Two-Dimensional Liquid Chromatography: Principles and Practical Applications [1st ed.] 9789811561894, 9789811561900

Table of contents :
Front Matter ....Pages i-xvi
Introduction to 2DLC (Oliver Jones)....Pages 1-11
Basic Principles (Oliver Jones)....Pages 13-24
Method Development (Oliver Jones)....Pages 25-38
Data Analysis (Oliver Jones)....Pages 39-48
Hyphenation (Oliver Jones)....Pages 49-59
Applications of 2DLC (Oliver Jones)....Pages 61-70
Conclusions and Future Developments (Oliver Jones)....Pages 71-74
Back Matter ....Pages 75-75

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SPRINGER BRIEFS IN MOLECULAR SCIENCE

Oliver Jones

Two-Dimensional Liquid Chromatography Principles and Practical Applications

SpringerBriefs in Molecular Science

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Oliver Jones

Two-Dimensional Liquid Chromatography Principles and Practical Applications

123

Oliver Jones Australian Center for Research on Separation Science, School of Science RMIT University, Bundoora West Campus Bundoora, VIC, Australia

ISSN 2191-5407 ISSN 2191-5415 (electronic) SpringerBriefs in Molecular Science ISBN 978-981-15-6189-4 ISBN 978-981-15-6190-0 (eBook) https://doi.org/10.1007/978-981-15-6190-0 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

This book is affectionately dedicated to my wife Michelle. Not only a skilful and compassionate leader in her own field of computational chemistry but a perfect companion for our joint journey in science. Our marriage is far more than the sum of its individual dimensions thanks to her.

Preface

The primary objective of this book is to identify and discuss the basic aspects of two-dimensional chromatography (2DLC) and make them available in a simple and easy to understand manner. An effort has been made to present useful information on key topics such as column selection, solvent considerations, method development, and data analysis. I have not attempted to completely cover the subject but rather to provide a basis for understanding key concepts, and provided references so that a keen reader can further his or her study as needed. While some familiarity with the terminology of chromatography and analytical chemistry is assumed in places, I have tried to keep jargon and mathematical formulae to a minimum. Should any readers want to brush up on HPLC theory, I highly recommend the excellent CHROMacademy website (http://www.chromacademy.com/). It covers a huge range of information on everything chromatography related. It includes some great tutorials, application notes and study guides. You can get five years of free access if you work or study at a university. I feel that 2D is coming of age and is now far easier to use than many chemists (and if you are reading this you are a chemist, at least in part) realise. I hope this book will go some way towards confirming that feeling and encourage more scientists to use the technique. The book is a result of my teaching and postgraduate supervision in both gas and liquid chromatography at RMIT University in Melbourne over the last eight years. I thank all the students I have had the pleasure of working with during this time; this work would not have been possible without them. I’d also like to acknowledge all the help and support I have received from industry partners at Agilent, Shimadzu, and LECO who have all provided, and continue to provide, superb technical help and thoughtful conversations. Melbourne, Australia

Oliver Jones

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Acknowledgements

This book comes with thanks to three groups of peoples. Firstly, my family, friends, and academic colleagues around the world who always support my endeavours. Secondly, the citizens of Australia, particularly the state of Victoria, who employ me to teach Chemistry to their sons and daughters. Finally, my fellow Australians who support the Australian research enterprise with their taxes through agencies such as the Australian Research Council. I am sure most of them don’t know much about separation science, or have even heard of it, but they helped this book come to fruition just the same.

ix

Contents

1 Introduction to 2DLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2 Basic Principles . . . . . . . . . . . . . . . . . . . 2.1 Background . . . . . . . . . . . . . . . . . 2.2 Column Orthogonality . . . . . . . . . 2.3 Peak Capacity and Undersampling . 2.4 Surface Coverage . . . . . . . . . . . . . 2.5 Gradient Elution Chromatography . 2.6 Solvent Considerations . . . . . . . . . 2.7 Column Temperature . . . . . . . . . . 2.8 How to Use 2DLC . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . .

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3 Method Development . . . . . . . . . . . . . 3.1 Background . . . . . . . . . . . . . . . . 3.2 What Factors Should I Consider? 3.3 Offline Method Development . . . . 3.4 Online Method Development . . . . 3.5 Column Selection (Orthogonality) 3.6 Solvents . . . . . . . . . . . . . . . . . . . 3.7 Modulation . . . . . . . . . . . . . . . . . 3.8 Conclusion . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . .

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Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Displaying the Data . . . . . . . . . . . . . . . . . . . . . . . . Algorithms and Processing Methods for 2DLC Data . In Silico Method Optimisation . . . . . . . . . . . . . . . . .

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4 Data 4.1 4.2 4.3 4.4

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4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Hyphenation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . 5.3 Multiple Chromatographic Methods . . . . . . . . . . 5.4 Combining Chromatography and Electrophoresis 5.5 LC  GC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Applications of 2DLC . . . . . . . . 6.1 Background . . . . . . . . . . . 6.2 Pharmaceuticals . . . . . . . . 6.3 Natural Product Chemistry 6.4 Metabolomics . . . . . . . . . . 6.5 Proteomics . . . . . . . . . . . . 6.6 Lipidomics . . . . . . . . . . . . 6.7 Environmental Science . . . 6.8 Forensic Toxicology . . . . . 6.9 Polymer Science . . . . . . . . 6.10 Conclusions . . . . . . . . . . . References . . . . . . . . . . . . . . . . .

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7 Conclusions and Future Developments 7.1 Background . . . . . . . . . . . . . . . . 7.2 3DLC . . . . . . . . . . . . . . . . . . . . 7.3 Column Technology . . . . . . . . . . 7.4 Miniaturisation . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . .

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Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Author

Oliver Jones is an analytical chemist based at RMIT University in Melbourne, where he holds the rank of professor and serves as the Associate Dean of Biosciences and Food Technology. Originally from Manchester in the UK, Oliver was awarded a B.Sc. (Honours) from Queen Mary University of London and obtained his M.Sc. and Ph.D. from Imperial College London. He then held a Postdoctoral Fellowship at the University of Cambridge. Oliver left Cambridge to take up a lectureship at the University of Durham before moving to RMIT in 2012. Oliver’s group conducts research in analytical methods and technologies, predominantly in multidimensional chromatography and NMR, for a range of applications, particularly metabolomics and the trace analysis of environmental pollutants. Oliver also has a passion for teaching and recently helped develop a mobile game app called “Chirality-2” to help teach organic chemistry. He is currently a member of the Australian Academy of Science National Committee for Chemistry.

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Abbreviations

1D 1DLC 2D 2DLC ACN AEX AMDE ANP API ASM C18 CCS CE CN DAD DCM FID FT-IR GC GC  GC HILIC HPLC HR IC ID IEX IMS KOH LC LC  LC

One dimension(al) One-dimensional high-performance liquid chromatography Two dimension(al) Two-dimensional high-performance liquid chromatography Acetonitrile Anion exchange chromatography Absorption distribution, metabolism and excretion Aqueous normal phase Active pharmaceutical ingredients Active solvent modulation Octadecylsilane Collision cross sections Capillary electrophoresis Cyanopropyldimethylsilane Diode array detector Dichloromethane Flame ionisation detector Fourier transform infrared Gas chromatography Comprehensive two‐dimensional gas chromatography Hydrophilic interaction chromatography High-performance liquid chromatography High resolution Ion chromatography Internal diameter Ion exchange chromatography Ion mobility spectrometry Potassium hydroxide Liquid chromatography Comprehensive two‐dimensional liquid chromatography

xv

xvi

Mb MeOH MPLC MS NARP NH2 NMR NP NPS PARAFAC PCA PFP PHPLC PLS QQQ QToF RP SAX SCX SEC SFC SPAM SPE STAMP UV VEW WAX WCX

Abbreviations

Megabyte Methanol Medium pressure liquid chromatography Mass spectrometry Non-aqueous reversed-phase chromatography Amino Nuclear magnetic resonance Normal phase Novel psychoactive substances Parallel factor analysis Principal component analysis Pentafluorophenyl Preparative liquid chromatography Partial least-square regression analysis Triple quadrupole Quadrupole time of flight Reversed phase Strong anion exchange Strong cation exchange Size exclusion chromatography Super critical fluid chromatography Stationary phase-assisted modulation Solid phase extraction Separation technology for a million peaks Ultraviolet Vacuum evaporation modulation Weak anion exchange chromatography Weak cation exchange chromatography

Chapter 1

Introduction to 2DLC

Abstract Today’s analytical chemists face increasing demands to maximize the number of compounds that can be separated and identified in a single run but peak overlap continues to be a problem in many chromatographic methods. One method that might help to overcome these issues is multidimensional liquid chromatography, which uses two columns of different phases. A sequential collection of aliquots is made from the first column and reinjected onto a second; the resulting data are then plotted in 2D or 3D space. The total peak capacity of such a system is the combined peak capacities of each column. The ‘offline’ version of this technique, using a fraction collector, was introduced over 40 years ago but with recent advances in instrumentation and software, particularly the ‘online’ approach, using automated switching valves, has led to increasing interest in the technique. Both offline and online methods can be carried out as a comprehensive procedure, or via ‘heartcutting’, in which only specific peaks are analysed in the second dimension. Applications include proteomics, natural product chemistry, forensic science, and pharmaceutical analysis. These successes are likely to be built on in the future as new column chemistries and bio-informatic approaches are developed. In this chapter, an overview of the two-dimensional liquid chromatography is presented to give the reader a basic understanding of this emerging technology and its potential future uses.

1.1 Background Modern analytical chemistry is heavily reliant on both increasingly advanced analytical instrumentation and the equally increasingly advanced software needed to control the instruments and process the resulting data (also increasingly complex). Nevertheless, due to the sheer diversity of compounds present in most modern samples, and their correspondingly large range of physicochemical properties, it is difficult to envisage a single analytical technique capable of analysing, let alone quantifying all the possible chemical substances that might be present in a sample. For these reasons, many studies make use of multiple analytical techniques, each of which has advantages and disadvantages. Nuclear magnetic resonance (NMR) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 O. Jones, Two-Dimensional Liquid Chromatography, SpringerBriefs in Molecular Science, https://doi.org/10.1007/978-981-15-6190-0_1

1

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1 Introduction to 2DLC

spectroscopy is, for example, fast and reliable but lacks sensitivity. Gas chromatography is sensitive and reproducible but is limited to compounds that are (or can be made) volatile. Liquid Chromatography has good range, good sensitivity and can potentially analyse almost any compound, but it is often hampered by peak resolution issues. If one could increase the separation power of liquid chromatography while retaining reproducibility and sensitivity one would have a very powerful analytical tool. This book is intended as a primer on one such method—Two-dimensional liquid chromatography (2DLC). Some familiarity with the instrumentation and techniques of standard HPLC is assumed of the reader but jargon, mathematical equations, and complex data analysis have been kept to a minimum. Two-dimensional liquid chromatography is an interesting technique in that it is both an old, and a young method. The basic principles have been around for over 40 years with the essential concepts being introduced in the late seventies (Erni and Frei 1978). Comprehensive 2D-LC (LC × LC) as it is mainly thought of today (i.e. where all of the effluent from the first column is passed to the second) was first described in 1990 (Bushey and Jorgenson 1990). At this stage, however, the method was restricted to specialists. Similar to the development of mass spectrometry (MS) it wasn’t until advances in technology (primarily column and instrument hardware) and software made it possible for a wide range of users to perform the very fast, high-efficiency separations demanded by modern analysts that development really started to pick up. In the early 21st century development had been pushed along to the stage where LC × LC separations yielding peak capacities of up to 2,100 in 60 min were reported (Stoll et al. 2006). This has led to a large growth in the number of papers in 2DLC over the last 20 years as shown in Fig. 1.1.

Fig. 1.1 Bar chart showing number of papers returned by year with the search term “two dimensional AND liquid chromatography” at http://www.sciencedirect.com/ in March 2020

1.1 Background

3

Fig. 1.2 Schematic diagram of a 2DLC system

But let us take a step back and examine the basic principles of 2DLC. In principle, it is not too different to standard, one dimensional HPLC. The extra hardware required comes down to a second (binary) pump, (a third pump may be used as we shall see in Chap. 3), some form of modulation device (e.g. a valve) to move the effluent from column one onto column 2-either entirely (comprehensive) or in part (heart cutting), and a second detector. Software to run all these systems smoothly and efficiently as well as process and display the resulting data is also required. This may be commercial software or custom-written code which is often written in MATLAB, Mathematica or R. A typical 2DLC set up is shown in Fig. 1.2. Here we can see that essentially a 1D HPLC system plus another 1D HPLC system equals a 2D LC system. Chemists and biochemists have actually been separating things in more than one dimension for some time via two-dimensional electrophoresis or two-dimensional thin layer chromatography for example. Even in modern HPLC there is more separation that just the chemical interactions in the column. A Diode array detector (DAD) separates via wavelength for example and a mass spectrometer separates via mass to charge ratio. The second dimension in 2DLC can be thought of as being like just like 1DLC with an extra chemically selective detector. This can of course then be extended with a further form of separation such as mass or wavelength. The major advantage of 2DLC is the increase in separation space and resolving power it gives to the user—and resolving power (in a reasonable time) is what it is all about in analytical separation science (Poppe 1997). The total peak capacity of a 2DLC system is theoretically, but realistically not quite (as we will explore further in Chap. 2), the product of peak capacities of the individual dimensions. The resulting separation space far exceeds that of standard 1DLC systems, an illustration of this is shown in Fig. 1.3. Additionally, when the column effluent from the first dimension is focussed and re-injected onto the second for rapid analysis, the effects of peak broadening can be greatly minimised. 2DLC comes in two flavours, offline and online; each brings slightly different benefits and challenges. Online (automated) multidimensional chromatography (either gas or liquid) involves coupling two columns, with uncorrelated retention

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1 Introduction to 2DLC

Fig. 1.3 Comparison of standard and comprehensive offline two-dimensional LC separation of mushroom metabolites. Data are shown as both a contour plot (left) and a three-dimensional chromatogram (right). The white dots in the left panel represent detected peaks Based on data from Pandohee et al. (2015)

mechanisms (e.g. polar and non-polar, or normal phase and reverse phase columns) in series. During the analysis, a sequential collection of aliquots is made from the first column (the first dimension) and reinjected onto a second column (the second dimension) in multiple repeated alternating cycles via an automated switching valve. Typically, an 8 or 10 port, two-position valve with two sampling loops is used. One loop is connected to, and filled by, the first dimension effluent, while the contents of the other loop are injected onto the second dimension column. The time between each valve switch is referred to as the modulation time and this equals the analysis time of the second dimension. The resulting data are then plotted in either 2D or 3D space. As we will see later, active modulation methods are an important part of method sensitivity. Offline 2DLC has been around for almost 40 years. It involves the use of a fraction collector at the end of the first column to collect fixed aliquots of the eluent into chromatography vials. The column of the unit can then be changed and each of the previously collected fractions is then run as a ‘new’ sample. This method is relatively simple and cheap to set up since all that is required is a standard one-dimensional LC system, a fraction collector and appropriate data processing software. However, since running multiple aliquots of multiple samples can add up to hundreds, if not thousands of individual runs, the total analysis time for the offline technique is often multiple days or weeks and is thus time and operator intensive.

1.1 Background

5

The use of longer columns in the second dimension can also cause samples to become highly diluted so overloading of the column is sometimes necessary to ensure a large enough signal is received by the detector. Integrating the two resulting datasets from offline 2DLC can also be challenging and require specialist knowledge as we shall see in Chap. 4. The left-hand image in Fig. 1.3 for example, was generated using custom written code in Mathematica by Dr Paul Stevenson (Stevenson and Guiochon 2013). It is also worth mentioning at this point that LC × LC chromatograms look quite different to conventional LC profiles. Instead of a series of peaks the data is plotted in a bidimensional plane where analyte “spots” are scattered about, as can be seen in the left hand plot of Fig. 1.3—although the data can also be plotted in a 3D format (right hand panel of Fig. 1.3). Online 2DLC is easier to run and the high-pressure capability of modern columns and instrumentation allows high flow rates and correspondingly fast analysis. Disadvantages included increased price and the fact that short second dimension columns are required in automated 2DLC since the separation of a sub-sample in the second dimension must be completed before the subsequent sub-sample from the first dimension is injected. The 2DLC approach can be divided further into two forms; comprehensive (LC × LC) and heart cutting (LC-LC). In comprehensive LC × LC all of the effluent from the first column is subjected to a second separation step. The second column has a limited analysis time as each fraction has to be off the column before the next fraction is injected to avoid mixing. This means the second column is usually run under ultra-high pressure (UHPLC) conditions and this may complicate subsequent MS hyphenation. It does however allow full characterisation of complex samples and it is quite popular for such applications. In heart-cutting 2DLC we only inject specific fractions of interest onto the second column (this could be one or multiple fractions). Contemporary software packages allow the user to select peaks of interest and automatic peak selection is also possible using user-defined time windows. This is illustrated in Fig. 1.4. The heart-cutting technique is often written as LC-LC to distinguish it from comprehensive 2DLC (which is written as LC × LC). Similar terminology is also employed in multidimensional gas chromatography. The advantage with heartcutting is that a fraction(s) or peak(s) cut out from the main sample can then be analysed with higher separation efficiency on the second column as there are fewer competing peaks to worry about. If multiple cuts are made the term multiple heart-cutting (mLC-LC) is used. This is a flexible and intuitive method relative to comprehensive LC × LC and can be readily combined with Mass Spectrometry (MS). It is popular for moderately complex samples and peak purity assessments. Operated in a heart-cutting mode, 2D-LC can also be used a preparative technique (sometimes termed 2D-PHPLC). Here the first dimension essentially serves as an extraction step, simplifying the sample matrix before separation of target analyte(s) in the second dimension.

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1 Introduction to 2DLC

Fig. 1.4 Illustration of the principle of (multiple) heartcutting or (m)LC-LC, subjecting one or more fractions of a chromatogram to a second separation mechanism

Fully automated 2DLC is a relatively new technique currently offered by systems such as the Agilent 1290 Infinity (Fig. 1.5), Shimadzu Nexera-e and Waters ACQUITY instruments. These instruments make use of multiple HPLC columns and switching valves in conjunction with advanced software (to control both the valve and the solvent pumps) to move eluent from the first column to the second automatically. Such systems have only been around for the last five years or so but having such off the shelf options has helped paved the way for the full exploration of 2D × LC, as has a coherent theoretical framework(s) to explain the reasons and principles behind multidimensional LC separations (Giddings 1995; Pirok et al. 2019). At this stage the reader may ask why we should care about increasing separations? To answer this question, we only have to step into any modern laboratory and look at the type of samples currently of interest to the modern-day chemist. Such samples are often amazingly complex be they protein extractions, metabolites, water/soil samples or food products. There is also a need to be able to measure more compounds, even in targeted analysis. For example, not long ago an analyst looking as pesticides may only need to test for 20–30 compounds, now they may need to look for 6000 or more. There is therefore an increasing desire to expand the number of compounds that can be separated in a single run. Although not yet widespread the increased separation power of two-dimensional chromatography has great potential to meet this need in a large variety of areas, particularly for the analysis of compounds that are too sensitive for mass spectrometry but contained in matrices too complex for standard LC analysis.

