN of NH-homologous series. Network (D) is produced by fusing the MDINs of (C) with the MDiN of the CO2-series and adding the MDiN of O-functionalities to (D) finally results in MDiN (E). The benefit of MDiNs over KMD- and VK-plots in terms of over-plotting becomes apparent on the CH2 and H2 panels. Connecting HMDB-nodes either using Δm(CH2) ¼ 14.01565 amu or Δm(H2) ¼ 2.01565 amu leads to well-structured, connected graph components with very consistent compound class assignments. ‘Graph’ is the mathematical term for ‘network’ and a connected graph component – or just graph component – is a connected subnetwork. Each graph component in the upper-half-MDiNs of Fig. 12.2 represents a homologous series given their respective Δm highlighted on their panel. The homologous series produced by O, CO2 and NH are clearly inferior to CH2 and H2 in terms of length and compound class consistency. Fusing the CH2 and H2 panels causes Di- and Triacylglycerols, as well as Phosphoethanolamines, Gangliosides and other compound classes to ‘self-organize’ in their own graph components (the color code for these compound classes is

380

Fundamentals and Applications of Fourier Transform Mass Spectrometry

pronounced in the bottom left panel of Fig. 12.2 once more). The class of Gangliosides, which did not occur in Fig. 12.1 because they have exact masses >1000 amu, was highlighted on Fig. 12.2A to outline the principle of MDiNs once more. The molecular formulas on nodes and compositional changes on the edges can be exchanged with exact masses and Δm’s, respectively. Adding the functionality Δm(NH) to MDiN (B), leading to MDiN (C), does not cause any further agglomeration of the lipid compound classes mentioned above. However, this insertion of a new functionality causes a partial fusion of amino acids (light blue), peptides (blue) and fatty acid esters (orange). A further addition of Δm(CO2) adds terpene glycosides (mint green) and flavonoid O-glycosides (grass green) to the main graph components in MDiN (D). Phosphoethanolamines (purple) are still separated into two distinct graph components at that point. Adding Δm(O) not only fuses both Phosphoethanolamine components, it attaches Triacylglycerols and the remaining Fatty Acid Ester components to the largest graph component of mixed compound classes. MDiNs allow for a free manipulation, visualization and extraction of both, homologous series and compound classes by means of an appropriate choice of Δm’s. Any further fusion of functionalities leads to an ever more cross-connected picture, eventually connecting all m/z values. The high density of MDiNs generated from only 10 different Δm’s leads to a core of trustworthy level 4 identifications once MDiNs are used for molecular formula assignment. However, pathway analysis and related secondary level techniques require a selective removal of the majority of MDiN edges later on (see “Mass difference networks in the visualization and secondary analysis of UHR-MS data” section).

MDiNs in primary data analysis MDiN’s for dereplication The primary analysis of UHR-MS data does not fundamentally differ from that of HR-MS techniques when it comes to the use of Δm values. Due to its higher mass accuracy, UHR-MS provides much better control over SMM’s, which is a key feature in primary analysis. A substantial use of MDiNs in primary analysis is their application in tracking molecular adducts, i.e., artifacts in electrospray ionization (ESI), of any kind: Two molecules M1 and M2 may occur as [Mi + H]+, [Mi + Na]+, [Mi-H2O + H]+ or [Mi-H2O + Na]+ adducts and/or auto-fragmentation products and they may occur as homodimers [2M1 + H]+ and its variants or heterodimers [M1 + M2 + H]+ and so on. Naturally, even higher oligomers are possible, as easily seen when subjecting an Arginine standard to ESI, which delivers a

Mass differences in mass-spectrometric metabolome analyses

381

series of up to homopentameric adduct signals in both ESI + and ESI- ionization modes. Some other amino acids can also show the same behavior under electrospray ionization. Naturally, all these variants can occur at different charge states and the production of each of these variants is necessarily dependent on one or multiple dissociation constants. Furthermore, some physical conditions, such as supersonic beam expansion as well as any de-clustering electric potential, which might be applied in the atmospheric pressure ESI source, have impact on cluster ion formation. Therefore, ion abundance of the monomeric variant of a molecular ion [M + H]+ alone is not bound to reflect the concentration of its molecule in solution. As mentioned before, Mahieu et al. [17, 65] showed how mass difference networking can alleviate this problem to that extent that corresponding relationships can be found. The intensities of degenerate peaks (“Mass differences in instrumental quality parameters” section) are usually considered to correlate across samples or chromatographic peak shapes. However, varying concentrations of solutes and ‘reaction partners’ for heterooligomer-formation can cause non-correlating behaviors. There is still no workflow that describes how to counter this problem. In fact, an earlier publication by Brown et al. [87] derived fundamental insights into the relative behavior of chemically related ion’s along mass differences and RTs in UHR-MS. These authors investigated all mass differences in LC-UHR MS runs (Orbitrap) and confronted them with chromatographic peak shape correlation and retention time differences. They found that the signal-pairs of the most correlating mass differences pertained to isotopologues of the same compound and a considerable number of correlations across Δm’s pertained to adducts with inorganic salt. Here, Brown et al. stated that the extent of sodium formation depended on the sample type specific abundance of, e.g., NaCl; this was true despite chromatographic separation. Next, they found that 14%–33% of all detected features represented homo-oligomeric adduct ions. Importantly, they reported that the number of homo-oligomeric ions per compound was not concentration dependent, but the intensity ratios – and thus the correlation of chromatographic peak shapes across Δm’s – was indeed concentration dependent. In addition, Forcisi et al. [47] observed concentration-dependent ratios of [Caffeine+H]+ and [Caffeine+Na]+ ions in positive ESI. Conclusively, correlations of chromatographic peak shapes across prespecified Δm’s seem to be less reliable when screening for pairs of inorganic salt adducts of one compound as compared to screening for pairs of its isotopologues. A further method published by Gipson et al. [88] in 2008 was primarily centered on

382

Fundamentals and Applications of Fourier Transform Mass Spectrometry

differentiating LC-MS artifact-Δm’s from biochemical Δm’s, whose definition was based on the KEGG database. As a side note – finding literature that employs mass differences can be hampered by different terminology which is dependent on the researcher’s scientific backgrounds. E.g., Gipson et al. termed mass differences – or their corresponding peaks – to be ‘compound interaction pairs’, Breitling et al. [42] or Rogers et al. [89] used the term ‘transformation’ and Brown et al. [87] used the term ‘mass difference’. The same diversity in terminology is met for networks based on Δm’s, which we proposed to call ‘mass difference networks’, which seems to be more faithful to what these data structures reflect physically. MDiN’s for third and fourth level identification Here, we present two particularly interesting methods of 2nd and 3rd level identification as previously discussed above (see “Primary analysis: Annotation, identification, knowns and unknowns” section). The first method, published by Rogers et al. [89], appreciates that all molecules’ m/z features in a spectrum are likely to be produced by a system of interconnected chemical reactions (Δm’s). Consider m1, which has a unique molecular formula assignment and m2, which has equivocal (multiple) molecular formula assignments, which are all allowed within a given mass tolerance window. How can we determine the correct formula for m2 among all alternative formulas? Rogers et al. could show: if m2 can be connected to m1 by a Δm corresponding to H2, the correct molecular formula assignment of m2 is that one, which differs by exactly 2H from the formula of m1. The same is true for other small functionalities such as CH2, O, CO and CO2 as well as NH. They constructed an MDiN-based algorithm that updates the probability for a formula’s correctness based on the correctness-probability of its neighbors in the MDiN. This approach basically predicts what Kilgour et al. [52] found empirically four years later; i.e., that MDiN nodes with the highest connectivity have the highest degree of trust in terms of formula assignment. In fact, the method of Rogers et al. should be added to all MDiN-based algorithms for level 4 identification, because their approach makes the degree of trust into formula assignments quantifiable. In summary, most MDiN-based algorithms for level 4 identification operate on first creating an MDiN from mass spectra, followed by the definition of starters – known molecular formulas – within this MDiN. Finally, all unassigned MDiN-nodes that neighbor a starter can be assigned by combining the starter-formula and the formula attributed to their respective Δm’s. Yet, there is one weak point

Mass differences in mass-spectrometric metabolome analyses

383

in all MDiN-based algorithms: The final identification-outcome depends on the correct definition of starter masses, which are provided to the program together with their assigned molecular formulas. If the starting formulas for MDiN level 4 identification are in fact incorrect, the results of the assignment procedure will be incorrect as well. The degree of trust into MDiNbased annotation improves with increasing the number of correctly chosen and provided initial molecular formulas for the calculation. To illustrate this fact: If the MS spectrum of a blood plasma metabolome was to be matched against a database for petroleomics, no true positive molecular formula assignments were to be expected. This brings us to a problem of database matching in general: As implied before and as will be substantiated in the next section, biological mass spectral databases are tremendously incomplete. And remembering the SMMs from the “Compositional space” section, there is a high likelihood to obtain a database hit from exact mass matching (even at sub ppm level), even if the truly correct hit is not present in the database. That is to say: A database-match on exact mass alone should never be considered as being more trustworthy than a molecular formula that was properly validated by isotopic patterns when starter-identities for MDiN-based annotations are defined. The second method, proposed by Weber [54] and Viant, called transformation mapping (TM), is included in the MI-Pack package and operates on a variant of the idea of Rogers et al., namely, that the (bio)chemical context of an experiment is a valuable advisor in terms of molecular formula identification. Briefly, Weber and Viant downloaded all biochemical substrateproduct pairs from KEGG, calculated the Δm’s between product (m2) and substrate (m1) as well as the mean of each pair’s masses. They calculated all Δm’s found in an empirical UHR mass spectrum and performed exact Δmmapping – ‘transformation mapping’ (TM) – of means (mi,mj) and their corresponding Δm’s on the basis of a mass error surface constraint. The mass error surface (ES) is a much more realistic approximation for mapping mass spectra against databases as compared to fixed error windows such as “1 ppm.” The ES is calculated by taking all mass pairs that fit a known biochemical Δm 30,000 organic signatures in a shale oil sample [15], suggesting a very diverse pool of organic compounds present in shale. Shale formations currently being drilled and hydraulically fractured can be as deep as 3500 m with temperatures reaching 160 °C and pressures exceeding 600–700 bar. A recent study on flowback and produced waters from the

FT-ICR MS and Orbitrap mass spectrometry approaches in environmental chemistry

415

Fig. 13.3 DOM composition during the process of hydraulic fracturing. (Adopted from J. Luek, Characterization of Organic Compounds in Hydraulic Fracturing Fluids (Ph.D. Thesis), University of Maryland Center for Environmental Science).

Marcellus Shale energy and Environment Laboratory (MSEEL.org) showed interesting transformations in the DOM pool between injection, flowback, early and late production suggesting a dynamic and continuously changing pool or organic compounds in hydraulic fracturing fluids (Fig. 13.3). Particularly interesting is the CHOS pool that appeared to be labile and is associated to the early flowback, because it is not observed during production and within processed water.

Non-targeted approaches in characterizing pollutants We use daily an unknown number of chemicals and that number will continue to increase. As a result, a diverse soup of pharmaceuticals, daily care products, pesticides and their breakdown products continuously enter our environment. Hence, specific targeted analysis may not be the most effective way to evaluate human and environmental exposure and alternative more holistic ways are needed. Targeted approaches in determining human and

416

Fundamentals and Applications of Fourier Transform Mass Spectrometry

environmental health are often hindered due to the shear complexity of possible contaminants in impacted aquatic systems. It is often the case that whole effluent toxicity (WET) tests revealed acute and chronic toxicity without knowing the stressor and hence targeted approaches may not well suited to determine which chemical or chemicals cause toxicity either. Non-target screening for unknown organic contaminants can be achieved using high resolution MS techniques, but a fundamental understanding of each specific analytical window is required. In this light, a detailed understanding of ion suppression and ionization efficiencies of contaminants is critical in designing environmental non-target approaches. However, advances have been made in recent years and more detailed approaches have been implemented. Separation techniques such as gas chromatography (GC) and liquid chromatography (LC) interfaced with ultrahigh resolution mass analyzers are one possible route in determining unknown compounds in surface waters and these non-targeted approaches are gaining momentum with the ability and further improvements in mass resolution of such instrumentations. For example, liquid and gas chromatography interfaced with high resolution Orbitrap MS has shown promise in non-target screening and identification of emerging contaminants in tandem with targeted analysis [17–19, 21, 64, 65] or in combination with deconvolution tools such as chemical databases and MS-MS approaches [66]. Promising algorithms have also been developed to assess homologous series according to their predicted mass and retention time shifts (http://www.envihomolog.eawag.ch/). Complex matrices such as surface waters remain challenging to screen for contaminants due to the high risk of false positive hits. This is especially true when the structure of the contaminants is relatively simple and could be easily matched by naturally occurring isomers. However, progress has been made and strategies in avoiding false positives as well as finding contaminants of unknown structures are implemented. The overall workflow is quite similar in the peer-reviewed literature, where often liquid chromatography (to a lesser extent GC) is interfaced with a high resolution mass spectrometer such as the Orbitrap MS. Potential contamination candidates are then fragmented to confirm structure. The combined information of retention time, very accurately measured mass and MS-MS fragmentation pattern are very powerful in finding unknown compounds, but also in screening for known contaminants. Accessible databases such as MassBank [67] (https://massbank. eu/MassBank/) or Mass Spectrum Interpreter [68] (https://chemdata.nist. gov/dokuwiki/doku.php?id¼chemdata:interpreter) are also helpful and can be used to match fragmentation patterns. A conceptual overview of this

FT-ICR MS and Orbitrap mass spectrometry approaches in environmental chemistry

417

Fig. 13.4 Conceptual scheme of the MS analysis of contaminants using targeted, suspect and non-targeted screening.

approach is given in Fig. 13.4. A few examples are given below to highlight non-targeted ultrahigh resolution mass spectrometric approaches used in surface water, groundwater and sediments.

Contaminants in surface and groundwater Detailed review papers have recently been published on non-targeted screening of contaminants using high resolution mass spectrometry [20–23] and another study summarized identified transformation products of emerging contaminants [21]. That same study proposed a work flow of combining targeted and non-targeted approaches which may assist in study designs. A review about potential biological and chemical transformation products of emerging pollutants analyzed by high-resolution mass spectrometry was also recently published [23]. Large scale comparison of non-targeted approaches has also been recently undertaken to evaluate if such different approaches give similar results. For example, Schymanski et al. initiated a

418

Fundamentals and Applications of Fourier Transform Mass Spectrometry

collaborative trial amongst 18 institutions on the same samples [20]. Each institution implemented targeted and non-targeted approaches with an impressive diversity of analytical methods. The main conclusion of this trial was that results were largely similar, but that the data processing remained time-consuming. It was also addressed that a fully automated identification approach is not yet in sight. FT-ICR MS is particularly useful to determine contamination plumes and to investigate the potential impact of organic contaminants in groundwater. For example, the molecular complexity of DOM impacted by a benzene, ethylbenzene, toluene and xylene (BTEX) contamination plume was recently determined across a contamination plume at a former gas work site in Germany that closed operation in 1967. High resolution well sampling revealed a remarkable change in organic compound composition across the plume and underlined the importance of non-target screening to determine the impact of a hydrocarbon contamination plume [16]. Furthermore, a complex indicative dissolved organic sulfur (DOS) pool could be associated with the zone of sulfate reduction by bacteria associated with the plume and highlighted secondary effects of groundwater contamination.

Contaminants in sediments It is well established that organic contaminants caused the majority of toxicity evaluated by sediment toxicity identification and evaluation (TIE) methods [69] and hence non-targeted screening of known and unknown contaminants and also their transformation products in sediments makes sense. For example, non-targeted screening of synthetic organo-bromine compounds (NSOBCs) was performed on Lake Michigan sediments using an LC Q Exactive Hybrid Quadrupole Orbitrap MS [24]. A data-independent precursor isolation and characteristic fragment (DIPIC-Frag) procedure was used to evaluate these previously unknown organo-bromine molecules and an astonishing number of 1593 unique NSOBCs were reported.

Conclusions and suggestions for future work Non-targeted screening approaches using ultrahigh resolution MS have substantially influenced the field of environmental chemistry in recent years and these techniques are explored in practically all aspects of assessing contamination of any environmental system. New instruments of high resolution also became more affordable and hence a lot more studies have been carried out in recent years. However, the workflow and more sophisticated

FT-ICR MS and Orbitrap mass spectrometry approaches in environmental chemistry

419

computer algorithms need to be further developed for the upcoming new studies to be better prepared for the challenges of ever increasing potential contaminants in the environment. New advancements such as true high resolution ion mobility will hopefully continue to transform the approaches and will further widen the analytical window. Network analysis and predictive tools to assess reactivity of contaminants might help to better understand secondary degradation products that also remain a big challenge.

Acknowledgments I particular express my gratitude to Jenna Luek for her willingness to share a figure (Fig. 13.3) from her just completed PhD thesis and the editors of this book for inviting me to write this book chapter. I also thank the Chesapeake Biological Laboratory to support my research. This is contribution 5509 of the University of Maryland Center for Environmental Science.

References [1] X. Wang, H.F. Zhang, Y.H. Zhang, Q. Shi, J. Wang, J.W. Yu, M. Yang, New insights into trihalomethane and haloacetic acid formation potentials: correlation with the molecular composition of natural organic matter in source water, Environ. Sci. Technol. 51 (2017) 2015–2021. [2] H.F. Zhang, Y.H. Zhang, Q. Shi, J.Y. Hu, M.Q. Chu, J.W. Yu, M. Yang, Study on transformation of natural organic matter in source water during chlorination and its chlorinated products using ultrahigh resolution mass spectrometry, Environ. Sci. Technol. 46 (2012) 4396–4402. [3] E.E. Lavonen, M. Gonsior, L.J. Tranvik, P. Schmitt-Kopplin, S.J. K€ ohler, Selective chlorination of natural organic matter: identification of previously unknown disinfection byproducts, Environ. Sci. Technol. 47 (2013) 2264–2271. [4] M. Gonsior, P. Schmitt-Kopplin, H. Stavklint, S.D. Richardson, N. Hertkorn, D. Bastviken, Changes in dissolved organic matter during the treatment processes of a drinking water plant in sweden and formation of previously unknown disinfection byproducts, Environ. Sci. Technol. 48 (2014) 12714–12722. [5] H. Zhang, Y. Zhang, Q. Shi, H. Zheng, M. Yang, Characterization of unknown brominated disinfection byproducts during chlorination using ultrahigh resolution mass spectrometry, Environ. Sci. Technol. 48 (2014) 3112–3119. [6] H. Zhai, X. Zhang, X. Zhu, J. Liu, M. Ji, Formation of brominated disinfection byproducts during chloramination of drinking water: new polar species and overall kinetics, Environ. Sci. Technol. 48 (2014) 2579–2588. [7] M. Gonsior, C. Mitchelmore, A. Heyes, M. Harir, S.D. Richardson, W.T. Petty, D. A. Wright, P. Schmitt-Kopplin, Bromination of marine dissolved organic matter following full scale electrochemical ballast water disinfection, Environ. Sci. Technol. 49 (2015) 9048–9055. [8] J.L. Luek, P. Schmitt-Kopplin, P.J. Mouser, W.T. Petty, S.D. Richardson, M. Gonsior, Halogenated organic compounds identified in hydraulic fracturing wastewaters using ultrahigh resolution mass spectrometry, Environ. Sci. Technol. 51 (2017) 5377–5385. [9] L.Y. Tseng, M. Gonsior, P. Schmitt-Kopplin, W.J. Cooper, P. Pitt, D. Rosso, Molecular characteristics and differences of effluent organic matter from parallel activated sludge and integrated fixed-film activated sludge (IFAS) processes, Environ. Sci. Technol. 47 (2013) 10277–10284.

