Serum/Plasma Proteomics: Methods and Protocols [3 ed.] 1071629778, 9781071629772

Table of contents :
Preface
Contents
Contributors
Part I: Blood Processing and Handling Strategies
Chapter 1: Protocols for the Isolation of Platelets for Research and Contrast to Production of Platelet Concentrates for Trans...
1 Introduction
2 Materials
2.1 Blood Collection and Platelet Sample Preparation
3 Methods
3.1 Blood Collection
3.2 Platelet-Rich Plasma (PRP) Preparation Method
3.3 Buffy Coat (BC) Platelet Preparation Method
3.4 How Blood Processing Facilities Prepare PCs for Transfusion
4 Notes
References
Chapter 2: Collection of Plasma Samples in Areas with Limited Healthcare Access
1 Introduction
2 Materials
2.1 Plasma Collection and Storage
2.2 Extraction of Intact Proteins: General Procedure
2.3 Extraction of Intact Proteins: SARS-CoV-2 Total Ab Assay
2.4 Extraction and Digestion of Proteins for Peptide-Based LC-MS Proteomics: Direct Digestion
2.5 Extraction and Digestion of Proteins for Peptide-Based LC-MS Proteomics: SP3 Digestion
2.6 Extraction of Metabolites for Global Profiling
3 Methods
3.1 Plasma Collection and Storage
3.2 Extraction of Intact Proteins: General Procedure
3.3 Extraction of Intact Proteins: SARS-CoV-2 Total Ab Assay
3.4 Extraction and Digestion of Proteins for Peptide-Based LC-MS Proteomics: Direct Digestion
3.5 Extraction and Digestion of Proteins for Peptide-Based LC-MS Proteomics: SP3 Digestion (See Note 16)
3.6 Extraction of Metabolites for Global Profiling (See Note 17)
4 Notes
References
Chapter 3: Blood Collection Processing and Handling for Plasma and Serum Proteomics
1 Introduction
2 Materials
2.1 Blood Collection
2.2 Plasma/Serum Generation
3 Methods
3.1 Participant Recruitment and Study Design
3.2 Venous Blood Collection for Plasma
3.3 Venous Blood Collection for Serum
3.4 Sample Preparation
3.5 Sample Thawing
4 Notes
Checklist for Addressing Pre-analytical Variables in a Proteomic Study
References
Chapter 4: Preparation of Cryoprecipitate and Cryo-depleted Plasma for Proteomic Research Analysis
1 Introduction
2 Materials
2.1 Whole Blood Collection, Plasma Preparation, and Frozen Storage
2.2 Cryoprecipitate/Cryo-depleted Plasma Preparation
3 Methods
3.1 Blood Collection/Phlebotomy
3.2 Cryoprecipitation
3.3 Thawing of Cryoprecipitate or Cryo-depleted Plasma
4 Notes
References
Part II: Discovery-Based Mass Spectrometry
Chapter 5: High-Throughput Proteome Profiling of Plasma and Native Plasma Complexes Using Native Chromatography
1 Introduction
1.1 Native Profiling of Protein Complexes
1.2 Native Separation of Plasma
1.3 HT Label-Free SEC-AutoP3 Approach
1.4 HT Isobaric Labeling AutoMP3 Approach
2 Materials
2.1 Native Fractionation Using SEC
2.2 Sample Preparation for AutoP3/HT AutoMP3 SEC Fractions
2.3 Sample Preparation for AutoMP3 for SEC Fractions
2.4 LC-MS Acquisition
2.5 LC-MS Data Analysis: Software Tools
3 Methods
3.1 Native Fractionation Using SEC
3.2 Sample Preparation Using SEC-AutoP3
3.3 Sample Preparation Using HT AutoMP3
3.4 LC-MS/MS Acquisition
3.4.1 LC-MS/MS Acquisition of SEC-AutoP3 Label-Free Samples
3.4.2 LC-MS Acquisition of TMT-Labeled Samples
3.5 Data Analysis
4 Notes
References
Chapter 6: High-Throughput and In-Depth Proteomic Profiling of 5 μL Plasma and Serum Using TMTpro 16-Plex
1 Introduction
2 Materials
2.1 Depletion of Top-Abundance Proteins in Plasma/Serum
2.2 Protein Digestion
2.3 TMT Labeling