1.1 Background

7

Fig. 1.5 Agilent 1290 2DLC system in the author’s laboratory at RMIT University in Melbourne Australia

Such areas include, but not limited to, animal health, biomedicine, dairy science, environmental toxicology, food science, functional genomics, pharmacology, plant biology toxicology and the omic sciences. The latest version of the Human Metabolome Database (version 4.0) for example lists 114,100 individual entries (Wishart et al. 2018). This is, nearly a threefold increase from version 3.0. This includes large quantities of predicted MS/MS and GCMS reference spectral data as well as predicted (physiologically feasible) metabolite structures. Even this large database does not include all possible compounds/isomers. This means that the actual number of human metabolites (both endogenous and exogenous) could potentially be far higher. Typically, however, metabolomics studies using standard commercial databases such as NIST and METLIN ‘identify’ only around 100 metabolites. In recent years, commercial libraries have been complemented by extensive open-access databases, such as mzCloud and the Human Metabolome Database, containing hundreds of thousands of spectra. The creation of in-house libraries of pure compounds may be of help here but, of course, there can be no in-house libraries made of, as yet, unknown metabolites. A range of in silico predictive tools have emerged to assist with the interpretation of high-resolution MS data in this regard. As yet none of these developments have led to thousands of metabolites per study being identified routinely. Indeed, da Silva et al. (2015) estimated that in some cases as few as 1.8% of the mass spectra obtained in metabolomics studies can be fully identified and many features identified in mass spectra are listed only as ‘unknown

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1 Introduction to 2DLC

compounds’. Of course, one has to separate all of these compounds from each other before one can identify them. The growth and success of proteomics (the analysis of the entire protein complement of a cell, tissue, or organism under a specific, defined set of conditions) has introduced a similar problem in that field. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of the least abundant as well as the most abundant proteins, as well as the analysis of post-translational modifications such as glycosylation and phosphorylation. This vastly increases the number of sample components. In short people want more samples analysis, in greater depth, faster. The bottleneck in compound separation and identification is an issue that will take the use of new research and analytical methods to solve. Recent developments, such as HILIC (hydrophilic interaction liquid chromatography) (Creek et al. 2011) and ANP (aqueous normal phase chromatography) (Pesek and Matyska 2012), have greatly increased compound coverage in biological samples via liquid chromatography, with the detection of over 1000 compounds being reported in certain sample types (Callahan et al. 2009). This level of analysis is not present in most papers and, since the theoretical maximum peak capacity for conventional liquid chromatography is ~1500 (Guiochon 2006) even this method will soon reach its limit. The use of very long columns is also required for such detailed analysis, meaning run times of hours or even days are often required to attain such high values. Liquid chromatography as a method can also still be hampered by lack of resolution. Some metabolites, especially those present in low concentrations, may simply be crowded out and go unseen. Many polar metabolites, such as sugars, and many amino acids are often not retained by conventional reverse phase (RP) LC columns for example (Callahan et al. 2009). In addition, the increased number of peaks results in correspondingly crowded chromatograms, likely obscuring important (bio)chemical details. The lack of ability to detect all metabolites present, and fully identify all metabolites detected, termed the dark metabolome by Jones (2018), means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publicly funded studies is being lost or unused each year. Similar problems exist for related areas such as proteomics and pharmaceutical analysis as well as unrelated but equally important ones such as polymer science. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Separation science needs to develop new tools and methods for compound identification since the separation of complex mixtures is of vital importance to modern chemistry. With the above in mind, it can hopefully now be seen why 2DLC has such potential. Indeed, new demonstrations of its capabilities appear almost weekly. That said, 2DLC is not without its problems. Despite all its positive traits and increasing number of applications and publications 2DLC has struggled to break into the academic or industrial laboratory as a routine analytical technique. Issues such as mobile phase compatibility issues, column orthogonality, band broadening and data analysis all need to be dealt with before this can happen. These issues are discussed in subsequent chapters.

1.1 Background

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There is also still a perception that 2DLC is not a mature method, that it is slow, the data too complex and/or that that it cannot be used outside the specialist laboratory. If this is your view, I hope perhaps this book will go some way to dispelling these beliefs. Before we go on to the more technical chapters of this book, I feel it is worth stepping back to review the history of the technique. Those not so interested in this aspect can skip forward to the next chapter if they wish. As any student of chemistry knows, the father of liquid chromatography is usually taken to be Russian botanist Mikhail Tsvet, who essentially developed liquid-adsorption column chromatography in 1900 during his research on plant pigments. Much has been written on Tsvet’s life and work (Abraham 2004; Ettre 1990; Livengood 2009; Sakodynskii 1981). The essentials are that he used calcium carbonate as adsorbent and petrol ether/ethanol mixtures as eluent to separate chlorophylls and carotenoids (Ettre 1990). He first used the term “chromatography” in print in 1906 and in 1907 he demonstrated his chromatograph for the German Botanical Society, but the development of the technique stopped there for several decades. Tsvet’s work was mostly ignored during his lifetime. Common factors cited for this include the violent political upheaval in Russia at the beginning of the 20th century, the fact that Tsvet originally published only in Russian (making his results largely inaccessible to western scientists); and a paper by two prominent scientists of the day who tried and failed to repeat Tsvet’s experiments and so denied the findings. Later it was found that they did not follow his methods exactly and so used an overly aggressive adsorbent (destroying the chlorophyll) and were not able to repeat his results. This shows the importance of following published method in detail when trying to recreate work. Tsvet’s method fell into relative (though not total) obscurity (Ettre 1990). The technique was not fully revived until 10 years after his death. Today of course, every student of chromatography knows the name Mikhail Tsvet. The history of chromatography (while fascinating) does not explain the origin of the term. Chromatography is a portmanteau of “chroma” from the Greek word for “colour”, combined with “graphy”, meaning writing or recording-giving us ‘colour writing’. Interestingly the word Tsvet in Russian can mean colour, which could give us the alternative meaning of “Tsvet’s writing”. This makes more sense and is a nice theory but, while certainly plausible there is no way to tell for sure if this is what was intended. Tsvet could just as easily have picked the term because he was working with plant pigments which were very colourful (see Fig. 1.6 for an example). The issue is further complicated by the often-overlooked fact that the term chromatography was common well before 1900. Indeed, it was in use throughout the 19th century in connection with artists’ materials, especially colours and pigments. George Field’s very famous and well-known book “Chromatography; or, a Treatise on Colours and Pigments and of their Powers in Paint” was published in 1835 some 71 years before Tsvet used the term (Abraham 2004). Interestingly, Field does not give the origin of the term either. This would indicate that either he was forgetful or that the word chromatography was so well known in 1835 that he saw no reason to spell out where it came from.

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Fig. 1.6 Plant pigments extracted using column chromatography similar to that described by Tsvet

The full history is probably now lost but it does illustrate that the history of science can be as fascinating as the science itself. Today the term chromatography is not used in relation to art or art materials at all. It is thus interesting to note that a term originally only used by artists is now almost exclusively used by scientists. Although as my first lab manager said to me when I started my Ph.D.—“You have to think of chromatography as an art as well as a science.” He did not mean it literally but I have always found this to be good advice.

References Abraham MH (2004) 100 years of chromatography—or is it 171? J Chromatogr A 1061(1):113–114 Bushey MM, Jorgenson JW (1990) Automated instrumentation for comprehensive two-dimensional high-performance liquid chromatography of proteins. Anal Chem 62(2):161–167 Callahan DL, Souza DD, Bacic A, Roessner U (2009) Profiling of polar metabolites in biological extracts using diamond hydride-based aqueous normal phase chromatography. J Sep Sci 32(13):2273–2280 Creek DJ, Jankevics A, Breitling R, Watson DG, Barrett MP, Burgess KEV (2011) Toward global metabolomics analysis with hydrophilic interaction liquid chromatography–mass spectrometry: improved metabolite identification by retention time prediction. Anal Chem 83(22):8703–8710 da Silva RR, Dorrestein PC, Quinn RA (2015) Illuminating the dark matter in metabolomics. PNAS 112(41):12549–12550 Erni F, Frei RW (1978) Two-dimensional column liquid chromatographic technique for resolution of complex mixtures. J Chromatogr A 149:561–569 Ettre LS (1990) Key moments in the evolution of liquid chromatography. J Chromatogr A 535:3–12

References

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Giddings JC (1995) Sample dimensionality: a predictor of order-disorder in component peak distribution in multidimensional separation. J Chromatogr A 703(1–2):3–15 Guiochon G (2006) The limits of the separation power of unidimensional column liquid chromatography. J Chromatogr A 1126(1–2):6–49 Jones OAH (2018) Illuminating the dark metabolome to advance the molecular characterisation of biological systems. Metabolomics 14(8):101 Livengood J(2009) Why was M. S. Tswett’s chromatographic adsorption analysis rejected? Stud Hist Philos Sci Part A 40(1):57–69 Pandohee J, Stevenson PG, Conlan XA, Zhou X-R, Jones OAH (2015) Off-line two-dimensional liquid chromatography for metabolomics: an example using Agaricus bisporus mushrooms exposed to UV irradiation. Metabolomics 11(4):939–951 Pesek JJ, Matyska MT (2012) A new approach to bioanalysis: aqueous normal-phase chromatography with silica hydride stationary phases. Bioanalysis 4(7):845–853 Pirok BWJ, Stoll DR, Schoenmakers PJ (2019) Recent developments in two-dimensional liquid chromatography: fundamental improvements for practical applications. Anal Chem 91(1):240– 263 Poppe H (1997) Some reflections on speed and efficiency of modern chromatographic methods. J Chromatogr A 778(1):3–21 Sakodynskii KI (1981) New data on M.S. Tswett’s life and work. J Chromatogr A 220(1):1–28 Stevenson PG, Guiochon G (2013) Cumulative area of peaks in a multidimensional high performance liquid chromatogram. J Chromatogr A 1308:79–85 Stoll DR, Cohen JD, Carr PW (2006) Fast, comprehensive online two-dimensional high performance liquid chromatography through the use of high temperature ultra-fast gradient elution reversedphase liquid chromatography. J Chromatogr A 1122(1):123–137 Wishart S, Feunang YD, Marcu A, Guo AC, Liang K, Vazquez-Fresno R, Sajed T, Johnson D, Li C, Karu N, Sayeeda Z, Lo E, Assempour N, Berjanskii M, Singhal S, Arndt D, Liang Y, Badran H, Grant J, Serra-Cayuela A, Liu Y, Mandal R, Neveu V, Pon A, Knox C, Wilson M, Manach C, Scalbert A (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46(D1):D608–d617

Chapter 2

Basic Principles

Abstract Two-dimensional liquid chromatography is not simple but neither is it as complex a technique as it is sometimes made out to be. New developments in technology mean the technique is more and more accessible and user-friendly in terms of method development, data analysis, and automation. While the extra dimension increases separation power, peak resolution, and reproducibility it also increases the complexity of the system. This chapter discusses some of the issues and constraints that it pays to at least be aware of and hopefully understand before starting to design 2DLC methods. Common issues such as expected peak capacity, undersampling and issues around columns and solvent compatibility are discussed. The point made that it is important to keep in mind that the power of this technology is best demonstrated when used to analyse samples containing hundreds or more components that cannot be separated using a one-dimensional chromatographic system. Keywords Analytical chemistry · Orthogonality · Peak capacity · Undersampling · Solvents · Surface coverage

2.1 Background Since its introduction in the 1940s column chromatography has developed significantly into modern high-pressure liquid chromatography (hence the abbreviation HPLC). As manufacturers of this form of instrumentation refined the technology, they changed the name to high-performance liquid chromatography (which meant no change in the abbreviation was necessary). In 2004 new technology allowed higher pressure to be utilised and the term ultra-high pressure liquid chromatography, and the new abbreviation, UHPLC, was introduced to the literature. This acronym was later updated to ultra-high-performance liquid chromatography (again requiring no change in abbreviation). Today much work done in this area is still considered to be high-performance liquid chromatography, although UPLC is gaining ground in many areas due to the fact it can make sample analysis a lot faster.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 O. Jones, Two-Dimensional Liquid Chromatography, SpringerBriefs in Molecular Science, https://doi.org/10.1007/978-981-15-6190-0_2

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The “next frontier” is two-dimensional liquid chromatography where the extra dimension increases separation power, peak resolution, and reproducibility. It also increases the complexity of the system (and what might go wrong) and introduces issues that are not considerations in standard HPLC. It pays to at least be aware of some of these issues before jumping into 2DLC method development. Let us have a look at some of them here.

2.2 Column Orthogonality The fundamental issue of LC × LC is the combination of two different separation mechanisms i.e. the combination of two columns which are different in both dimensions and the nature of the stationary phase (and hence likely the mobile phase) (Pandohee et al. 2015). Somewhat confusingly to some, chromatographers often refer to the difference between phases in 2DLC in terms of the columns being orthogonal to each other. The word orthogonal comes from the Greek orthog¯onios (“ortho” meaning right and “gon” meaning angled). The term is used, in this case, to describe events that are statistically independent, or do not affect one another in terms of outcome. Readers may also be familiar with the term orthogonal from applications such as principle components analysis (PCA) in which the related term orthogonal projection describes a method for drawing three-dimensional objects with linear perspective. For those more used to speaking about orthogonality in terms of principle components it is important to note that reference to orthogonal columns does not mean that they are literally connected perpendicularly to each other but simply that the column chemistries are diverse (and unrelated) (Pandohee et al. 2015). The greater the difference in column chemistry comparted to the sample and each other the better the separation (Giddings 1984). We will go into this in more depth in Chap. 3. To ensure high-resolution separation the columns should be different phases. The choice of the most appropriate column set to achieve the desired separation is difficult since the user must consider the different flow geometries and chemical interactions occurring in the two columns. Choosing the first and second columns is the most crucial and challenging factor as this determines the separation capacity of the system. The best result is achieved when two columns separate the compounds via different mechanisms because retention of the compounds by the stationary phase in the first dimension is different from the retention mechanism of the second dimension. Nonorthogonal separation will lead to no separation over the separation space, the peaks will be clustered and along a diagonal line from the bottom left corner to the upper right corner. For example, Fig. 2.1 shows a 2D intensity plot that might result if a sample was run on a two-dimensional system with two identical columns (two C18 columns say) run in identical conditions. Note that this latter point can be quite important. For example, a reverse phase (RP) × reverse phase 2DLC system run with a significantly different pH in each column can have a high peak capacity since in such a case the chemistry in each dimension is quite different (Gilar et al. 2005).

2.2 Column Orthogonality

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Fig. 2.1 Stylized 2DLC separation using columns with identical phases and conditions

We can see in Fig. 2.1 that the separation times are identical in each dimension and so the separation is not improved by the second column. Ideally, we would make use of all the possible separation space (the blue square). In actuality this does not usually occur, and the separation tends to occur left and right from the central diagonal line (red lines in Fig. 2.2) This is known as the spreading angle. The wider the spreading angle the better the separation. We must remember however that we are unlikely to ever make full use of the available separation space (though we can get quite close). This is because while in an ideal separation science world all the peaks would be evenly spaced out (and resolved) over the available separation space, in real life the peaks are randomly distributed. This means that some areas of the separation space may still have coeluting peaks while other parts are unused. No separation mode in 2DLC is likely to offer complete orthogonality of separation and the practical peak capacity of 2D separation systems is reliant on a number of factors such as the number of collected fractions and the system chemistry-mobile phase, column conditions (e.g. pH and temperature), column type etc. Some of these factors are discussed below. An excellent discussion on optimising separations in online comprehensive two-dimensional liquid chromatography is given in Pirok et al. (2017).

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Fig. 2.2 Stylised 2DLC separation using columns with different phases showing the spreading angle (black)

2.3 Peak Capacity and Undersampling Having discussed above that it is not possible to use all the separation space it is worth delving into how many peaks we can reasonably expect from a 2D method before we cover method development in Chap. 3. This is so that we are not overly optimistic early on (and then disappointed later). Indeed, one of the first questions generally asked about 2DLC is “how many peaks can it resolve”? Here one might be tempted to use what has become known as the “product rule” for calculating the theoretical peak capacity. The product rule states that under ideal conditions the total peak capacity (PCt ) of a two-dimensional liquid chromatography system is the product of the peak capacities of the first (PC1 ) and the second (PC2 ) dimensions. This can be written as the following simple equation. PCt = PC1 × PC2 This equation, while a useful starting point usually overestimates the true resolving power as it underestimates the (often quite severe) effects of under-sampling and surface coverage (fcoverage ). But what do we mean by these terms? Let’s start with under-sampling? This is a very important factor in 2DLC and indeed nearly all areas of analytical chemistry. Modern analytical instrumentation usually provides us with digital data. This means the readings are not continuous but discreate measurements separated by time. A peak leaving a column, for example, is measured multiple times by the detector.

2.3 Peak Capacity and Undersampling

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Fig. 2.3 Illustration of the effect of sampling frequency on peak shape

What we see as a continuous peak on the computer screen is in reality a series of measurements joined together by the computer, even though the response they are measuring is usually continuous. We also need to keep in mind that commercial chromatography and mass spectrometry software may perform various data transformations (such as data smoothing) in the background that we are unaware of. This is one reason for the recent push towards open data standards such as mzTAB (Griss et al. 2014). An example of the effect of sampling frequency on peak shape is shown in Fig. 2.3. It can hopfully be seen that fast sampling across multiple coeluting peaks is essential if they are to be adequality resolved. As we learned in chapter one 2DLC works by taking aliquots of the effluent from the first dimension and injecting them onto the second. We also learnt that the second column can be thought of as a type of chemically sensitive detector. Other forms of detector such as mass spectrometers or spectroscopic techniques cannot measure continuously but must cycle through values of mass or wavelength, or wait for a light source to reflect from the target. Similarly, sample aliquots cannot be moved instantaneously from the first to the second dimension. If nothing else, no matter how fast it is, the switching valve takes some amount of time to move between the load and the inject positions. This means we always lose some sample from the first dimension and thus the “sampling rate” across the first-dimension separation has a potentially large influence on the final peak capacity that is not accounted for in the product rule equation above. There will always be “undersampling” of the first dimension and this necessitates the application of a correction factor (Cf ) to account for it. This is often referenced as the Greek letter Beta (β) in the 2DLC literature. A modified equation may then be written as PCt = (PC1 /Cf ) × PC2 The number of times one samples across a peak as it leaves the first dimension can have a large effect on how that peak is seen in the final chromatogram. Two closely

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eluting peaks could easily be plotted as one if there are not enough sampling points across the peaks to identify when one ends and the next begins. By now one might have realised that choosing the proper sampling time is very important in practical 2DLC work. One might next ask what correction factor must be used? This is a tricky question to answer since there is a strong interaction between the sampling time and other system parameters, such as the first-dimension gradient time and first dimension peak width, and thus first-dimension peak capacity. So how does one determine peak capacity in 2DLC? In many cases the product rule described above will be adequate as the number of peaks in the sample will be far fewer than the maximum resolving power available for the system.

2.4 Surface Coverage Now let’s look at surface coverage or fcoverage . This can be defined as the fraction of the available separation space that is occupied by peaks. Ideally this latter variable would, of course, be as close to 100% as possible while still having every peak resolved from each other. As we shall see this is rarely the case because while we would like peaks to be evenly spaced out throughout a chromatogram in real life peaks are subject to random distribution and so will overlap. This is the basis of statistical overlap theory developed by Davis and Giddings (1983). This is a mathematically complex model but, in short, it means that in many cases a lot of potential separation space is not used. Davis and Giddings showed, for example, that, relative to the maximum peak content for closely spaced speaks, a randomly distributed chromatogram will never contain/seperate more than around 37% of its potential peaks; 2DLC was later shown to be potentially worse in this regard (Davis 1991). Maximising the value for fcoverage is thus usually one of the main aims in 2DLC method development. If we modify our equation for total peak capacity to include the fcoverage term, we get something like PCt = (PC1 /Cf ) × PC2 × fcoverage There is also a scan delay for non-chemical directors so we could, if we were quite picky, include a correction factor for the PC2 as well. This could be written as PCt = (PC1 × PC2 × fcoverage )/Cf or PCt = (PC1 /Cf1 ) × (PC2 /Cf2 ) × fcoverage However, any such Cf is likely to be minimal and not affect the overall results so can likely be ignored. For most 2DLC users, particularly those just starting out in the field, using the product rule to estimate peak capacity will be satisfactory (as long as one bears in

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mind the answers won’t be exact) as the number of peaks that need to be resolved will be less than the separation space available. We will look at how to maximize the available peak capacity in Chap. 3. For those who wish to delve into this issue further Li et al. (2009) recently published a very useful equation for estimating peak capacity in 2DLC. This incorporated a correction for under-sampling of the first dimension and explored this issues in some mathematical depth. Although they only tested their equation on low molecular weight compounds there is no reason to think that larger molecules would show much difference. Their results showed that not only is the speed of the second dimension separation important for reducing the overall analysis time (which is not surprising since the faster the second dimension separation the faster one can sample from the first dimension) but it also plays a vital role in determining the overall peak capacity when the first dimension is under-sampled. A surprising subsidiary finding was that for relatively short 2DLC separations (much less than a couple of hours) the first-dimension peak capacity was far less important than is commonly believed. This means that when first dimension gradient times are 30 min or less it does not pay to use very small particles or take any other steps to improve the first-dimension peak capacity above a value of about 50–100. Only in very slow (>2 h) 2DLC separations, which are rare, does it make sense to improve the first-dimension peak capacity. Their second important finding was that the dependence of the productivity of the second-dimension separation (peak capacity per unit of gradient cycle time) dictates the choice of the rate at which the first-dimension separation should be sampled to maximize the total 2DLC peak capacity. Of greater practical consequence is the finding that the optimum values of the second-dimension productivity occur at second dimension gradient cycle times, in the order of 20 s, which are within reach of existing instrumentation. Other workers such as Pirok et al. (2017) have shown that smaller particles will improve peak capacity for fast separations but only up to a point. This means that other factors, such as column chemistry and solvent composition are better areas to focus on for method optimisation than using ever smaller particle sizes in your column (with associated back pressure and friction problems).