420

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[10] S. Shakeri Yekta, M. Gonsior, P. Schmitt-Kopplin, B.H. Svensson, Characterization of dissolved organic matter in full scale continuous stirred tank biogas reactors using ultrahigh resolution mass spectrometry: a qualitative overview, Environ. Sci. Technol. 46 (2012) 12711–12719. [11] M. Gonsior, M. Zwartjes, W.J. Cooper, W. Song, K.P. Ishida, L.Y. Tseng, M.K. Jeung, D. Rosso, N. Hertkorn, P. Schmitt-Kopplin, Molecular characterization of effluent organic matter identified by ultrahigh resolution mass spectrometry, Water Res. 45 (2011) 2943–2953. [12] A.C. Maizel, C.K. Remucal, The effect of advanced secondary municipal wastewater treatment on the molecular composition of dissolved organic matter, Water Res. 122 (2017) 42–52. [13] X. Wang, J. Wang, K. Li, H. Zhang, M. Yang, Molecular characterization of effluent organic matter in secondary effluent and reclaimed water: comparison to natural organic matter in source water, J. Environ. Sci. (2017). [14] P. Phungsai, F. Kurisu, I. Kasuga, H. Furumai, Molecular characterization of low molecular weight dissolved organic matter in water reclamation processes using Orbitrap mass spectrometry, Water Res. 100 (2016) 526–536. [15] E. Bae, J.-G. Na, S.H. Chung, H.S. Kim, S. Kim, Identification of about 30 000 chemical components in shale oils by electrospray ionization (ESI) and atmospheric pressure photoionization (APPI) coupled with 15 T Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and a comparison to conventional oil, Energy Fuels 24 (2010) 2563–2569. [16] S.E.M. Dvorski, M. Gonsior, N. Hertkorn, J. Uhl, H. M€ uller, C. Griebler, P. SchmittKopplin, Geochemistry of dissolved organic matter in a spatially highly resolved groundwater petroleum hydrocarbon plume cross-section, Environ. Sci. Technol. 50 (2016) 5536–5546. [17] A.C. Hogenboom, J.A. van Leerdam, P. de Voogt, Accurate mass screening and identification of emerging contaminants in environmental samples by liquid chromatography–hybrid linear ion trap Orbitrap mass spectrometry, J. Chromatogr. A 1216 (2009) 510–519. [18] A. M€ uller, W. Schulz, W.K.L. Ruck, W.H. Weber, A new approach to data evaluation in the non-target screening of organic trace substances in water analysis, Chemosphere 85 (2011) 1211–1219. [19] C. Hug, N. Ulrich, T. Schulze, W. Brack, M. Krauss, Identification of novel micropollutants in wastewater by a combination of suspect and nontarget screening, Environ. Pollut. 184 (2014) 25–32. [20] E.L. Schymanski, H.P. Singer, J. Slobodnik, I.M. Ipolyi, P. Oswald, M. Krauss, T. Schulze, P. Haglund, T. Letzel, S. Grosse, N.S. Thomaidis, A. Bletsou, C. Zwiener, M. Iba´n˜ez, T. Portoles, R. de Boer, M.J. Reid, M. Onghena, U. Kunkel, W. Schulz, A. Guillon, N. Noyon, G. Leroy, P. Bados, S. Bogialli, D. Stipanicev, P. Rostkowski, J. Hollender, Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis, Anal. Bioanal. Chem. 407 (2015) 6237–6255. [21] A.A. Bletsou, J. Jeon, J. Hollender, E. Archontaki, N.S. Thomaidis, Targeted and nontargeted liquid chromatography-mass spectrometric workflows for identification of transformation products of emerging pollutants in the aquatic environment, TrAC Trends Anal. Chem. 66 (2015) 32–44. [22] V. Leendert, H. Van Langenhove, K. Demeestere, Trends in liquid chromatography coupled to high-resolution mass spectrometry for multi-residue analysis of organic micropollutants in aquatic environments, TrAC Trends Anal. Chem. 67 (2015) 192–208.

FT-ICR MS and Orbitrap mass spectrometry approaches in environmental chemistry

421

[23] Y. Pico´, D. Barcelo´, Transformation products of emerging contaminants in the environment and high-resolution mass spectrometry: a new horizon, Anal. Bioanal. Chem. 407 (2015) 6257–6273. [24] H. Peng, C. Chen, D.M.V. Saunders, J. Sun, S. Tang, G. Codling, M. Hecker, S. Wiseman, P.D. Jones, A. Li, K.J. Rockne, J.P. Giesy, Untargeted identification of organo-bromine compounds in lake sediments by ultrahigh-resolution mass spectrometry with the data-independent precursor isolation and characteristic fragment method, Anal. Chem. 87 (2015) 10237–10246. [25] J.J. Rook, Formation of haloforms during chlorination of natural waters, Water Treat. Exam. (1974) 234–243. [26] R.J. Bull, D.A. Reckhow, X. Li, A.R. Humpage, C. Joll, S.E. Hrudey, Potential carcinogenic hazards of non-regulated disinfection by-products: haloquinones, halocyclopentene and cyclohexene derivatives, N-halamines, halonitriles, and heterocyclic amines, Toxicology 286 (2011) 1–19. [27] N. Costet, R. Garlantezec, C. Monfort, F. Rouget, B. Gagniere, C. Chevrier, S. Cordier, Environmental and urinary markers of prenatal exposure to drinking water disinfection by-products, fetal growth, and duration of gestation in the PELAGIE Birth Cohort (Brittany, France, 2002-2006), Am. J. Epidemiol. 175 (2012) 263–275. [28] K. Waller, S.H. Swan, G. DeLorenze, B. Hopkins, Trihalomethanes in drinking water and spontaneous abortion, Epidemiology 9 (1998) 134–140. [29] D.A. Savitz, P.C. Singer, A.H. Herring, K.E. Hartmann, H.S. Weinberg, C. Makarushka, Exposure to drinking water disinfection by-products and pregnancy loss, Am. J. Epidemiol. 164 (2006) 1043–1051. [30] U.S. EPA, National Primary Drinking Water Regulations: Stage 2 Disinfectants and Disinfection Byproducts Rule, Federal Register, 2006, p. 388. [31] K. Ichihashi, K. Teranishi, A. Ichimura, Brominated trihalomethane formation in halogenation of humic acid in the coexistence of hypochlorite and hypobromite ions, Water Res. 33 (1999) 477–483. [32] G. Hua, D. Reckhow, Determination of TOCl, TOBr and TOI in drinking water by pyrolysis and off-line ion chromatography, Anal. Bioanal. Chem. 384 (2006) 495–504. [33] S.D. Richardson, Disinfection by-products: formation and occurrence in drinking water, in: J. Nriagu (Ed.), Encyclopedia of Environmental Health, Elsevier Science, Burlington, 2011, pp. 110–136. [34] S.W. Krasner, H.S. Weinberg, S.D. Richardson, S.J. Pastor, R. Chinn, M.J. Sclimenti, G.D. Onstad, A.D. Thruston, Occurrence of a new generation of disinfection byproducts, Environ. Sci. Technol. 40 (2006) 7175–7185. [35] S.D. Richardson, M.J. Plewa, E.D. Wagner, R. Schoeny, D.M. DeMarini, Occurrence, genotoxicity, and carcinogenicity of regulated and emerging disinfection by-products in drinking water: a review and roadmap for research, Mutat. Res., Rev. Mutat. Res. 636 (2007) 178–242. [36] L. Boutegrabet, B. Kanawati, I. Gebef€ ugi, D. Peyron, P. Cayot, R.D. Gougeon, P. Schmitt-Kopplin, Attachment of chloride anion to sugars: mechanistic investigation and discovery of a new dopant for efficient sugar ionization/detection in mass spectrometers, Chem. A Eur. J. 18 (2012) 13059–13067. [37] D. Khiari, S.W. Krasner, C.J. Hwang, R. Chinn, S. Barrett, Effects of chlorination and chloramination on the molecular weight distribution of natural organic matter and the production of high-molecular-weight disinfection by-products, in: AWWA Water Technology Conference, American Water Works Association, Boston, MA, 1996. [38] X. Zhang, R.A. Minear, Characterization of high molecular weight disinfection byproducts resulting from chlorination of aquatic humic substances, Environ. Sci. Technol. 36 (2002) 4033–4038.

422

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[39] E.E. Lavonen, D.N. Kothawala, L.J. Tranvik, M. Gonsior, P. Schmitt-Kopplin, S. J. K€ ohler, Tracking changes in the optical properties and molecular composition of dissolved organic matter during drinking water production, Water Res. 85 (2015) 286–294. [40] X. Wang, J. Wang, Y. Zhang, Q. Shi, H. Zhang, Y. Zhang, M. Yang, Characterization of unknown iodinated disinfection byproducts during chlorination/chloramination using ultrahigh resolution mass spectrometry, Sci. Total Environ. 554–555 (2016) 83–88. [41] Y. Pan, W. Li, H. An, H. Cui, Y. Wang, Formation and occurrence of new polar iodinated disinfection byproducts in drinking water, Chemosphere 144 (2016) 2312–2320. [42] C. Postigo, C.I. Cojocariu, S.D. Richardson, P.J. Silcock, D. Barcelo, Characterization of iodinated disinfection by-products in chlorinated and chloraminated waters using Orbitrap based gas chromatography-mass spectrometry, Anal. Bioanal. Chem. 408 (2016) 3401–3411. [43] A. Kolkman, B.J. Martijn, D. Vughs, K.A. Baken, A.P. van Wezel, Tracing nitrogenous disinfection byproducts after medium pressure UV water treatment by stable isotope labeling and high resolution mass spectrometry, Environ. Sci. Technol. 49 (2015) 4458–4465. [44] B. Werschkun, S. Banerji, O.C. Basurko, M. David, F. Fuhr, S. Gollasch, T. Grummt, M. Haarich, A.N. Jha, S. Kacan, A. Kehrer, J. Linders, E. Mesbahi, D. Pughiuc, S.D. Richardson, B. Schwarz-Schulz, A. Shah, N. Theobald, U. von Gunten, S. Wieck, T. H€ ofer, Emerging risks from ballast water treatment: the run-up to the International Ballast Water Management Convention, Chemosphere 112 (2014) 256–266. [45] G. Ding, X. Zhang, M. Yang, Y. Pan, Formation of new brominated disinfection byproducts during chlorination of saline sewage effluents, Water Res. 47 (2013) 2710–2718. [46] EIA, Annual Energy Outlook 2012 with Projections to 2035, U.S. Energy Information Administration, 2012, p. 239. [47] J.-P. Nicot, B.R. Scanlon, Water use for shale-gas production in Texas, US, Environ. Sci. Technol. 46 (2012) 3580–3586. [48] R.D. Vidic, S.L. Brantley, J.M. Vandenbossche, D. Yoxtheimer, J.D. Abad, Impact of shale gas development on regional water quality, Science 340 (2013). [49] R. Barati, J.-T. Liang, A review of fracturing fluid systems used for hydraulic fracturing of oil and gas wells, J. Appl. Polym. Sci. 131 (2014). [50] W.T. Stringfellow, J.K. Domen, M.K. Camarillo, W.L. Sandelin, S. Borglin, Physical, chemical, and biological characteristics of compounds used in hydraulic fracturing, J. Hazard. Mater. 275 (2014) 37–54. [51] T.J. Gallegos, B.A. Varela, Trends in Hydraulic Fracturing Distributions and Treatment Fluids, Additives, Proppants, and Water Volumes Applied to Wells Drilled in the United States From 1947 Through 2010—Data Analysis and Comparison to the Literature, (U.S. Geological Survey Scientific Investigations Report), U.S. Geological Survey, 2015. 15 p. [52] A.L. Maule, C.M. Makey, E.B. Benson, I.J. Burrows, M.K. Scammell, Disclosure of hydraulic fracturing fluid chemical additives: analysis of regulations, New Solut. 23 (2013) 167–187. [53] J.S. Harkness, G.S. Dwyer, N.R. Warner, K.M. Parker, W.A. Mitch, A. Vengosh, Iodide, bromide, and ammonium in hydraulic fracturing and oil and gas wastewaters: environmental implications, Environ. Sci. Technol. 49 (2015) 1955–1963. [54] E.L. Rowan, M.A. Engle, T.F. Kraemer, K.T. Schroeder, R.W. Hammack, M. Doughten, Geochemical and isotopic evolution of water produced from Middle Devonian Marcellus Shale gas wells, Appalachian basin, Pennsylvania, AAPG Bull. 99 (2014) 181–206.

FT-ICR MS and Orbitrap mass spectrometry approaches in environmental chemistry

423

[55] E. Barbot, N.S. Vidic, K.B. Gregory, R.D. Vidic, Spatial and temporal correlation of water quality parameters of produced waters from devonian-age shale following hydraulic fracturing, Environ. Sci. Technol. 47 (2013) 2562–2569. [56] G.A. Kahrilas, J. Blotevogel, P.S. Stewart, T. Borch, Biocides in hydraulic fracturing fluids: a critical review of their usage, mobility, degradation, and toxicity, Environ. Sci. Technol. 49 (2015) 16–32. [57] P. Westerhoff, P. Chao, H. Mash, Reactivity of natural organic matter with aqueous chlorine and bromine, Water Res. 38 (2004) 1502–1513. [58] N.Z. Jiao, G.J. Herndl, D.A. Hansell, R. Benner, G. Kattner, S.W. Wilhelm, D. L. Kirchman, M.G. Weinbauer, T.W. Luo, F. Chen, F. Azam, The microbial carbon pump and the oceanic recalcitrant dissolved organic matter pool, Nat. Rev. Microbiol. 9 (2011). [59] N. Jiao, G.J. Herndl, D.A. Hansell, R. Benner, G. Kattner, S.W. Wilhelm, D. L. Kirchman, M.G. Weinbauer, T.W. Luo, F. Chen, F. Azam, Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean, Nat. Rev. Microbiol. 8 (2010) 593–599. [60] S.A. Gross, H.J. Avens, A.M. Banducci, J. Sahmel, J.M. Panko, B.E. Tvermoes, Analysis of BTEX groundwater concentrations from surface spills associated with hydraulic fracturing operations, J. Air Waste Manage. Assoc. 63 (2013) 424–432. [61] R.B. Jackson, A. Vengosh, T.H. Darrah, N.R. Warner, A. Down, R.J. Poreda, S. G. Osborn, K. Zhao, J.D. Karr, Increased stray gas abundance in a subset of drinking water wells near Marcellus shale gas extraction, Proc. Natl. Acad. Sci. U. S. A. 110 (2013) 11250–11255. [62] S.J. Maguire-Boyle, A.R. Barron, Organic compounds in produced waters from shale gas wells, Environ. Sci.: Processes Impacts 16 (2014) 2237–2248. [63] W. Orem, C. Tatu, M. Varonka, H. Lerch, A. Bates, M. Engle, L. Crosby, J. McIntosh, Organic substances in produced and formation water from unconventional natural gas extraction in coal and shale, Int. J. Coal Geol. 126 (2014) 20–31. [64] M. Ruff, M.S. Mueller, M. Loos, H.P. Singer, Quantitative target and systematic nontarget analysis of polar organic micro-pollutants along the river Rhine using highresolution mass-spectrometry—identification of unknown sources and compounds, Water Res. 87 (2015) 145–154. [65] J. Hollender, E.L. Schymanski, H.P. Singer, P.L. Ferguson, Nontarget screening with high resolution mass spectrometry in the environment: ready to go? Environ. Sci. Technol. 51 (2017) 11505–11512. [66] R. Bade, A. Causanilles, E. Emke, L. Bijlsma, J.V. Sancho, F. Hernandez, P. de Voogt, Facilitating high resolution mass spectrometry data processing for screening of environmental water samples: an evaluation of two deconvolution tools, Sci. Total Environ. 569–570 (2016) 434–441. [67] H. Horai, M. Arita, S. Kanaya, Y. Nihei, T. Ikeda, K. Suwa, Y. Ojima, K. Tanaka, S. Tanaka, K. Aoshima, Y. Oda, Y. Kakazu, M. Kusano, T. Tohge, F. Matsuda, Y. Sawada, M.Y. Hirai, H. Nakanishi, K. Ikeda, N. Akimoto, T. Maoka, H. Takahashi, T. Ara, N. Sakurai, H. Suzuki, D. Shibata, S. Neumann, T. Iida, K. Tanaka, K. Funatsu, F. Matsuura, T. Soga, R. Taguchi, K. Saito, T. Nishioka, MassBank: a public repository for sharing mass spectral data for life sciences, J. Mass Spectrom. 45 (2010) 703–714. [68] A. Mayorov, Y. Mirokhin, D. Tchekovskoi, S.E. Stein, Enhancements to NIST MS Interpreter for Modeling High Mass Accuracy Tandem Mass Spectra, American Society for Mass Spectrometry, San Diego, 2018. [69] K.T. Ho, R.M. Burgess, What’s causing toxicity in sediments? Results of 20 years of toxicity identification and evaluations, Environ. Toxicol. Chem. 32 (2013) 2424–2432.

CHAPTER 14

Identification of biologically active peptides by means of Fourier transform mass spectrometry Sergey V. Kovalev, Albert T. Lebedev

Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia

Contents Introduction FTMS basics FTMS for mass fingerprinting of peptides Fourier transform tandem mass spectrometry Collisional activation Electron-based dissociation Photoactivation Combining different activation methods Conclusion References

425 426 428 431 433 439 446 447 458 459

Introduction Bioactive natural peptides are ubiquitous in all life forms. In this review we shorten our scope to animal peptides (vertebrates and invertebrates) that were identified by the Fourier transform mass spectrometry (FTMS). These peptides mediate a number of physiological processes and responses, they protect organism against infections, inflammation and tumor development, act as antibiotics, hormones, neurotransmitters, immune regulators. Exogenous peptides can protect animal that secret them against predators as well. Uncovering the structure of bioactive peptides at the amino acid sequence level is important for understanding their mechanism of action and developing possible analogs of natural peptides for biological and medical applications. This is the area of peptidomics—the comprehensive qualitative and Fundamentals and Applications of Fourier Transform Mass Spectrometry https://doi.org/10.1016/B978-0-12-814013-0.00014-4

© 2019 Elsevier Inc. All rights reserved.

425

426

Fundamentals and Applications of Fourier Transform Mass Spectrometry

quantitative analysis of all peptides in a biological sample, new “omics” field that was introduced in the beginning of 2000s [1, 2]. It has emerged as a branch of proteomics and has been advanced by the development of the new separation strategies, analytical detection methods such as mass spectrometry, and bioinformatic technologies. Excellent comprehensive reviews dealing with peptidomics can be found in Refs. [3–6]. Bioactive animal peptides mostly come from two sources: secretions and tissue extracts. Secretions are represented by skin secretions (especially amphibian) and venoms. Amphibian skin secretions are intensively studied because of the unique chemical properties of skin peptides, their biosynthesis pathways, and because of their potential clinical applications. By 2015 more than 100 peptide families with about 2000 amphibian skin peptides were reported [7]. An area of venomics covers diverse groups of animals like cnidarians, arthropods, mollusks and reptiles [8–10] and it is estimated that venoms contain more than 20 million different peptides with only several thousand identified [11]. Tissue extracts provide different classes of peptides involved in the most of physiological processes like extra- and intercellular signaling (especially neurotransmitters and hormones), immune system response, pain, circadian rhythms, etc. Particularly important are neuropeptides because charting of the neuropeptidome is the first step toward understanding peptidergic signaling in animals [5, 12, 13]. Peptidomic approaches are also used in biomarker discovery [4, 6]. А process of the peptide identification include a number of steps: (i) sample collection, (ii) sample preparation, (iii) separation of individual components with various chromatographic, capillary electrophoretic, and gel-based techniques, (iv) analysis of individual peptides (different mass spectrometry methods and Edman degradation), (v) interpretation of mass spectra, and de novo sequencing with the aid of powerful bioinformatics methods (Fig. 14.1). The tantalizing task of the complete review of the whole pipeline is out of scope of this article and can be found in Refs. [3, 4, 14–19]. Here we focus only on the FTMS techniques for identification of bioactive animal peptides.

FTMS basics Thanks to the ongoing progress in experimental techniques, mass spectrometry (MS) has become a competitive alternative to the Edman degradation

Identification of biologically active peptides by means of FTMS

427

Fig. 14.1 Mass-spectrometry based bioactive peptide identification workflow.

[20, 21] for sequencing amino acids in peptides even in cases of isomeric leucine/isoleucine residues [22, 23]. MS based proteome and peptidome studies have been established as a powerful procedure for non-standard amino acids and posttranslational modifications (PTMs) identification and localization. MS provides a plethora of different techniques for peptidomics and proteomics research that are thoroughly reviewed in Ref. [24] and references therein. The most useful methods for peptide ionization are electrospray ionization (ESI) [25, 26] and matrix-assisted laser desorption ionization (MALDI) [27, 28]. FTMS provides the highest mass resolving power and mass accuracy with fairly high sensitivity. It is of crucial importance for а reliable establishing of ions composition in mass spectra and confirmation of peptide sequence. There are two types of instruments in FTMS: Fourier transform ion cyclotron resonance (FT ICR) and Orbitrap mass spectrometers. The FT ICR method proposed in 1974 [29, 30] is based on the measurement of frequencies of the cyclotron movement of ions in a magnetic field by measuring the signal induced by ions between the capacitor sheets and the Fourier transform analysis of that signal. Orbitrap mass analyzer was presented in 1999 and became commercially available in 2005 [31]. It is based on the idea of Kingdon ion trap where ions are held by electrostatic fields of cylindrical symmetry without application of a magnetic field, and

428

Fundamentals and Applications of Fourier Transform Mass Spectrometry

uses the Fourier transform. By 2017 FT-ICR and Orbitrap instruments have unsurpassed resolution of 1,000,000 at m/z 2700 for 21 Tesla FT-ICR [32] and at m/z 200 for Orbitrap [33]. It is worth mentioning that Orbitrap resolving power is inversely proportional to the square root of m/z while that of FT-ICR inversely scales with m/z. So for small m/z FT-ICR is beneficial (for m/z < 300 in magnitude mode and m/z < 1000 in absorption mode as compared to a high-field compact orbital trap) and for large m/z Orbitrap is more appropriate [34]. Another advantage of Orbitrap is its smaller size. Actually a smaller orbital trap possesses better analytical characteristics than bigger one being besides a benchtop instrument. Despite the bigger size of the analyzer, FT-ICR instruments possess an unsurpassed ability to accept ions with low and very low energies and to trap them practically indefinitely, and this can be combined with various fragmentation techniques (UV, IR, collisional, etc.). For example, it is possible to select a precursor ion of accurate mass directly in the FTICR cell, excite it, analyze, then de-excite and reanalyze. Orbitrap analyzer does not possess this flexibility. High-energy ions dissociating or colliding with neutrals or other ions, are usually lost after their mass measurement.