2.4 Basic pH Reversed Phase Liquid Chromatography (bRPLC)
2.5 LC-MS/MS
2.6 Software for MS Data Analysis
3 Methods
3.1 Depletion of High-Abundance Proteins
3.2 Protein Digestion and Peptide Desalting
3.3 TMT Labeling (See Note 11)
3.4 High pH Fractionation by Basic pH Reversed Phase Liquid Chromatography
3.5 LC-MS/MS
3.6 MS Data Analysis
4 Notes
References
Chapter 7: An Optimized Data-Independent Acquisition Strategy for Comprehensive Analysis of Human Plasma Proteome
1 Introduction
2 Materials
2.1 Plasma Collection and Abundant Plasma Protein Depletion
2.2 Single-Pot Solid-Phase-Enhanced Sample Preparation and Enzymatic Digestion
2.3 Proteomic Profiling Using NanoLC-MS/MS
2.4 MS Data Processing and Analysis
2.5 DIA Method Development and Optimization (Optional)
3 Methods
3.1 Plasma Collection and Abundant Plasma Protein Depletion
3.2 Single-Pot Solid-Phase-Enhanced Sample Preparation and Enzymatic Digestion
3.3 Proteomic Profiling Using NanoLC-MS/MS
3.4 MS Data Processing and Analysis
3.5 DIA Method Development and Optimization (Optional)
4 Notes
References
Chapter 8: In-Depth Blood Proteome Profiling by Extensive Fractionation and Multiplexed Quantitative Mass Spectrometry
1 Introduction
2 Materials
2.1 Plasma Protein Precipitation
2.2 In-Solution Digesting
2.3 Peptide Desalting
2.4 TMT Labeling and Desalting of Peptides
2.5 Offline Basic pH RPLC
2.6 Acidic pH RPLC MS/MS Analysis
2.7 MS Data Analysis
3 Methods
3.1 Plasma/Serum Collection
3.2 Protein Precipitation and In-Solution Digestion
3.2.1 Protein Precipitation
3.2.2 In-Solution Digestion and Peptide Desalting
3.3 TMTpro 18-Plex Labeling, Pooling, and Desalting
3.4 Basic pH RPLC Extensive Fractionation
3.5 Acidic pH RPLC-MS/MS Analysis
3.6 Computational Analysis
3.6.1 Database Search of MS Raw Data
3.6.2 Peptide Identification After Data Filtering
3.6.3 Quantification of Peptides/Proteins
4 Notes
References
Chapter 9: Early Cancer Biomarker Discovery Using DIA-MS Proteomic Analysis of EVs from Peripheral Blood
1 Introduction
2 Materials
2.1 Serum Sample Preparation
2.2 Extracellular Vesicle (EV) Purification
2.3 EV Protein Extraction for LC-MS/MS
2.4 Protein Quantitation (EZQ Protein Quantitation Kit) (See Note 8)
2.5 Sample Preparation (Reduction and Alkylation) for LC-MS/MS
2.6 Single-Pot Solid-Phase-Enhanced Sample Preparation (SP3) Protocol (See Note 11)
2.7 Enzymatic Digestion of Protein
2.8 Peptide Sample Desalting Using ZipTips and Spin Columns
2.9 Off-Line Peptide Fractionation by Microflow High pH RP-HPLC
2.10 LC-MS/MS Analysis of Peptide Samples
2.11 LC-MS/MS Data Processing
2.12 Statistical Analysis
3 Methods
3.1 Serum Sample Preparation
3.2 Extracellular Vesicle Extraction
3.3 EV Protein Extraction for LC-MS/MS
3.4 Protein Quantitation (EZQ Protein Quantitation Kit) (See Note 8)
3.5 Sample Preparation (Reduction and Alkylation) for LC-MS/MS
3.6 Single-Pot, Solid-Phase-Enhanced Sample Preparation (SP3) Protocol (See Note 27)
3.7 Enzymatic Digestion of Proteins and Peptide Desalting (See Note 41)
3.8 Off-Line Peptide Separation by High pH Reversed Phase HPLC
3.9 DDA-MS Data Processing and Spectral Library Generation
3.10 DIA-MS Data Processing
3.11 Statistical Analysis
4 Notes
References
Part III: Developments in Technologies for Plasma Proteomics
Chapter 10: Strategies to Enrich, Identify, and Characterize Glycoproteome in Blood Plasma Using Liquid Chromatography High-Re...