2.5 Gradient Elution Chromatography Most people who work with liquid chromatography will be familiar with the difference between isocratic and gradient elution so one could be forgiven for wondering why I am discussing solvents here. The reason is that 2DLC introduces some extra considerations that it is worth being aware of before moving onto method development. Let’s start by reminding ourselves of the differences between isocratic chromatography and gradient elution chromatography as this is very important in 2DLC. In isocratic conditions, the mobile phase composition is held constant during the separation while in gradient elution chromatography the mobile phase strength is varied from an initially weak eluent composition to a stronger one (usually via a simple

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linear gradient). The major operational difference between isocratic and gradient elution is therefore that in gradient elution chromatography both the instrument and the column must be re-equilibrated to the initial eluent composition at the end of the run (Stoll et al. 2006). There are some other important differences. In isocratic elution the peaks are relatively broad and the peak width increases with retention time. In gradient elution, the peaks are narrow with almost equal peak widths. In isocratic chromatography, the retention time of a particular compound is linear with its retention factor (e.g. constant in the initial eluent composition) so the higher the retention factor the longer the elution time, all other factors being equal. One cannot assign a fixed retention factor value to a compound when gradient elution is used as this value changes during elution (the retention factor decreases throughout the gradient run). We can, however, calculate the average retention factor value for the whole of the separation. What this boils down to is that in gradient work a much wider range of solute retention values can be handled in a given separation time. Retention time in gradient elution chromatography must also take into account what is called the delay time to account for the time it takes the mobile phase composition changes to propagate through the system and get to the column inlet. Fortunately, the impact of the gradient delay can be compensated for by deliberately starting the solvent gradient before the sample injection is made (known as a delayed injection). The concept of the gradient delay is extremely important in the second dimension of 2D-LC systems. All of the above can have a large impact on how we do 2DLC.

2.6 Solvent Considerations An easy way to lose efficiency from a HPLC separation is to pay little attention to sample preparation, in particular the solvent(s) in which the analyte is dissolved. As most undergrad chemists will know, the solvent within the sample vial need only vary a little in strength compared to the mobile phase (at the initial composition for a gradient separation) for peak broadening to occur. This can quickly start to erode the baseline between closely resolved peaks. If the solvent strength mis-match is even higher, significant band/peak distortion can occur. In a 2DLC separation, small aliquots of a chromatographic separation (the first dimension) are sequentially transferred to a different separation environment (the second dimension). This adds an extra layer of complexity since, as we have already established 2DLC works best with two columns of differing chemistries and thus potentially differing mobile phases. The use of two columns means that mobile phase compatibility is an important issue to consider in 2DLC method. While the use of different mobile phases adds to the change in selectivity when performing a separation on two orthogonal columns (HILIC × RPLC or SEC × RPLC), it can also result in problems such as completely immiscible solvents, which would make the coupling of the columns impossible. It

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is ideal if the mobile phase from the first column contains less organic phase than the second column. A shallow gradient program in the first dimension will often provide better peak capacity, while both isocratic and gradient elution can be used in the second dimension. The use of isocratic conditions in the second dimension avoids the need for column re-equilibration at the end of the separation, which is highly desirable for fast separations. However, gradient programs are more efficient in eluting well-retained compounds such that the chances of wrap-arounds are minimised (Lesins and Ruckenstein 1989). It is therefore often preferable to do gradient elution chromatography in the second dimension even though on a fast time scale it is much easier to do isocratic elution. All of the above means that trying to complete a 2DLC separation using a gradient in the first dimension is inherently challenging because the concentration of the aliquot transferred between dimensions (i.e. the second-dimension injection solvent) is continually increasing. The solvent strength miss-match can deform the peak just as it would in normal HPLC and remove any ability to extract meaningful data from a separation. An innovative solution to this problem was developed by Stevenson et al. (2014), who utilised a third pump into the 2D instrumental configuration. This allowed them to introduce a “counter gradient” to offset the changing mobile phase produced by the first dimension. They also found that the second-dimension injection solvent could be artificially manipulated so that a consistent composition was sent to the second dimension. The counter gradient has significance in RP × RP, but also HILIC × RP separations, where the mobile phases are at the opposite ends of the solvent strength spectrum. The authors also created a free-to-use interactive application that calculates the required counter method for a single step gradient to produce the required transfer solvent strength. Unfortunately, the counter gradient approach also has its limitations. It was found that the mobile phase concentration after the counter gradient was limited to approximately 20% of the solvent component. Below that threshold several experimental design issues arose, including very slow first dimension times; counter gradient flow rates above instrument max; analytical scale sample loops of insufficient size; and first dimension dilution leading a worse limit of detection (Stevenson et al. 2014). There is also the not insignificant problem of the conceptual and instrumental complexity of plumbing in a third solvent pump to the system and ensuring all solvents are miscible and of equal viscosity at each stage of the separations. As yet nobody has tried a counter gradient method with reverse and normal phases of chromatography, but it could be anticipated that the inclusion of a solvent that is miscible in both of these mobile phase environments will improve their compatibility. This could dramatically enhance the time and space efficiency of 2D-HPLC analyses.

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2.7 Column Temperature In gas chromatography separations the temperature program used is an essential part of a successful separation. The use of elevated temperature in HPLC has a similar theoretical promise as using smaller particles with UHPLC-namely better performance and reduced analysis time. If the column temperature is increased, the chromatographic separation process becomes faster and, in general, more efficient. Both lower mobile phase viscosity and increased temperature improve diffusion during the chromatographic process. However, the percentage decrease in retention time is usually not the same for all compounds of a sample mixture and changes in peak spacing are common. Aqueous solvents are known to exhibit less polar characteristics as they are heated and therefore if used under isocratic conditions, temperature programming can provide the benefits of gradient chromatography in manipulating retention, whilst maintaining a constant mobile phase eluent. Temperature programming is a potentially valuable tool for 2DLC and suited for applications that demand fast analysis times and high peak capacity as the deleterious effects of solvent mismatch and reduced sensitivity from other complex interfacing solutions are avoided. Two columns with different chemistries can be housed with independent temperature control for each column, providing versatility for orthogonal separations. Just as in HPLC in general, there has so far been less focus on temperature primarily because metal LC columns and the separation beds are slow to heat (and cool) and difficult to heat uniformly and separations can be effectively achieved using other methods. The above notwithstanding Holland et al. (2016) used temperature programming to manipulate solvent elution strength in place of a mobile phase concentration gradient. This ensured that all eluent fractions transferred into the second dimension were of identical solvent composition, i.e. the second-dimension injection solvent did not increase during the analysis. When applied to a complex natural product extract of coffee, the separation was completed in 35 min and had an orthogonality value of 35% and a spreading angle of 52°. The use of temperature programming avoided solvent incompatibility at the separation interface, ensuring peaks in the second dimension retained symmetrical profiles which led to improved peak capacity and reduced retention times. At first glance temperature programming seems promising, so why is it not more widespread? The need for samples and HPLC columns that are stable at high temperatures is limiting for many applications. The extended thermal re-equilibration times, in the order of 40 min, are also unfavourable for high throughput routine analysis, requiring further attention from the instrumental and column design viewpoint.

2.8 How to Use 2DLC

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2.8 How to Use 2DLC A question that naturally arises is; what kinds of problems are best addressed using 2DLC? The simple answer is complex ones. This can be seen in the literature where the vast majority of applications deal with very complex naturally occurring mixtures (biological cells, blood, urine, environmental samples and so forth). It is also worth keeping in mind the adage that one does not take the Ferrari to do the supermarket shopping (unless you want to show off of course). The real power in 2DLC is in being able to resolve complex mixtures quickly. Indeed today, what once took 6 h can take as little as 30 min. If you have fewer than 150–200 peaks to resolve in a reasonable timeframe of around 30 min you are likely better sticking with HPLC (with UHPLC if between 300–500) (Poppe 1997). Today’s samples, however, increasingly call for the separation of thousands of componants and so 2DLC could have use in a wide range of areas. The increased separation power of two-dimensional chromatography is of great potential in a large variety or areas, particularly for compounds that are too sensitive for mass spectrometry but contained in matrices too complex for standard LC analysis. The combinations of new column chemistries that could be used are many and varied and the use of size exclusion and chiral columns in the first dimension offers up many further opportunities for novel applications.

References Davis JM (1991) Statistical theory of spot overlap in two-dimensional separations. Anal Chem 63(19):2141–2152 Davis JM, Giddings JC (1983) Statistical theory of component overlap in multicomponent chromatograms. Anal Chem 55(3):418–424 Giddings JC (1984) Two-dimensional separations: concept and promise. Anal Chem 56(12):1258A– 1270A Gilar M, Olivova P, Daly AE, Gebler JC (2005) Orthogonality of separation in two-dimensional liquid chromatography. Anal Chem 77(19):6426–6434 Griss J, Jones AR, Sachsenberg T, Walzer M, Gatto L, Hartler J, Thallinger GG, Salek RM, Steinbeck C, Neuhauser N, Cox J, Neumann S, Fan J, Reisinger F, Xu Q-W, del Toro N, Pérez-Riverol Y, Ghali F, Bandeira N, Xenarios I, Kohlbacher O, Vizcaíno JA, Hermjakob H (2014) The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience. Mol Cell Proteomics 13(10):2765–2775 Holland BJ, Conlan XA, Francis PS, Barnett NW, Stevenson PG (2016) Overcoming solvent mismatch limitations in 2D-HPLC with temperature programming of isocratic mobile phases. Anal Methods 8(6):1293–1298 Lesins V, Ruckenstein E (1989) Gradient flow programming: a coupling of gradient elution and flow programming. J Chromatogr A 467:1–14 Li X, Stoll DR, Carr PW (2009) Equation for peak capacity estimation in two-dimensional liquid chromatography. Anal Chem 81(2):845–850

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Pandohee J, Holland BJ, Li B, Tsuzuki T, Stevenson PG, Barnett NW, Pearson JR, Jones OA, Conlan XA (2015) Screening of cannabinoids in industrial-grade hemp using two-dimensional liquid chromatography coupled with acidic potassium permanganate chemiluminescence detection. J Sep Sci 38(12):2024–2032 Pirok BWJ, Gargano AFG, Schoenmakers PJ (2017) Optimizing separations in online comprehensive two-dimensional liquid chromatography. J Sep Sci 41(1):68–98 Poppe H (1997) Some reflections on speed and efficiency of modern chromatographic methods. J Chromatogr A 778(1):3–21 Stevenson PG, Bassanese DN, Conlan XA, Barnett NW (2014) Improving peak shapes with counter gradients in two-dimensional high performance liquid chromatography. J Chromatogr A 1337:147–154 Stoll DR, Cohen JD, Carr PW (2006) Fast, comprehensive online two-dimensional high performance liquid chromatography through the use of high temperature ultra-fast gradient elution reversedphase liquid chromatography. J Chromatogr A 1122(1):123–137

Chapter 3

Method Development

Abstract Two-dimensional liquid chromatography offers a way to greatly increase the number of compounds that can be separated, detected, and quantified in a single analytical run to a greater degree of sensitivity and selectivity than standard HPLC. Many potential users of two-dimensional technology are put off by its apparent complexity, but modern 2DLC today is greatly facilitated by advances in software that makes the technology relatively easy to run. The high-pressure capability of modern instrumentation allows high flow rates and correspondingly high-speed analysis and one no longer has to count individual peaks by hand. Multidimensional chromatography offers a potential solution to many separation problems but method development in 2DLC is more complex that standard HPLC and users must consider factors such as the need for two independent retention mechanisms (column orthogonality), solvent mismatch, and back-mixing (sample wraparound) while ensuring robust analytical performance from increased instrumental complexity. Some basic considerations in method development for 2DLC are also discussed here. Keywords Columns · Computability · Modulation · Offline · Online · Separation mechanisms

3.1 Background Most analytical chemists reading this will have had to develop and apply new methods, probably for a range of instruments, as part of their everyday job. To do so they would consider the sample type and then think of the best column chemistries to use. At the end of the day, once you understand the principles of method development it can be repeated many times over. Now, since, as we have discussed in Chaps. 1–3, an LC × LC method is “simply” the product of two 1D separations should not method development be similarly within reach of most chromatographers? The answer is not as simple as one might think. Before we go much further I should make readers aware that there is an excellent technical resource in the form of a searchable 2DLC method database available. A collaboration between two leaders in the field, Dwight Stoll of Gustavus Adolphus © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 O. Jones, Two-Dimensional Liquid Chromatography, SpringerBriefs in Molecular Science, https://doi.org/10.1007/978-981-15-6190-0_3

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College and Bob Pirok of the University of Amsterdam it can be accessed at www.mul tidlc.org and is a comprehensive source of information on multi-dimension separations. To make sense of all the material on this site however once needs to understand some of the important factors in LC × LC method development. Understanding the principles is also of use if one is trying to develop a totally new method for a new sample types.

3.2 What Factors Should I Consider? The important factors in 2DLC are just the same as in standard LC. The choice of column packing material, particle size, flow rate and mobile phase(s) employed are critical factors in driving the separation that must be optimised to obtain the best result. When performing two-dimensional (2D) chromatography two separate separations need to be optimised simultaneously. We also need to keep in mind that there are two main forms of 2DLC (comprehensive and heart cutting) as well as two main types of method development (offline and online). The use of two columns and thus multiple mobile phase setups introduces extra complications that the analyst must consider. These include fundamental factors such as column selection, sampling rate, solvent compatibility, second dimension analysis time, and sample loop volumes among others. All need to be considered and optimised for a successful analysis (Schoenmakers et al. 2006). This makes the development of a two-dimensional liquid chromatography method a little more challenging. So, what are the main issues when developing a method that uses the second dimension? Before we jump into detailed discussion let’s look at some of the more common ones. They include the following. • Means to transfer the peak from first dimension to second dimension. Some form of modulator/transfer valve is usually used for this. System pressure differences are likely to be an issue if transferring an analyte from the first dimension to the second dimension directly. • If using a sample loop, the loop size, transfer volume, and percentage filling (of the loop) all need to be considered. • If using a trap column (which we will discuss later), consideration of the column chemistry is critical. • Mobile phase compatibility and associated solvent effects between both dimensions are a common issue and if the two methods contain incompatible mobile phases (such as ion exchange (IEX) × RP), the compatibility of high salt content with organic solvent (s) needs to be considered. • Mobile phase compatibility with any associated detector is also a concern. For example, if MS is the downstream characterization then MS unfriendly mobile phases need to be removed before the effluent reaches the MS detector. We should also perhaps consider the differences between offline and online (automated) method development.

3.3 Offline Method Development

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3.3 Offline Method Development Offline 2DLC has actually been around for around 40 years but has recently undergone something of a resurgence. It is simpler than online 2DLC and has been applied in studies on food, (Dugo et al. 2004) traditional medicines, (Ma et al. 2006) peptide digests, (Marchetti et al. 2008) metabolomics and lipidomics (Li et al. 2014) amongst others. It does not require complicated instrumental set-up and can be performed with a standard 1DLC system consisting of an autosampler, a pump (usually binary or quaternary), a column oven, a detector, and a fraction collector to sample the first dimension. The sample is injected and the eluent from the first column is collected in sample vials using the fraction collector. These fractions are then re-analysed via the second column. Since the two dimensions are independent there is no restraint on the analysis time of the second dimension. This implies that longer columns that will provide more theoretical plates can be used in both the first and second dimensions. Hence, the offline mode often affords a greater total peak capacity. Moreover, the volume of eluent being transferred can be increased accordingly with the volume of the second-dimension format to improve the sensitivity. Fraction collection also allows manipulation of the sample, such as preconcentration, derivatisation, and/or reconstitution into the appropriate mobile phase, prior to injection into the second dimension (Fairchild et al. 2009a). Although the instrumental set-up for offline 2DLC mode is simpler, it is also more labour intensive and since the eluent is in contact with more tubes there is a higher risk of cross-contamination, sample loss and the introduction of artefacts. The total analysis time of an offline analysis is significantly higher (a few hours or days) than an online analysis (Fairchild et al. 2009a). Protocols for offline 2DLC method development have been outlined previously by Horváth et al. (2009). Important factors to note are that the first and second dimension conditions (column type, mobile phase composition, flow rate, gradient program and temperature) must usually be optimised separately. The choice of which two columns to use for a given separation is usually obtained via selectivity studies using multiple column types in conjunction with knowledge of column chemistry and this was recently the subject of a detailed review by Bassanese et al. (2015). A good example of this in action can be seen in Pandohee et al. (2015). The final separation can be maximized by optimising solvent composition, flow rate and temperature, for example, just as in standard LC. A sampling rate is then determined to maintain separation efficiency and avoid undersampling. For a truly comprehensive separation at least 3 or 4 fractions per peak should be analysed (Bassanese et al. 2015).