FTMS for mass fingerprinting of peptides An approach of accurate mass determining (mass fingerprinting) is commonly used in mass spectrometry imaging (MSI) experiments that allow analyzing tissue samples with high spatial and mass resolution [35]. High resolution mass spectra provide information about elemental composition of the analyzed molecules with mass accuracy of 0.1 ppm achieved with 21 Tesla FT-ICR [32]. MALDI is the most widespread ionization source in MSI but other sources are possible [36]. In MALDI-MSI after laser impact at specific locations on the target plate molecules are desorbed from the sample, allowing spatial mapping of compounds. Peptides have high ionization efficiency and convenient mass range so they are perfect targets for MSI. MSI-based peptidomics has been particularly useful in neurobiology. Excellent protocol overview for MSI can be found in Ref. [37]. Guenther et al. used linear ion trap orbital trapping mass spectrometer combined with an atmospheric pressure MALDI imaging source to get neuropeptides images in mouse pituitary gland with a spatial resolution of 5 μm [38]. They identified 10 neuropeptides by their accurate mass (with errors 3-fold (corresponding to a 32% increase in peptide identifications) using 30 μm-i.d. columns. compared to the standard 75-μm-i.d. Higher LC resolution also requires a higher sampling rate with a high frequency of MS/MS experiments and a higher efficiency of ion transmission so that less abundant species can be identified. They reported an increase of peptide identifications by a factor of 2.9 and an increase of identified protein groups by a factor of 1.7 using the latest generation of tribrid Q-LQT-OT MS instruments compared to the first one (Lumos vs. LTQ Orbitrap Fusion), due to electrodynamic ion funnel [119] and the reduction of MS duty cycle time by a factor of 3 times in Top10 DDA mode (1 s vs. 3.6 s). High sensitivity can be achieved by tuning the ion fill time (optimizing the amount of a given species collected prior to fragmentation) that requires ion counting and optimizing the associated selection mass windows (from 1.3 to 4 mass units). Longer fill time and larger

Proteomics and proteoforms

543

windows increase sensitivity and decrease sampling frequency and specificity [120]. Because the FTMS acquisition is long and more specifically for orbitraps (the transient duration is 32 ms at a resolution of 15,000 (FWHM) for m/z 200 but it can be increased to more than 1 s for achieving a resolution of 450,000 for m/z 200, and one must add the ion accumulation time on the top of it). Most of the technological changes have focused on the parallelization of the different stages (filling, counting, fragmentation, and ion detection). The family of FT-Orbitrap instruments has evolved into two subgroups, one has kept coupling to an LQT and has evolved with the tribrid Q-LQT-OT (Quadrupole-linear quadrupole ion trap-Orbitrap) configuration, and the other has kept coupling to an upstream quadrupole analyzer (Q-OT). In both cases, an ion counting device before detection by OT FT-MS is implemented. Similarly, FT-ICR instruments have been equipped with a multipolar counting accumulation device to be effectively coupled with LC. Finally, a good sensitivity requires a good ionization efficiency, with a stable spray despite variations in the organic solvent content due to the LC gradient, which for example can be optimized by varying the voltage applied to the spray source with the instantaneous content of organic solvent [117].

Scan modes, targeted analysis and data independent analysis In the quest for better sample coverage with a gain in sensitivity, dynamic range and peak capacity acquisition modes have evolved tremendously in recent years. The MS/MS acquisition was originally done in DDA where the MS/MS analysis is triggered by the detection of a precursor above a defined signal intensity threshold in one MS, with the succession of cycles “1 MS survey scan, N DDA MS/MS scans” in the so-called TopN DDA acquisition mode. With this, it was recognized that many species remained undetected [121] because they either coeluted with abundant species or were too scarce to be selected [122]. Other strategies complemented this mode of operation. In order to increase the coverage of our sample, multiplex analyzes were introduced [123]. It is thus possible to select several precursors either continuously over a window of more or less restricted mass, or discretely by isolating various precursors of different masses, and subsequently analyze the composite MS/MS spectrum obtained from fragmentation of these multiple precursors. It is necessary to deconvolute this composite spectrum: this has been achieved by using the retention times, in particular in MSE or All ion fragmentation mode (AIF) [50,124] or by

544

Fundamentals and Applications of Fourier Transform Mass Spectrometry

working with spectral databases, in DIA or SWATH [125]. Originally developed with TOF analyzers, this approach has also been proposed with FT ICR analyzers [126] and orbitraps [127]. The systematization of this strategy has given rise to data independent acquisition (DIA) analysis, the MS/MS mode analysis is carried out sequentially and blindly on small mass windows which just supposed to cover the whole mass range of interest. The interpretation of this type of data requires the construction of an LC MS/MS database (retention time, precursor mass, fragmentation profile) with a very precise alignment of retention times. LC alignment is performed by relying on a set of standard peptides spiked in the sample of interest [128]. For example, the DIA multiplex combination has been used for histone analysis [129] but the same type of database can also be exploited to perform targeted analyses on some of the compounds in the scheduled Parallel Reaction Monitoring (PRM) strategy [130], according to the principle of the multiple reaction monitoring (MRM) strategy. Targeted studies are of course more limiting in terms of the number of compounds detected but can improve the sensitivity of the analysis [131,132]. The advantage of PRM for targeted analyses is that it does not require prior selection transitions of interest, it offers high resolution and high measurement accuracy for precursor and fragment ions, which reduces interferences. Adding a standard set of peptides has very little influence on the result of an LC-MS/MS analysis and can align very efficiently the chromatograms within a campaign in your lab. This is not the case for inter-laboratory comparisons or even inter-experimental set-ups. In order to pool LC-MS/MS databases it has therefore recently been proposed to introduce other types of information, such as the level of useful acetonitrile HI (% ACN) for the elution of a peptide instead of the retention time and distinguish the experimental and theoretical retention times (calculated with SSRCalc DB) [133]. Finally, the chromatographic conditions (stationary phase and mobile phase) as well as the type of sample injected seem to affect the retention properties of the peptides and must therefore be controlled: a correlation coefficient higher than 0.995 has been recommended to be able to use a bank of data from one DIA run to another. Recently a study on the effects of ion isolation and accumulation on the sensitivity and quantitative accuracy of targeted proteomics was performed on a tribrid instrument coupled to a multidimensional nanoLC separation [117]. The authors reported a sensitivity of 1 yoctomole/ms or 100 molecules per scan for a pure standard peptide (yM, 10.E-24 mol) with a range of linearity for quantification that reaches 7 orders of magnitude. At this level of

Proteomics and proteoforms

545

sensitivity, the authors see unexpectedly no influence of the ion fill time with comparable signal intensities for ion fill times ranging between 25 and 2500 ms. To achieve this amazing sensitivity, the applied voltage to stabilize the electrospray is decreased with the increasing concentration of acetonitrile in the mobile phase during the LC gradient. The used acquisition mode is a high-resolution targeted mode that derives from the PRM mode, and which is based on the indication of a list of peptides of interest with their exact masses and a window of expected retention time (AIM mode or Accumulated Ion Monitoring). In AIM mode, the tribrid configuration is fully exploited with the selection of the precursor on a window of 8 amu and ion accumulation in the multipole, a high-resolution MS quantitation step in the orbitrap and an identification of the MS/MS fragmentation sequence in the linear ion trap. The detection sensitivity drops when the analytes are presented in a more complex matrix. While many strategies have been proposed to improve the rate of analysis in MS/MS mode, little work has been done to optimize MS detection. When acquiring the survey scan, a maximum number of ions is used in order not to deteriorate the spectral quality by space charge effects. This favors the detection of the most abundant species, but many species are then detected with a low signal-to-noise ratio and their identification is then compromised. In DIA mode where a small mass range is analyzed each time it is possible to insert an MS spectrum between each MS/MS spectrum for each mass window. In this case, the analysis is analogous to what was done in Selected Ion Monitoring (SIM) mode in LTQ FT-ICR, which significantly improves the signal-to-noise ratio of the minor species and the accuracy of mass measurement at the expense of proteome coverage performed in DDA [134]. This idea was taken up in the BoxCar approach, which is applicable to any instrument capable of making a precursor selection upstream of the detection [135]. Briefly, the method uses the selection quadrupole upstream of the C-trap to sequentially transmit mass ranges of fixed or variable width and adapted to the number of charges contained in each range with a fixed filling rate per window (100,000 ions). These windows are grouped in two or three series, disjoined within each series, and allow a recovery of 1 Da between each window in the joined series. This is to create a multiplex SIM that covers the entire mass range. A Top5 DDA MS/MS acquisition is then performed on this multiplexed SIM MS spectrum. Globally the signal MS is equalized, i.e., the windows containing the major species will be limited to the same number of ions as the windows containing less abundant species. The set of two or three acquisition series is then assembled to

546

Fundamentals and Applications of Fourier Transform Mass Spectrometry

proceed with the data processing that has been modified from the database search engine MaxQuant [136]. The implementation of the BoxCar strategy on 1 μg of proteolytic peptides from HeLa cell protein extract identified 60,228 single peptide sequences and 7775 protein groups, with one order of magnitude improvement in the dynamic range of the analysis. It can be adapted to other strategies such as DIA approaches. One of the limitations of the method is the time-consuming SIM approach that must be adjusted to the time required to perform the MS/MS analyses and the resolution of the LC separation.

Quantification The identification of peptides and the resulting inferences of proteins are generally insufficient to answer the problems raised in biology and must be supplemented by semi-quantitative information (sample comparison) or even quantitative information (protein assay). The diverse nature of the obtained information is a major obstacle since on one hand proteins are not directly identified and on the other hand mass spectrometry is not a quantitative method of detection per se. It is possible to divide quantitative approaches in bottom-up proteomics into two categories from a methodological point of view (label free approaches, stable isotope labeling methods), or from an application point of view (untargeted or targeted approaches). This field is constantly evolving and has been the subject of regular reviews, such as the review by Ankney et al. [137]. Here we reconsider the general principles and some recent results derived from the above conventional methods. The quantification of a species in mass spectrometry is based on the comparison of the signal recorded for this species in two (or more) samples. This intensity is related to the physicochemical properties of the species and it is therefore not possible to directly compare the signals of different species. The ionization efficiency can be modulated by the environment (matrix effect), but in general this influence is neglected as a first approximation. Variation in intensity can also be reflected in the number of times (spectral counting) where this species exceeds a certain intensity threshold, such as the one set to switch to MS/MS mode. It is therefore possible to estimate it by considering the spectral counting. To compare two samples, it is possible to compare the signals of the separate acquisitions for each sample (label free approach, non-targeted in DDA/DIA or targeted in PRM for example) or to mark this species with a label of the same molecular structure and

Proteomics and proteoforms

547

different mass (stable isotopic labeling approach) and compare the intensities of the signals obtained in a single analysis of stoichiometrically mixed samples. This labeling can be carried out either chemically by derivation, enzymatically, or metabolically by incorporation of labeled amino acids during protein synthesis. Finally, the isotopic label can be differentiated either directly at the MS level (different isotopic mass labels, for example in SILAC [138]/AACT [139] or in enzymatic labeling 18O [140]), either at the MS/MS level (isobaric isotopic labels but generating distinct reporters mass in fragment ions, for example with isobaric Tags for Relative And Absolute Quantitation (iTRAQ) or with Tandem Mass Tag (TMT) labeling [141]). The use of very high-resolution mass analyzers also makes it possible to work with neutron markers (NeuCode) using amino acids whose monoisotopic mass with 6mDa mass difference [142]. Combined with stable isotopic labeling such as SILAC, this increases the number of samples that can be quantified simultaneously [143], but also enables absolute quantitative measurements using internal standards with stable isotopic labels. Each approach has its limits and its advantages [144]. The cost of the required reagents must be compared with the cost of the analyses, since label-free approaches are instrument time-consuming. Metabolic labeling is not always possible since it is a priori relevant for auxotrophic models for labeled amino acids, even if models of living organisms have been used: Drosophila [145], C. elegans [146], C. albicans [147]. The use of 18O labeling by incorporation of a H18 2 O molecule during proteolysis is also of interest. Although not offering a large mass increment, this strategy was applied to identify Nglycosylation sites after treatment of glycoproteins with a PNGase F [148]. The chemical derivatization of proteins has the advantage of being applicable to any protein. However, when the targeted reactive residues coincide with the proteolytic digestion sites, this can inhibit proteolysis. This is the case with the TMT and iTRAQ labeling that target lysine for tryptic digestion. In addition to cost, another disadvantage of this ubiquitous labeling is the risk of co-selection of multiple precursors in a complex mixture. The reporter ions resulting from the MS/MS fragmentation are then identical regardless of the co-selected precursors and their superposition causes a compression of the intensity ratios of these reporters [149]. For the same reason, the DIA mode is incompatible with this approach, and also not compatible with the SILAC strategy because, in addition to the major increase of the complexity of the sample in multiplex mode, the labeled peptides can be splitted on several DIA windows. Reducing the mass window of the markers is then very interesting, and the NeuCode marking has been applied successfully.

548

Fundamentals and Applications of Fourier Transform Mass Spectrometry

More recently, a mass defect-based data-independent acquisition strategy called MdFDIA has been described, using a metabolic labeling using 13 15 C6 N2-lysine (+8.0142 Da, light) and D8-lysine (+8.0512 Da, heavy) and a chemical derivatization on the C-terminal part of the peptides with light (213CD2H, +34.06312 Da) and heavy (2CD3, +34.06896 Da) dimethyl groups, respectively. So, a peptide (and its C-terminal fragments) generated from four differentially labelled samples will be detected as a quadruplet with mass increments of 5.84, 31.16 and 5.84 mDa. High mass resolution obtained from DIA MS/MS experiments allowed to resolve the peaks to perform a differential quantification and to characterize protein changes in different breast cancer cells proteomes [150]. Absolute quantification of a species ideally requires the use of a labeled standard compound. This is not realistic for proteome-scale applications, so various algorithms have been proposed to correct the fact that two peptides of the same abundance do not give the same intensity in MS with approaches like the Exponentially Modified Protein Abundance Index (emPAI) [151] derived from spectral counting, and Intensity Based Absolute Quantification (iBAQ) [152] or l’APEX [153] based on the intensity of the peaks or the Top3 and its variant [154,155] based on the intensity of the peaks and the number of peptides identified. Recently a new LFAQ algorithm has been proposed to correct biases for minor proteins notably by introducing the concept of machine learning system [156] to integrate the concept of detectability in the form of the quantitative factor (Q-factor). This algorithm was evaluated on two sets of data obtained in DDA FTMS from total protein extracts of Saccharomyces cerevisiae strain BY4743 and Mus musculus RAW264.7 cells. The obtained correlation coefficients for the quantification of 48 standard UPS2 proteins that have been spiked in both complex samples at six different concentrations ranging from 0.05 fmol to 5000 fmol have shown that this Q-factor makes it possible to improve the quality of the quantification (correlation factor R2 ¼ 0.931, 0.902, 0.888, 0.790 in yeast and R2 ¼ 0.908, 0.897, 0.849, 0.820 in mouse respectively with LFAQ, IBAQ, Top3). The acquisition modes are also very important for quantitative analysis. To circumvent the ratio compression effect of the reporter ions in TMT DDA MS2, the use of a tribrid in MultiNotch mode MS3 TMT-MS3 eliminates the chemical noise obtained in MS2 by considering the reporters of the fragments in MS3. The results of this type of analysis should be recovered and exported in editable format [157], which was a strong limitation for orbitrap data processing. The TMTc + mode was introduced recently

Proteomics and proteoforms

549

(c+ stands for the integrated deconvolution of complement reporter ion intensities). In TMTc+ mode the complement ions that are formed as a result of the fragmentation of the precursor peptide are considered in a simplified strategy that does not require the use of the tribrid mass analyzer. This approach, however, requires precise deconvolution of those species whose isotopes are superimposed [158]. Moreover, TMT quantification requires a fragmentation energy that can be deleterious to localize the PTMs on the peptides. Phosphorylation sites are usually lost due to neutral loss fragmentation. In the tribrid configuration, if this fragmentation energy is lowered to identify the modified peptide in MS/MS then the fragments are submitted to MS3 analysis to perform the quantitation on the reporters in the MS3-IDQ mode as presented for example for the phosphoproteome study [159] which can then be combined with the analytical strategies developed for the enrichment of phosphorylated peptides [160].

Top-down proteomics Tandem mass spectrometry for protein analysis is not recent since the first MS/MS spectra on albumin were published in 1990 [161] with a series of multiply charged b fragments obtained in CID by fragmentation in the source on a triple Quadrupole [162]. Moreover, the identification and quantification of proteoforms directly from whole molecules is in many ways attractive: on one hand, we can distinguish different states of modifications of a protein in order to correlate them with their function, which is impossible in bottom-up, and on the other hand we can avoid the pitfalls of the statistical processing of data necessary for the protein inference step from the sequences of proteolytic peptides which is unavoidable in the bottom-up approach. However, the very large protein polypeptides suffer from technical difficulties that have always delayed top-down approaches compared to bottom-up approaches. Yet the term top-down proteomics was introduced first [163] before the term bottom-up proteomics. Closer to the final functional bricks, proteins and proteoforms also have a greater diversity of physicochemical properties, and their unbiased handling is therefore more delicate. They differ in their solubility, and their specific purification, separation, and storage are more critical. Moreover, the analysis of these macrobiomolecules in MS and in MS/MS is also more demanding in terms of specifications. In MALDI or ESI and under non-denaturing conditions (where low charge states are obtained), the required mass range for ion detection is relatively high. In ESI under denaturing conditions, very high

550

Fundamentals and Applications of Fourier Transform Mass Spectrometry

charge states require high mass resolution and high mass accuracy in order to correctly discriminate the species involved. Finally, data processing is more complex because the fragmentation rules are less established for these MS/MS spectra with the need of deconvolution of superimposed multiple charge states, and especially because the activation methods best suited to obtain good sequence coverage, namely ECD/ETD and UVPD modes, are still in full development as we previously reported. The development of top-down approaches is regularly reviewed [164], and gave birth to an international consortium, in a willingness to redouble its efforts to get closer to the analytical capacity of the bottom up as quickly as possible.

Purification and separation of proteins Despite the lower number of components of the protein samples when the proteins are not proteolyzed, a separation and solubilization step is essential [17]. The solubilization of proteins often requires the addition of surfactants that can interact with stationary chromatography phases and limit the ionization efficiency of proteins in MS. To extract and solubilize proteins the most common surfactant is sodium dodecyl sulfate (SDS), although it is poorly tolerated in MS and LC [165], because it could not be effectively replaced. Various methods have been developed to remove SDS by performing acetone precipitation, membrane filtration (FASP) or electrophoresis transmembrane (TME) [166], which are accompanied with problems of aggregation of the proteins depleted in SDS. This problem of manipulation and processing of samples is probably one of the major limitations of top-down approaches. The separation of proteins is mainly carried out either in liquid chromatography (RPLC, HILIC, Size exclusion Chromatography SEC or IE mainly), or in electrophoresis (GELFrEE, IEF or capillary electrophoresis) [167]. RPLC is most frequently coupled to MS because of its high compatibility with the ESI ionization mode. It combines a non-polar stationary phase (silica in general) whose hydrophobicity is modulated by grafting of alkyl chains (C1 to C18 generally) with a polar acidic mobile phase, containing volatile components. The length of the alkyl chain is adapted to the size of the compounds of interest. Other parameters can influence the quality of the separation, such as the particle size of the silica particles or granulometry (with a smaller size giving a lower theoretical plateau height and therefore a better resolving power), and porosity (ideally 200–450 A˚, too small pores for better separation will increase the risk of

Proteomics and proteoforms

551

clogging and cross-contamination) [168]. The length of the column directly increases the number of theoretical plates but at the cost of greater pressure drop, which can be improved by a higher separation temperature (45–60 °C). The effect of temperature has been used to apply a chromatographic elution gradient [169]. Alternatives to silica have been proposed, with the use of polymeric support [170]. To be coupled efficiently with good sensitivity, most approaches work in nanoLC mode with small diameter (75 μm internal diameter usually) columns. RPLC is rarely used alone because its resolving power is insufficient for proteins, and two-dimensional approaches are generally proposed. Like the bottom-up proteomics, the “hi RP/lo RP” coupling [171], that exploits the orthogonality of RPLC dimensions in basic and acidic conditions, has been applied to top-down approaches. [172]. HILIC separations are less developed as final separation techniques and this is certainly due to the difficulties of working in capillary mode, as well as its more limited phase capacity and less understanding of the underlying mechanisms, which reduces the robustness of these mechanisms. However recent developments are trying to exploit their specificities [173]. The applications for the analysis of glyco-proteoforms seem very promising [174]. Separations SEC and IE are often dedicated to a first-dimension separation in on-line or off-line mode [175,176], and this is due in particular to a lack of miniaturization and the poor compatibility of the solvents with the MS. Electrophoretic approaches are historically the first to have demonstrated the diversity of proteoforms. Thus, post-translational modifications such as glycosylation or phosphorylation are traditionally highlighted by 2D gel electrophoresis (GE) [177,178]. On the other hand, the extraction of proteins is not easy from Polyacrylamide Gel Electrophoresis (SDS-PAGE) gels [179]. Other approaches have been developed such as GELFrEE (i.e., geleluted liquid fraction entrapment electrophoresis) [180] which was originally presented for small proteins (240,000 at m/z 200) but which remain below the performance of an FT-ICR. Fragmentation patterns for protein sequence coverage are one of the most active topics of study today. They provide additional information. Activated ion-electron transfer dissociation (AI-ETD) which combines IR irradiation with ETD, increases the sequence coverage by generating ECD/ETD-like fragments of type c and z [190] and the sequencing of proteins of higher molecular mass (up to 70 kDa) have been reported [191]. UV photodissociation [77] also produces complete sequence coverage and is suitable for studying the composition of intact protein complexes [76,192], with the study of disulfide bridges and the localization of protonated protein species in the gas phase as was done for small proteins such as ubiquitin and beta-lactoglobulin [193]. The UVPD/HCD combination in LCMS/MS on the HeLa cell proteome demonstrated that UVPD was complementary to HCD and allowed identification of fewer proteins but with better sequence coverage. [78]. The combined analysis of intact top-down protein and associated polypeptides from middle-down proteolysis on Ides-digested IgG1 (150 kDa), which releases fragments Fc/2 and Fab’/2 (25 and 50 kDa respectively), achieves 40% sequence coverage through the combination of UVPD and EThcD [194]. This approach, however, was carried out on a monoclonal antibody without limitation due to the superposition of multiple endogenous proteoforms of IgG.