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3.4 Online Method Development An online 2DLC method can be complicated to develop and involves compromises in terms of volume of effluent transferred, analysis time of the second dimension and solvent compatibility (Fairchild et al. 2009b). Schoenmakers et al. (2006) proposed a protocol for online 2DLC method development covering suitable column dimensions (length and diameters), particle sizes, flow rates, and second-dimension injection volumes (i.e. loop sizes). The protocol suggests that the experimenter selects the first dimension parameter details, which can include the maximum workable pressure drop, the smallest column diameter, and the maximum acceptable (or essentially the total) analysis time, per sample (Schoenmakers et al. 2006). Poppe plots (Poppe 1997), which offer a clear and unambiguous way to discuss the performance limits of separation systems, are then constructed for these columns and a compromise is made for the best conditions to factors such as particle size, flow-resistance factor, eluent viscosity and analyte diffusivity (Schoenmakers et al. 2006). These are then used to determine the second-dimension parameters including particle diameter, number of plates, column length, and peak capacity. One of the main reasons for the increase in complexity for online 2DLC is that the speed of the second separation must be considerably higher compared to the first dimension. Ideally, the sample should be clear of the second dimension prior to the next injection otherwise this results in additional stress to the secondary pumps, as they are working at higher pressures. A further disadvantage is the increased price compared to single dimension systems. For the successful development of an online LC × LC method, it is again important that the two dimensions are orthogonal to allow good use of the two-dimensional separation space. Gilar et al. (2005) investigated the selectivity of various modes such as RP, SCX (Strong Cation Exchange), SEC (Size Exclusion Chromatography), and HILIC to identify LC × LC setups with useful orthogonality. It was found that an RP phenyl column × RP C18 combination, both working at a pH of 2.6 would be a correlated system, while the use of a RP C18 × RP C18 at a pH of 2.6 and 10 in the first and second dimension, respectively, would use a bigger portion of the separation space. Moreover, combining a RP C18 × HILIC or RP C18 × SCX would provide orthogonal separations that were especially good for peptides. The analysis time of the second dimension is the next parameter to optimise. Since the speed of the second separation dictates the sampling rate and flow rate of the first dimension, short and rapid second dimension separations allow a higher number of fractions to be transferred to the second dimension (thus maintaining peak capacity) as well as appropriate sampling of the first dimension (remember this is 3–4 fractions per peak). Numerous strategies, such as the use of monolithic columns, high temperature chromatography with zirconia columns and/or the use of sub 2 µm particles have been developed to increase the second analysis speed. The latter can be highly advantageous as the use of small particles provides more theoretical plates and hence an increase in resolution in the second dimension. The use of core-shell particles is also very attractive for instruments that do not possess a UPLC pump as

3.4 Online Method Development

29

their second pump as they operate at lower backpressure than conventional particle stationary phases but still provide high-resolution separations. The second-dimension formats used are usually short (30–50 mm in length) and are conventional bore columns (internal diameter of 4.6 mm) to provide the shortest analysis time possible and allow the introduction of relatively large fraction volumes from the first dimension. The first-dimension flow rate can be then adjusted (usually in the order of nano or micro litres per minute) so as the peaks are being sampled 3–4 times. In doing so, the resolution obtained in the first dimension is maintained throughout the second dimension. Murphy et al. (1998) showed that if the first dimension is under-sampled, that is the modulation ratio is less than 4, then some of the first dimension is lost in the sample loop. It has also been observed that at a modulation ratio of 3 the loss in resolution did not exceed 10% (Davis et al. 2007).The use of narrow bore (i.d. 1.0 mm or 2.1 mm) or capillary columns is therefore highly recommended. In the online mode, the optimum resolution is mainly attributed to the first dimension, with the second dimension providing the change in selectivity. To maximise the efficiency, long columns or serially coupled columns, are often used. Mobile phase compatibility in the two dimensions is an important issue to consider when developing a 2DLC method. While the use of different mobile phases adds to the change in selectivity when performing a separation on 2 orthogonal columns (HILIC × RP or SE × RP), it can also result in completely immiscible solvents, which would make the coupling of the columns impossible. It is therefore ideal that the mobile phase from the first column contains less organic phase compared to the second column. In the first dimension, a shallow gradient program should be used because this will provide better peak capacity, while both isocratic and gradient elution are used in the second dimension. The use of isocratic conditions in the second dimension avoids the need for column re-equilibration at the end of the separation, which is desirable for fast separations. However, gradient programs are more efficient in eluting well-retained compounds (Lesins and Ruckenstein 1989). A 2DLC can be coupled with all LC detectors including ultra-violet, photo-diode array, mass spectrometer, or evaporative light scattering detection. It is important to note that a high acquisition rate is needed to ensure that enough data points are being collected from the sharp peaks obtained from the high-speed second analysis. Moreover, the use of a mass spectrometer, which is orthogonal to LC separations (since it separates by mass to charge ratio), offers additional information on the sample components. Therefore, when co-eluting compounds from highly complex samples they can be identified according to their mass. A summary of the different forms of liquid chromatography is given in Table 3.1. Of course, an HPLC column also needs a mobile phase if it is to be of any use and these bring their own challenges.

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Table 3.1 Summary of advantages and disadvantages of offline and online two-dimensional liquid chromatography Offline

Online

Interface

Fraction collector

Valve

Sample treatment/pre-concentration before second dimension injection

Yes, including pre-concentration Not always possible and derivatisation

Total analysis time

Significantly high (from hours to days)

Analysis time of second dimension

No time limit on separation time Second dimension analysis of second dimension time has to be less than first dimension analysis time so that the next sample can be injected

Peak capacity

Very high peak capacity can be obtained by using two long columns

High

Wrap around

None

Will occur if second dimension is not finished with all compounds eluted before the next injection

Automation

Not automated, risk of contamination, labour intensive

Automated

Data representation

Stacked or contour plots

2D (contour or projection) and/or 3D

Relatively fast (almost as long as a one-dimensional separation)

3.5 Column Selection (Orthogonality) One critical point that is worth spending some time on is column orthogonality. The fundamental issue of LC × LC is the combination of two different separation mechanisms i.e. the combination of two columns which are different in both dimensions and the nature of the stationary phase (and hence likely the mobile phase). We also need to keep in mind the fact that the second column is usually shorter than the first. This can be seen in the 2DLC set up in Fig. 3.1. The selection of two orthogonal columns for LC × LC separation can be a labour intensive and time-consuming process and in many cases is an entirely trial-and-error approach. Knowledge of the column and sample chemistries involved can help speed up the process of conducting a selectivity study comparing multiple columns to see which two produces the most different results with the same sample and then using these two columns as the two different dimensions. Pandohee et al. (2015) for example tested six different stationary phases (Luna Cyano (CN), Luna C18, Luna NH2 , Luna pentafluorophenyl (PFP), Luna Phenylhexyl and a Kinetex C18. All columns had dimensions of 150 mm by 4.6 mm, a

3.5 Column Selection (Orthogonality)

31

Fig. 3.1 Agilent Technologies 2D-LC system showing the two columns. The primary (C-18) on the left of the picture and the shorter secondary (phenyl-hexyl) column on the top right have been highlighted in red and blue boxes respectively

particle diameter of 5 µm and a pore size of 100 Å. The selectivity study involved comparing the previously listed columns using a mobile phase composition that started at 5% methanol (95% water) and rose to 100% methanol by 20 min; the methanol was held at 100% for another 10 min to ensure that all components were eluted from the column. The quality of the separation was determined by comparing the number of peaks and the use of the total separation space for each column. The same procedure was repeated for all columns that could potentially be used as a second dimension and the two columns providing the most dissimilar separations (the CN and the C18) were chosen for the first and second dimensions respectively. Both dimensions were then further optimised (solvent composition, flow rate, temperature) in the laboratory so that the analysis time was as short as possible. The differences in the 1DLC chromatograms obtained for each column (with all other variables being kept identical) are shown in Fig. 3.2. An alternative approach is to use in silico testing to predict retention times on different columns. For example Burns et al. (2016) showed that in silico optimisation in the form of a data processing pipeline, created in the open-source application OpenMS, could be developed to map the components within the mixture of equal mass across a library of HPLC columns. LC × LC separation space utilisation was compared by measuring the surface coverage. This allowed for significantly quicker 2DLC phase selection and the predicted separation space utilisation closely matched actual results.

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Fig. 3.2 Selectivity study of 4 stationary phases A: CN, B: NH2 , C: PFP, D: Phenyl-Hexyl and E: C18. All columns were Luna columns of 150 mm × 4.6 mm × 5 µm. The solvent protocol started from 5% methanol to 100% by 20 min, the organic phase was held at 100% for 10 min (after Pandohee et al. 2015)

A recent development in this area is the use of the commercial software to predict compound retention times as well as the effects of varying multiple parameters such as pH, temperature, and buffer concentration. This software allows the choice of columns and solvents etc. to be optimised in silico before running experiments (Andrighetto et al. 2014). As such, in silico testing has clear, financial, temporal and environmental benefits. A recent review of 2DLC looked at which column combinations were most common for both comprehensive 2DLC and heart cutting 2DLC (Pirok et al. 2019). For the former, RPLC × RPLC was the set up in ~35% of published papers. This was followed by HILIC × RPLC (~15%), IEX × RPLC (~5%) and SEC × RPLC (~4%). In LC-LC the order was different. RPLC-RPLC was again in the lead (~36% of published papers) but second was SEC-RPLC (~11%), then lastly IEX-RPLC (~7%) and HILIC-RPLC (also ~7%).

3.6 Solvents

33

3.6 Solvents In gas chromatography (GC) the mobile phase is specifically chosen to be inert to the solutes and has no role in relative retention or selectivity of the analytes (solutemobile phase interactions are near zero) and a solute’s vapour pressure controls distribution. With LC, solute-mobile phase interactions are significant and solute distribution into the mobile phase is increased well above what would be provided by the solute vapour pressure alone. The 1DLC separations that are to be combined into an LC × LC method must be carefully matched in terms of selectivity, compatibility, orthogonality, and resolution. A good review of this topic (with some very useful tables and diagrams) is given in Pirok et al. (2017). Understanding solvent chemistry is also vital in 2DLC. Using two columns comes with an additional issue around solvent mismatch (for example if one wanted to combine normal and reverse and phase columns). The use of ancillary solvents such as n-butanol can be used to reduce solvent mismatch and column re-equilibration times. It is also vital that solvents that are carried between the columns do not have different viscosities or miscibility or there may be issues with viscous fingering or flow patterns. Viscous fingering (also known as Saffman–Taylor instability) is the unstable displacement of a more viscous fluid by a less viscous fluid. It can occur even in the absence of porous media. In terms of HPLC, it is a phenomenon discussed extensively by Shalliker et al. (2007). Peak separation can be improved in 2DLC using variable second-dimension gradients. These were discussed a little in chapter two. Let’s look at the effect they can have on a separation. Figure 3.3 below shows four different reverse phase 2DLC elution programs. In each case the solvents were 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B) for the first dimension, and 0.1% formic acid

Fig. 3.3 Solvent composition of four different reverse phase 2DLC elution programs

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Fig. 3.4 Identical sample mix run through the four different solvent composition programs in Fig. 3.3

in water (A) and 0.1% formic acid in methanol (B) for the second dimension. The red line in each graph refers to the constant increase in % of solvent B of the first dimension over the run. The blue line refers to the %B of the second dimension. We can see that in each case this rises to a certain percentage and then returns to 5% before the next injection, except for method 4 where it gradually increases with each new injection. If we take these methods and use them to run a test mix containing a range of simple, yet closely related compounds, namely uracil, sulfamethazine, 2hydroxyquinoline, phenol, acetanilide, methyl paraben, acetophenone, ethyl paraben, propiophenone, propyl paraben, N-N-diethyl-m-toluamide, butyrophenone, butyl paraben, toluene benzophenone, valerophenone, heptyl paraben what will the results be? This is shown in Fig. 3.4. As can be seen that while there is a lot of similarities, method two makes the best use of the separation space. Optimising even this relatively simple separation took a several days. As a result, it is possible to spend more than twice as long to develop an acceptable LC × LC method than to develop two 1D LC methods. This can dramatically increase the method development costs of LC × LC relative to 1D LC and other analytical techniques, which can be a high barrier if one is faced with an urgent analytical question. However, there have been a number of advances in 2DLC particularly around modulation and method optimisation software which have made this task much easier.

3.7 Modulation

35

3.7 Modulation The modulation (or switching) valve is the heart of the (online) 2DLC system. In standard 2DLC the modulation is passive-fractions from the first dimension are shifted onto the second. It has been proposed that modulation periods are most favourable when they are adjusted to be ∼2.2−4 times the standard deviation of a first dimension peak in order to avoid the need for excessively short run times in the second dimension (Horie et al. 2007). But there is far more to modulation than timing. Recently several researchers have started to introduce forms of active modulation in which some modification of the effluent from the first dimension is undertaken before it is moved on. Such systems include Stationary Phase Assisted Modulation (SPAM), Active Solvent Modulation (ASM) and Vacuum Evaporation modulation (VEM). An excellent review of fundamental improvement in the practical applications of 2DLC with active modulation (and other factors) is given in Pirok et al. (2019) but we will discuss the topic briefly here. In SPAM, rather than using large storage loops, analytes are effectively filtered out of the first dimension effluent using low-volume trapping columns which can trap and concentrate the analytes (Baglai et al. 2018; Gargano et al. 2016). Optionally, the first column effluent may be diluted using a weak eluent to facilitate retention on the traps. Although the system relies on compound retention in the traps it results in a reduction of analysis time and removal of incompatible fractions. The modulation volume is now no longer a limiting factor due to the trap column. Developed for RP in the second dimension, ASM sends part of the second dimension flow back, via a bypass capillary, to act as a diluent for the first dimension effluent fraction injected into the second column (Stoll et al. 2017). This method is robust and significantly reduces breakthrough effects and enables on column focussing of the injected analyte. Modulation volume is still a limiting factor in this approach. In VEM the incompatible solvent is removed from the first-dimension effluent via evaporation (which can be vacuum-assisted). This method is highly experimental at the time of writing (April 2020). It has only been demonstrated for combinations of NPLC and RPLC to date and analyte loss is a concern, although this can be addressed with the use of a suitable membrane. Some analytes may also only redissolve slowly (if at all) once one solvent is evaporated (Groeneveld et al. 2019). It should also be noted that all modulators increase the complexity of the already challenging method development for 2DLC. A lot of research work is currently being undertaken to try and solve this problem, including extensive use of computer-aided development. Some readers may be wondering if they can use a guard column (if they are using one) as a trap column? Here it pays to remember that trap columns and guard columns are different. A guard column generally has the same ID particle size and material and the same frit size as the analytical column. It is used to protect the analytical column. A trap column is typically packed with a different packing material than the analytical column. The range of packing materials vary and could include RP, IEX and mixed-mode stationary phases with particle sizes from 10 to 30 microns. Trap

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columns can also have a larger frit size to allow higher flow rates during the loading process, up to 5 mL/min, while keeping the back pressure within the optimum range of the pumps. It is generally advisable not to use a guard column as a trap column, primarily because of its variable particle size packing. A guard column could result in high back pressure at higher flow rates during the loading step. The mass of the guard column would also most likely not be sufficient for effective trapping. Compared with directly transferring an analyte from a first dimension to a second dimension column, a trap column reduces system pressure at transfer due to larger particle size and shorter length than a typical LC column. The main use of a trap column is to retain the analyte in a range of volumes to facilitate quantification in the second dimension. Along with at/on-column dilution, it can effectively focus the analyte and prevent break-through. A sample loop thus won’t have the same functions as a trap column. If one thinks about it, the technology platform that has been developed for 2DLC also creates a number of other possibilities. All kinds of physical (e.g. dissolution) or chemical (e.g. enzymatic or light-induced degradation or detection) processes can be made to take place between the two separation stages in 2DLC. This allows for a wide variety of experiments to be performed within a single, efficient, automated analysis (Groeneveld et al. 2019). This may lead to many novel experimental approaches in future, and it will be exciting to see where this goes.

3.8 Conclusion The complications that can be a concern in 2DLC method development are gradually being removed. Detector-sensitivity and phase-system compatibility issues can largely be solved by using active-modulation strategies. A large number of applications are published in the literature and robust instruments are commercially available. The recent developments in modulation technology have unlocked new possibilities in hyphenation of 2DLC with mass spectrometry and solvent incompatibility is often no longer an issue. Similarly to its big brother GC × GC, the advance in computer processing power has facilitated automated method optimization to the end that 2DLC has matured well enough for routine applications in analytical labs and there has been great activity in the development and application of 2D-LC techniques. Both heart-cut (LC-LC) and comprehensive (LC × LC) techniques appear to have a bright future.

References

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References Andrighetto LM, Stevenson PG, Pearson JR, Henderson LC, Conlan XA (2014) DryLab® optimised two-dimensional high performance liquid chromatography for differentiation of ephedrine and pseudoephedrine based methamphetamine samples. Forensic Sci Int 244(1):302–305 Baglai A, Blokland MH, Mol HGJ, Gargano AFG, van der Wal S, Schoenmakers PJ (2018) Enhancing detectability of anabolic-steroid residues in bovine urine by actively modulated online comprehensive two-dimensional liquid chromatography–high-resolution mass spectrometry. Anal Chim Acta 1013:87–97 Bassanese DN, Holland BJ, Conlan XA, Francis PS, Barnett NW, Stevenson PG (2015) Protocols for finding the most orthogonal dimensions for two-dimensional high performance liquid chromatography. Talanta 134:402–408 Burns NK, Andrighetto LM, Conlan XA, Purcell SD, Barnett NW, Denning J, Francis PS, Stevenson PG (2016) Blind column selection protocol for two-dimensional high performance liquid chromatography. Talanta 154:85–91 Davis JM, Stoll DR, Carr PW (2007) Effect of first-dimension undersampling on effective peak capacity in comprehensive two-dimensional separations. Anal Chem 80(2):461–473 Dugo P, Favoino O, Tranchida PQ, Dugo G, Mondello L (2004) Off-line coupling of non-aqueous reversed-phase and silver ion high-performance liquid chromatography–mass spectrometry for the characterization of rice oil triacylglycerol positional isomers. J Chromatogr A 1041(1–2):135– 142 Fairchild JN, Horváth K, Guiochon G (2009a) Approaches to comprehensive multidimensional liquid chromatography systems. J Chromatogr A 1216(9):1363–1371 Fairchild JN, Horváth K, Guiochon G (2009b) Theoretical advantages and drawbacks of online, multidimensional liquid chromatography using multiple columns operated in parallel. J Chromatogr A 1216(34):6210–6217 Gargano AFG, Duffin M, Navarro P, Schoenmakers PJ (2016) Reducing dilution and analysis time in online comprehensive two-dimensional liquid chromatography by active modulation. Anal Chem 88(3):1785–1793 Gilar M, Olivova P, Daly AE, Gebler JC (2005) Orthogonality of separation in two-dimensional liquid chromatography. Anal Chem 77(19):6426–6434 Groeneveld G, Pirok Bob WJ, Schoenmakers PJ (2019) Perspectives on the future of multidimensional platforms. Faraday Discuss 218:72–100 Horie K, Kimura H, Ikegami T, Iwatsuka A, Saad N, Fiehn O, Tanaka N (2007) Calculating optimal modulation periods to maximize the peak capacity in two-dimensional HPLC. Anal Chem 79(10):3764–3770 Horváth K, Fairchild J, Guiochon G (2009) Optimization strategies for off-line two-dimensional liquid chromatography. J Chromatogr A 1216(12):2511–2518 Lesins V, Ruckenstein E (1989) Gradient flow programming: a coupling of gradient elution and flow programming. J Chromatogr A 467:1–14 Li M, Tong X, Lv P, Feng B, Yang L, Wu Z, Cui X, Bai Y, Huang Y, Liu H (2014) A not-stopflow online normal-/reversed-phase two-dimensional liquid chromatography–quadrupole timeof-flight mass spectrometry method for comprehensive lipid profiling of human plasma from atherosclerosis patients. J Chromatogr A 1372:110–119 Ma S, Chen L, Luo G, Ren K, Wu J, Wang Y (2006) Off-line comprehensive two-dimensional highperformance liquid chromatography system with size exclusion column and reverse phase column for separation of complex traditional Chinese medicine Qingkailing injection. J Chromatogr A 1127(1–2):207–213 Marchetti N, Fairchild JN, Guiochon G (2008) Comprehensive off-line, two-dimensional liquid chromatography. Application to the separation of peptide digests. Anal Chem 80(8):2756–2767 Murphy RE, Schure MR, Foley JP (1998) Effect of sampling rate on resolution in comprehensive two-dimensional liquid chromatography. Anal Chem 70(8):1585–1594

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Pandohee J, Stevenson P, Zhou X-R, Spencer M, Jones O (2015) Multi-dimensional liquid chromatography and metabolomics, are two dimensions better than one? Curr Metabol 3(1):10–20 Pirok BWJ, Gargano AFG, Schoenmakers PJ (2017) Optimizing separations in online comprehensive two-dimensional liquid chromatography. J Sep Sci 41(1):68–98 Pirok BWJ, Stoll DR, Schoenmakers PJ (2019) Recent developments in two-dimensional liquid chromatography: fundamental improvements for practical applications. Anal Chem 91(1):240– 263 Poppe H (1997) Some reflections on speed and efficiency of modern chromatographic methods. J Chromatogr A 778(1–2):3–21 Schoenmakers PJ, Vivó-Truyols G, Decrop WMC (2006) A protocol for designing comprehensive two-dimensional liquid chromatography separation systems. J Chromatogr A 1120(1–2):282–290 Shalliker RA, Catchpoole HJ, Dennis GR, Guiochon G (2007) Visualising viscous fingering in chromatography columns: high viscosity solute plug. J Chromatogr A 1142(1):48–55 Stoll DR, Shoykhet K, Petersson P, Buckenmaier S (2017) Active solvent modulation: a valve-based approach to improve separation compatibility in two-dimensional liquid chromatography. Anal Chem 89(17):9260–9267

Chapter 4

Data Analysis

Abstract 2DLC can resolve far more peaks than standard HPLC but it also produces far more complex data. We are at a point in analytical science where running samples and generating terabytes of data is often the simplest part of the workflow and analysing the data properly takes up the majority of the analyst’s time. The question then becomes how to draw meaningful insights from these datasets, and once the data’s salient features are extracted, how can we best identify them, and infer meaning from that list of identified compounds. The data must be processed properly to create useful chromatograms, identify all the peaks, and generate new knowledge from the data obtained. There are a number of different ways to do this ranging from preprocess smoothing algorithms to geometric approach factor analysis. Understanding these methods as well as the format of the raw data is an important part of the 2DLC workflow. Keywords Algorithm · Chemometrics · Deconvolution · In silico · Processing · Software

4.1 Background It will come as no surprise to learn that that the strengthening of the separation power, with peaks coming from two dimensions of separations, leads to the formation of a correspondingly more crowded data plot. It is perhaps also not surprising therefore that LC × LC files are much bigger than their one-dimensional counterparts. Indeed, if we imagine an entire profile of a complex sample, it is easy to understand that the amount of data coming from the 1D separation, once expanded to the orthogonal plane, becomes very large. Each modulation produces continuous short, fast and practically isocratic 2D chromatograms, leading to final sizes of 500 Mb or more. File sizes are also strictly linked to acquisition frequency. The substantial number of large data files produced by 2DLC require advanced software for data processing, graphing and interpretation (Guiochon et al. 2008). Dealing with two dimensions of separation requires a large amount of processing power to enable visualisation and analysis (both quantitative and quantitative). This © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 O. Jones, Two-Dimensional Liquid Chromatography, SpringerBriefs in Molecular Science, https://doi.org/10.1007/978-981-15-6190-0_4

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introduces two problems to be solved. One, how to collect and save the data files and two, how to easily handle and process the acquired data. Sample pre-treatment and pre-separation are used to reduce the complexity of the sample in conjunction with deconvolution software; however these are not always successful (Nikitas et al. 2001; Vivó-Truyols et al. 2002). They can increase analysis time, sometimes introduce artefacts into the data and may not allow a good representation of the entire sample. 2DLC has therefore also been reliant on the development of new software capable of meeting the requirements of the modern analyst. In the early days of 2DLC the general trend was to develop home-made software via programming languages such as such as Mathematica and MATLAB or freeware such as R or Python. One of the simplest ways to do the work in such software is to quantity all the separated spots present in the bi-dimensional plane via the summation of all the single peak areas that could be linked to the same constituent. This is timeconsuming and requires advanced programming knowledge but usually gives good results. Today, commercial 2DLC systems come with very powerful software that automatically generates the required chromatograms and data tables. However, while perhaps not a concern for most analysts. The fact is that most commercial programs are proprietary. One can’t see what they are doing with the data or how they are recording it in the first place. Is a signal smoothing algorithm applied to the raw signal from the detector before the data is plotted? In many cases, we simply don’t know. It is worthwhile therefore having some discussion about data analysis in 2DLC. This chapter aims to give the reader a general overview of the area and its importance. Readers that wish to dive into the applications of chemometrics in one- and two-dimensional chromatography in more depth are directed to the excellent recent review of the subject by Bos et al. (2020).

4.2 Displaying the Data Because they contain a retention time and a detector measurement for each dimension 2DLC chromatograms are generally displayed as contour plots (similar to map data) rather than standard 1D chromatograms. The way I explain this to my students is to imagine you are standing at ground level looking at a mountain range. You can only see the peaks closest to you. Small peaks at the back may be hidden by large ones at the front and even if the larger mountains are in the distance it may be hard to fully distinguish them from smaller peaks in front. Such peaks may just look like one mountain to the naked eye, just as two coeluting peaks can look like one larger one in a standard HPLC trace. Now imagine you are floating above the mountains looking down. Now you have a contour map type view of a much larger area. You can see many more peaks going in all directions, but you may be unable to tell the relative heights of each peak, and smaller peaks may still be hidden by larger ones (foothills lost in the shadows of the

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Fig. 4.1 Simplified plots showing chromatograms as single peaks (upper panel) and as a contour plot (lower panel)

mountains so to speak). There may also be so many peaks visible that it is impossible to count them all. A very basic illustration of this is given in Fig. 4.1. Modern commercial 2DLC deconvolution software tends to take a series of slices through the 3D landscape at different heights. The user can zoom in and out and also generate a list of peaks that simply can’t be seen by eye using a single 3D map from one perspective. There are also multiple alignment algorithms for the correction of minor retention time shifts between multiple runs. But how do we get the map in the first place? The data obtained from an offline 2DLC separation consist of several single 1D chromatographic files (one datafile per cut), which can be presented as an overlay plot. With advanced computer software it is possible to convert the raw datafiles into a matrix consisting of the first dimension, second dimension and signal intensity values. This matrix then can then be converted to a visual form which usually takes the form of a coloured contour plot with spots corresponding to the peaks of each separated component. A colour gradient is then used to show the intensity of the peaks as shown in Fig. 4.2.

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Fig. 4.2 Comprehensive, but un-optimised, 2D LC analysis of black tea. The same data are shown as a contour plot (panel A) and a three-dimensional chromatogram (panel B)

The data can also be displayed as a 3D plot if desired (as seen in Fig. 4.2) however this does not always make it easier to see the data. This means of course that any software must be able to recognise which 2D peaks correspond to the same compounds. This is especially relevant when one is interested in quantitative analysis. Understanding how data is processed and analysed is important because if you don’t understand how the data was generated and displayed it is much harder to be certain of the inferences you can draw from it.

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4.3 Algorithms and Processing Methods for 2DLC Data The identification and counting of peaks in a 2DLC chromatogram are very important, especially for large scale high throughput analysis. In such cases advanced software and algorithms are needed. Chromatographic data are often pre-processed for a number of reasons. These can be generalised as follows (Bos et al. 2020) (i) (ii) (iii) (iv) (v)

denoising and smoothing baseline (drift) correction retention time alignment peak deconvolution and resolution enhancement data compression

The Savitsky-Golay filter (Savitzky and Golay 1964) has become a universal method to remove noise from scientific measurements. This algorithm not only smooths noisy data but also provides the first to third derivatives of recorded chromatograms, which in turn are used to examine the shape of the chromatographic curve, allowing overlapping and shouldering peaks to be distinguished and providing retention and area information of LC peaks (Danielsson et al. 2002; Vivó-Truyols et al. 2005). Many systems, such as the free to use XCMS software platform extracts peak information in this way (Pierce and Mohler 2011). If you put your sample in at the front and take your data out at the back, you have a limited idea of how the data was generated nor if an alternative signal processing method might have been better. I have often thought that analytical scientists could potentially learn a lot from the signal processing advances made in electronic engineering in recent years that enable internet and phone signals to be processed to such an extent that my voice can travel almost instantaneously from one side of the world to the other and be as recognisable, with no distortion as if I were standing right next to the listener. Some researchers have tried alternative methods. The penalised least squares smoothing algorithm which is based on the method first described by Whittaker (1922) has been applied to 2DLC (Stevenson et al. 2013a, b) This method has the advantage of not only reducing signal noise but also approximating the baseline following an asymmetric least squares approach. Wavelet transforms have been used to remove noise from HPLC and LC-MS data in the MetSign and OpenMS systems (Tautenhahn et al. 2008; Wei et al. 2011) Interestingly, wavelet analysis has also been applied to NMR spectroscopy data with some success (Rubtsov and Griffin 2007). There are many other sophisticated chemometric analyses that can be used to extract meaningful information from complex 2DLC data. Commonly used methods are principal component analysis (PCA), partial least-square regression analysis (PLS) (Tistaert et al. 2012) parallel factor analysis (PARAFAC) (Allen and Rutan 2011) or the Generalised Rank Annihilation Method (GRAM) (Ramos et al. 1987; Sanchez et al. 1987). Most chemometric methods are used for the deconvolution of overlapping peaks, reduction of dimensionality of an analyte of interest, and comparison of samples, in addition to the improvement in data quality by removing artifacts

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or correcting the baseline and remove erroneous data (e.g. data generated from instrumentation spikes). Raw data can be analysed by dividing the separation area into bins and calculating the area under the resultant curves (Pierce and Mohler 2011). Techniques to extract peak information from traditional chromatographic spectra can also be applied to 2DLC data. However, as several fractions per peak are transferred between dimensions a further processing step must be completed to assign a single multidimensional retention time per eluted compound. Peters et al. (2007) reported a protocol to combine features in neighbouring fractions by comparing the peak start and end times measured from the second derivative of chromatographic measurements; peaks were joined when the overlap surpassed a defined threshold. Vivó-Truyols (2012) refined this process by joining peaks on the basis of their conformation to Bayesian statistics. Similar processes have been applied by researchers to assess similarities in retention characteristic between the two separation environments (Stevenson et al. 2010) and find the cumulative area of 2DLC peaks by fitting Gaussian, or exponentially modified, Gaussian models to each successive chromatogram (Stevenson and Guiochon 2013). In the methods discussed above each fraction is analysed as a discrete chromatogram and the data then looked at as a whole. Alternatively, 2DLC data can be examined as a single three-dimensional dataset (where the dimensions are longitude, latitude and altitude-going back to our mountain range metaphor) with the watershed algorithm (Beucher and Lantuejoul 1979). This has been adapted by Reichenbach and co-worker to 2DLC chromatograms and is available as the GC Image software (Reichenbach 2009) which is relatively easy to use but also expensive. Essentially, the watershed algorithm is used to find geographical pits. When applied to 2DLC, inverted peaks are represented as basins that are subsequently filled with a hypothetical fluid. An algorithm can then be applied that filters all catchment basins that are below a given threshold thus creating a two-dimensional retention map. However, it has been reported that the watershed method is limited in that it cannot resolve the most complex datasets because it requires a crest between basins. This means that it has limited ability to distinguish between severely overlapping and shouldering peaks (Ramos 2009) and has been found to fail approximately 15% of the time in GC × GC experiments (Vivó-Truyols and Janssen 2010). As noted by Bos et al. (2020) the water-shed algorithm is often outperformed by the other techniques, but it may find new application in the field of polymer analysis. Polymer separations typically do not yield individually separated components (i.e. peaks), but envelopes or distributions (sometimes called “smears”), which are difficult to treat with curve-fitting or derivative-based methods. Whatever software is used one must also be careful of the phenomenon known as wrap-around. This occurs when the 2D retention time of a peak exceeds the modulation period and so is dragged into the next modulation cycle. It is commonly seen when a particular analyte possesses a very high affinity for the stationary phase and hence moves slowly through the column. Wraparound causes the formation of broader peaks, the width of which can mask other peaks from further modulations. The distorted and larger peaks that result from wraparound are usually picked up automatically by current software but are better prevented than cured.

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When peak information is in hand, chemometric processes can also applied to 2DLC measurements, usually to find changes in datasets. Principal component analysis (PCA) is a broad unsupervised approach to data analysis where samples are grouped and aligned along axes representing the greatest differences in separation behaviour (Eriksson et al. 1999) As such, it is a valuable tool to rapidly identify similarities and differences between unknown datasets, or to confirm hypotheses (Cook and Rutan 2014). Often studies are trying to find differences between two distinct classes of sample, e.g. sick and healthy. In such cases, a supervised approach can be taken such as partial least squares-discriminant analysis (PLS-DA) (Wold and Andersson 1973). This has the benefit of training the algorithm with data from known classes, which is then used to create models from linear combinations of complex chromatographic data that differ from the provided quantitative information; these models can then be used to classify new unknown data/chromatograms but care must be taken not to overfit the data. Work on improved peak clustering algorithm for comprehensive two-dimensional liquid chromatography data analysis continues (Bos et al. 2020; Xu et al. 2019) and there is ample research space in this area for the interested data analyst or chemometrician.

4.4 In Silico Method Optimisation The use of statistical models to predict retention factors is well developed in chromatography, for example, the Kovats retention index used to convert retention times into system-independent constants (Zellner et al. 2008). More recently in silico screening has also been used to optimise the separation set up of complex 2D separations such as the Ion Chromatography (IC) × Capillary Electrophoresis (CE) separation of low molecular-mass organic acids (Ranjbar et al. 2017) and a blind optimisation protocol for column selection from natural product extraction. In the latter case a data processing pipeline, created in the open source ‘OpenMS’ software, was developed to map the components within the mixture of equal mass across a library of HPLC columns to find the best combination before the experiments were run (Burns et al. 2016). This approach allowed for a significantly quicker selection of two orthogonal columns. A recent development in this area is the use of software, such as the commercial program Drylab, to predict not only compound retention times but also the effects that varying multiple parameters such as pH, temperature, and buffer concentration will have on the retention time. This software allows the choice of columns and solvents etc to be optimised in silico prior to running experiments (Andrighetto et al. 2014). Optimising experimental design in advance of running a physical experiment has clear financial, temporal, and environmental benefits not least of which is the avoidance of any potential need to waste precious samples. Commercial software such as Drylab does come with a cost. Recently, however, the C++ based MUSCLE (Multi-platform Unbiased Optimization of Spectrometry via Closed-Loop Experimentation) software was created, which also allows the

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Fig. 4.3 Optimised model of caffeine interacting with an NVP monomer of Oasis HLB sorbent

in silico optimisation of LC separations (Bradbury et al. 2015). Although not as advanced as Drylab, and reliant on the Windows operating system, MUSCLE has the advantage of being free and open source. Both DryLab and MUSCLE can be used to predict metabolite retention times given a certain set of operating conditions. Theoretically, this means they could also be used to work backward and predict the likely metabolite/chemical structure of an unknown peak from a chromatogram if given the starting conditions—though this would take a lot of experimental work to prove. Such automated software could also help with the active-modulation interfaces discussed in Chap. 3 where they would greatly reduce sensitivity effects and solvent incompatibility. Other research in this area is in computer-based modelling of sorbent/sorbate interactions in chromatographic systems. An example of this is shown in Fig. 4.3. Combining computational and experimental approaches was recently used to select chromophores to enable the detection of fatty acids via HPLC, further demonstrating the potential uses of this form of method development (Pandohee et al. 2019). The new knowledge of such interactions could potentially advance our understating of the underlying chemical theory and help further optimise 2DLC separations.

4.5 Conclusions Data analysis is a critical step in obtaining useful information from complex samples using high throughput analysis and/or the increasingly advanced analytical tools such as 2DLC. Many data processing methods in 2DLC are based on 1DLC methods or

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techniques brought from 2DGC (2DLC’s bigger brother). In the future, methods, such as peak alignment, for use in 2DLC are likely to be those designed specifically for this technique (e.g. be designed to operate in two dimensions not just one). Data processing methods are often difficult to compare however, because the results greatly depend on the quality of the original data and the aim of the analyst. For example, is the aim to separate as many compounds as possible or just to separate relatively few (or even only one) peaks that are very hard to isolate? While useful results can be obtained using today’s increasingly powerful commercial software the better the user understands their sample, the aim of the experiment, how their data is pre- and post-processed and presented the better the outcome will generally be.

References Allen RC, Rutan SC (2011) Investigation of interpolation techniques for the reconstruction of the first dimension of comprehensive two-dimensional liquid chromatography–diode array detector data. Anal Chim Acta 705(1–2):253–260 Andrighetto LM, Stevenson PG, Pearson JR, Henderson LC, Conlan XA (2014) DryLab(R) optimised two-dimensional high performance liquid chromatography for differentiation of ephedrine and pseudoephedrine based methamphetamine samples. Forensic Sci Int 244:302–305 Beucher S, Lantuejoul C (1979) Use of watersheds in contour detection Bos TS, Knol WC, Molenaar SRA, Niezen LE, Schoenmakers PJ, Somsen GW, Pirok BWJ (2020) Recent applications of chemometrics in one- and two-dimensional chromatography. J Sep Sci n/a(n/a) Bradbury J, Genta-Jouve G, Allwood JW, Dunn WB, Goodacre R, Knowles JD, He S, Viant MR (2015) MUSCLE: automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis. Bioinformatics 31(6):975–977 Burns NK, Andrighetto LM, Conlan XA, Purcell SD, Barnett NW, Denning J, Francis PS, Stevenson PG (2016) Blind column selection protocol for two-dimensional high performance liquid chromatography. Talanta 154:85–91 Cook DW, Rutan SC (2014) Chemometrics for the analysis of chromatographic data in metabolomics investigations. J Chemom 28(9):681–687 Danielsson R, Bylund D, Markides KE (2002) Matched filtering with background suppression for improved quality of base peak chromatograms and mass spectra in liquid chromatography–mass spectrometry. Anal Chim Acta 454(2):167–184 Eriksson L, Johansson E, Kettaneh-Wold N, Wold S (1999) Introduction to multi- and megavariate data analysis using projection methods (PCA and PLS). Umetrics, Umeå, Sweden Guiochon G, Marchetti N, Mriziq K, Shalliker RA (2008) Implementations of two-dimensional liquid chromatography. J Chromatogr A 1189(1–2):109–168 Nikitas P, Pappa-Louisi A, Papageorgiou A (2001) On the equations describing chromatographic peaks and the problem of the deconvolution of overlapped peaks. J Chromatogr A 912(1):13–29 Pandohee J, Rees RJ, Spencer MJS, Raynor A, Jones OAH (2019) Combining computational and experimental approaches to select chromophores to enable the detection of fatty acids via HPLC. Anal Methods 11(23):2952–2959 Peters S, Vivó-Truyols G, Marriott PJ, Schoenmakers PJ (2007) Development of an algorithm for peak detection in comprehensive two-dimensional chromatography. J Chromatogr A 1156(1–2 SPEC. ISS.):14–24 Pierce KM, Mohler RE (2011) A review of chemometrics applied to comprehensive twodimensional separations from 2008–2010. Sep Purif Rev 41(2):143–168 Ramos L (2009) Comprehensive two dimensional gas chromatography. Elsevier

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Ramos LS, Sanchez E, Kowalski BR (1987) Generalized rank annihilation method: II. Analysis of bimodal chromatographic data. J Chromatogr A 385(0):165–180 Ranjbar L, Talebi M, Haddad PR, Park SH, Cabot JM, Zhang M, Smejkal P, Foley JP, Breadmore MC (2017) In silico screening of two-dimensional separation selectivity for ion chromatography × capillary electrophoresis separation of low-molecular-mass organic acids. Anal Chem 89(17):8808–8815 Reichenbach SE (2009) Quantification in comprehensive two-dimensional liquid chromatography. Anal Chem 81(12):5099–5101 Rubtsov DV, Griffin JL (2007) Time-domain Bayesian detection and estimation of noisy damped sinusoidal signals applied to NMR spectroscopy. J Magn Reson 188(2):13–13 Sanchez E, Scott Ramos L, Kowalski BR (1987) Generalized rank annihilation method: I. Application to liquid chromatography—diode array ultraviolet detection data. J Chromatogr A 385(0):151–164 Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639 Stevenson PG, Bassanese DN, Barnett NW, Conlan XA (2013a) Improved 2D-HPLC of red wine by incorporating pre-process signal-smoothing algorithms. J Sep Sci 36(21–22):3503–3510 Stevenson PG, Conlan XA, Barnett NW (2013b) Evaluation of the asymmetric least squares baseline algorithm through the accuracy of statistical peak moments. J Chromatogr A 1284:107–111 Stevenson PG, Guiochon G (2013) Cumulative area of peaks in a multidimensional high performance liquid chromatogram. J Chromatogr A 1308:79–85 Stevenson PG, Mnatsakanyan M, Guiochon G, Shalliker RA (2010) Peak picking and the assessment of separation performance in two-dimensional high performance liquid chromatography. Analyst 135(7):1541–1550 Tautenhahn R, Böttcher C, Neumann S (2008) Highly sensitive feature detection for high resolution LC/MS. BMC Bioinf 9(1):1–16 Tistaert C, Bailey HP, Allen RC, Heyden YV, Rutan SC (2012) Resolution of spectrally rankdeficient multivariate curve resolution: alternating least squares components in comprehensive two-dimensional liquid chromatographic analysis. J Chemom 26(8–9):474–486 Vivó-Truyols G (2012) Bayesian approach for peak detection in two-dimensional chromatography. Anal Chem 84(6):2622–2630 Vivó-Truyols G, Janssen H-G (2010) Probability of failure of the watershed algorithm for peak detection in comprehensive two-dimensional chromatography. J Chromatogr A 1217(8):1375– 1385 Vivó-Truyols G, Torres-Lapasió JR, Caballero RD, Garc´ıa-Alvarez-Coque MC (2002) Peak deconvolution in one-dimensional chromatography using a two-way data approach. J Chromatogr A 958(1–2):35–49 Vivó-Truyols G, Torres-Lapasió JR, van Nederkassel AM, Vander Heyden Y, Massart DL (2005) Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: part II: peak model and deconvolution algorithms. J Chromatogr A 1096(1–2):146–155 Wei X, Sun W, Shi X, Koo I, Wang B, Zhang J, Yin X, Tang Y, Bogdanov B, Kim S, Zhou Z, McClain C, Zhang X (2011) MetSign: a computational platform for high-resolution mass spectrometry-based metabolomics. Anal Chem 83(20):7668–7675 Whittaker ET (1922) On a new method of graduation. Proc Edinb Math Soc 41:63–75 Wold S, Andersson K (1973) Major components influencing retention indices in gas chromatography. J Chromatogr A 80(1):43–59 Xu J, Zheng L, Su G, Sun B, Zhao M (2019) An improved peak clustering algorithm for comprehensive two-dimensional liquid chromatography data analysis. J Chromatogr A 1602:273–283 Zellner BdA, Bicchi C, Dugo P, Rubiolo P, Dugo G, Mondello L (2008) Linear retention indices in gas chromatographic analysis: a review. Flavour Fragr J 23(5):297–314