Epilogue Protein analysis by mass spectrometry is not restricted to proteomic analyses (i.e., identification and quantification of proteoforms in one given sample). Other studies are as exciting such as the structural and conformational analysis of proteins by limited proteolysis [195,196], by H/D exchange [197,198], or as protein imaging [199,200] or related derivatives imaging [201]. This often implies again developments for the treatment of the sample

Proteomics and proteoforms

555

[202] or for its ionization [203]. The use of a mass analyzer combining ultra-high resolution, high precision of mass measurement with a high speed of acquisition is required to address such applications and these are new opened fields where Fourier transform mass spectrometry could bring a lot of invaluable information.

Acknowledgments We would like to the SMBP ESPCI group and more especially Chiara Giangrande for the histone code study, Giovanni Chiappetta and Yann Verdier for the lactalbumine characterization, Emmanuelle Demey for help on the preparation of the illustrations.

References [1] V.C. Wasinger, S.J. Cordwell, A. Cerpa-Poljak, J.X. Yan, A.A. Gooley, M. R. Wilkins, M.W. Duncan, R. Harris, K.L. Williams, I. Humphery-Smith, Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium, Electrophoresis 16 (1995) 1090–1094. [2] M.R. Wilkins, J.C. Sanchez, A.A. Gooley, R.D. Appel, I. Humphery-Smith, D.F. Hochstrasser, K.L. Williams, Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it, Biotechnol. Genet. Eng. Rev. 13 (1996) 19–50. [3] P. Kahn, From genome to proteome: looking at a cell’s proteins, Science 270 (1995) 369–370. [4] D. Swinbanks, Government backs proteome proposal, Nature 378 (1995) 653. [5] M. Karas, D. Bachmann, U. Bahr, F. Hillenkamp, Matrix-assisted ultraviolet laser desorption of non-volatile compounds, Int. J. Mass Spectrom. Ion Process. 78 (1987) 53–68. [6] C.M. Whitehouse, R.N. Dreyer, M. Yamashita, J.B. Fenn, Electrospray interface for liquid chromatographs and mass spectrometers, Anal. Chem. 57 (1985) 675–679. [7] K. Tanaka, H. Waki, Y. Ido, S. Akita, Y. Yoshida, T. Yoshida, T. Matsuo, Protein and polymer analyses up tom/z 100 000 by laser ionization time-of-flight mass spectrometry, Rapid Commun. Mass Spectrom. 2 (1988) 151–153. [8] B. Chait, S. Kent, Weighing naked proteins: practical, high-accuracy mass measurement of peptides and proteins, Science 257 (1992) 1885–1894. [9] G.A. Valaskovic, N.L. Kelleher, D.P. Little, D.J. Aaserud, F.W. McLafferty, Attomole-sensitivity electrospray source for large-molecule mass spectrometry, Anal. Chem. 67 (1995) 3802–3805. [10] M.R. Emmett, R.M. Caprioli, Micro-electrospray mass spectrometry: ultra-highsensitivity analysis of peptides and proteins, J. Am. Soc. Mass Spectrom. 5 (1994) 605–613. [11] M. Wilm, M. Mann, Analytical properties of the nanoelectrospray ion source, Anal. Chem. 68 (1996) 1–8. [12] N.L. Anderson, N.G. Anderson, Proteome and proteomics: new technologies, new concepts, and new words, Electrophoresis 19 (1998) 1853–1861. [13] M. Mann, P. Højrup, P. Roepstorff, Use of mass spectrometric molecular weight information to identify proteins in sequence databases, Biol. Mass Spectrom. 22 (1993) 338–345.

556

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[14] P. Roepstorff, Mass spectrometry in protein studies from genome to function, Curr. Opin. Biotechnol. 8 (1997) 6–13. [15] N.H. Packer, M.J. Harrison, Glycobiology and proteomics: is mass spectrometry the holy grail? Electrophoresis 19 (1998) 1872–1882. [16] The Consortium for Top Down Proteomics, L.M. Smith, N.L. Kelleher, Proteoform: a single term describing protein complexity, Nat. Methods 10 (2013) 186–187. [17] F.E. Regnier, J. Kim, Proteins and proteoforms: new separation challenges, Anal. Chem. 90 (2018) 361–373. [18] R. Aebersold, J.N. Agar, I.J. Amster, M.S. Baker, C.R. Bertozzi, E.S. Boja, C.E. Costello, B.F. Cravatt, C. Fenselau, B.A. Garcia, Y. Ge, J. Gunawardena, R.C. Hendrickson, P.J. Hergenrother, C.G. Huber, A.R. Ivanov, O.N. Jensen, M.C. Jewett, N.L. Kelleher, L.L. Kiessling, N.J. Krogan, M.R. Larsen, J.A. Loo, R.R. Ogorzalek Loo, E. Lundberg, M.J. MacCoss, P. Mallick, V.K. Mootha, M. Mrksich, T.W. Muir, S.M. Patrie, J.J. Pesavento, S.J. Pitteri, H. Rodriguez, A. Saghatelian, W. Sandoval, H. Schl€ uter, S. Sechi, S.A. Slavoff, L.M. Smith, M.P. Snyder, P.M. Thomas, M. Uhlen, J.E. Van Eyk, M. Vidal, D.R. Walt, F.M. White, E.R. Williams, T. Wohlschlager, V.H. Wysocki, N.A. Yates, N.L. Young, B. Zhang, How many human proteoforms are there? Nat. Chem. Biol. 14 (2018) 206–214. [19] E.A. Ponomarenko, E.V. Poverennaya, E.V. Ilgisonis, M.A. Pyatnitskiy, A.T. Kopylov, V.G. Zgoda, A.V. Lisitsa, A.I. Archakov, The size of the human proteome: the width and depth, Int. J. Anal. Chem. 2016 (2016) 1–6. [20] The UniProt Consortium, UniProt: the universal protein knowledgebase, Nucleic Acids Res. 45 (2017) D158–D169. [21] Y. Zhang, B.R. Fonslow, B. Shan, M.-C. Baek, J.R. Yates, Protein analysis by shotgun/bottom-up proteomics, Chem. Rev. 113 (2013) 2343–2394. [22] A. Shevchenko, O.N. Jensen, A.V. Podtelejnikov, F. Sagliocco, M. Wilm, O. Vorm, P. Mortensen, A. Shevchenko, H. Boucherie, M. Mann, Linking genome and proteome by mass spectrometry: large-scale identification of yeast proteins from two dimensional gels, Proc. Natl. Acad. Sci. U. S. A. 93 (1996) 14440–14445. [23] J.R. Yates, S. Speicher, P.R. Griffin, T. Hunkapiller, Peptide mass maps: a highly informative approach to protein identification, Anal. Biochem. 214 (1993) 397–408. [24] X. Zhan, H. Yang, F. Peng, J. Li, Y. Mu, Y. Long, T. Cheng, Y. Huang, Z. Li, M. Lu, N. Li, M. Li, J. Liu, P.R. Jungblut, How many proteins can be identified in a 2DE gel spot within an analysis of a complex human cancer tissue proteome? Electrophoresis 39 (2018) 965–980. [25] J.R. Yates, The revolution and evolution of shotgun proteomics for large-scale proteome analysis, J. Am. Chem. Soc. 135 (2013) 1629–1640. [26] K. Biemann, Sequencing of peptides by tandem mass spectrometry and high-energy collision-induced dissociation, Methods Enzymol. (1990) 455–479. [27] A.J. Oosterkamp, E. Gelpı´, J. Abian, Quantitative peptide bioanalysis using columnswitching nano liquid chromatography/mass spectrometry, J. Mass Spectrom. 33 (1998) 976–983. [28] M.T. Davis, D.C. Stahl, S.A. Hefta, T.D. Lee, A microscale electrospray interface for on-line, capillary liquid chromatography/tandem mass spectrometry of complex peptide mixtures, Anal. Chem. 67 (1995) 4549–4556. [29] J. Abian, A.J. Oosterkamp, E. Gelpı´, Comparison of conventional, narrow-bore and capillary liquid chromatography/mass spectrometry for electrospray ionization mass spectrometry: practical considerations, J. Mass Spectrom. 34 (1999) 244–254. [30] R.H. Robins, J.E. Guido, Design, construction and application of a simple packed capillary high performance liquid chromatography/electrospray mass spectrometry system, Rapid Commun. Mass Spectrom. 11 (1997) 1661–1666.

Proteomics and proteoforms

557

[31] Y. Chen, F.E. Leach, N.K. Kaiser, X. Dang, Y.M. Ibrahim, R.V. Norheim, G.A. Anderson, R.D. Smith, A.G. Marshall, Improved ion optics for introduction of ions into a 9.4-T Fourier transform ion cyclotron resonance mass spectrometer, J. Mass Spectrom. 50 (2015) 280–284. [32] M.E. Belov, S.R. Ellis, M. Dilillo, M.R.L. Paine, W.F. Danielson, G.A. Anderson, E.L. de Graaf, G.B. Eijkel, R.M.A. Heeren, L.A. McDonnell, Design and performance of a novel interface for combined matrix-assisted laser desorption ionization at elevated pressure and electrospray ionization with Orbitrap mass spectrometry, Anal. Chem. 89 (2017) 7493–7501. [33] A.H. Payne, G.L. Glish, Tandem mass spectrometry in quadrupole ion trap and ion cyclotron resonance mass spectrometers, Methods Enzymol. 402 (2005) 109–148. [34] M.R. Hoopmann, R.L. Moritz, Current algorithmic solutions for peptide-based proteomics data generation and identification, Curr. Opin. Biotechnol. 24 (2013) 31–38. [35] N.L. Kelleher, H.Y. Lin, G.A. Valaskovic, D.J. Aaserud, E.K. Fridriksson, F.W. McLafferty, Top down versus bottom up protein characterization by tandem high-resolution mass spectrometry, J. Am. Chem. Soc. 121 (1999) 806–812. [36] B.T. Chait, Mass spectrometry: bottom-up or top-down? Science 314 (2006) 65–66. [37] B. Bogdanov, R.D. Smith, Proteomics by FTICR mass spectrometry: top down and bottom up, Mass Spectrom. Rev. 24 (2005) 168–200. [38] B. Meyer, D.G. Papasotiriou, M. Karas, 100% protein sequence coverage: a modern form of surrealism in proteomics, Amino Acids 41 (2011) 291–310. [39] L.M. Smith, N.L. Kelleher, Proteoforms as the next proteomics currency, Science 359 (2018) 1106–1107. [40] T.K. Toby, L. Fornelli, N.L. Kelleher, Progress in top-down proteomics and the analysis of proteoforms, Annu. Rev. Anal. Chem. 9 (2016) 499–519. [41] M. Mann, N.L. Kelleher, Precision proteomics: the case for high resolution and high mass accuracy, Proc. Natl. Acad. Sci. 105 (2008) 18132–18138. [42] K.D. Henry, E.R. Williams, B.H. Wang, F.W. McLafferty, J. Shabanowitz, D. F. Hunt, Fourier-transform mass spectrometry of large molecules by electrospray ionization, Proc. Natl. Acad. Sci. U. S. A. 86 (1989) 9075–9078. [43] E. Shishkova, A.S. Hebert, J.J. Coon, Now, more than ever, proteomics needs better chromatography, Cell Syst. 3 (2016) 321–324. [44] J.S. Brodbelt, Ion activation methods for peptides and proteins, Anal. Chem. 88 (2016) 30–51. [45] K. Biemann, Contributions of mass spectrometry to peptide and protein structure, Biol. Mass Spectrom. 16 (1988) 99–111. [46] P. Roepstorff, J. Fohlman, Letter to the editors: proposal for a common nomenclature for sequence ions in mass-spectra of peptides, Biol. Mass Spectrom. 11 (1984) 601. [47] J.V. Olsen, B. Macek, O. Lange, A. Makarov, S. Horning, M. Mann, Higher-energy C-trap dissociation for peptide modification analysis, Nat. Methods 4 (2007) 709–712. [48] C. Shao, Y. Zhang, W. Sun, Statistical characterization of HCD fragmentation patterns of tryptic peptides on an LTQ Orbitrap Velos mass spectrometer, J. Proteomics 109 (2014) 26–37. [49] A. Michalski, E. Damoc, J.-P. Hauschild, O. Lange, A. Wieghaus, A. Makarov, N. Nagaraj, J. Cox, M. Mann, S. Horning, Mass spectrometry-based proteomics using Q exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer, Mol. Cell. Proteomics 10 (2011) M111.011015. [50] T. Geiger, J. Cox, M. Mann, Proteomics on an Orbitrap benchtop mass spectrometer using all-ion fragmentation, Mol. Cell. Proteomics 9 (2010) 2252–2261. [51] C.D. Kelstrup, D.B. Bekker-Jensen, T.N. Arrey, A. Hogrebe, A. Harder, J.V. Olsen, Performance evaluation of the Q Exactive HF-X for Shotgun Proteomics, J. Proteome Res. 17 (2018) 727–738.

558

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[52] H.J. Cooper, K. Ha˚kansson, A.G. Marshall, The role of electron capture dissociation in biomolecular analysis: electron capture dissociation, Mass Spectrom. Rev. 24 (2005) 201–222. [53] M.A. van Agthoven, L. Chiron, M.-A. Coutouly, M.-A. Delsuc, C. Rolando, Twodimensional ECD FT-ICR mass spectrometry of peptides and glycopeptides, Anal. Chem. 84 (2012) 5589–5595. [54] M.A. van Agthoven, C.A. Wootton, L. Chiron, M.-A. Coutouly, A. Soulby, J. Wei, M.P. Barrow, M.-A. Delsuc, C. Rolando, P.B. O’Connor, Two-dimensional mass spectrometry for proteomics, a comparative study with Cytochrome c, Anal. Chem. 88 (2016) 4409–4417. [55] F. Floris, L. Chiron, A.M. Lynch, M.P. Barrow, M.-A. Delsuc, P.B. O’Connor, Topdown deep sequencing of ubiquitin using two-dimensional mass spectrometry, Anal. Chem. 90 (2018) 7302–7309. [56] F. Floris, M.A. van Agthoven, L. Chiron, C.A. Wootton, P.Y.Y. Lam, M.P. Barrow, M.-A. Delsuc, P.B. O’Connor, Bottom-up two-dimensional electron-capture dissociation mass spectrometry of calmodulin, J. Am. Soc. Mass Spectrom. 29 (2018) 207–210. [57] K.L. Fort, C.N. Cramer, V.G. Voinov, Y.V. Vasil’ev, N.I. Lopez, J.S. Beckman, A.J.R. Heck, Exploring ECD on a Benchtop Q exactive Orbitrap mass spectrometer, J. Proteome Res. 17 (2018) 926–933. [58] J.E.P. Syka, J.J. Coon, M.J. Schroeder, J. Shabanowitz, D.F. Hunt, Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry, Proc. Natl. Acad. Sci. 101 (2004) 9528–9533. [59] N.D. Udeshi, P.D. Compton, J. Shabanowitz, D.F. Hunt, K.L. Rose, Methods for analyzing peptides and proteins on a chromatographic timescale by electron-transfer dissociation mass spectrometry, Nat. Protoc. 3 (2008) 1709–1717. [60] S. Tamara, R.A. Scheltema, A.J.R. Heck, A.C. Leney, Phosphate transfer in activated protein complexes reveals interaction sites, Angew. Chem. Int. Ed. 56 (2017) 13641–13644. [61] P. L€ ossl, A.M. Brunner, F. Liu, A.C. Leney, M. Yamashita, R.A. Scheltema, A.J.R. Heck, Deciphering the Interplay among multisite phosphorylation, interaction dynamics, and conformational transitions in a tripartite protein system, ACS Cent. Sci. 2 (2016) 445–455. [62] V. Franc, Y. Yang, A.J.R. Heck, Proteoform profile mapping of the human serum complement component C9 revealing unexpected new features of N -, O -, and C-glycosylation, Anal. Chem. 89 (2017) 3483–3491. [63] F. Sobott, S.J. Watt, J. Smith, M.J. Edelmann, H.B. Kramer, B.M. Kessler, Comparison of CID versus ETD based MS/MS fragmentation for the analysis of protein ubiquitination, J. Am. Soc. Mass Spectrom. 20 (2009) 1652–1659. [64] M.-S. Kim, A. Pandey, Electron transfer dissociation mass spectrometry in proteomics, Proteomics 12 (2012) 530–542. [65] G.C. McAlister, D. Phanstiel, D.M. Good, W.T. Berggren, J.J. Coon, Implementation of electron-transfer dissociation on a hybrid linear ion trap  Orbitrap mass spectrometer, Anal. Chem. 79 (2007) 3525–3534. [66] N.M. Riley, J.J. Coon, The role of electron transfer dissociation in modern proteomics, Anal. Chem. 90 (2018) 40–64. [67] F. Liu, P. L€ ossl, B.M. Rabbitts, R.S. Balaban, A.J.R. Heck, The interactome of intact mitochondria by cross-linking mass spectrometry provides evidence for coexisting respiratory supercomplexes, Mol. Cell. Proteomics 17 (2018) 216–232. [68] K.R. Durbin, R.T. Fellers, I. Ntai, N.L. Kelleher, P.D. Compton, Autopilot: an online data acquisition control system for the enhanced high-throughput characterization of intact proteins, Anal. Chem. 86 (2014) 1485–1492.

Proteomics and proteoforms

559

[69] C. Singh, C.G. Zampronio, A.J. Creese, H.J. Cooper, Higher energy collision dissociation (HCD) product ion-triggered electron transfer dissociation (ETD) mass spectrometry for the analysis of N-linked glycoproteins, J. Proteome Res. 11 (2012) 4517–4525. [70] J. Saba, S. Dutta, E. Hemenway, R. Viner, Increasing the productivity of glycopeptides analysis by using higher-energy collision dissociation-accurate mass-productdependent electron transfer dissociation, Int. J. Proteomics 2012 (2012) 1–7. [71] J. Laskin, J.H. Futrell, Activation of large lons in FT-ICR mass spectrometry, Mass Spectrom. Rev. 24 (2005) 135–167. [72] W. Li, C.L. Hendrickson, M.R. Emmett, A.G. Marshall, Identification of intact proteins in mixtures by alternated capillary liquid chromatography electrospray ionization and LC ESI infrared multiphoton dissociation Fourier transform ion cyclotron resonance mass spectrometry, Anal. Chem. 71 (1999) 4397–4402. [73] G. van der Rest, F. He, M.R. Emmett, A.G. Marshall, S.J. Gaskell, Gas-phase cleavage of PTC-derivatized electrosprayed tryptic peptides in an FT-ICR trapped-ion cell: mass-based protein identification without liquid chromatographic separation, J. Am. Soc. Mass Spectrom. 12 (2001) 288–295. [74] S. Bourgoin-Voillard, N. Leymarie, C.E. Costello, Top-down tandem mass spectrometry on RNase A and B using a Qh/FT-ICR hybrid mass spectrometer, Proteomics. 14 (2014) 1174–1184. [75] J.B. Shaw, W. Li, D.D. Holden, Y. Zhang, J. Griep-Raming, R.T. Fellers, B.P. Early, P.M. Thomas, N.L. Kelleher, J.S. Brodbelt, Complete protein characterization using top-down mass spectrometry and ultraviolet photodissociation, J. Am. Chem. Soc. 135 (2013) 12646–12651. [76] L.J. Morrison, J.S. Brodbelt, 193 nm ultraviolet photodissociation mass spectrometry of tetrameric protein complexes provides insight into quaternary and secondary protein topology, J. Am. Chem. Soc. 138 (2016) 10849–10859. [77] R.R. Julian, The mechanism behind top-down UVPD experiments: making sense of apparent contradictions, J. Am. Soc. Mass Spectrom. 28 (2017) 1823–1826. [78] T.P. Cleland, C.J. DeHart, R.T. Fellers, A.J. VanNispen, J.B. Greer, R.D. LeDuc, W.R. Parker, P.M. Thomas, N.L. Kelleher, J.S. Brodbelt, High-throughput analysis of intact human proteins using UVPD and HCD on an Orbitrap mass spectrometer, J. Proteome Res. 16 (2017) 2072–2079. [79] J.B. Shaw, E.W. Robinson, L. Pasˇa-Tolic, Vacuum ultraviolet photodissociation and Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry: revisited, Anal. Chem. 88 (2016) 3019–3023. [80] M.A. Halim, M. Girod, L. MacAleese, J. Lemoine, R. Antoine, P. Dugourd, Combined infrared multiphoton dissociation with ultraviolet photodissociation for ubiquitin characterization, J. Am. Soc. Mass Spectrom. 27 (2016) 1435–1442. [81] M.A. Halim, L. MacAleese, J. Lemoine, R. Antoine, P. Dugourd, M. Girod, Ultraviolet, infrared, and high-low energy photodissociation of post-translationally modified peptides, J. Am. Soc. Mass Spectrom. 29 (2018) 270–283. [82] Y. Zhang, X. Xie, X. Zhao, F. Tian, J. Lv, W. Ying, X. Qian, Systems analysis of singly and multiply O-glycosylated peptides in the human serum glycoproteome via EThcD and HCD mass spectrometry, J. Proteomics 170 (2018) 14–27. [83] J.A. Madsen, H. Xu, M.R. Robinson, A.P. Horton, J.B. Shaw, D.K. Giles, T. S. Kaoud, K.N. Dalby, M.S. Trent, J.S. Brodbelt, High-throughput database search and large-scale negative polarity liquid chromatography–tandem mass spectrometry with ultraviolet photodissociation for complex proteomic samples, Mol. Cell. Proteomics 12 (2013) 2604–2614. [84] S.M. Greer, M. Bern, C. Becker, J.S. Brodbelt, Extending proteome coverage by combining MS/MS methods and a modified bioinformatics platform adapted for