Chapter 5

Hyphenation

Abstract The identification of unknown compounds is of fundamental importance for a range of applications in chromatography including, but not limited to, environmental pollution, food/natural product analysis, metabolomics, sports drug testing and petrochemicals, and biofuel analysis. Critical to the success of each such application is the ability to separate the compounds of interest both from each other and the sample matrix-which may be present at concentrations many orders of magnitude higher than the target compounds. While two-dimensional chromatography increases the available separation space there can still be problems identifying all the peaks in a sample especially due to dilution of low concentration components in the second dimension. Selectivity and sensitivity are key to solving such problems. Both may be increased by the use of multiple dimensions of separation. In this chapter, some of the methods of extending the power of 2DLC by hyphenating it with other dimensions of separation are presented and discussed. Keywords Collision cross section · Ion chromatography · Electrophoresis · Eluent · Ion mobility · Mass spectrometry

5.1 Background If, as stated in Chap. 2 of this book, the best separations can be achieved using the most varied columns as possible then could better separations be achieved using two, totally different techniques? The short answer is yes but the long answer needs to consider how these two systems can be connected. It could be argued that a truly multidimensional system utilises two dimensions that have different mechanisms of response, for example where a chemical separation dimension is coupled with a spectroscopic dimension. For any multidimensional separation system, the following conditions can generally be thought of as applicable: (i) The components of a mixture are subject to two or more separation steps, in which their displacements depend on different factors.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 O. Jones, Two-Dimensional Liquid Chromatography, SpringerBriefs in Molecular Science, https://doi.org/10.1007/978-981-15-6190-0_5

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(ii) When two or more components are separated in any single step, they always stay separated until the total separation operation is finished (Kouremenos et al. 2016). To respond to these challenges many analysts utilise advanced mass spectrometry (MS) and data processing software to deconvolute complex overlapping chromatograms. This approach is not always optimum. No software is perfect and mass spectrometry has also been accused of telling you the answer you want, not the answer you need. Sometimes compound specific detectors such as electron capture (for halogens) or the nitrogen–phosphorus detector (NPD) may give more definitive results. It may not be too unfair to say that some hard-core (and perhaps even some not so hard core) chromatographers regard mass spectrometry as just another detector while some mass spectrometrists tend to think of chromatography as just a fancy method of sample preparation. In truth, both sides have a point, and both also need to see the wider picture and work together, not separately. Chromatography is more than sample preparation and mass spectrometry (not spectroscopy!) is a valid form of separation science that separates on mass/charge ratio rather than retention time. The two techniques provide complementary information. LC-MS is the current gold standard in analytical chemistry and so LC × LC-MS is likely to be even more useful in many cases. Hyphenation of LC to MS somewhat relaxes the demands placed on chromatographic separation of species because of the inherent selectivity of MS detection, especially in the form of tandem or high-resolution (HR) MS. However, MS also suffers from some limitations, such as distinguishing between isomeric species and its susceptibility to matrix effects. Chemically sensitive detectors and extra dimensions of separation are other options but there is no perfect method. The simplest way to identify an unknown compound is still to first separate it from other compounds in the sample. This is, of course, the whole point of two-dimensional liquid chromatography, but can we do more, and if so, what are the best dimensions to add to a 2DLC system? One way to answer this question might be to try and conceptualise where the overlap between the various forms of separation techniques might be. Figure 5.1 illustrates a conceptual “separation space” that encompasses the majority of relevant chromatographic techniques that could be hyphenated together and illustrates the extent of the distinction and overlap among them. It is based on the concept originally proposed in Talebi et al. (2016).

5.2 Mass Spectrometry I have noted elsewhere in the book (but it is important so bears repeating) that there is no one best method of analysis since the best method to use depends on the aim of the analyst. If your aim is to identify as many things as possible as quickly as possible the one “obvious” extra dimension to add to a 2DLC system is mass spectrometry, which

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Fig. 5.1 An illustration of a conceptual separation space adapted from Talebi et al. (2016)

comes in almost as many varieties as chromatography. Which is best? Well, resolving power is crucial in 2DLC and this is where MS with fast scan speed (such as time of flight MS) can be very helpful. One form of spectrometry that shows particular use in this area is ion mobility-spectrometry mass spectrometry (IMS) because it adds together separation by mass and by drift time. Indeed linking ion mobility with two dimensional chromatography can give four dimensions of separation and such systems are currently starting to appear in the literature linked to both GC × GC (Lipok et al. 2018) and LC × LC systems (Stephan et al. 2016a). For those unfamiliar with the technique, IMS separates ions drifting through a tube filled with buffer gas in a weak electric field according to their shape-to-charge ratio (rather than mass to charge in standard MS) (Kanu et al. 2008). This gives IMS the ability to measure collision cross sections (CCS) of the ions in question. The CCS is a specific value for an ion and can act as an additional parameter to exact mass, increasing certainty in the identification of compounds. A very handy application of CCS and IMS is that it provides the analyst with the ability to separate isobaric species (ions with identical masses but different atomic compositions) when they differ in their size and/or shape. Even the best high-resolution MS would deliver only one peak for two isobaric ions because they have the same mass-to-charge (m/z) ratio. The separated ions are then introduced into a mass analyser in a second step where their mass to charge ratios can be determined on a microsecond timescale. Drift time can then be added to retention time in the analysis. Comprehensive twodimensional liquid chromatography (LC × LC) and ion mobility spectrometry–mass

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spectrometry (IMS–MS) are increasingly being used to address challenges associated with the analysis of highly complex samples (Causon and Hann 2015). The advantage of ion mobility for characterizing unknown compounds by their CCS and accurate mass in a non-target approach is not insignificant. Venter et al. (2018) evaluated the potential of a comprehensive three-dimensional (HILIC) × reversed phase LC (RP-LC) × IMS–high-resolution MS to analyse a range of phenolic compounds, including hydrolysable and condensed tannins, flavonoids, and phenolic acids in several natural products. A protocol for the extraction and visualization of the four-dimensional data obtained using this approach was developed as part of the process. The benefits associated with the incorporation of IMS include improved MS sensitivity and mass-spectral data quality. IMS also provided separation of trimeric procyanidin isomeric species that could not be differentiated by HILIC × RP-LC or HR-MS. The performance of the LC × LC × IMS system was characterised in terms of practical peak capacity and separation power, using established theory and taking undersampling and orthogonality into account. Overall an average increase in separation performance by a factor of 13 was found when IMS was incorporated into the HILIC × RP-LC–MS workflow. The authors did note however that IMS separation performance and the extent of second dimension undersampling depended on the upper mass scan limit, which might present a limitation for the analysis of larger molecular ions such as proteins or polymers. Stephan et al. (2016b) used LC × LC, in combination with ion mobility-highresolution mass spectrometry to separate components of various complex samples in four dimensions. Their system worked as a continuous multi heart-cutting LC system, using a long modulation time of 4 min. This allowed the complete transfer of most of the first-dimension peaks to the second-dimension without fractionation meaning that each compound showed up as only one peak in the second dimension. This is useful as it simplifies the data handling even when ion mobility spectrometry as a third and mass spectrometry as a fourth dimension were introduced. The authors tested the system on the analysis of a plant extract and showed the separation power of this four-dimensional separation method had a potential total peak capacity of >8700. Natural products such as those used in the two studies detailed above are often used to test the effectiveness of separation techniques due to their complex sample matrix and the fact that such samples usually contain not only a large number of very similar individual compounds but also a large number of compound classes which can vary by orders of magnitude in size, concentration and polarity (McCance et al. 2018). Natural products are complex samples to be sure, but they are not the only ones. Comprehensive analysis has also become an important tool in the field of environmental science and toxicology since a huge variety of pollutants from different sources are can be found in the environment, ranging from pesticides and pharmaceuticals (Pérez and Barceló 2007) to perfluorinated compounds such as per-and poly-fluoroalkyl substances (PFAS) as well as disinfection by-products (Alexandrou et al. 2019). Liquid chromatography coupled to high-resolution mass spectrometry is currently the gold standard for the identification of such complex

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mixtures of compounds. Non-targeted analysis of a wastewater sample with a fourdimensional separation platform with HPLC coupled to an ion mobility-quadrupoletime of flight mass spectrometer (IM-QToF-MS) revealed 53 different compounds, identified by exact mass and CCS, in the examined wastewater sample. Fifty-three compounds may not sound like a huge number but critically this method was able to distinguish between isobaric structures such as cyclophosphamide and ifosfamide which would have been impossible to do with standard MS.

5.3 Multiple Chromatographic Methods Many analysts will be familiar with normal phase (NP) and reverse phase (RP) HPLC. However, there are multiple forms of chromatography and nearly all have been combined at some point to explore approaches to LC × LC. These including reversed-phase liquid chromatography (RPLC) coupled to size-exclusion chromatography (SEC)(Li et al. 2015), RPLC to RPLC (Donato et al. 2011), ion chromatography (IC) to SEC (Xu et al. 2018), IC to RPLC (Brudin et al. 2010) ion-exchange chromatography with a strong cation-exchange column (SCX) to RPLC (Kajdan et al. 2008), normal-phase liquid chromatography (NPLC) to RPLC (Wei et al. 2009), supercritical fluid chromatography to RPLC (Yang et al. 2020), hydrophilic interaction chromatography to RPLC (Cao et al. 2018), and even IC to IC and CE (Fa et al. 2018). Most of these tend to be rather specialised applications and the most common combination of 2DLC is RPLC × RPLC (Pirok et al. 2019)-although the combination of HILIC and RP provides powerful separation capability for the analysis of polar compounds in samples with complex matrices. Despite high orthogonality, on-line combinations of organic normal-phase and reversed-phase chromatography are less frequent, due to the limited mobile phase compatibility. Hence, NP systems are usually combined with RP systems off-line. The compatibility problem is less prohibitive, though it is still important, with direct coupling of reversed-phase and hydrophilic interaction liquid chromatography, HILIC, (aqueous-organic normal phase) systems (RP × HILIC, HILIC × RP). Because of good compatibility, the combination of the same principles, but with different stationary and mobile phases, are popular in RP × RP based methods. Although it is more difficult to select fully two-dimensional systems with a high degree of orthogonality when the same format is used in both dimensions it is possible to increase orthogonality by utilising a different pH in each column. Generally, there are no significant problems with mobile phase compatibility in RP × RP, RP × IEC, SEC × RP or SEC × NP two-dimensional LC separation systems. In contrast it is usually not easy to couple organic normal-phase and reversed-phase modes on-line, because of mobile phase immiscibility. NP systems should generally not be used in the second dimension as water transferred in aqueous-organic mobile phases from the first, RP, dimension may deactivate the polar adsorbent used for NP separations in a non-aqueous mobile phase and destroy the separation. Further,

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large viscosity differences between the mobile phases used in the two dimensions may distort peak shape due to flow instability (viscous fingering effect). Occasionally, the system incompatibility problems can be less important when an organic NP system is used in the first dimension and the RP system in the second dimension. Anyway, RP-NP systems are usually connected off-line. For on-line connection, an interface was recently introduced, in which volatile organic mobile phase is evaporated from the first-dimension NP fractions, before introduction into the second, RP dimension, often at a cost of incomplete recovery of sample compounds, especially those with low boiling points. The system compatibility problems are avoided when non-aqueous reversed-phase chromatography (NARP) is combined with normal-phase chromatography, in a 2D NARP × NP LC on-line setup but this solution can usually be used only for low-polarity samples, such as lipids. Polar compounds can be separated in coupled reversed phase × HILIC systems, which often show complementary selectivity. The mobile phases used in HILIC contain low concentrations of water in an organic solvent such as acetonitrile and are well miscible with aqueous-organic mobile phases used in both RP × HPLC and RP × HILIC combinations still present significant elution strength mismatch problems. Highly organic HILIC mobile phases are strong eluents in the reversed-phase systems and if transferred as the fraction solvent into the RP second dimension, they may significantly deteriorate the band shape and resolution. This also applies for RP × HILIC setups, as the RP mobile phases usually contain water concentrations high enough to interfere with the HILIC separation in the second dimension. None of the combinations listed above are likely to offer true orthogonality in the mathematical sense (i.e. being statistically independent) although some of them come very close. They also come with specific problems. Ion chromatography usually involves KOH for example, and this is not compatible with RP or MS systems. Linking IC to MS is made possible however by neutralising the first-dimension effluent, containing KOH, before transferring it to the second-dimension reversedphase column (Brudin et al. 2010). Similarly linking RPLC to NPLC requires modification of mobile phases to ensure they are compatible between columns. This can be done of course and the major driver to apply 2DLC methods to maximize peak capacity to tackle complex samples means that even more novel combinations of chromatography columns are likely in future.

5.4 Combining Chromatography and Electrophoresis Multidimensional electrophoretic and chromatographic separations can be powerful complementary techniques for the analysis of lower molecular mass molecules, in addition to proteins and peptides. The first comprehensively coupled multidimensional separation approach combining liquid chromatography and capillary electrophoresis (LC × CE) was described by Bushey and Jorgenson (1990). This work offered remarkably enhanced resolving power compared to its single dimensional

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building blocks. Since this time there have been a surprisingly large number of 2D separations with LC and CE (perhaps this is only surprising only you don’t work with CE) and these were recently detailed in an excellent and comprehensive review by Ranjbar et al. (2017). In the environmental space, a recent 2D separation was accomplished using a comprehensively coupled online two-dimensional ion chromatography-capillary electrophoresis (IC × CE) system. The system was successfully used to resolve a suite of haloacetic acids, dalapon, and common inorganic anions in water. The system was custom built and employed a sequential injection-capillary electrophoresis instrument and a non-focusing modulation interface, comprising a tee-piece and a six-port two-position injection valve, to allow comprehensive sampling of the IC effluent. High electric field strength (+2 kV/cm) enabled rapid second-dimension separations in which each peak eluted from the first-dimension separation column was analysed at least three times in the second dimension. Two-dimensional peak capacity was 498 with a peak production rate of 9 peaks/min. In the biochemical area there have been some novel approaches to linking IC to CE coupled with mass spectrometry (IC × CE-MS) to study ionic metabolites such as the organic acids and phosphorylated species that comprise glucose metabolism and the tricarboxylic acid (TCA) cycle (Burgess et al. 2011; Ramautar et al. 2017). The disadvantage of these systems is they are complex to run and require on-line desalting to turn high salt eluents from the IC step into MS compatible water, but this is not impossible to do with modern chromatographic technology (Beutner et al. 2018). Stationary phase-assisted modulation (as discussed in Chap. 3) can help in this regard as it allows salts or organic solvents from a first-dimension ion-exchange separation to be removed before aqueous reverse-phase LC separation using MS. A major drawback to widespread adoption of this technology is that libraries of standard compounds are currently lacking for IC and CE-MS systems. Notwithstanding the seemingly ideal mechanistic considerations, there are numerous practical constraints impeding LC × CE. Effective transfer of LC effluent to CE is the main challenge to address when coupling the two techniques but other issues include the fact that the typical peak volume in LC is substantially larger than the injection volume in CE and the hydrodynamic flow in LC affects the electrophoretic separation. As a result of complicated interfacing, LC × CE has not experienced the wide employment or development of GC × GC or LC × LC. But what about linking LC to GC?

5.5 LC × GC The linking of an LC to a GC system was first published by Majors in 1980, who used it to determine anthrazine in a sorghum sample via regular vaporisation and reinjection of a small part of the HPLC fraction onto a GC (Majors 1980). LC-GC systems have been studied since the 1990s primarily by Grob and Biedermann in Switzerland and the Mondello group in Italy (Biedermann and Grob 2012; Grob et al.

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Fig. 5.2 A view of GC column (left) and the silica particles that are tightly packed in LC columns (right) under an electron microscope to illustrate some of the differences between the two techniques. Images created at the RMIT University Microscopy and Microanalysis Facility (RMMF)

1991; Mondello et al. 1999; Zoccali et al. 2015). They have primarily been used for the analysis of food contact materials and these studies have been reviewed recently (Biedermann and Grob 2012). Such systems can be used for more than just food testing. LC-GC × GC is also attractive for trace analysis of pesticides for example due to the excellent detection limits afforded by large volume injection and the lack of sample pre-treatment, which may not extract all pesticides in a sample (Godula et al. 2001). It should be noted, however, that the coupling of LC to GC is not a trivial issue, as both the LC and GC operate with mobile phases that are in two different physical states, at different pressures and which contain different additives. Some of the differences between the two are shown in Fig. 5.2. Normal-phase liquid chromatography is more easily coupled with GC than reversed-phase because the eluent is usually a non-polar volatile solvent and thus NP based LC tends to be the favoured method. There are two possibilities to transfer the LC eluent to a GC. One must either regulate the LC flow according to the requirements of the two-dimensional GC unit, which necessitates using µ-LC, which in turn may limit sample detection, or introduce very large volumes into the GC. The latter is usually preferable as it allows for lower detection limits. A good guide to how to set up such a system is given in Kouremenos et al. (2016). There are also (very complicated) commercial systems such as the Shimadzu 5D Ultra-e LC-GC × GC-MS/MS system which offers five dimensions of separation. This is perhaps a little over the top and not needed outside very specialised applications. In general LC × GC (whether linked to MS system or not) is still considered a complex and specialised technique, so few laboratories use it continually and the applications to date have been limited. The future of LC-GC × GC will rely on (i)

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available and easily implemented user-friendly technology, (ii) more reported applications, and perhaps most crucially (iii) the development of proper data handling techniques.

5.6 Conclusions Recent developments in modulation technology and automated computational method optimisation have unlocked new possibilities in hyphenation of different forms of LC. RPLC × RPLC is currently the most used form of LCLC with HILIC gaining popularity, boosted somewhat by the fact that new modulation techniques can overcome solvent incompatibilities and sample dilution in the second dimension. The combinations of new column chemistries that could be used are many and varied and the use of size exclusion and chiral columns in the first dimension offers up many further opportunities for novel applications. Novel hyphenations such as IC × CE and LC-GC are still rather specialist tools but show promise. Ultimately the use of novel hyphenations to push the boundaries of separation science can only be for the better.