560

[85] [86] [87]

[88] [89] [90] [91] [92] [93] [94] [95] [96]

[97] [98]

[99] [100] [101]

Fundamentals and Applications of Fourier Transform Mass Spectrometry

database searching of positive and negative polarity 193 nm ultraviolet photodissociation mass spectra, J. Proteome Res. 17 (2018) 1340–1347. J.S. Cottrell, Protein identification using MS/MS data, J. Proteomics 74 (2011) 1842–1851. K. Verheggen, L. Martens, F.S. Berven, H. Barsnes, M. Vaudel, Database search engines: paradigms, challenges and solutions, Adv. Exp. Med. Biol. 919 (2016) 147–156. E. Audain, J. Uszkoreit, T. Sachsenberg, J. Pfeuffer, X. Liang, H. Hermjakob, A. Sanchez, M. Eisenacher, K. Reinert, D.L. Tabb, O. Kohlbacher, Y. PerezRiverol, In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics, J. Proteomics 150 (2017) 170–182. C. Bamberger, S. Martı´nez-Bartolome, M. Montgomery, S. Pankow, J.D. Hulleman, J.W. Kelly, J.R. Yates, Deducing the presence of proteins and proteoforms in quantitative proteomics, Nat. Commun. 9 (2018) 2320. X.-D. Feng, L.-W. Li, J.-H. Zhang, Y.-P. Zhu, C. Chang, K.-X. Shu, J. Ma, Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process, BMC Genomics 18 (2017) 143. B.B. Misra, Updates on resources, software tools, and databases for plant proteomics in 2016-2017, Electrophoresis 39 (13) (2018) 1543–1557. R.L. Gundry, Bioinformatics for mass spectrometry-based proteomics, in: D.R.M. Graham, D.E. Ott (Eds.), HIV-1 Proteomics, Springer New York, New York, NY, 2016, pp. 99–112. K.D.B. Anapindi, E.V. Romanova, B.R. Southey, J.V. Sweedler, Peptide identifications and false discovery rates using different mass spectrometry platforms, Talanta 182 (2018) 456–463. D. Bergeron, A.-M.S. Tremblay, Algorithms for optimized maximum entropy and diagnostic tools for analytic continuation. Phys. Rev. E 94 (2016). https://doi.org/ 10.1103/PhysRevE.94.023303. M. Bern, T. Caval, Y.J. Kil, W. Tang, C. Becker, E. Carlson, D. Kletter, K.I. Sen, N. Galy, D. Hagemans, V. Franc, A.J.R. Heck, Parsimonious charge deconvolution for native mass spectrometry, J. Proteome Res. 17 (2018) 1216–1226. W. Cai, H. Guner, Z.R. Gregorich, A.J. Chen, S. Ayaz-Guner, Y. Peng, S.G. Valeja, X. Liu, Y. Ge, MASH suite pro: a comprehensive software tool for top-down proteomics, Mol. Cell. Proteomics 15 (2016) 703–714. R.D. LeDuc, V. Schw€ammle, M.R. Shortreed, A.J. Cesnik, S.K. Solntsev, J.B. Shaw, M.J. Martin, J.A. Vizcaino, E. Alpi, P. Danis, N.L. Kelleher, L.M. Smith, Y. Ge, J.N. Agar, J. Chamot-Rooke, J.A. Loo, L. Pasa-Tolic, Y.O. Tsybin, ProForma: a standard proteoform notation, J. Proteome Res. 17 (2018) 1321–1325. J. Eriksson, D. Feny€ o, Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs, Nat. Biotechnol. 25 (2007) 651–655. D.B. Bekker-Jensen, C.D. Kelstrup, T.S. Batth, S.C. Larsen, C. Haldrup, J. B. Bramsen, K.D. Sørensen, S. Høyer, T.F. Ørntoft, C.L. Andersen, M.L. Nielsen, J.V. Olsen, An optimized shotgun strategy for the rapid generation of comprehensive human proteomes, Cell Syst. 4 (2017) 587–599.e4. H. Mirzaei, M. Carrasco (Eds.), Modern Proteomics – Sample Preparation, Analysis and Practical Applications, Springer International Publishing, Cham, 2016. J. Beri, M.M. Rosenblatt, E. Strauss, M. Urh, M.S. Bereman, Reagent for evaluating liquid chromatography-tandem mass spectrometry (LC-MS/MS) performance in bottom-up proteomic experiments, Anal. Chem. 87 (2015) 11635–11640. M.S. Bereman, J. Beri, V. Sharma, C. Nathe, J. Eckels, B. MacLean, M.J. MacCoss, An automated pipeline to monitor system performance in liquid chromatography-

Proteomics and proteoforms

[102]

[103] [104] [105] [106] [107]

[108]

[109] [110]

[111]

[112]

[113]

[114] [115]

561

tandem mass spectrometry proteomic experiments, J. Proteome Res. 15 (2016) 4763–4763–4769. K. Gevaert, F. Impens, P. Van Damme, B. Ghesquie`re, X. Hanoulle, J. Vandekerckhove, Applications of diagonal chromatography for proteome-wide characterization of protein modifications and activity-based analyses, FEBS J. 274 (2007) 6277–6289. D. Gussakovsky, H. Neustaeter, V. Spicer, O.V. Krokhin, Sequence-specific model for peptide retention time prediction in strong cation exchange chromatography, Anal. Chem. 89 (2017) 11795–11802. M.-Z. Zhu, N. Li, Y.-T. Wang, N. Liu, M.-Q. Guo, B. Sun, H. Zhou, L. Liu, J.-L. Wu, Acid/salt/pH gradient improved resolution and sensitivity in proteomics study using 2D SCX-RP LC–MS, J. Proteome Res. 16 (2017) 3470–3475. O.V. Krokhin, P. Ezzati, V. Spicer, Peptide retention time prediction in hydrophilic interaction liquid chromatography: data collection methods and features of additive and sequence-specific models, Anal. Chem. 89 (2017) 5526–5533. V.R. Richard, D. Domanski, A.J. Percy, C.H. Borchers, An online 2D-reversed-phase – reversed-phase chromatographic method for sensitive and robust plasma protein quantitation, J. Proteomics 168 (2017) 28–36. H. Lee, D.-G. Mun, J.E. So, J. Bae, H. Kim, C. Masselon, S.-W. Lee, Efficient exploitation of separation space in two-dimensional liquid chromatography system for comprehensive and efficient proteomic analyses, Anal. Chem. 88 (2016) 11734–11734–11741. V. Spicer, P. Ezzati, H. Neustaeter, R.C. Beavis, J.A. Wilkins, O.V. Krokhin, 3D HPLC-MS with reversed-phase separation functionality in all three dimensions for large-scale bottom-up proteomics and peptide retention data collection, Anal. Chem. 88 (2016) 2847–2855. O.V. Krokhin, G. Anderson, V. Spicer, L. Sun, N.J. Dovichi, Predicting electrophoretic mobility of tryptic peptides for high-throughput CZE-MS analysis, Anal. Chem. 89 (2017) 2000–2008. M. Di Venere, S. Viglio, D. Sassera, M. Fumagalli, A. Bardoni, R. Salvini, M. Cagnone, P. Iadarola, Do the complementarities of electrokinetic and chromatographic procedures represent the “Swiss knife” in proteomic investigation? An overview of the literature in the past decade: proteomics and 2DE, Electrophoresis 38 (2017) 1538–1550. T. Vehus, H. Roberg-Larsen, J. Waaler, S. Aslaksen, S. Krauss, S.R. Wilson, E. Lundanes, Versatile, sensitive liquid chromatography mass spectrometry - implementation of 10 μm OT columns suitable for small molecules, peptides and proteins, Sci. Rep. (2016) 37507. P. Yu, S. Petzoldt, M. Wilhelm, D.P. Zolg, R. Zheng, X. Sun, X. Liu, G. Schneider, A. Huhmer, B. Kuster, Trimodal mixed mode chromatography that enables efficient offline two-dimensional peptide fractionation for proteome analysis, Anal. Chem. 89 (2017) 8884–8891. V. Gladilovich, U. Greifenhagen, N. Sukhodolov, A. Selyutin, D. Singer, D. Thieme, P. Majovsky, A. Shirkin, W. Hoehenwarter, E. Bonitenko, E. Podolskaya, A. Frolov, Immobilized metal affinity chromatography on collapsed Langmuir-Blodgett iron(III) stearate films and iron(III) oxide nanoparticles for bottom-up phosphoproteomics, J. Chromatogr. A 1443 (2016) 181–190. B. Jiang, Q. Wu, L. Zhang, Y. Zhang, Preparation and application of silver nanoparticle-functionalized magnetic graphene oxide nanocomposites, Nanoscale. 9 (2017) 1607–1615. M. Olkowicz, A. Radulska, J. Suraj, A. Kij, M. Walczak, S. Chlopicki, R.T. Smolenski, Development of a sensitive, accurate and robust liquid

562

[116] [117]

[118]

[119] [120] [121] [122] [123] [124] [125]

[126] [127]

[128] [129] [130] [131]

Fundamentals and Applications of Fourier Transform Mass Spectrometry

chromatography/mass spectrometric method for profiling of angiotensin peptides in plasma and its application for atherosclerotic mice, J. Chromatogr. A 1393 (2015) 37–46. D. Payto, C. Heideloff, S. Wang, Sensitive, simple, and robust nano-liquid chromatography-mass spectrometry method for amyloid protein subtyping, Methods Mol. Biol. 1378 (2016) 55–60. P. Cifani, A. Kentsis, High sensitivity quantitative proteomics using automated multidimensional nano-flow chromatography and accumulated ion monitoring on Quadrupole-Orbitrap-linear ion trap mass spectrometer, Mol. Cell. Proteomics 16 (2017) 2006–2016. Y. Zhu, R. Zhao, P.D. Piehowski, R.J. Moore, S. Lim, V.J. Orphan, L. Pasˇa-Tolic, W.-J. Qian, R.D. Smith, R.T. Kelly, Subnanogram proteomics: impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples, Int. J. Mass Spectrom. 427 (2018) 4–10. R.T. Kelly, A.V. Tolmachev, J.S. Page, K. Tang, R.D. Smith, The ion funnel: theory, implementations, and applications, Mass Spectrom. Rev. 29 (2) (2010) 294–312. B. Sun, J.R. Kovatch, A. Badiong, N. Merbouh, Optimization and modeling of quadrupole Orbitrap parameters for sensitive analysis toward single-cell proteomics, J. Proteome Res. 16 (2017) 3711–3721. A. Michalski, J. Cox, M. Mann, More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS, J. Proteome Res. 10 (2011) 1785–1793. G. Alves, A.Y. Ogurtsov, S. Kwok, W.W. Wu, G. Wang, R.-F. Shen, Y.-K. Yu, Detection of co-eluted peptides using database search methods, Biol. Direct. 3 (2008) 27. J.D. Chapman, D.R. Goodlett, C.D. Masselon, Multiplexed and data-independent tandem mass spectrometry for global proteome profiling, Mass Spectrom. Rev. 33 (2014) 452–470. G.H.M.F. Souza, P.C. Guest, D. Martins-de-Souza, LC-MSE, multiplex MS/MS, ion mobility, and label-free quantitation in clinical proteomics, Methods Mol. Biol. 1546 (2017) 57–73. L.C. Gillet, P. Navarro, S. Tate, H. R€ ost, N. Selevsek, L. Reiter, R. Bonner, R. Aebersold, Targeted data extraction of the MS/MS spectra generated by dataindependent acquisition: a new concept for consistent and accurate proteome analysis, Mol. Cell. Proteomics 11 (2012) O111.016717. C.R. Weisbrod, J.K. Eng, M.R. Hoopmann, T. Baker, J.E. Bruce, Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification, J. Proteome Res. 11 (2012) 1621–1632. J.D. Egertson, A. Kuehn, G.E. Merrihew, N.W. Bateman, B.X. MacLean, Y.S. Ting, J.D. Canterbury, D.M. Marsh, M. Kellmann, V. Zabrouskov, C.C. Wu, M.J. MacCoss, Multiplexed MS/MS for improved data-independent acquisition, Nat. Methods 10 (2013) 744–746. O.V. Krokhin, V. Spicer, Peptide retention standards and hydrophobicity indexes in reversed-phase high-performance liquid chromatography of peptides, Anal. Chem. 81 (2009) 9522–9530. S. Sidoli, R. Fujiwara, B.A. Garcia, Multiplexed data independent acquisition (MSX-DIA) applied by high resolution mass spectrometry improves quantification quality for the analysis of histone peptides, Proteomics 16 (2016) 2095–2105. A.C. Peterson, J.D. Russell, D.J. Bailey, M.S. Westphall, J.J. Coon, Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics, Mol. Cell. Proteomics 11 (2012) 1475–1488. S. Gallien, A. Bourmaud, S.Y. Kim, B. Domon, Technical considerations for largescale parallel reaction monitoring analysis, J. Proteomics 100 (2014) 147–159.

Proteomics and proteoforms

563

[132] N. Rauniyar, Parallel reaction monitoring: a targeted experiment performed using high resolution and high mass accuracy mass spectrometry, Int. J. Mol. Sci. 16 (2015) 28566–28581. [133] O.V. Krokhin, V. Spicer, Generation of accurate peptide retention data for targeted and data independent quantitative LC-MS analysis: chromatographic lessons in proteomics, Proteomics 16 (2016) 2931–2936. [134] W. Haas, B.K. Faherty, S.A. Gerber, J.E. Elias, S.A. Beausoleil, C.E. Bakalarski, X. Li, J. Villen, S.P. Gygi, Optimization and use of peptide mass measurement accuracy in shotgun proteomics, Mol. Cell. Proteomics 5 (2006) 1326–1337. [135] F. Meier, P.E. Geyer, S. Virreira Winter, J. Cox, M. Mann, BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes, Nat. Methods 15 (2018) 440–448. [136] J. Cox, M. Mann, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat. Biotechnol. 26 (2008) 1367–1372. [137] J.A. Ankney, A. Muneer, X. Chen, Relative and absolute quantitation in mass spectrometry-based proteomics, Annu. Rev. Anal. Chem. 11 (2018) 49–77. [138] S.-E. Ong, B. Blagoev, I. Kratchmarova, D.B. Kristensen, H. Steen, A. Pandey, M. Mann, Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics, Mol. Cell. Proteomics 1 (2002) 376–386. [139] H. Zhu, S. Pan, S. Gu, E.M. Bradbury, X. Chen, Amino acid residue specific stable isotope labeling for quantitative proteomics, Rapid Commun. Mass Spectrom. 16 (2002) 2115–2123. [140] X. Ye, B. Luke, T. Andresson, J. Blonder, 18O stable isotope labeling in MS-based proteomics, Brief. Funct. Genomic. Proteomic. 8 (2009) 136–144. [141] A. Thompson, J. Sch€afer, K. Kuhn, S. Kienle, J. Schwarz, G. Schmidt, T. Neumann, R. Johnstone, A.K.A. Mohammed, C. Hamon, Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS, Anal. Chem. 75 (2003) 1895–1904. [142] A.E. Merrill, A.S. Hebert, M.E. MacGilvray, C.M. Rose, D.J. Bailey, J.C. Bradley, W.W. Wood, M. El Masri, M.S. Westphall, A.P. Gasch, J.J. Coon, NeuCode labels for relative protein quantification, Mol. Cell. Proteomics 13 (2014) 2503–2512. [143] G.K. Potts, E.A. Voigt, D.J. Bailey, C.M. Rose, M.S. Westphall, A.S. Hebert, J. Yin, J.J. Coon, Neucode labels for multiplexed, absolute protein quantification, Anal. Chem. 88 (2016) 3295–3303. [144] A.F.M. Altelaar, C.K. Frese, C. Preisinger, M.L. Hennrich, A.W. Schram, H.T. M. Timmers, A.J.R. Heck, S. Mohammed, Benchmarking stable isotope labeling based quantitative proteomics, J. Proteomics 88 (2013) 14–26. [145] M.D. Sury, J.-X. Chen, M. Selbach, The SILAC fly allows for accurate protein quantification in vivo, Mol. Cell. Proteomics MCP. 9 (2010) 2173–2183. [146] M. Miyagi, Monitoring protein synthesis in Caenorhabditis elegans using SILAC, Methods Enzymol. 585 (2017) 77–89. [147] I.N. Kaneva, J. Longworth, P.E. Sudbery, M.J. Dickman, Quantitative proteomic analysis in Candida albicans using SILAC-based mass spectrometry, Proteomics 18 (2018) e1700278. [148] J.M. Smeekens, W. Chen, R. Wu, Mass spectrometric analysis of the cell surface N-glycoproteome by combining metabolic labeling and click chemistry, J. Am. Soc. Mass Spectrom. 26 (2015) 604–614. [149] S.Y. Ow, M. Salim, J. Noirel, C. Evans, P.C. Wright, Minimising iTRAQ ratio compression through understanding LC-MS elution dependence and high-resolution HILIC fractionation, Proteomics 11 (2011) 2341–2346.

564

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[150] Y. Di, Y. Zhang, L. Zhang, T. Tao, H. Lu, MdFDIA: a mass defect based four-plex data-independent acquisition strategy for proteome quantification, Anal. Chem. 89 (2017) 10248–10255. [151] B. Zybailov, A.L. Mosley, M.E. Sardiu, M.K. Coleman, L. Florens, M.P. Washburn, Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae, J. Proteome Res. 5 (2006) 2339–2347. [152] B. Schwanh€ausser, D. Busse, N. Li, G. Dittmar, J. Schuchhardt, J. Wolf, W. Chen, M. Selbach, Global quantification of mammalian gene expression control, Nature. 473 (2011) 337–342. [153] P. Lu, C. Vogel, R. Wang, X. Yao, E.M. Marcotte, Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation, Nat. Biotechnol. 25 (2007) 117–124. [154] J.C. Silva, M.V. Gorenstein, G.-Z. Li, J.P.C. Vissers, S.J. Geromanos, Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition, Mol. Cell. Proteomics 5 (2006) 144–156. [155] M. Blein-Nicolas, M. Zivy, Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics, Biochim. Biophys. Acta 1864 (2016) 883–895. [156] C. Chang, Z. Gao, W. Ying, Y. Zhao, Y. Fu, S. Wu, M. Li, G. Wang, X. Qian, Y. Zhu, F. He, LFAQ: towards unbiased label-free absolute protein quantification by predicting peptide quantitative factors, Anal. Chem. (2018) Epub 2018 Dec 10. [157] K.A. Kovalchik, S. Moggridge, D.D.Y. Chen, G.B. Morin, C.S. Hughes, Parsing and quantification of raw Orbitrap mass spectrometer data using RawQuant, J. Proteome Res. 17 (6) (2018) 2237–2247. [158] M. Sonnett, E. Yeung, M. W€ uhr, Accurate, sensitive, and precise multiplexed proteomics using the complement reporter ion cluster, Anal. Chem. 90 (2018) 5032–5039. [159] M.J. Berberich, J.A. Paulo, R.A. Everley, MS3-IDQ: utilizing MS3 spectra beyond quantification yields increased coverage of the phosphoproteome in isobaric tag experiments, J. Proteome Res. 17 (2018) 1741–1747. [160] B. Bai, H. Tan, V.R. Pagala, A.A. High, V.P. Ichhaporia, L. Hendershot, J. Peng, Deep profiling of proteome and phosphoproteome by isobaric labeling, extensive liquid chromatography, and mass spectrometry, Methods Enzymol. 585 (2017) 377–395. [161] R.D. Smith, J.A. Loo, C.G. Edmonds, C.J. Barinaga, H.R. Udseth, New developments in biochemical mass spectrometry: electrospray ionization, Anal. Chem. 62 (1990) 882–899. [162] J.A. Loo, C.G. Edmonds, R.D. Smith, Tandem mass spectrometry of very large molecules: serum albumin sequence information from multiply charged ions formed by electrospray ionization, Anal. Chem. 63 (1991) 2488–2499. [163] N.L. Kelleher, S.V. Taylor, D. Grannis, C. Kinsland, H.-J. Chiu, T.P. Begley, F. W. Mclafferty, Efficient sequence analysis of the six gene products (7-74 kDA) from the Escherichia coli thiamin biosynthetic operon by tandem high-resolution mass spectrometry, Protein Sci. 7 (1998) 1796–1801. [164] A.D. Catherman, O.S. Skinner, N.L. Kelleher, Top down proteomics: facts and perspectives, Biochem. Biophys. Res. Commun. 445 (2014) 683–693. [165] C. Kachuk, A.A. Doucette, The benefits (and misfortunes) of SDS in top-down proteomics, J. Proteomics. 175 (2018) 75–86. [166] N. Unterlander, A.A. Doucette, Membrane-based SDS depletion ahead of peptide and protein analysis by mass spectrometry, Proteomics 18 (2018) e1700025. [167] A.L. Capriotti, C. Cavaliere, P. Foglia, R. Samperi, A. Lagana`, Intact protein separation by chromatographic and/or electrophoretic techniques for top-down proteomics, J. Chromatogr. A 1218 (2011) 8760–8776.

Proteomics and proteoforms

565

[168] Y. Shen, N. Tolic, P.D. Piehowski, A.K. Shukla, S. Kim, R. Zhao, Y. Qu, E. Robinson, R.D. Smith, L. Pasˇa-Tolic, High-resolution ultrahigh-pressure long column reversed-phase liquid chromatography for top-down proteomics, J. Chromatogr. A 1498 (2017) 99–110. [169] H. Kanazawa, T. Okano, Temperature-responsive chromatography for the separation of biomolecules, J. Chromatogr. A 1218 (2011) 8738–8747. [170] M.J. Roth, D.A. Plymire, A.N. Chang, J. Kim, E.M. Maresh, S.E. Larson, S.M. Patrie, Sensitive and reproducible intact mass analysis of complex protein mixtures with superficially porous capillary reversed-phase liquid chromatography mass spectrometry, Anal. Chem. 83 (2011) 9586–9592. [171] F. Yang, Y. Shen, D.G. Camp, R.D. Smith, High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis, Expert Rev. Proteomics 9 (2012) 129–134. [172] Z. Wang, H. Ma, K. Smith, S. Wu, Two-dimensional separation using high-pH and low-pH reversed phase liquid chromatography for top-down proteomics, Int. J. Mass Spectrom. 427 (2018) 43–51. [173] A.F.G. Gargano, L.S. Roca, R.T. Fellers, M. Bocxe, E. Domı´nguez-Vega, G.W. Somsen, Capillary HILIC-MS: a new tool for sensitive top-down proteomics, Anal. Chem. 90 (2018) 6601–6609. [174] E. Domı´nguez-Vega, S. Tengattini, C. Peintner, J. van Angeren, C. Temporini, R. Haselberg, G. Massolini, G.W. Somsen, High-resolution glycoform profiling of intact therapeutic proteins by hydrophilic interaction chromatography-mass spectrometry, Talanta 184 (2018) 375–381. [175] D.C. Simpson, S. Ahn, L. Pasa-Tolic, B. Bogdanov, H.M. Mottaz, A.N. Vilkov, G.A. Anderson, M.S. Lipton, R.D. Smith, Using size exclusion chromatographyRPLC and RPLC-CIEF as two-dimensional separation strategies for protein profiling, Electrophoresis 27 (2006) 2722–2733. [176] S.G. Valeja, L. Xiu, Z.R. Gregorich, H. Guner, S. Jin, Y. Ge, Three dimensional liquid chromatography coupling ion exchange chromatography/hydrophobic interaction chromatography/reverse phase chromatography for effective protein separation in top-down proteomics, Anal. Chem. 87 (2015) 5363–5371. [177] H. Kosako, K. Motani, Global identification of ERK substrates by phosphoproteomics based on IMAC and 2D-DIGE, Methods Mol. Biol. 1487 (2017) 137–149. [178] K. Motani, H. Kosako, Phosphoproteomic identification and functional characterization of protein kinase substrates by 2D-DIGE and Phos-tag PAGE, Biochim. Biophys. Acta 186 (1) (2019) 57–61. [179] B.T. Kurien, R.H. Scofield, Extraction of proteins from gels: a brief review, Methods Mol. Biol. 869 (2012) 403–405. [180] J.E. Lee, J.F. Kellie, J.C. Tran, J.D. Tipton, A.D. Catherman, H.M. Thomas, D.R. Ahlf, K.R. Durbin, A. Vellaichamy, I. Ntai, A.G. Marshall, N.L. Kelleher, A robust two-dimensional separation for top-down tandem mass spectrometry of the low-mass proteome, J. Am. Soc. Mass Spectrom. 20 (2009) 2183–2191. [181] Y. Zhao, L. Sun, G. Zhu, N.J. Dovichi, Coupling capillary zone electrophoresis to a Q exactive HF mass spectrometer for top-down proteomics: 580 proteoform identifications from yeast, J. Proteome Res. 15 (2016) 3679–3685. [182] R.A. Lubeckyj, E.N. McCool, X. Shen, Q. Kou, X. Liu, L. Sun, Single-shot topdown proteomics with capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry for identification of nearly 600 Escherichia coli proteoforms, Anal. Chem. 89 (2017) 12059–12067. [183] E.N. McCool, R.A. Lubeckyj, X. Shen, D. Chen, Q. Kou, X. Liu, L. Sun, Deep topdown proteomics using capillary zone electrophoresis-tandem mass spectrometry: identification of 5700 proteoforms from the Escherichia coli proteome, Anal. Chem. 90 (2018) 5529–5533.

566

Fundamentals and Applications of Fourier Transform Mass Spectrometry

[184] R.W. Nelson, D. Dogruel, P. Williams, Mass determination of human immunoglobulin IgM using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, Rapid Commun. Mass Spectrom. 8 (1994) 627–631. [185] M.E. Belov, E.N. Nikolaev, G.A. Anderson, H.R. Udseth, T.P. Conrads, T.D. Veenstra, C.D. Masselon, M.V. Gorshkov, R.D. Smith, Design and performance of an ESI interface for selective external ion accumulation coupled to a Fourier transform ion cyclotron mass spectrometer, Anal. Chem. 73 (2001) 253–261. [186] M.E. Belov, R. Zhang, E.F. Strittmatter, D.C. Prior, K. Tang, R.D. Smith, Automated gain control and internal calibration with external ion accumulation capillary liquid chromatography-electrospray ionization Fourier transform ion cyclotron resonance, Anal. Chem. 75 (2003) 4195–4205. [187] S.M. Patrie, J.P. Charlebois, D. Whipple, N.L. Kelleher, C.L. Hendrickson, J.P. Quinn, A.G. Marshall, B. Mukhopadhyay, Construction of a hybrid quadrupole/fourier transform ion cyclotron resonance mass spectrometer for versatile MS/MS above 10 kDa, J. Am. Soc. Mass Spectrom. 15 (2004) 1099–1108. [188] A. Makarov, E. Denisov, Dynamics of ions of intact proteins in the Orbitrap mass analyzer, J. Am. Soc. Mass Spectrom. 20 (2009) 1486–1495. [189] D. Nolting, R. Malek, A. Makarov, Ion traps in modern mass spectrometry, Mass Spec. Rev. 38 (2019) 50–168. [190] N.M. Riley, M.S. Westphall, J.J. Coon, Activated ion-electron transfer dissociation enables comprehensive top-down protein fragmentation, J. Proteome Res. 16 (2017) 2653–2659. [191] N.M. Riley, M.S. Westphall, J.J. Coon, Sequencing larger intact proteins (30-70 kDa) with activated ion electron transfer dissociation, J. Am. Soc. Mass Spectrom. 29 (2018) 140–149. [192] S. Warnke, G. von Helden, K. Pagel, Analyzing the higher order structure of proteins with conformer-selective ultraviolet photodissociation, Proteomics 15 (2015) 2804–2812. [193] L.J. Morrison, J.S. Brodbelt, Charge site assignment in native proteins by ultraviolet photodissociation (UVPD) mass spectrometry, Analyst 141 (2016) 166–176. [194] L. Fornelli, K. Srzentic, R. Huguet, C. Mullen, S. Sharma, V. Zabrouskov, R.T. Fellers, K.R. Durbin, P.D. Compton, N.L. Kelleher, Accurate sequence analysis of a monoclonal antibody by top-down and middle-down Orbitrap mass spectrometry applying multiple ion activation techniques, Anal. Chem. 90 (14) (2018) 8421–8429. [195] Y. Feng, G. De Franceschi, A. Kahraman, M. Soste, A. Melnik, P.J. Boersema, P.P. de Laureto, Y. Nikolaev, A.P. Oliveira, P. Picotti, Global analysis of protein structural changes in complex proteomes, Nat. Biotechnol. 32 (2014) 1036–1044. [196] S. Schopper, A. Kahraman, P. Leuenberger, Y. Feng, I. Piazza, O. M€ uller, P.J. Boersema, P. Picotti, Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry, Nat. Protoc. 12 (2017) 2391–2410. [197] G. Wang, I.A. Kaltashov, Approach to characterization of the higher order structure of disulfide-containing proteins using hydrogen/deuterium exchange and top-down mass spectrometry, Anal. Chem. 86 (2014) 7293–7298. [198] K.D. Rand, M. Zehl, T.J.D. Jørgensen, Measuring the hydrogen/deuterium exchange of proteins at high spatial resolution by mass spectrometry: overcoming gas-phase hydrogen/deuterium scrambling, Acc. Chem. Res. 47 (2014) 3018–3027. [199] E.H. Seeley, K. Schwamborn, R.M. Caprioli, Imaging of intact tissue sections: moving beyond the microscope, J. Biol. Chem. 286 (2011) 25459–25466. [200] B.M. Prentice, D.J. Ryan, R. Van de Plas, R.M. Caprioli, J.M. Spraggins, Enhanced ion transmission efficiency up to m/z 24 000 for MALDI protein imaging mass spectrometry, Anal. Chem. 90 (2018) 5090–5099.

Proteomics and proteoforms

567

[201] M.K. Passarelli, A. Pirkl, R. Moellers, D. Grinfeld, F. Kollmer, R. Havelund, C.F. Newman, P.S. Marshall, H. Arlinghaus, M.R. Alexander, A. West, S. Horning, E. Niehuis, A. Makarov, C.T. Dollery, I.S. Gilmore, The 3D OrbiSIMS-label-free metabolic imaging with subcellular lateral resolution and high mass-resolving power, Nat. Methods 14 (2017) 1175–1183. [202] R. Longuespee, R. Casadonte, M. Kriegsmann, C. Pottier, G. Picard de Muller, P. Delvenne, J. Kriegsmann, E. De Pauw, MALDI mass spectrometry imaging: a cutting-edge tool for fundamental and clinical histopathology, Proteomics Clin. Appl. 10 (2016) 701–719. [203] M. Wisztorski, J. Quanico, J. Franck, B. Fatou, M. Salzet, I. Fournier, Droplet-based liquid extraction for spatially-resolved microproteomics analysis of tissue sections, Methods Mol. Biol. 1618 (2017) 49–63.

Further reading [204] S. Si Ahmed Zennia, A. Mati, F. Saulnier, Y. Verdier, G. Chiappetta, G. Mulliert, L. Miclo, J. Vinh, J.-M. Girardet, Identification by FT-ICR-MS of Camelus dromedarius α-lactalbumin variants as the result of nonenzymatic deamidation of Asn-16 and Asn-45, Food Chem. 187 (2015) 305–313.

CHAPTER 18

Gas phase ion-molecule reactions of inorganic compounds in FT-ICR-MS Karl Peter Wanczeka, Basem Kanawatib a

Institute of Inorganic and Physical Chemistry, University of Bremen, Bremen, Germany Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M€ unchen, Neuherberg, Germany

b

Contents Introduction Hydrogen and rare gases Boron Carbon, silicon, and germanium Nitrogen, phosphorus, and arsenic Oxygen, sulfur, selenium, and tellurium Fluorine and chlorine Conclusion References

569 570 572 572 574 582 584 585 585

Introduction In this review, reactions between ions and neutral molecules (or atoms) in the gas phase at single collision conditions (low pressure) will be discussed, and due to the limited space available, restricted to inorganic chemistry of non-metal compounds. The material is arranged according to the groups of the Periodic System. “If organic chemistry is defined as the chemistry of hydrocarbon compounds and their derivatives, inorganic chemistry can be described broadly as the chemistry of “everything else.” This includes all the remaining elements of the periodic table, as well as carbon, which plays a major role in many inorganic compounds. Organometallic chemistry, a very large and rapidly growing field, bridges both areas by considering compounds containing direct metal-carbon bonds” [1]. Ion-molecule reactions are known for more than 100 years. In 1913, Thomson [2] detected a substance with three times the mass of the hydrogen

Fundamentals and Applications of Fourier Transform Mass Spectrometry https://doi.org/10.1016/B978-0-12-814013-0.00018-1

© 2019 Elsevier Inc. All rights reserved.

569

570

Fundamentals and Applications of Fourier Transform Mass Spectrometry

atom. He concluded that this must be the H+3 ion. In 1916, Dempter [3] directly detected H+3 in the mass spectrum of hydrogen molecules, showed the pressure dependence of its intensity, and classified the reaction as an ionmolecule reaction: “The positive molecules so formed are able to dissociate the gas. When this occurs the complex H3 is formed.” However, there was little interest in this type of reactions for a long time. In 1952, Talroze and Ljubimova [4] detected CH+5 in the mass spectrum of methane. Independently, the same ion was found by Stevenson and Schissler [5] three years later. The situation changed completely after the invention of ion cyclotron resonance mass spectrometry in 1965 by Llewellyn [6] and of flowing afterglow in 1966 by Fehsenfeld et al. [7]. For approximately the next 30 years there was a keen interest in inorganic ion-molecule reactions. In the more recent years, analytical applications are in the main focus. McDaniel’s [8] book on collision phenomena in ionized gases, published in 1964 is the basic reference until today. Several books, which deal with ion-molecule reactions have been published in the following years. Two comprehensive books on ion-molecule reactions, edited by Franklin [9] and by Bowers [10] cover the whole field, instrumentation and reactions. Ausloos [11] edited a volume on the interaction between ions and molecules, which also covers the whole field. It contains contributions from a NATO advanced study institute, held in 1974. Futrell [12] edited a volume on general and physical aspects of gaseous ion chemistry. The book edited by Russell [13], focuses on inorganic gas phase chemistry of metal ions and metal clusters. All books include ICR studies. A large collection of cation-molecule reaction data was published by Anicich [14].

Hydrogen and rare gases Many detailed studies have been published of the reactions of hydrogen and rare gas ions with simple neutral atoms and molecules. The Futrell group published several studies in the field: Clow and Futrell [15] determined the rate constants of the H+3 , D+3 , H2D+, and HD+2 formation in the ionmolecule reactions of H2, D2, and HD. The experimental results agreed well with the values calculated with Langevin polarization theory. With their tandem spectrometer, Smith and Futrell [16] measured the dependence of the rate constant of the reaction of D+3 with H2 on D+3 vibrational energy. H2/rare gas systems [17–19] were investigated in great detail. Smith et al. [20] reacted Kr+(2P3/2) and Kr+(2P1/2) with H2. The reaction energies of

Gas phase ion-molecule reactions of inorganic compounds in FT-ICR-MS

571

hydrogen atom transfer were determined as 0.17 eV and 0.82 eV, respectively. Both reactions have negligible activation energy. Kr+(2P1/2) reacts faster. With double resonance technique, Smith and Futrell [21] showed that the rate constant of the symmetric charge transfer reaction 3 He+ + 4He ! 4He+ + 3He increased monotonically in the kinetic energy range between 0.1 eV and 25 eV. The same authors [22] determined the rate constants and product distributions at near thermal energies of ion-molecule reactions of ArD+ with Ne, Kr, N2, O2, CO, CO2, N2O, COS, and NH3. The energy of ArD+ was changed by a certain number of internal energy relaxing collisions with D2 in the ion source of the tandem spectrometer. Bruce and Eyler [23] employed the kinetic energy dependence of the Ar+ + N2 ion-molecule reaction as a chemical “thermometer.” Anicich et al. [24] measured the product distributions of thermal energy charge transfer reactions of He+, Ne+, and Ar+ ions with N2, O2, CO, NO, CO2, and N2O. Except for the He+-N2 reaction, only dissociative charge transfer is observed for the highly exothermic He+ and Ne+ reactions. The corresponding observations were made by Mauclaire et al. [25] for the Ne+ + O2 and Ar+ + O2 reactions by product ion kinetic energy measurements. With the same method, the same group [26] studied the charge transfer of Ar+ + H2O. Emission spectroscopy of the H2O+ ions was also reported. Smith and Futrell [27] measured the thermal energy rate constants for proton and hydrogen atom transfer from H+2 and H2 to CO2 and CO+2 . In their study of charge exchange reactions in the system He+ + N2*, Kemper and Bowers [28] discovered that the majority of the initially formed N+2 * ions must have lifetimes at least 104–103 s. With their ICR spectrometer with temperature controlled ICR cell, the same group [29] found an unusual isotope effect in the reactions of Kr+ with H2, D2, and HD in the temperature range between 80 and 400 K. Kim et al. [30] studied the proton transfer reactions of H+3 with N2, O2, and CO. The same group [31] studied hydrogen atom abstraction in systems X+ + H2 ! XH+ + H. X+ ¼ CH+, CH+2 , CH+4 , N+, NH+, NH+2 , NH+3 , O+, OH+, H2O+, CO+, N+2 , C+2 , and C2H+. For many reactions, a large fraction of collisions was found to be not reactive. Marx et al. [32] concluded that at least 60% of the N+ ions, formed in the dissociative charge transfer reaction He+ + N2, comes from excited shortlived N+2 *. McAllister and Pitman [33] determined the rate constants of the reactions in the CO2/H2 and CO2/D2 mixtures with a four-section ICR cell and compared the values with the literature. As an early example for the many measurements of gas phase proton affinities, the work of

572

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Brauman et al. [34] is referred. The relative gas phase acidities of simple hydrides decrease in the order: H2S > AsH3 > PH3 > SiH4 > H2O > NH3 > CH4. The order of the acidities can thus be analyzed in terms of the position of the central atom in the Periodic Table.

Boron Dunbar has in detail investigated the ion chemistry of boron hydrides and related compounds: Ion-molecule reactions of positive and negative ions of diborane [35], of more complicated boron hydrides [36]: B4H10, B5H9, B5H11, and B6H10, and the ion-molecule reactions of diborane with several alcohols and dimethyl ether [37]. Murphy and Beauchamp [38] studied the ion chemistry of trimethyl borane and determined the proton affinity of (CH3)2B ¼CH–2 to 365  5 kcal/mol. The protonation of trimethyl borane yielded (CH3)2B+ and CH4 but no protonated molecule. Doiron et al. [39] studied the protonation of borazine B3N3H6 theoretically and experimentally and determined the proton affinity to 196.4  0.2 kcal/mol. Dixon [40] investigated the ion chemistry of two closo carboranes: 1,6-C2B4H6 and 2,4-C2B5H7. The proton affinities were determined to 208  4 kcal/mol and 173  1 kcal/mol, respectively. The condensation reactions of the molecular ions with the neutral molecule both produced H2 as the neutral product. Abboud et al. [41] showed that the protonation of ammonia borane and 16 alkyl substituted amine boranes yielded H2 in every case. Armentrout and Beauchamp [42] studied the ion chemistry of uranium(IV) tetrahydroborate and determined the heat of formation of ΔHf[U(BH4)4] ¼ 7  14 kcal/mol.

Carbon, silicon, and germanium Jaffe and Klein [43] have determined the rate constants for the ion-molecule reactions in CO and CO2. The reaction CO+* + CO ! C2O+ + O has also been studied by Bowers et al. [44] in great detail. The rate constant for the formation of C2O+ was 8.5  1.5  1010 cm3 s1 molecule1, the lifetime of the CO+* 102 s, the ratio of CO+* to the reminder of CO+ formed in the electron beam was (3.8  0.4)  103. Jaffe and Klein [43] found for the same reaction of excited CO+* ion a rate constant of 2.0  0.3  1012 cm3 s1 molecule1. Romanzin et al. [45] studied the ion-molecule reactions CN and C3N anions and HC3N molecules, which are of interest in planetary atmospheres. Wight and Beauchamp

Gas phase ion-molecule reactions of inorganic compounds in FT-ICR-MS

573

[46] examined the ion chemistry of isocyanic acid. The acidity was measured to be D(OCN–H+) ¼ 344.7  2 kcal/mol. The protonation of isocyanic acid by ion-molecule reactions indicated the structure HOCNH+. McAllister [47] studied the ion chemistry of the two isomers methyl thiocyanate and isothiocyanate. The proton affinities were identical within the measurement uncertainty. Su and Kevan [48] investigated the ion-molecule reactions of several perfluorocarbons: C2F6, C3F8, C3F6, c-C4F8, and 2-C4F8. Fluoride transfer and collision-induced dissociation reactions dominate the ion chemistry. Karpas and Klein [49] determined the energy dependence of dissociative electron attachment to the carbonyl halides Cl2CO, F2CO, and ClFCO. Negative ion-molecule reactions yielded COX 3 (X ¼ Cl or F) via halide ion transfer. Henis and coworkers published several studies on the ion chemistry of silane. The primary ions of silane [50] were Si+, SiH+, SiH+2 , and SiH+3 . Many reactions yielded products with two silicon atoms. SiH+2 , and SiH+3 underwent H– transfer. Product ions with two silicon atoms were formed in endothermic ion-molecule reactions [51]. Si+2 was formed only endothermically from reactions of Si+ and SiH+2 with neutral silane. Si2H+, Si2H+2 , and Si2H+3 were formed in exothermic and endothermic reactions. The silanium ion SiH+5 was produced by a reaction of CH+5 with SiH4, [52]. Its reactions with ammonia were studied. The mechanism of these reactions was confirmed by double resonance experiments and deuteration: SiD4H+ + NH3 ! SiNH3D+3 + HD. The hydrogen elimination was also studied [53] with the aid of the reactions of SiH+2 + CD4 and SiD+2 + CH4. The analysis showed that elimination from the silicon atom was favored by a factor of 10. Tertiary reactions formed products with three silicon atoms [54]. The implications of reactions of Si+ for recoil atom studies were discussed. Luebkemann and Wanczek [55] studied the ion chemistry of positive and negative ions of (CH3)3SiCF3 and (CH3)3SiC2F5, and supported the ICR spectrometric results by DFT calculations. Fluoronium ions like (CH3)6FSi+2 were the major product ions for positive ion-molecule reactions, and five coordinated siliconate ions like (CH3)3SiFCF 3 in the negative mode. Xavier et al. [56] investigated ligand exchange reactions of (CH3)4Si, (CH3)4Ge, Si(O(CH3))4, and Ge(O(CH3))4. Ab initio calculations were carried out to better understand the reaction pathways. A typical ligand exchange reaction was: Me3Si+ + Ge(OMe)4 ! MeOSiMe+2 + MeGe(OMe)3. Reed and Brauman [57] determined in their photodetachment studies the electron affinities of SiH3 (1.44  0.03 eV) and GeH3 (1.74  0.04 eV) radicals.