References Alexandrou LD, Bowen C, Jones OAH (2019) Fast analysis of multiple haloacetic acids and nitrosamines in recycled and environmental waters using liquid chromatography-mass spectrometry with positive–negative switching and multiple reaction monitoring. Anal Methods 11(30):3793–3799 Beutner A, Piendl SK, Wert S, Matysik F-M (2018) Methodical studies of the simultaneous determination of anions and cations by IC × CE–MS using arsenic species as model analytes. Anal Bioanal Chem 410(24):6321–6330 Biedermann M, Grob K (2012) On-line coupled high performance liquid chromatography–gas chromatography for the analysis of contamination by mineral oil. Part 1: Method of analysis. J Chromatogr A 1255(0):56–75 Brudin SS, Shellie RA, Haddad PR, Schoenmakers PJ (2010) Comprehensive two-dimensional liquid chromatography: ion chromatography × reversed-phase liquid chromatography for separation of low-molar-mass organic acids. J Chromatogr A 1217(43):6742–6746 Burgess K, Creek D, Dewsbury P, Cook K, Barrett MP (2011) Semi-targeted analysis of metabolites using capillary-flow ion chromatography coupled to high-resolution mass spectrometry. Rapid Commun Mass Spectrom 25(22):3447–3452 Bushey MM, Jorgenson JW (1990) Automated instrumentation for comprehensive two-dimensional high-performance liquid chromatography of proteins. Anal Chem 62(2):161–167 Cao J-L, Wang S-S, Hu H, He C-W, Wan J-B, Su H-X, Wang Y-T, Li P (2018) Online comprehensive two-dimensional hydrophilic interaction chromatography × reversed-phase liquid chromatography coupled with hybrid linear ion trap Orbitrap mass spectrometry for the analysis of phenolic acids in Salvia miltiorrhiza. J Chromatogr A 1536:216–227 Causon TJ, Hann S (2015) Theoretical evaluation of peak capacity improvements by use of liquid chromatography combined with drift tube ion mobility-mass spectrometry. J Chromatogr A 1416:47–56

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Donato P, Cacciola F, Sommella E, Fanali C, Dugo L, Dachà M, Campiglia P, Novellino E, Dugo P, Mondello L (2011) Online comprehensive RPLC × RPLC with mass spectrometry detection for the analysis of proteome samples. Anal Chem 83(7):2485–2491 Fa Y, Yu Y, Li F, Du F, Liang X, Liu H (2018) Simultaneous detection of anions and cations in mineral water by two dimensional ion chromatography. J Chromatogr A 1554:123–127 Godula M, Hajšlová J, Maštouska K, Kˇrivánková J (2001) Optimization and application of the PTV injector for the analysis of pesticide residues. J Sep Sci 24(5):355–366 Grob K, Lanfranchi M, Egli J, Artho A (1991) Determination of food contamination by mineral oil from jute sacks using coupled LC-GC. J Assoc Off Anal Chem 74(3):506–512 Kajdan T, Cortes H, Kuppannan K, Young SA (2008) Development of a comprehensive multidimensional liquid chromatography system with tandem mass spectrometry detection for detailed characterization of recombinant proteins. J Chromatogr A 1189(1):183–195 Kanu AB, Dwivedi P, Tam M, Matz L, Hill HH Jr (2008) Ion mobility–mass spectrometry. J Mass Spectrom 43(1):1–22 Kouremenos KA, Jones OAH, Morrison PD, Marriott PJ (2016) Development of an online LCLVI-GC × GC system: design and preliminary applications. Chromatographia 79(1):79–87 Li Y, Gu C, Gruenhagen J, Zhang K, Yehl P, Chetwyn NP, Medley CD (2015) A size exclusionreversed phase two dimensional-liquid chromatography methodology for stability and small molecule related species in antibody drug conjugates. J Chromatogr A 1393:81–88 Lipok C, Hippler J, Schmitz OJ (2018) A four dimensional separation method based on continuous heart-cutting gas chromatography with ion mobility and high resolution mass spectrometry. J Chromatogr A 1536:50–57 Majors RE (1980) Multidimensional high performance liquid chromatography. J Chromatogr Sci 18(10):571–579 McCance W, Jones OAH, Edwards M, Surapaneni A, Chadalavada S, Currell M (2018) Contaminants of Emerging Concern as novel groundwater tracers for delineating wastewater impacts in urban and peri-urban areas. Water Res 146:118–133 Mondello L, Dugo P, Dugo G, Lewis AC, Bartle KD (1999) High-performance liquid chromatography coupled on-line with high resolution gas chromatography state of the art. J Chromatogr A 842(1–2):373–390 Pérez S, Barceló D (2007) Application of advanced MS techniques to analysis and identification of human and microbial metabolites of pharmaceuticals in the aquatic environment. TrAC Trends Anal Chem 26(6):494–514 Pirok BWJ, Stoll DR, Schoenmakers PJ (2019) Recent developments in two-dimensional liquid chromatography: fundamental improvements for practical applications. Anal Chem 91(1):240– 263 Ramautar R, Somsen GW, de Jong GJ (2017) CE–MS for metabolomics: developments and applications in the period 2014–2016. Electrophoresis 38(1):190–202 Ranjbar L, Foley JP, Breadmore MC (2017) Multidimensional liquid-phase separations combining both chromatography and electrophoresis–A review. Anal Chim Acta 950:7–31 Stephan S, Hippler J, Köhler T, Deeb AA, Schmidt TC, Schmitz OJ (2016a) Contaminant screening of wastewater with HPLC-IM-qTOF-MS and LC + LC-IM-qTOF-MS using a CCS database. Anal Bioanal Chem 408(24):6545–6555 Stephan S, Jakob C, Hippler J, Schmitz OJ (2016b) A novel four-dimensional analytical approach for analysis of complex samples. Anal Bioanal Chem 408(14):3751–3759 Talebi M, Park SH, Taraji M, Wen Y, Amos RIJ, Haddad PR, Shellie RA, Szucs R, Pohl CA, Dolan JW (2016) Retention time prediction based on molecular structure in pharmaceutical method development: a perspective. LCGC N Am 34(8):550–558 Venter P, Muller M, Vestner J, Stander MA, Tredoux AGJ, Pasch H, de Villiers A (2018) Comprehensive three-dimensional LC × LC × ion mobility spectrometry separation combined with high-resolution ms for the analysis of complex samples. Anal Chem 90(19):11643–11650

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Wei Y, Lan T, Tang T, Zhang L, Wang F, Li T, Du Y, Zhang W (2009) A comprehensive twodimensional normal-phase × reversed-phase liquid chromatography based on the modification of mobile phases. J Chromatogr A 1216(44):7466–7471 Xu J, Zheng L, Lin L, Sun B, Su G, Zhao M (2018) Stop-flow reversed phase liquid chromatography × size-exclusion chromatography for separation of peptides. Anal Chim Acta 1018:119–126 Yang L, Nie H, Zhao F, Song S, Meng Y, Bai Y, Liu H (2020) A novel online two-dimensional supercritical fluid chromatography/reversed phase liquid chromatography–mass spectrometry method for lipid profiling. Anal Bioanal Chem 412(10):2225–2235 Zoccali M, Tranchida PQ, Mondello L (2015) On-line combination of high performance liquid chromatography with comprehensive two-dimensional gas chromatography-triple quadrupole mass spectrometry: a proof of principle study. Anal Chem 87(3):1911–1918

Chapter 6

Applications of 2DLC

Abstract It has been shown that two-dimensional liquid chromatography offers a way to greatly increase the number of compounds that can be separated, detected and quantified in a single analytical run. The potential of 2DLC is high but to gain more widespread acceptance by academia and especially industry it is necessary to show that multidimensional LC can be applied to real-world analytical problems and produce useful results. This chapter therefore gives an overview of some of the many (and growing) applications that admirably demonstrate the utility of 2DLC, including pharmaceutical analysis (for both small molecules and biopharmaceuticals), natural product chemistry, polymer analysis, forensic sciences and the ‘omic sciences (metabolomics, lipidomics and proteomics). Keyword Forensics · Natural products · Lipids · Pharmaceuticals · Polymers · Proteins

6.1 Background Now that we have understood the background and theoretical underpinnings of 2DLC let’s cover some of its potential applications. Note that we won’t go into all potential applications here as that would very easily be a book in its own right. All fields in which both high peak capacity and high selectivity are needed could benefit from multidimensional LC. These include, but are not limited to, the following. • • • • • •

Pharmaceuticals (including biopharmaceuticals) Natural products and herbal medicines Omic sciences (metabolomics, lipidomics, proteomics) Polymers (including surfactants) Forensic science and toxicology Environmental science and ecotoxicology

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 O. Jones, Two-Dimensional Liquid Chromatography, SpringerBriefs in Molecular Science, https://doi.org/10.1007/978-981-15-6190-0_6

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2DLC could also be used for the analysis of fuels and related petrochemicals although these types of samples are already well served by 2DGC (Frysinger et al. 2002; Giri et al. 2017; Pandohee et al. 2020). It would be impossible to list all the applications here and in some sense it would be redundant to do so since there is a comprehensive list of applications that can be accessed for free online https://www.morepeaks.org/pirok/2dlc-applications.php and http://multidlc.org/literature/2DLC-Applications. We will, however, have a look at some examples in the literature to gain some idea of what is possible with the technique.

6.2 Pharmaceuticals Many pharmaceutical products are highly complex mixtures that may contain multiple Active Pharmaceutical Ingredients (APIs). A single drug product may contain both the active ingredient and impurities. There may be APIs with multiple chiral centers, products that contain both small and large molecules (e.g., proteindrug formulations with surfactant excipients) and drug products with impurities that need to be identified and removed. The use of an achiral × chiral column setup would be ideally suited for the analysis of impurities in chiral pharmaceutical substances for example. It is likely that 2DLC will play an important role in this area in future. For example, heart-cutting with a 2DLC-MS platform enables characterization of monoclonal antibodies and antibody-drug-conjugates during all stages of the product life cycle (Largy et al. 2016; Ouyang et al. 2015; Williams et al. 2017). In recent years 2DLC has been used in pharmaceutical analysis to address challenges such as resolving peak co-elution, impurity quantification, peak purity assessment, stability/degradation testing, and chiral separations (Zhang et al. 2013). Kiffe et al. (2007) for example, compared both one and two dimensional liquid chromatography (in the offline mode) in conjunction with MS to analyse urine and faeces from rats and mice for the structural elucidation of metabolites from drug candidate compounds during absorption distribution, metabolism and excretion (AMDE) studies. This work may not sound glamourous, but it is essential for pharmaceutical development. The research also made use of heart cutting to transfer fractions of interest to a second dimension and is worth including here since the compounds of interest had to be separated out from complex biological matrices in the same way as biological metabolites are. The radiolabelled version of the compound being assessed was created and fed to the test animals. Urine and faeces were then collected, and the metabolites extracted. The samples were then run on standard one-dimensional liquid chromatography. Effluent from the first dimension was then collected in 96 well plates and potential metabolites were located by testing each fraction for radioactivity. A positive result indicated that the fraction held either the radiolabelled parent compound or a radiolabelled metabolite derived from it. The fraction with radioactive metabolite fractions was then injected onto a second LC system with hyphenated MS detection

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for the identification and subsequent structural determination. The study showed that applying the 2DLC-MS approach enabled characterisations of major, minor, and trace metabolites and was a useful method to separate out radiolabelled materials from all the other compounds present in a particular biological sample. Such a method could be used to track many different compounds though many complex biochemical pathways.

6.3 Natural Product Chemistry Natural products chemistry is an old technique that has undergone somewhat of a resurgence in recent years. Today it is a major resource for the study of naturally occurring biologically active substances. The traditional way of studying natural products is pre-treatment and fractionation of a complex matrix followed by separation and isolation of the individual components using repeat liquid/column chromatography, in an off-line mode. The off-line approach is very easy but presents several disadvantages: it is time-consuming, operationally intensive, and difficult to automate and in some cases difficult to reproduce. There may be hundreds if not thousands of possible compounds isolated from natural products which will vary in size, polarity and stability. The natural product chemist may be interested in one or two such compounds or a class of very closely related compounds. Qiu et al. (2014) created an on-line comprehensive medium pressure liquid chromatography (MPLC) × preparative LC system connected using a solid phase trapping column. The system allowed for the automated multi-step preparative separation of 25 compound with enough of each that their structures could be identified by MS, and both proton and carbon NMR. The 2DLC approach, therefore, offered great advantages in analytical efficiency and sample treatment capacity compared with conventional methods. Since this study was published there have been numerous reports of similar methods of sample enrichment and comprehensive characterization (Liu et al. 2019; Zhao et al. 2019). In a similar vein to natural product chemistry, the enhanced peak capacity and resolution of 2DLC has led it to be used in the profiling of Chinese medicines including the development and introduction of novel stationary phases for such research (Zhou et al. 2020). Indeed, one of the most common untargeted LC × LC applications is the profiling of natural products in plant extracts, particularly those used in traditional Chinese medicines (Brandão et al. 2019). In both areas the huge number of possible (but in many cases unknown) compounds that might be of interest/responsible for a supposed therapeutic effect makes the extra separation space of 2DLC particularly valuable.

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6.4 Metabolomics Metabolomics is the integrated analytical biochemistry and bioinformatics based study of the biological metabolites. It involves profiling as many small molecules as possible in a biological system through large-scale, non-targeted or targeted, high throughput determination of metabolites in a biological system. The challenge of metabolomics is to comprehensively cover the analysis of as many compounds as possible. This is hindered by the wide chemical diversity (size, weight, polarity, stability) of metabolites in a sample, high availability of abundance, and establishment of the different approaches for extraction, separation, detection, and quantitation, and identification of novel compounds. Recent work on the application of 2DLC to metabolomics has shown that it can analyse both aqueous and organic phase metabolites in one sample run. A good review of 2DLC in metabolomics can be found in Pandohee et al. (2015b) but a few examples are discussed below. Fairchild et al. (2010) used off-line 2DLC with tandem mass spectrometry detection (2DLC/MS-MS) to assess aqueous metabolites extracted from Escherichia coli and Saccharomyces cerevisae cultures using an SCX × HILIC column set up. They developed a single set of chromatographic conditions for both positive and negative ionization modes and were able to detect a total of 141 metabolite species (92 for E. Coli and 95 for S. cerevisae with 46 in common). The method gave an overall peak capacity of approximately 2500. The work demonstrated that a single twodimensional separation method is sufficient and practical when a pair or more of one-dimensional separations are used in metabolomics studies. In order to obtain sample high throughput, the authors relied upon what they described as a ‘workhorse approach’ in which the first dimension was short and only a small number of fractions were collected. The second dimension and the detector were relied upon for much of the separation power which in turn meant that the total peak capacity was lower than it might have been since some chromatographic performance was sacrificed to reduce the overall analysis time. Pandohee et al. (2015a) also used the offline 2DLC approach with a diode array detector to study how the metabolic profiles of Agaricus bisporus mushrooms changed upon UV exposure. The authors tested several metabolite extraction methods and then used selectivity studies to find the most orthogonal columns for the analysis, settling on a Cyano and a C18 for the first and second dimensions, respectively. The data were processed using custom written code in Mathematica. The method allowed the detection of 158 peaks from several different compound classes including sugars, amino, organic and fatty acids and phenolic compounds in a single analytical run; although only 51 of these could be identified. The study gives a good overview of the 2DLC method development process and demonstrates the increased peak capacity and separation space of 2DLC and its potential in metabolomics since the method was able to identify a wider array of compounds than 1DLC, nuclear magnetic resonance spectroscopy or GC-MS. The run time, however, was several hours per sample and thus the method had the potential to be improved via the use of online 2DLC.

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Fully automated online 2DLC was used by Klavins et al. (2014) in conjunction with tandem mass spectrometry (MS/MS) to detect and quantify sugar phosphates in cell extracts from the methylotrophic yeast Pichia pastoris. An anion exchange column was used as the first dimension and porous graphitized carbon as the second. This particular study used the heart cutting technique and careful method development enabled chromatographic separation on the second dimension to be optimized for each transferred fraction, thus minimizing the separation time and ensuring complete removal of the salt constituents of the first column eluent. The method gave sub-µM limits of detection ranging between 0.03 and 0.19 µM for the investigated compounds. A total of 10 sugar phosphates (glucose-1-phosphate, glyceraldehyde 3-phosphate, glucose-6-phosphate, mannose-6-phosphate, fructose-6-phosphate (F6P), ribose-5-phosphate, sedoheptulose 7-phosphate (S7P), 3-phosphoglyceric acid, 6-phosphogluconic acid and fructose-1,6-bisphosphate) were identified and perhaps, more crucially, were quantified by this technique. At first glance a method identifying only ten compounds does not sound like it is taking full advantage of the 2DLC technique, but quantification of sugar phosphates is a very complex task. The ability to separate the phosphates of interest from the hundreds of other compounds in a typical yeast extract is of high importance, and this method combined the efficiency of strong anion exchange (SAX) chromatography with the selectivity of MS/MS detection.

6.5 Proteomics Proteomics is the analysis of all the proteins in a particular sample. Research in this area involves the analysis of very complex samples, often comprising hundreds of thousands of proteins or peptides which may be fragmented or modified (e.g. phosphorylated or glycosylated) versions of the parent compound. Because the LC separation and MS identification of intact proteins are difficult, the LC−MS analysis is typically performed on a peptide level, after protein digestion with suitable proteolytic enzymes. This further increases the sample complexity and reinforces the demands for efficient separation methods which makes 2DLC very useful. There are huge numbers of papers on 2DLC proteomics. The most common 2DLC setup for proteomics is a combination of either SCX chromatography with RP or RP × RP-LC but this may not suit everybody and a large challenge for proteomic applications is still working out the most orthogonal column set to use. Fortunately, the peptide separation orthogonality for 16 different 2D LC-MS systems has recently been determined using a retention data set of ~ 30 000 tryptic peptides for each 2D pairing (Yeung et al. 2020). This study noted that separation orthogonality generally increases in the order RP < SCX < HILIC < SAX, with the exception of high pH RP–low pH RP system, which showed the second-best orthogonality. A good review of multi-dimensional liquid chromatography in proteomics can be found in Zhang et al. (2010).

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6.6 Lipidomics Li et al. (2014) made use of online 2DLC, coupling normal and reversed-phase columns to a quadrupole time-of-flight mass spectrometer (QToF-MS) and applying the method to the comprehensive profiling of lipids in human plasma. This method enabled the determination of 540 endogenous lipid species from 17 classes with the limit of detections of 19 validation standards in the ng/mL range. The authors also assessed the difference in lipid metabolism products from healthy individuals and those suffering from atherosclerosis. Levels of galactosylceramides in atherosclerosis patients were 1.5–2.8-fold higher than glucosylceramides in atherosclerosis patients compared to controls. Although the sample sizes were small (there were only 12 subjects in total), the study demonstrates the potential of 2DLC to both lipidomic (and atherosclerosis) research. More recently a novel online two-dimensional supercritical fluid chromatography/reversed-phase liquid chromatography–triple-quadrupole mass spectrometry (2D SFC/RPLC-QQQ MS) method using a vacuum solvent evaporation interface was developed and tested for use in lipid profiling of human plasma (Yang et al. 2020). This approach allowed lipid classes to be separated by Super Critical Fluid Chromatography (SFC) and then different lipid molecular species within those classes were further separated by the second-dimension RPLC. The method allowed the identification of 370 endogenous lipid species from ten lipid classes, including diacylglycerol, triacylglycerol, ceramide, glucosylceramide, galactosylceramide, lactosylceramide, sphingomyelin, acylcarnitine, phosphatidylcholine, and lysophosphatidylethanolamine, in human plasma to be separated within 38 min. The limit of detection was in the order of nanograms per millilitre. This is a nice approach, but not every lab has SFC of course.

6.7 Environmental Science One dimensional LC-MS (particularly QQQ mass spectrometry) is currently the gold standard in environmental laboratories worldwide due to its sensitivity and the fact that it is not limited to (semi) volatile compounds (or compounds that can be made volatile by chemical modification) as GC-MS is. It may be surprising therefore that 2DLC has not yet been widely used in environmental science. This is probably explained by the fact that environmental samples can be very dirty. Wastewater and river water samples can have high sediment and particulate loads and/or large compounds such as fulvic and humic acids. Particulates can be filtered out of the sample but if the compounds of interest are bound to these substances they will not be seen in the final analysis. Large substances that give colour to the sample can interfere with ionisation in the mass spec and removing them can also remove the compounds of interest.