574

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Nitrogen, phosphorus, and arsenic The ion-molecule reactions of N+2 with N2 in the ground and excited states were studied by two groups, by Jaffe et al. [58], and in great detail by Bowers et al. [59]. The second group determined the lifetime of the excited N+2 * > 0.01 s and ΔHf (N+3 ) ¼ 372  4 kcal/mol. Huntress et al. [60] measured the relative rates and their kinetic energy dependence in the ion chemistry of ammonia. Charge transfer competes with proton transfer over the whole energy range studied from thermal to 50 eV. For the same system, Marx and Mauclaire [61] derived different values. In a follow up paper, Huntress [62] indeed stated an error, due to an incorrect equation in their publication. However, the disagreement remains. Huntress supposed, that the disagreement may be caused by incomplete ion ejection before reaction in the experiments of Marx and Mauclaire. Anicich et al. [63] studied rate constants of reactions in ammonia and water mixtures after electron impact ionization. The reactions were dominated by proton and charge transfer. Electron impact ionization above threshold yielded a large fraction of NH3*+ in vibrationally excited states, which reacted by proton transfer to H2O. This reaction is endothermic for ground state NH+3 . A statistical phase space theoretical treatment for this reaction system was given by Chesnavich and Bowers [64] and vibrational excitation of NH+3 was considered. Azeim and van der Rest [65] were able to generate the ammonia-water ionized hetero-dimer [NH3, H2O]+ and to study its charge and proton transfer reactions. Cacace et al. [66] generated the fluorodiazonium ion FN+2 and the ion F2HN+2 with ion-molecule reactions in an ionized mixture of HN3 and NF3. The most stable structure of F2HN+2 was calculated as FN2-FH+. The same group [67] synthesized F2NO+ in the ion-molecule reaction: NF+2 + N2O ! F2NO+ + N2. FT-ICR and mass-analyzed ion kinetic energy (MIKE) spectrometry were employed. Schindler et al. [68] reacted chlorine nitrate ClONO2 with large ionic water clusters H+(H2O)n, O(H2O)n, and OH(H2O)n, (n ¼ 1–100). In a hydrolysis reaction hypochlorous acid, HOCl, was formed. Huntress and Anicich [69] measured the product distribution for the three product channels O+2 + N, NO+ + O, and O+ + NO of the reaction of N+ and O2. Pearson et al. [70] concluded from an unusual low photodetachment threshold of the NO 2 ion that there are three isomers present: the usual bent isomer, a peroxo isomer, and a ring isomer. The relative energies of the three isomers were calculated with ab initio self-consistent-field method.

Gas phase ion-molecule reactions of inorganic compounds in FT-ICR-MS

575

Marx et al. [71] investigated the energy dependence of the reaction between O– and N2O with both ICR and flowing afterglow techniques. The only products observed were NO + NO. In good agreement, the rate constants measured at room temperature with flowing afterglow were 2.2  0.4  1010 cm3/s and with ICR 2.5  0.5  1010 cm3/s. Aschi et al. [72] analyzed the structure of protonated peroxynitric acid, (HOONO2)H+, experimentally and theoretically. The ion was formed by protonation of peroxynitric acid with H3O+. Its lowest energy structure was HOOH-NO+2 . Holtz et al. [73] determined the basicity of nitrogen trifluoride to be 151  10 kcal/mol with the aid of reactions of NF3 with CH+5 and H2Cl+. Gross and Lin [74] emphasized the analytical applications of ion-molecule reactions by studying the gas phase reactions of several cyclopropane molecular ion derivatives with NH3 in a Varian ICR-9 mass spectrometer, equipped with a dual inlet. They found that association complex ions between R1,R2substituted cyclopropanes and ammonia play a major role to form N]C bonds in the produced product ions [CH2]NH2]+, R1CH]NH+2 and R2CH]NH+2 . They also found that the C3H∙+ 6 radical ions, which are produced by electron impact ionization of cyclopropane keep their cyclic structure [75]. Sieck et al. [76] studied the energy-dependent ring opening of cycloalkane parent ions and studied the gas phase ion-molecule reaction of these parent ions and NH3 to yield CH2NH+2 ΔH ¼ 27 kcal/mol and CH2NH+3 product ions with ΔH ¼  16 kcal/mol. Gross and Lin could also determine the rate constant for the production of several product ions as a result of ion-molecule reactions between several ionized cyclopropane derivatives and ammonia. They indicated that the reactivity of different cyclopropane derivatives decreases with increasing molecular size of the substituted cyclopropane ions. One discovered reason for this is the possibility of side ion-molecule reactions to interfere, such as the protonation reaction of ammonia by various substituted cyclopropane ions, as confirmed by double resonance experiments. They also found that possible isomerization of larger substituted cyclopropane ions with ring opening can produce unreactive aliphatic ions, which do not react with ammonia. Chau and Bowers [77] determined in a drift cell ion cyclotron resonance mass spectrometer absolute rate constants of the neutrals PH3 and NH3, when each of them react with He+, Ne+, Ar+, Kr+, Xe+, CO+, and CO+2 . The product distribution of these ion-molecule reactions was determined by double resonance ejection approach. They determined the mechanism of these thermal energy charge transfer reactions, where electron

576

Fundamentals and Applications of Fourier Transform Mass Spectrometry

transfer represented the dominant reaction channel, while hydrogen atom transfer was only observed in these systems in minor signal intensities. The driving force of the above mentioned ion-molecule reactions between rare gas ions and reactive PH3 and NH3 neutrals is the fact that the electronrare gas cation recombination energy is greater that the ionization potential of each of the PH3 and NH3 neutrals. The former lies in the range (12–24.6) eV, while the latter lies in the range (8–15) eV for most neutrals. Thus, their work was the introduction of chemical ionization technique, which utilizes rare gas cations for ionization of neutral molecules in the gas phase. The energy of such ion-molecule reactions, which are utilized for chemically ionizing neutrals is deposited in the form of internal energy in the generated product ions M+. Corderman and Beauchamp [78] determined the gas phase proton affinity of PF3 to 160  5 kcal/mol. They also determined the homolytic bond dissociation energy of PF+3 –H to 113.2 kcal/mol and could also determine the energetics of formation of PF+2 , PF+4 , HPF+3 and CH3PF+3 by the use of ICR. Ion-molecule reactions of PF3 in mixtures with SiF4, BF3, SF6, NF3, CH3F, and (CH3)2CO were reported. They determined the heat for formation ΔHf(CH3PF+3 ) to 8.5  5 kcal/mol by studying the ion-molecule reaction between PF+3 and CH3F. They also studied reaction between PF+3 and NF3 leading to the product ion PF+4 in great detail and found the role of acetone traces to produce the new product ion (CH3)2CF+. Hartman et al. [79] investigated more sophisticated phosphor containing systems (Ylides), which have key importance in current organic chemistry (Wittig reaction). They utilized a 30 eV energetic electron impact beam to ionize Trimethylmethylenephosphorane. They could perform gas phase ion-molecule reactions between the fragment and molecular ions and the neutral molecule (CH3)3P]CH2. The double bond in this compound is significantly polarized due to electronegativity difference of C and P so that this system can be described as a phosphonium cation, which is bound to a carbanion anion. The authors found that the parent ion (CH3)3PCH+2 is stable under 30 eV electron impact conditions and that primary ion formation by methyl elimination is more favorable than methylene elimination. Gas phase ion rearrangements could also be observed due to the experimental findings for the production of PdH and CdC bonds in some formed primary ions. The molecular, phosphonium radical ion undergoes an ion-molecule condensation reaction in ICR:

Gas phase ion-molecule reactions of inorganic compounds in FT-ICR-MS



577

∙ + ðCH3 Þ3 P─CH2 + ðCH3 Þ3 P═CH2  + ! ðCH3 Þ3 PCHPðCH3 Þ3 + CH3 ∙

The same group has also found that two other main ion-molecule reactions with this radical ion also takes place:  ∙ +  + ðCH3 Þ3 P─CH2 + ðCH3 Þ3 P═CH2 ! ðCH3 Þ4 P + PC4 H10 ∙  ∙ + ðCH3 Þ3 P─CH2 + ðCH3 Þ3 P═CH2  ∙ + ! ðCH3 Þ2 PðCH2 ÞC2 H5 + PðCH3 Þ3 They could successfully determine the rate constant of the former reaction to 2.1  1010 cm3 molecule1 s1 and for the latter ion-molecule reaction to 2.7  1010 cm3 molecule1 s1. The same group also studied the ion-molecule reaction of ionized acetone (at 15 eV) with trimethylmethylenephosphorane to yield a trimethylphosphine oxide radical cation, a Wittig type reaction:  ∙ + ½CH3 COCH3 ∙ + + ðCH3 Þ3 P═CH2 ! ðCH3 Þ3 PO + C4 H8 Wanczek and Hartmann [80] studied the gas phase ion chemistry of dimethylaminodifluorophosphine by the use of Varian Syrotron ICR mass spectrometer. Besides determining the formed primary ions, which were produced as a result of 30 eV electron impact ionization of (CH3)2NPF2, they could also observe ion-molecule reactions between the fragment ions [NC2H4]+ and (CH3)2NPF2 as follows:  + ½NC2 H4  + + ðCH3 Þ2 NPF2 ! ðCH3 Þ2 HNPF2 + NC2 H3 They determined the rate constant to the above mentioned protonation reaction to 1  1010 cm3 molecule1 s1. The same group could also observe other ion-molecule reactions involving [NC2H4]+ as follows: ½NC2 H4  + + ðCH3 Þ2 NPF2 ! ½NC3 H8  + + ðH2 CNPF2 Þ  + ½NC2 H4  + + ðCH3 Þ2 NPF2 ! H4 C2 NðF2 ÞPNðCH3 Þ2 Mixtures of (CH3)2NPF2 with CH3PF2 could also be studied in ICR by the same group to yield diphosphinofluoronium cations:  +  + ðCH3 Þ2 NPF + ðCH3 Þ2 PF ! ðCH3 Þ2 P  F  PðFÞNðCH3 Þ2  +  + ðCH3 Þ2 PF + ðCH3 Þ2 NPF2 ! ðCH3 Þ2 P  F  PðFÞNðCH3 Þ2

578

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Similar reactions could also be observed in the mixture (CH3)2NPF2 with and (CH3)2PF. Wanczek and R€ oschenthaler [81] produced the ICR mass spectra and studied the ion-molecule reactions of dimethylfluorophosphine and methyldifluorophosphine in the gas phase. They ran double resonance experiments on all the detected ions and investigated the mass spectra at different pressures and irradiating electron energies. The same group also determined appearance potentials of the major ions in the ICR spectra of methyldifluorophosphine and dimethylfluorophosphine. Fluoride anion transfer could be observed in the ion-molecule reaction: PF2 + + CH3 PF2 ! CH3 PF + + PF3 : The PF+2 ion is also capable to catch an electron from methyldifluorophosphine in the following charge transfer ion-molecule reaction to two radical species PF2 + + CH3 PF2 ! CH3 PF2∙ + + PF2 ∙ Many other product ions could be observed when methyldifluoropho+ sphine was let to react with CH3PF∙+ 2 and CH3PF . The mechanism of these reactions could be revealed and the same group found that fluoride atom in a neutral CH3PF2 can attack electrophilic phosphorous center in the cation CH3PF+ to yield several product ions. Ions with two phosphorous atoms such as (CH3)2P2F+3 could also be observed as a result of this mentioned ion-molecule reaction. However, Wanczek and R€ oschenthaler found that no covalent PdP bond can be established in such product ion, because the connected fluoride ions withdraw electron density from the PdP bond. They suggested a fluoride bridge in the structure of diphosphinofluoronium ion [F(CH3)P-F-P(CH3)F]+. Hodges et al. [82] studied nucleophile reactions of several anions (SF 6,         SF5 , SO2F, F , F , CF Cl , Cl , CD O , DNO , OH , and NH 2 3 3 2 ), obtained by either electron capture or by dissociative electron capture of the corresponding neutral gases with trimethylphosphate (CH3O)3PO in ICR and could confirm the observed reaction pathways by performing double resonance experiments. Contrary to the well-known attack of a nucleophile on phosphorous, in case of (CH3O)3PO in the condensed phase, they found that the carbon atom, instead of the phosphorous atom, was attacked, when ion-molecule reactions between several nucleophilic anions and the phosphorous ester (CH3O)3PO were performed. They also found that  SF 6 is a very stable anion, so that fluoride ion transfer from SF6 to (CH3O)3PO does not occur. They added with their results a significant contribution to the negative ion chemical ionization technique.

Gas phase ion-molecule reactions of inorganic compounds in FT-ICR-MS

579

In another study, Hodges et al. [83] investigated the effects of the molecular structure on gas phase basicity and proton affinity of monocyclic and bicyclic phosphite esters. They found out that the proton affinity of the phosphorous electron lone pair decreases with increasing the steric effect of the studied phosphites. The same group also observed stereo differences in basicity of cyclic six-membered phosphites, depending on whether the electron loan pair of the phosphorous is axially or equatorially oriented. Relationships between the first ionization potential of the studied cyclic phosphites and the gas phase proton affinity could also be obtained. They concluded from their study that phosphites are first ionized from the phosphorus electron lone pair orbital. Aue et al. [84] obtained photoelectron spectra and studied gas phase proton affinities of several three-membered ring heterocycles, such as aziridine, oxirane, thiirane and phosphirane. They found out that increased s electronic orbital character in the heteroatom of the cyclic ring decreases the electron lone pair density in that cite, where proton attachment can be made, so that the proton affinity inversely correlates with the increase of the s orbital character. Sullivan and Beauchamp [85] studied gas phase nucleophilic reactions of      several anions: NH 2 , OH , CH3CH2O , HNO , HS , and SF6 with phosphorous trifluoride and trifluorophosphine oxide by ICR and could determine the rate constants of these reactions. They also revealed the mechanism of these reactions with OPF3 and PF3 to proceed via penta- and tetracoordinate ionic intermediates, respectively. The ion-molecule reactions  between SF 5 or SF6 and PF3O indicate that fluoride transfer reactions  can occur to form F4PO. Both SF 5 or SF6 were capable to show fluoride transfer in their ion-molecule reactions with (CH3)3B. The same group  showed also that the ion-molecule reaction SF 6 + PF3 ! SF5 + PF4  occurs. PF4 was only formed in minor signal intensity at thermal ion energy. The same group also studied the interesting ion molecule reaction between (CH3O)2PO+ and trimethyl phosphate and could find that two equivalent pathways are feasible. However, the structure of the formed product ion (CH3O)5P2O+2 was not determined. ðCH3 OÞ2 PO + + ðCH3 OÞ3 PO ! ðCH3 OÞ4 P + + ðCH3 OÞOPO ðCH3 OÞ2 PO + + ðCH3 OÞ3 PO ! ðCH3 OÞ5 P2 O2 + Wanczek [86] studied the gas phase ion-chemistry of methylphosphine, dimethylphosphine as well as dimethyldeuterophosphine in ICR by implying an electron energy (30 eV) pulse technique and he performed double

580

Fundamentals and Applications of Fourier Transform Mass Spectrometry

resonance experiments at different pressures and irradiating field strengths. He showed that the ICR mass spectra of CH3PH2 and (CH3)2PH change as a function of pressure inside the ICR cell, indicating the gas phase ionmolecule reactions between the most abundant ions and the neutrals. The base peak in both methylphosphine and dimethylphosphine is CH3P∙+ radical ion. Several ion-molecule reactions between this radical ion and CH3PH2 could be observed to yield: CH3PH+3 and CH2P∙ in a proton transfer, CH3P2H+2 and methyl radical in a phosphorous cation transfer, or as an acceptor of a methyl group to yield: (CH3)2P+ and PH∙2. Hydrogen atom transfer reaction could also be observed to form CH3PH+ and CH3PH∙ radical. He also observed an additional pathway for the above mentioned reaction between CH3P∙+ radical ion and methylphosphine to yield phosphonium ion, which contains two phosphorous atoms (CH3)2P2H+. The mechanism for the formation of this product ion is suggested to result from phosphourous electron loan pair attack on the electrophilic phosphorous center of the CH3P∙+ radical ion, to produce an intermediate ion, which can be stabilized by dissociation of a hydrogen atom: CH3P∙+ + CH3PH2 ! (CH3)2P2H+ + H%. Other diphosphorous product ions could also be observed but their abundance decreased in the order: (CH3)2P2H+ > CH3P2H+2 > (CH3)3P+2 . Wanczek found that these product ions can be formed as a result of a phosphorous transfer reaction: CH3 P∙ + + ðCH3 Þ2 PH ! ðCH3 Þ2 P2 H + + CH∙3 CH2 P + + ðCH3 Þ2 PH ! CH3 P2 H2 + + C2 H4 In summary, he concluded that the ion chemistry of methylphosphine and dimethylphosphine is indeed versatile. He indicated that the ionmolecule reactions of these reactive compounds can be classified in four categories: Reactions yielding phosphonium ions with one phosphorous atom, with two phosphorous atoms, as well as experimentally observed CID fragment ions and charge transfer reactions. Kanawati and Wanczek [87] investigated the production of several phosphoranide anions in the gas phase in a long cyclindrical five-sections ICR cell. They studied the ion chemistry of tris(trifluoromethyl)phosphine and the gas phase ion-molecule reactions of its phosphide anion (CF3)2P, which was mainly produced as a result of dissociative electron attachment to (CF3)3P. Under a pressure of 2  107 mbar and when the produced phosphide anion (CF3)2P was accelerated, intensive signals of produced   phosphoranides CF3PF 3 , (CF3)2PF2 and (CF3)3PF could be observed.

Gas phase ion-molecule reactions of inorganic compounds in FT-ICR-MS

581

Fragment ions such as F and CF 3 could also be observed as a result of this phosphide anion acceleration. The main ion-molecule reaction is a fluoride anion transfer from the phosphide anion to trifluoromethylphosphine: ðCF3 Þ2 P + ðCF3 Þ3 P ! ðCF3 Þ3 PF + CF3 ─P═CF2 This reaction is slightly endothermic (ΔE ¼ +2.1 kcal/mol) and therefore requires kinetic acceleration of the phosphide anion inside the ICR cell, in order to induce internal energy deposition via increased collision frequency of the accelerated phosphide with the (CF3)3P neutrals, so that this ion-molecule reaction can proceed. Kanawati and Wanczek [88] could reveal the mechanism of this reaction by the use of sophisticated density functional theory (DFT) calculations at high level of theory B3LYP 6-311 +G(3df )//6-31+G(2d) and we could indeed identify that transition state (TS) geometry, which describes this fluoride anion transfer reaction. The phosphide anion (CF3)2P acts as a fluoride anion donor [89] and the driving force for this reactivity is the production of a stable neutral, which can undergo electron delocalization F3CdPdCF3 $ F + F2C] PdCF3 $ F3CdP]CF2 + F.  The other produced lower mass phosphoranides (CF3)2PF 2 and CF3PF3 were subsequently produced from the larger phosphoranide (primary product ion) (CF3)3PF as a result of dissociative collisions with the rest gas molecules of tristrifluoromethylphosphine. Kanawati and Wanczek could also resolve the mechanism of this low mass phosphoranide ions formation by identifying the transition state geometry, which is capable to eliminate difluorocarbene CF2 from (CF3)3PF to yield the lower mass phosphora nides (CF3)2PF 2 and CF3PF3 in one and two successive CF2 elimination steps, respectively. The transition: (CF3)3PF ! (CF3)2PF 2 + CF2 has a forward energy barrier height of 30.5 kcal/mol and is +27.2 kcal/mol endo thermic. The transition: (CF3)2PF 2 ! CF3PF3 + CF2 has a higher forward energy barrier height (+54.0 kcal/mol) and is +34.5 kcal/mol endothermic. Thus, the authors could explain the original need for phosphide acceleration for all these above-mentioned phosphoranides to be experimentally obtained in the gas phase. They concluded that the design of a trifluoromethylation reagent cannot be achieved with the phosphoranide (CF3)3PF, because it is prone to eliminate successive CF2 neutrals to produce other lower mass phosphoranides. This research helped scientists, who perform synthesis in the condensed phase to reveal opportunities for production of interesting phosphoranides starting from the reactive phosphide and (CF3)3P.

582

Fundamentals and Applications of Fourier Transform Mass Spectrometry

With photodetachment from amide and arsenide ions, Smyth and Brauman [90] obtained the electron affinities of NH2 and AsH2 radicals as 0.744  0.022 eV and 1.27  0.03 eV, respectively. Wyatt et al. [91] investigated the ion chemistry of arsine in detail. Arsine fragment ions undergo condensation reactions with the neutral molecule. The proton affinity of AsH 2 was determined as 360 + 10 kcal/mol. Doiron and McMahon [92] studied the ion chemistry of AsF3. They found the proton affinity of AsF3 to be 153  2 kcal/mol and the order of gas phase basicities NF3 < AsF3 < PF3.

Oxygen, sulfur, selenium, and tellurium Ajello et al. [93] produced HO+2 by a reaction of metastable O+2 * with H2. Cacace and Speranza [94] generated protonated ozone by protonation of ozone with strong acids like H+3 or KrH+. The proton affinity of ozone was determined to 148  3 kcal/mol. The ion chemistry of HO+3 and H2O+3 was studied by Speranza [95]. The groups of Bondybey [96] and Beyer published a number of studies of positive and negative water cluster ions. Positive water cluster ions reacted with HCl and formed mixed cluster ions [97]. It took 10 water molecules to solvate a single molecule of HCl, 13 water molecules to solvate a second HCl molecule in the same cluster. Negative water cluster ions [98],  (H2O) anion n , n  60–130, reacted with SF6 to form the hydrated F and the SF5 radical. The electron affinity of SF5 was determined to 4.27  0.25 eV. Lengyel et al. [99] reacted HNO3 with negative water clus ter ions—hydrated electrons—(H2O) n (n ¼ 35–65). OH(H2O)n was the major reaction product. Niednerschatteburg et al. [100] studied the reactions of sulfur cluster ions with ammonia. Of the cations S+n (n ¼ 1, …, 8) and anions S n (n ¼ 2, …, 6), only the cations were reactive, S+4 was most reactive. Laudenslager and Huntress [101] determined reaction channels and rate constants for the H2S-NH3 and H2S-H2O mixtures. Many proton transfer reactions were detected. Condensation reactions were observed only for the H2S-NH3 mixture. McIver and Eyler [102] measured the equilibrium constant for the reaction SH + HCN $ CN + H2S to 9.0  0.6. CS2H+ and CSH+ ions are the most important products in the ion chemistry [103] of CS2 mixtures with H2 and CH4. CS+2 primary ions are unreactive. Wanczek et al. [104] showed the preponderance of S atom transfer reactions in the ion chemistry of SSF2, S3F+2 is formed with great yield

Gas phase ion-molecule reactions of inorganic compounds in FT-ICR-MS

583

(k ¼ 2  109 cm3 molecule1 s1) and S4F+2 . Hartmann et al. [105] proved the domination of S and Cl transfer in the ion-molecule reactions of SCl2 and S2Cl2. Foster et al. [106] and Odom et al. [107] studied electron attachment to sulfur hexafluoride and the lifetime of the SF 6 ions formed. These ions are stable. The excited SF * is stabilized by collisions at higher pressure and 6 radiative relaxation. Pepi et al. [108] showed that the gas phase protonation of trifluoromethyl sulfur pentafluoride yielded mostly dissociation products, HF, CF4, and SF+3 , but also the protonated molecule. Collisional activation experiments and theoretical calculations showed the loosely bounded ion-molecule complex HF-CF4-SF+3 as the most stable isomer. The proton affinity of SF5CF3was determined to 152.5  3 kcal/mol. Several compounds with a sulfur oxygen bond have been studied. McAllister [109] and Nixon et al. [110] found that in the ion chemistry of dimethylsulfoxide the protonated molecule and the proton-bound dimer are abundant product ions. The ion-molecule reactions of methanesulfonic acid and related compounds were investigated by De Petris et al. [111]. CH3SO+2 and CH3SO3H+2 were formed from CH3SO3H, and CH3SO2OCH3H+ from methyl methanesulfonate. No rearrangement processes were detected in most ion-molecule reactions. The proton affinity of methanesulfonic acid was measured to 184  2 kcal/mol. Sullivan and Beauchamp [112] investigated the rich ion chemistry of the sulfuryl halides SO2F2, SO2Cl2, and SO2ClF. The negative ion-molecule reactions were dominated by halide transfer. Positive ions showed reactions, which are dependent on the relative SdF and SdCl bond strength. Morgon et al.  [113] generated the NSO 2 ion by the ion-molecule reaction of NH2 and SO2F2. ΔH(acid) ¼ 330  5 kcal/mol for NSO2H was experimentally determined. Pepi et al. [114] studied the ion chemistry of SOF2H+, produced by corona discharge of SF6/air mixtures. Two structures were detected and investigated theoretically: The ion-molecule complex HF-SOF+ reacted with nucleophiles as SOF+ donor, and the covalently bounded H-OSF+2 as protonating agent. Baykut et al. [115] studied in detail the cyclic structures in the ion chemistry of thiirane, ethanediol-1,2, ethanedithiol-1,2, and 2-mercaptoethanol with the aid of characteristic reactions. Dixon et al. [116] determined the proton affinity of HSe to 339  5 kcal/mol and of H2Se to 170  3 kcal/mol. The condensation reactions of Se+ and SeH+ with SeH2 yielded neutral H2. Karpas [117] measured the proton affinities of H2CS (l84.7  1.0 kcal/mol), H2Se

584

Fundamentals and Applications of Fourier Transform Mass Spectrometry

(171.2  0.2 kcal/mol), SeCO (157  3 kcal/mol), and H2CSe (186  1 kcal/mol). H2Te was studied by Gal et al. [118]. The proton affinity of TeH was obtained as 331.3  0.8 kcal/mol.

Fluorine and chlorine Cipollini et al. [119] reacted F+2 with a number of molecules, mainly electron transfer and fragmentation products occurred. With Ar, CO, and N2 a formal F+ transfer was observed. The proton affinity of F2 was determined as 79  5 kcal/mol. Foster and Beauchamp [120] observed only a proton transfer reaction of the HF+ ion: HF+ + HF ! H2F+ + F. The proton affinity of HF was determined as 112  2 kcal/mol. Asubiojo et al. [121] reacted vibrationally excited Cl* with simple 2 organic compounds. Endothermic Cl transfer was observed. De Petris et al. [122] investigated the ion-molecule reactions of protonated chlorine Cl2H+ with H2, D2, and CH4. With H2, protonated hydrochloric acid ClH+2 was formed. With D2, ClD+2 but no ClHD was detected, indicating an insertion of the terminal Cl of Cl2H+ into the HdH bond. This mechanism was confirmed by theoretical calculations. CH4 reacted the same way to form protonated methyl chloride, CH3ClH+. The same group [123] employed the transfer of Cl+ to chlorine by Cl2H+ and Cl+2 to yield Cl+3 . Cl2F+ was generated in the ion-molecule reaction of XeF+ + Cl2. Cl+ transfer reactions to nucleophiles like H2, HCl, Xe, Cl2, CH4, CO, CH3CN, and HCN were investigated. Lias [124] studied the slow charge transfer reactions of Xe+, Kr+, and Ar+ to HI, HBr, HCl, Br2, and Cl2, and from O+2 and N+2 to HI, HBr, and HCl. Kr+, Ar+, and N+2 also underwent hydrogen transfer with halogen hydrides. Preliminary results of dissociative charge transfer reaction of the doubly charged rare gas ions were also given. The following mechanism was assumed: Kr2+ + HCI ! Kr+ + H + CI+. Three groups have studied ion-molecule reactions of xenon ions and the formation of their ionic xenon compounds. Armentrout et al. [125] reacted Xe+(2P1/2) ions state selectively with NF3 and obtained XeF+. The excited state of Xe+(2P1/2) radiatively decayed to Xe+(2P3/2) with a transition probability of 18  4 s1. Riveros et al. [126] generated XeCl in the ion-molecule reaction of COCl with xenon. The dissociation energy of XeCl into Xe and Cl was estimated to be 110 years ago that infrared radiation might activate chemical reactions [15], and soon after disproved [16]. Instead, Lindemann [17] and Hinshelwood [18] established an explanation of observed pressure dependent changes of reaction orders in terms of collisional activation of unimolecular reactions. Only in the void, namely in the absence of collisions, and on long time scales, stored ions may undergo unimolecular activation by absorption of ambient black body radiation. First recordings of such processes by McMahon [19] were ably interpreted by Dunbar [20] and labeled as Black Body Induced Radiative Dissociation (BIRD) by Williams who helped to establish the effect firmly, among others [21–24].

596

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Collisional activation is regularly described at the level of textbook knowledge: Product formation of A+ ! P+ + N involves an activated intermediate A+*, which forms in collisions A+ + B with background or buffer gas B and may decay back to A spontaneously or proceed on towards products and neutral fragments P+ + N: k1

A+ + B

!

k∗

A +∗ + B ! P + + N + B

(19.5)

k1

Well known to the MS community as Collision Induced Dissociation (CID) this scheme is not only energy dependent. It also involves an explicit pressure dependence—in the first step. Once the pressure is high enough this dependence saturates, and the second step becomes rate determining. At low pressure, however, the collisional activation is rate determining. Other than expected, the zero pressure limit of activation turned out to be non-zero in case of weakly bound ions, such as molecular cluster ions within a high vacuum ion trap. Ambient radiation is the only source of energy uptake. The above reaction scheme thus expands by two collisionless processes, the + + photo absorption of A+ by rate kA abs, and the photo emission of A * by rate + + kA em , and the new rate law for A * thus reads: d ½A +∗  + + ¼ k1 ½A + ½B + kAabs ½A +   k∗ ½A +∗   kAem ½A +∗   k1 ½A +∗ ½B ¼ 0 dt (19.6) It vanishes in the usual steady state approximation. Solving for A+* the product rate law becomes:   + + k∗ k1 ½A + ½B + kAabs ½A +  d ½P +  k∗ kAabs ½A +  +∗ ∗  (19.7) ¼ k ½A  ¼ + + dt k∗ + kAem ∗ k∗ + kAem ∗ + k1 ½B The latter approximation holds for low pressures of B such as e.g. high or ultra high vacuum. In cases of weakly bound complexes or clusters which +∗ usually stem from cold ion sources, the emission rate kA em —which goes by T4—is certainly negligible. This allows for further simplification of Eq. (19.7) to: d ½P +  +  kAabs ½A +  dt

(19.8)

Cryo trapping by FT-MS for kinetics and spectroscopy

597

At least in those cases where barrierless direct dissociation dominates, the energy uptake from ambient radiation becomes the rate limiting step for dis+ sociation. The absorption rate kA abs is determined by the spectral properties of + A+, which is its vibrational IR absorption spectrum σ A vib (ν), and by the ambient black body radiation flux, which is given by the Planck distribution ρ(ν, T ) times speed of light c: Z νmax + A+ kabs ¼ σ Avib ðνÞ c ρðν, T Þdν (19.9) νmin

Of course, such arguments hold for anions alike. In any case, the energy uptake by radiation determines the fragmentation of labile ions in a trap. It is obvious that decisive cooling of the ion trap walls and surroundings helps to control and even eliminate such fragmentation by reduction of the black body radiation field. A conceptual example may help to elucidate the relevance of such effects. It is straightforward to compute vibrational spectra of complex molecules and molecular complexes at reasonable accuracy and across large ranges of such compounds. Certain pitfalls and caveats are potentially troublesome such as e.g., exceedingly large vibrational anharmonicities in acidic hydrogen bonds. Other than that it is equally straightforward to evaluate adsorption rates by Eq. (19.9) and check e.g. for isotope effects as depicted in the example of hydrated glycine cations in Figs. 19.1 and 19.2. In such a case the temperature dependent overlap of the black body radiation with the vibrational modes of the trapped ions under investigation not only varies by the temperature but also by its isotopes. The observable isotope effect on the fragmentation rate may even invert by the temperature. Such a combination of variable temperature and deuteration experiments have not been conducted thus far. There are noticeable prior studies on other systems which did tune the temperature without incorporating deuteration experiments, e.g., [25, 26]. When held isolated in a room temperature ICR trap various hydrocarbon cations have revealed an increase of fragmentation rate upon deuteration [19, 27, 28] This reflects the enhanced overlap of the lower C-O-D bending and O-D stretching vibrations of the deuterated compounds with the blackbody radiation. Other low frequency modes seem weak and contribute in a less significant way. Cryo studies of BIRD were subsequently introduced by Williams et al. They determined the temperature dependence of BIRD rate constants of singly and doubly protonated bradykinin and interpreted these data in terms

598

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Fig. 19.1 Molecular ion-radiation interaction within an ion trap. Folding the calculated IR spectrum of GlycineH+(H2O)4 (blue) with Planck’s law of black body radiation at 300 A+ K (red) and integrating yields the energy uptake rate kabs (red stepped curve). While stored in a room temperature trap, stored ions may thus take up enough energy from the ambient black body radiation field, here 24 kJ/mol/s, such that e.g. weak (cluster) bonds activate and break according to Eq. (19.8), commonly known as the BIRD process. Perdeuterated ions possess weaker and shifted IR absorptions; they would take up 14 kJ/mol/s. Note, that the black body radiation stems from the ion trap environment and is thus independent of the stored ion, but varies strongly by the trap temperature.

of Arrhenius activation parameters which are highly sensitive to small changes in ion structure. They concluded in a salt-bridge structure of the singly protonated bradykinin [21]. Further BIRD experiments of e.g., lithiated and sodiated glutamine and its derivatives, both either naked or mono hydrated or doubly hydrated, revealed a preference for nonzwitterionic structures in all cases but for sodiated glutamine, where a zwitterionic structure becomes isoenergetic [29, 30]. The influence of charge on the thermal dissociation of gaseous, protonated, homodimeric, protein ecotin ions was investigated by BIRD at elevated temperatures from 126 to 175 °C. The charge distribution in the 15+ and 16+ dimer ions influences the dissociation kinetics, with the more asymmetric distribution resulting in greater reactivity, and as opposed to the 17+ dimer, where the charge distribution has no measurable effect on the dissociation kinetics [31].

Cryo trapping by FT-MS for kinetics and spectroscopy

599

Fig. 19.2 Temperature dependent ratios of BIRD fragmentation rates kabs,H and kabs,D. The depicted ratios originate from kabs (GlycineH+(H2O)4) as depicted in Fig. 19.1 and according evaluations for GlycineD+6 (D2O)4 while varying the radiation temperature Trad. The expected normal isotope effect turns inverse at low temperatures. This reflects the temperature dependent overlap of vibrations with the radiation field. To the best of our knowledge, according experiments—e.g., by an appropriate cryo-FTICR-trap—were not conducted thus far.

There were independent BIRD studies by ICR ion traps on isolated water clusters which revealed a linear dependence of fragmentation rates on the cluster size and a normal isotope effect at room temperature [32]. The fragmentation of such size-selected clusters ranging from n ¼ 1 to above n
100 was explored in detail, and the consistently found linear scaling of fragmentation rates by the cluster size clearly indicates the number of IR active absorbers in accord with Eqs. (19.8) and (19.9). Much of this and subsequent work was reviewed shortly thereafter [33]. Of course, the initial ion or cluster ion temperature as emerging from the ion source is an issue as well, which falls beyond the scope of this review, however.

The evolution of cryo FT-MS In chemistry The earliest attempts of buffer gas cooling to cryogenic temperatures within an ICR cell date back to 1988, when Smalley et al. set up a combination of

600

Fundamentals and Applications of Fourier Transform Mass Spectrometry

laser vaporization-cluster ion source and FT-ICR instrument with liquid Nitrogen cooled ICR cell [34]. A 10 s injection process was followed by an additional thermalization period of a train of neon gas pulses for a duration ranging 1–5 s. The ICR cell was cooled with liquid nitrogen to approximately 100 K throughout this time. Both through collisions with the neon gas and by infrared emission towards the cold walls of the cell, the niobium clusters in that study were cooled much below 300 K. Exact temperatures though were not determined. Later on this approach was given up for undisclosed reasons. There have been some exploratory studies of sympathetic cooling of trapped molecular anions with self cooled electrons which seemingly found no continuation [35]. In 2003, Williams et al. devised a new cryo ICR setup for BIRD measurements at or below ambient temperatures [36] (Fig. 19.3). It was used for measuring threshold dissociation energies of weakly bound alkaline-earth metal water clusters, X2+(H2O)n, X ¼ Mg, Ca and n ¼ 8–10 [36]. Very much in parallel, Heeren et al. [37] applied liquid nitrogen cooling to an open ICR cell which improves pumping efficiency and allowed for collision-activated dissociation (CAD) experiments with leucine enkephaline of equilibrated temperatures (at 173, 296, and 393 K) [37] (Fig. 19.4).

Fig. 19.3 The Berkeley cryo ICR cell. Schematic diagram of the thermal jacket around the ICR cell with three thermocouples T1, T2, and T3. The thermal jacket can be either resistively heated, or cooled by liquid nitrogen. A source-side cap reduces the solid angle subtended by the axial opening, effectively decreasing the flux of photons from the vacuum chamber into the ICR cell. Reproduced with permission from: R.L. Wong, K. Paech, E.R. Williams, Blackbody infrared radiative dissociation at low temperature: hydration of X2+(H2O)n, for X ¼ Mg, Ca, Int. J. Mass Spectrom. 232 (2004) 59–66, © Elsevier (2004).

Cryo trapping by FT-MS for kinetics and spectroscopy

601

Fig. 19.4 The Amsterdam cryo ICR cell. The cooling pipe accepts lq. N2, while the heater elements allow for regulation of the desired wall temperature around the open type ICR cell. Reproduced with permission from: X. Guo, M. Duursma, A. Al-Khalili, L.A. McDonnell, R.M.A. Heeren, Design and performance of a new FT-ICR cell operating at a temperature range of 77–438 K, Int. J. Mass Spectrom. 231 (2004) 37–45, © Elsevier (2004).

Other than the previous cryo designs, O’Conner et al. choose in 2007 a very much different design: Putting an ICR cell inside the open cold bore of a superconducting magnet, they achieved superior cooling of ICR wall temperatures down to 4 Kelvin, as well as an ultra low background gas pressure of
1016 mbar [38]. Moreover, the cooling of the preamplifier achieved a noise reduction by almost one order of magnitude. In retrospect, the highly innovative approach proved volatile, and it eventually came to an end by the loss of the magnet. Some nuclear physics experiments took up this concept later on (see below) (Fig. 19.5).

602

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Fig. 19.5 The Munich cryo ICR cell. It is based on a Bruker Infinity™ cell, and it is cooled by a continuous flow of liquid Nitrogen which suffices to guarantee an ICR wall temperature close to 100 K. Some 300 K black body radiation leaks in through unavoidable openings of the shields. Reproduced with permission from: O.P. Balaj, C.B. Berg, S.J. Reitmeier, V.E. Bondybey, M.K. Beyer, A novel design of a temperaturecontrolled FT-ICR cell for low-temperature black-body infrared radiative dissociation (BIRD) studies of hydrated ions, Int. J. Mass Spectrom. 279 (2009) 5–9, © 2009 Elsevier.

Subsequently, Beyer et al. adopted a more “conventional” cooling design for their ICR cell, and paid much attention to the definition of the black body radiation field [39]. By lq. N2 cooling they conducted BIRD experiments on hydrated silver cations, which allowed for an interpretation in terms of an effective radiation temperature within the ICR cell of Trad ¼ 160 K. They emphasized a possible use of their design for future

Cryo trapping by FT-MS for kinetics and spectroscopy

603

ion-molecule reactions under variable and well-defined collision-gas temperatures. At Toulouse university, Joblin et al. set up the so called PIRENEA experiment, which comprises of a home built FT-ICR-instrument that aims to approach physical conditions of interstellar space through cooling of the ICR cell to Twall ¼ 35 K by application of a closed cycle cryo cooler [40]. They managed to record electronic spectra of buffer gas cooled polycyclic aromatic hydrocarbons, and they found strong evidence for photonic heating in the process of multi photon dissociation (MPD) [41]. For the purpose of gas phase magnetic studies by synchroton based X-ray radiation of transition metal (TM) clusters, our laboratory devised a cryo FT-ICR scheme that was coined the GAMBIT experiment [42, 43]. We applied excessive radiation shielding in combination with a two stage closed cycle cryo cooler and managed to obtain ICR wall temperatures Twall < 20 K, which we believe to determine both the kinetic gas temperature Tgas and the radiation temperature Trad. The latter was ensured by a design that invokes almost complete embedding of the ICR cell in a large area cold shield environment in combination with steric angle narrowing of the remaining two open ports towards 300 K black body radiation. The high sensitivity of the X-ray induced Magnetic Circular Dichroism (XMCD) effect towards the cluster ion temperature proved helpful in the interpretation of the obtained data, and it allowed for a reliable conformation of our equilibrium assumption.(Fig. 19.6). In 2015, the successful cryo ICR cell design was duplicated and brought into operation at the Kaiserslautern FRITZ instrument in order to perform cryo kinetic and cryo spectroscopic experiments with isolated TM clusters (see discussion below) [44]. The GAMBIT instrument is currently in transfer from the prior operation at the BESSY synchrotron to the SwissLightSource (SLS) of the Paul Scherrer Institute (PSI) at Villigen, Switzerland. In 2011 Oliver Hampe et al. devised a cryo ICR cell similar to the Berkeley, Amsterdam and Munich design [45]. A sophisticated balance of heating and cooling allowed for a temperature definition to within 1 Kelvin while limiting the gradients across the cell to