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There have been two studies using 2DLC with environmental samples and both involved online RPLC × RPLC coupled to MS to assess the potential for semitargeted analysis of test wastewater samples. These studies allowed for the identification of 23–65 compounds, including analgesics such as paracetamol and tramadol, the herbicides diuron and monuron, benzotriazole (a corrosion inhibitor), and antidepressants such as venlafaxine and sertraline (Haun et al. 2013; Leonhardt et al. 2015). This perhaps does not sound like a lot, but the ability to detect multiple compound classes at ng/L concentrations quickly is a great starting point for this area. 2DLC has also been applied to look at the molecular size continuum and associated lightabsorption properties of chemically distinct pools of urban organic air particles (Paula et al. 2016) showing that it can contribute to the understanding of more than just the aquatic compartment of the environment.

6.8 Forensic Toxicology The focus of a forensic toxicology laboratory is generally to determine the presence or absence of drugs in biological samples such as urine, blood, oral fluid, or hair, to see if an illicit drug or toxin played a role in a person’s death. The sensitivity of the analytical method is critical because scientists need to detect chemical compounds in very small amounts. A particularly tricky issue in this regard are novel psychoactive substances (NPS). Specific NPSs are banned but illicit drug manufacturers often respond by altering the NPS chemical structures to make new (but similar) compounds. A large range of analogues can be made in this way (Plummer et al. 2016). This not only circumvents legislation (unless an entire class of compounds is banned rather an individual compound) but each time a new structure is introduced, there is a possibility that it has not been previously recorded in mass spectral databases. As analytical chemists, we know that you generally only see what you look for. So if you use targeted analytical methods that rely on libraries of known compounds to identify drugs in samples you may well miss new compounds that may or may not coelute with known substances. The use of 2DLC to separate and identify multiple compounds that may have very similar properties is of great potential benefit in forensic toxicology. High-resolution mass spectrometry is a good way to screen a wide variety of analytes because of its high sensitivity and mass accuracy but you still need to separate the isomeric and/or structurally similar compounds out from each other. RP × RP systems have been used for the separation of co-eluting and isomeric synthetic cannabinoids in blood and urine (Eckberg 2018) and for the detection of cocaine and metabolites in bone (Mella et al. 2017), to the parts per billion level.

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6.9 Polymer Science The characterization of complex polymers that feature multiple, independent distributions is an obvious fit for LC × LC because of the large number of structurally related compounds coupled with the difficulty of applying MS for characterizing high-molecular-weight polymers. Indeed, polymer science is probably the second most common application in 2DLC after proteomic analysis (although one could of course argue that proteins are a type of polymer). 2DLC has been used to look at many applications from complex mixtures to block co-polymers (Im et al. 2007). Many 2DLC analyses of synthetic polymers have employed SEC for the second dimension analysis due to the relatively short run time in addition to its wide use in polymer analysis. By virtue of high temperature operation (leading to lower solvent viscosity and high diffusivity of the polymer molecules), some authors have been able to use normal length SEC column at a high flow rate with little loss in resolution (Im et al. 2009). Reliable SEC separations of polymers with molecular weights up to ca. 50 kDa can potentially by achieved in less than 1 min at pressures of about 66 MPa (Uliyanchenko et al. 2011). However, this also means some quite labour-intensive work, especially for some new polymer systems that require more exotic separation conditions, such as high temperatures, toxic eluents and the use of salts in, for example, SEC. All of this makes fractionation and especially sample prep of the fractions even more complex and in many cases the traditional off-line approach is still more efficient. Semi-preparative fractionation in the first dimension and reinjection (after elimination of the first-dimension solvent and some further sample prep) on the second dimension is usually helpful, especially as the resulting fractions can still then be taken for further analysis via NMR or differential scanning calorimetry (DSC).

6.10 Conclusions Although not yet widespread the increased separation power of two-dimensional chromatography is of great potential in a large variety of scientific areas, particularly for compounds that are too sensitive for mass spectrometry but contained in matrices too complex for standard LC analysis. The combinations of new column chemistries that could be used are many and varied and the use of size exclusion and chiral columns in the first dimension offers up many further opportunities for novel applications. Since 2DLC technology is now relatively easy to run and the high-pressure capability of modern instrumentation allows high flow rates and correspondingly high-speed analysis (and one no longer has to count individual peaks by hand) the number of applications of the field is likely to continue to grow.

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References Brandão PF, Duarte AC, Duarte RMBO (2019) Comprehensive multidimensional liquid chromatography for advancing environmental and natural products research. TrAC Trends Anal Chem 116:186–197 Eckberg MN (2018) Forensic toxicological screening and confirmation of 800 + novel psychoactive substances by LC-QTOF-MS and 2D-LC analysis psychoactive substances by LC-QTOF-MS and 2D-LC analysis. Florida International University https://digitalcommons.fiu.edu/etd/3923 Fairchild JN, Horvath K, Gooding JR, Campagna SR, Guiochon G (2010) Two-dimensional liquid chromatography/mass spectrometry/mass spectrometry separation of water-soluble metabolites. J Chromatogr A 1217(52):8161–8166 Frysinger GS, Gaines RB, Reddy CM (2002) GC × GC—A new analytical tool for environmental forensics. Environ Forensics 3(1):27–34 Giri A, Coutriade M, Racaud A, Okuda K, Dane J, Cody RB, Focant J-F (2017) Molecular characterization of volatiles and petrochemical base oils by photo-ionization GC × GC-TOF-MS. Anal Chem 89(10):5395–5403 Haun J, Leonhardt J, Portner C, Hetzel T, Tuerk J, Teutenberg T, Schmidt TC (2013) Online and splitless nanoLC × capillaryLC with quadrupole/time-of-flight mass spectrometric detection for comprehensive screening analysis of complex samples. Anal Chem 85(21):10083–10090 Im K, Park H-W, Kim Y, Chung B, Ree M, Chang T (2007) Comprehensive two-dimensional liquid chromatography analysis of a block copolymer. Anal Chem 79(3):1067–1072 Im K, Park H-W, Lee S, Chang T (2009) Two-dimensional liquid chromatography analysis of synthetic polymers using fast size exclusion chromatography at high column temperature. J Chromatogr A 1216(21):4606–4610 Kiffe M, Graf D, Trunzer M (2007) Two-dimensional liquid chromatography/mass spectrometry set-up for structural elucidation of metabolites in complex biological matrices. Rapid Commun Mass Spectrom 21(6):961–970 Klavins K, Chu DB, Hann S, Koellensperger G (2014) Fully automated on-line two-dimensional liquid chromatography in combination with ESI MS/MS detection for quantification of sugar phosphates in yeast cell extracts. Analyst 139(6):1512–1520 Largy E, Catrain Q, Van Vyncht G, Delobel A (2016) 2D-LC–MS for the analysis of monoclonal antibodies and antibody–drug conjugates in a regulated environment. Curr Trends Mass Spectrom 14:29–35 Leonhardt J, Teutenberg T, Tuerk J, Schlüsener MP, Ternes TA, Schmidt TC (2015) A comparison of one-dimensional and microscale two-dimensional liquid chromatographic approaches coupled to high resolution mass spectrometry for the analysis of complex samples. Anal Methods 7(18):7697–7706 Li M, Tong X, Lv P, Feng B, Yang L, Wu Z, Cui X, Bai Y, Huang Y, Liu H (2014) A not-stopflow online normal-/reversed-phase two-dimensional liquid chromatography–quadrupole timeof-flight mass spectrometry method for comprehensive lipid profiling of human plasma from atherosclerosis patients. J Chromatogr A 1372:110–119 Liu D, Jin H, Wang J, Zhou H, Liu Y, Feng J, Liang X (2019) Offline preparative 2-D polarcopolymerized reversed-phase chromatography × zwitterionic hydrophilic interaction chromatography for effective purification of polar compounds from Caulis Polygoni Multiflori. J Chromatogr B 1118–1119:70–77 Mella M, Schweitzer B, Mallet CR, Moore T, Botch-Jones S (2017) Detection of cocaine and metabolites in bone following decomposition using 2D LC–MS-MS. J Anal Toxicol 42(4):265– 275 Ouyang Y, Zeng Y, Rong Y, Song Y, Shi L, Chen B, Yang X, Xu N, Linhardt RJ, Zhang Z (2015) Profiling analysis of low molecular weight heparins by multiple heart-cutting two dimensional chromatography with quadruple time-of-flight mass spectrometry. Anal Chem 87(17):8957–8963

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Pandohee J, Hughes JG, Pearson JR, Jones AHO (2020) Chemical fingerprinting of petrochemicals for arson investigations using two-dimensional gas chromatography-flame ionisation detection and multivariate analysis. Sci Justice Pandohee J, Stevenson P, Conlan X, Zhou X-R, Jones OAH (2015a) Off-line two-dimensional liquid chromatography for metabolomics: an example using Agaricus bisporus mushrooms exposed to UV irradiation. Metabolomics 11(4):939–951 Pandohee J, Stevenson P, Zhou X-R, Spencer M, Jones O (2015b) Multi-dimensional liquid chromatography and metabolomics, are two dimensions better than one? Curr Metabolomics 3(1):10–20 Paula AS, Matos JTV, Duarte RMBO, Duarte AC (2016) Two chemically distinct light-absorbing pools of urban organic aerosols: A comprehensive multidimensional analysis of trends. Chemosphere 145:215–223 Plummer CM, Breadon TW, Pearson JR, Jones OAH (2016) The synthesis and characterisation of MDMA derived from a catalytic oxidation of material isolated from black pepper reveals potential route specific impurities. Sci Justice 56(3):223–230 Qiu Y-K, Chen F-F, Zhang L-L, Yan X, Chen L, Fang M-J, Wu Z (2014) Two-dimensional preparative liquid chromatography system for preparative separation of minor amount components from complicated natural products. Anal Chim Acta 820:176–186 Uliyanchenko E, Schoenmakers PJ, van der Wal S (2011) Fast and efficient size-based separations of polymers using ultra-high-pressure liquid chromatography. J Chromatogr A 1218(11):1509–1518 Williams A, Read EK, Agarabi CD, Lute S, Brorson KA (2017) Automated 2D-HPLC method for characterization of protein aggregation with in-line fraction collection device. J Chromatogr B 1046:122–130 Yang L, Nie H, Zhao F, Song S, Meng Y, Bai Y, Liu H (2020) A novel online two-dimensional supercritical fluid chromatography/reversed phase liquid chromatography–mass spectrometry method for lipid profiling. Anal Bioanal Chem 412(10):2225–2235 Yeung D, Mizero B, Gussakovsky D, Klaassen N, Lao Y, Spicer V, Krokhin OV (2020) Separation orthogonality in liquid chromatography–mass spectrometry for proteomic applications: comparison of 16 different two-dimensional combinations. Anal Chem 92(5):3904–3912 Zhang KW, Tsang JM, Wigman L, Chetwyn N (2013) Two-dimensional HPLC in pharmaceutical analysis. Am Pharm Rev 16:39–44 Zhang X, Fang A, Riley CP, Wang M, Regnier FE, Buck C (2010) Multi-dimensional liquid chromatography in proteomics–a review. Analytica chimica acta 664(2):101–113 Zhao H, Lai C, Zhang M, Zhou S, Liu Q, Wang D, Geng Y, Wang X (2019) An improved 2DHPLC-UF-ESI-TOF/MS approach for enrichment and comprehensive characterization of minor neuraminidase inhibitors from Flos Lonicerae Japonicae. J Pharm Biomed Anal 175:112758 Zhou W, Liu Y, Wang J, Guo Z, Shen A, Liu Y, Liang X (2020) Application of two-dimensional liquid chromatography in the separation of traditional Chinese medicine. J Sep Sci 43(1):87–104

Chapter 7

Conclusions and Future Developments

Abstract There has been a major shift in how multidimensional LC is perceived in the last few years. Many more analysts are aware of 2DLC, but it is still not a routine technique. More attention to robustness and method development strategies is still required, but 2D-LC is no longer a technique that suffers from solvent-compatibility, cumbersome method development, and reduced detection sensitivity. Instead, it is a rapidly maturing technique with clear applications in academia and industry. But how can we move it forward and what might the future hold? In this chapter we will discuss some of the answers to these questions.

7.1 Background We have now reached the last chapter of this book. By this point the readers will hopefully (if I have done my job as author properly) now have an understanding of the background of 2DLC, its basic principles and applications, and have some idea where to start with method development, hyphenation and data analysis. So, where to from here, what is needed to make 2DLC mainstream? If you want to get a technique used widely then the best way is to make it easy to use. Future developments should, therefore, focus on supporting method development as shown by Peter Schoenmakers’ group at the University of Amsterdam (Pirok et al. 2018). In particular, better solutions for method development (perhaps using advanced software solutions) and data analysis are required when extremely complex samples (such as biological samples) are analysed; for example, comprehensive 2D-LC coupled to IMS/QTOF mass spectrometry. Advances in these areas and wider recognition of the separation power achievable will push 2DLC into more routine use. The other main challenge in 2DLC is education, which is part of the reason behind this book. Analytical scientists need to not only understand the technology but also when and where to use it. Applying 2DLC when it isn’t needed or without a proper understanding of potential problems is likely to lead to disappointment. There are a huge range of excellent publications and free resources that can be accessed with only © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 O. Jones, Two-Dimensional Liquid Chromatography, SpringerBriefs in Molecular Science, https://doi.org/10.1007/978-981-15-6190-0_7

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Fig. 7.1 3D printed 2D chromatogram created by the author and printed at the advanced manufacturing precinct at RMIT University

a few clicks on a computer. One particularly detailed one is the Agilent Primer on 2DLC which can be accessed online at https://www.agilent.com/cs/library/primers/ public/5991-2359EN.pdf. One way we might increase education in 2DLC is to think of new ways to display the chromatograms. The use of 3D printing is quite common for making molecules for chemistry education (Jones and Spencer 2018). Taking this one stage further; once we have a 3D image of the chromatogram in the computer it is possible to map that surface and create a file that can be taken to a 3D printer. Yes, this means it is possible to 3D print your 2D chromatograms (this also works for 2D NMR data). An example of this is shown in Fig. 7.1. In this case the sample that was analysed was ink from a standard biro.

7.2 3DLC Going further into the future it may also be possible to perform three dimensional LC where the first two dimensions are time-based and the third is space-based (Wouters et al. 2015). This requires a conceptual leap. Rather than a standard HPLC one must imagine some form of 3D block or cube. The sample starts in one corner

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then moves along one surface, then another, and then another. At the end of the run each compound in the mixture is somewhere in the 3D structure of the block. If the chemical properties of the block varied along its length one would effectively have hundreds or even thousands of mini columns which could be used to conduct all the differing column chemistries discussed in the methods and hyphenation chapters of this book. We would then have to use something like Raman spectroscopy or imaging mass spectroscopy to make a 3D depiction of the block over time to create a 3D depiction (or movie) of the separations. A key issue here would be to achieve adequate flow control and confinement of the analytes to the desired regions. The image would serve as the detector so fast image capture and processing would also be needed. There is a lot of existing technology that could be plugged into this sort of system, but one can see that the geometry of such a setup would get very complex very quickly. I am not going to pretend I am anywhere near clever enough to understand how it would all fit together. Luckily I don’t have to as the design and manufacture of such a system is part of the Separation Technology For A Million Peaks (STAMP) project (https://www.stamp-uva.eu/ProjectDescription) running at the Van ’t Hoff Institute for Molecular Sciences at the University of Amsterdam.

7.3 Column Technology I think development in column technology, particular monolith columns are likely to help play a role in 2DLC. A standard HPLC column uses packed columns in which tiny beads of an inert substance, typically a functionalised silica, are packed tightly together. A monolith column has no beads but instead consist of continuous beds containing multiple channels. The channels usually possess a defined bimodal pore structure with macro- and mesopores in the micro- and nanometer range and a high surface area available for reactivity. The high permeability and porosity of the silica skeleton, and the resulting low back pressure allow for more flexible flow rates than conventional particulate columns. The main advantage of the monolithic technology is the ability to use high flow rates without significant loss of efficiency. Although not yet widely used outside specific applications, the wider use of monolith technology could lead to very much faster 2DLC and even nanoscale 2DLC, separations of complex mixtures in a much shorter timeframe (Kimura et al. 2004). They also allow for specialist separations such as that of intact, rather than digested, proteins (Eeltink et al. 2009).

7.4 Miniaturisation Another area I feel will really drive advances in 2DLC and analytical chemistry in general will be the miniaturisation of analytical instrumentation. For instance, back in the 1980s mass spectrometers were the size of a small room and required

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a computer almost as big, as well as highly trained staff to run them, and, as such, they were the preserve of the few. It wasn’t until mass spectrometers were small and robust enough to be used routinely in the lab that their use and applications grew. The new wave of benchtop NMR spectrometers is a great step in the direction of smaller instruments. Applications of this technique have already started to take off; I hope this is just the start. Imagine if you had an LC the size of a computer chip or lab slide), and/or a mass spectrometer robust enough for you to transport in (and perhaps even power from) your car, and as easy to operate as your smartphone? This will require developments in chip-based columns and microfluidics to facilitate but the potential this would have for the analysis of samples from huge numbers of people could drive some incredible work in the health and medical sciences as well as a range of other fields.

References Eeltink S, Dolman S, Detobel F, Desmet G, Swart R, Ursem M (2009) 1 mm ID poly(styreneco-divinylbenzene) monolithic columns for high-peak capacity one- and two-dimensional liquid chromatographic separations of intact proteins. J Sep Sci 32(15–16):2504–2509 Jones OAH, Spencer MJS (2018) A simplified method for the 3D printing of molecular models for chemical education. J Chem Educ 95(1):88–96 Kimura H, Tanigawa T, Morisaka H, Ikegami T, Hosoya K, Ishizuka N, Minakuchi H, Nakanishi K, Ueda M, Cabrera K, Tanaka N (2004) Simple 2D-HPLC using a monolithic silica column for peptide separation. J Sep Sci 27(10–11):897–904 Pirok BWJ, Gargano AFG, Schoenmakers PJ (2018) Optimizing separations in online comprehensive two-dimensional liquid chromatography. J Sep Sci 41(1):68–98 Wouters B, Davydova E, Wouters S, Vivo-Truyols G, Schoenmakers PJ, Eeltink S (2015) Towards ultra-high peak capacities and peak-production rates using spatial three-dimensional liquid chromatography. Lab Chip 15(23):4415–4422

Further Reading

I hope at the end of this book you have gained an appreciation of what twodimensional liquid chromatograph is and how it can be used. If you want to learn more, I have provided a list here of what I consider some of the key papers in the field. Pirok, B. W. J., et al. (2019). “Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications.” Analytical Chemistry 91, 240–263. Groeneveld, G., et al. (2019). “Perspectives on the future of multi-dimensional platforms.” Faraday Discussions 218, 72–100. Pirok, B. W. J., et al. (2018). “Optimizing separations in online comprehensive two-dimensional liquid chromatography.” Journal of Separation Science 41, 68–98. Venter, P., et al. (2018). “Comprehensive Three-Dimensional LC × LC × Ion Mobility Spectrometry Separation Combined with High-Resolution MS for the Analysis of Complex Samples.” Analytical Chemistry 90, 11643–11650. Stoll, D. R. and P. W. Carr (2017). “Two-Dimensional Liquid Chromatography: A State of the Art Tutorial.” Analytical Chemistry 89, 519–531. Gargano, A. F. G., et al. (2016). “Reducing Dilution and Analysis Time in Online Comprehensive Two-Dimensional Liquid Chromatography by Active Modulation.” Analytical Chemistry 88, 1785–1793. Causon, T. J. and S. Hann (2015). “Theoretical evaluation of peak capacity improvements by use of liquid chromatography combined with drift tube ion mobility-mass spectrometry.” Journal of Chromatography A 1416, 47–56. Wouters, B., et al. (2015). “Towards ultra-high peak capacities and peakproduction rates using spatial three-dimensional liquid chromatography.” Lab on a Chip 15, 4415–4422. Giddings, J. C. (1995). “Sample dimensionality: a predictor of order-disorder in component peak distribution in multidimensional separation.” Journal of Chromatography A 703, 3–15. Davis, J. M. and J. C. Giddings (1983). “Statistical theory of component overlap in multicomponent chromatograms.” Analytical Chemistry 55, 418–424. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 O. Jones, Two-Dimensional Liquid Chromatography, SpringerBriefs in Molecular Science, https://doi.org/10.1007/978-981-15-6190-0

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