1 Introduction
2 Materials
2.1 Plasma Sample Collection and Storage
2.2 Enrichment of Glycoproteins Using Lectin Affinity Chromatography (LAC)
2.3 Glycoprotein Digestion and Peptide Preparation
2.4 Selective Enrichment of Glycopeptides by LAC and HILIC, Deglycosylation, and O18 Labeling
2.5 MS Acquisition (Nano-LC-HR-MS/MS)
2.6 LC-HRMS Data Analysis: Software Tools
3 Methods
3.1 Blood Collection, Isolation of Plasma, and Storage
3.1.1 Isolation of Plasma from Blood Samples
3.2 Glycoprotein Enrichment from Plasma Samples
3.2.1 Selective Enrichment of Plasma Glycoproteins through Lectin Affinity Chromatography (LAC)
3.3 Glycoprotein Digestion
3.4 Glycopeptide Enrichment, Deglycosylation, and O18 Labeling
3.4.1 Glycopeptide Enrichment Using LAC-HILIC, PNGase-Mediated Deglycosylation, and O18 Labeling
3.5 HR-MS Acquisition (Nano-LC-HR Orbitrap MS)
3.6 Software-Based Data Analysis
4 Notes
References
Chapter 11: Proteome Analysis of Whole Blood Collected by Volumetric Absorptive Microsampling
1 Introduction
2 Materials
2.1 Blood Collection
2.2 VAMS Processing
2.3 Tryptic Digestion and Peptide Purification
2.4 Mass Spectrometry
3 Methods
3.1 Blood Collection
3.2 VAMS Processing
3.3 Tryptic Digestion
3.4 Mass Spectrometry Acquisition
3.5 Peptide Identification
4 Notes
References
Chapter 12: Proteomic Applications and Considerations: From Research to Patient Care
1 Introduction
1.1 Recent Advancements in Proteomics
2 Analytical Strategies in Proteomics
2.1 Label-Based Proteomics
2.2 Label-Free-Based Proteomics
3 Proteomic Biomarker Studies
4 Blood
4.1 Plasma- and Serum-Derived Extracellular Vesicles
5 Blood-Based Proteomic Biomarkers
5.1 Cancer
5.2 Stroke and Venous Thromboembolism (VTE)
5.3 Mild Traumatic Brain Injury (mTBI)
6 The Future of Proteomics
6.1 Machine Learning
6.2 Proteogenomics
7 Considerations for Biomarker Studies
References
Part IV: Enrichment & Detection Strategies
Chapter 13: Immunoaffinity Mass Spectrometry Diagnostic Tests for Multi-Biomarker Assays
1 Introduction
2 Materials
2.1 Antibody-Bead Production
2.2 Calibrator Preparation
2.3 Immunoaffinity Bead Method
2.4 MS Acquisition (Microflow-LC-MS/MS)
2.5 MS Data Evaluation: Software Tools
3 Methods
3.1 Antibody-Bead Preparation
3.2 Calibrator Preparation
3.3 Synthetic Label Preparation
3.4 IA-MS Method
3.5 LCMS Acquisition
3.6 MS Data Analysis
4 Notes
References
Chapter 14: Secretome Profile of Leukocyte-Platelet-Rich Fibrin (L-PRF) Membranes
1 Introduction
2 Materials
2.1 Blood Collection
2.2 L-PRF Membrane Culture and Secretome Collection
2.3 Protein Precipitation
2.4 Protein Quantitation
2.5 Gel-Based Proteomics: Qualitative Proteomics
2.5.1 Trypsin in-Gel Digestion
2.6 Gel-Based Proteomics: MS Analysis
2.7 Differential Secretome Protein Quantitation by SWATH-MS
3 Methods
3.1 Blood Collection: L-PRF Generation
3.2 L-PRF Membrane Culture and Secretome Collection
3.3 Protein Precipitation
3.4 Protein Quantitation
3.5 Qualitative Proteomics Analysis
3.5.1 Gel-Based Protein Separation
3.5.2 Trypsin in-Gel Digestion (See Note 9)
3.5.3 MS Analysis
3.6 Differential Quantitative SWATH-MS-Based Proteomic Analysis of L-PRF Secretomes
3.7 Systems Biology
4 Notes
References
Chapter 15: Quantification of Proteins in Blood by Absorptive Microtiter Plate-Based Affinity Purification Coupled to Liquid C...
1 Introduction
2 Materials
2.1 Antibody-Based Immunocapture
2.2 Affimer-Based Immunocapture
2.3 Direct (in-Well) Tryptic Digestion
2.4 Indirect Tryptic Digestion
2.5 LC-SRM/MS Analysis
3 Methods
3.1 Antibody-Based Immunocapture
3.2 Affimer-Based Immunocapture
3.3 Direct (in-Well) Tryptic Digestion
3.4 Indirect Tryptic Digestion
3.5 μLC-SRM/MS Analysis
4 Notes
References
Chapter 16: Glycomics-Assisted Glycoproteomics Enables Deep and Unbiased N-Glycoproteome Profiling of Complex Biological Speci...
1 Introduction
2 Materials
2.1 Initial Protein Handling
2.2 N-Glycomics Workflow
2.2.1 Protein Immobilization on PVDF Membrane
2.2.2 Release of N-Glycans
2.2.3 Reduction of N-Glycans
2.2.4 Desalting of N-Glycans Using SCX-C18-SPE
2.2.5 Desalting of N-Glycans Using PGC-C18-SPE
2.2.6 PGC-LC-MS/MS
2.2.7 Data Analysis (Manual- and Software-Aided Annotation): Software
2.3 N-Glycoproteomics Workflows
2.3.1 Tryptic Digestion and Peptide Desalting Using Oligo R3-C18-SPE
2.3.2 TMT-Labeling of Peptides (Optional)
2.3.3 N-Glycopeptide Enrichment Using ZIC-HILIC-C8-SPE
2.3.4 Peptide De-N-glycosylation
2.3.5 High-pH Prefractionation Using R2-C18-SPE (Optional)
2.3.6 Reversed-Phased LC-MS/MS of Intact N-Glycopeptides, De-N-glycopeptides, and Non-modified Peptides
2.3.7 Data Analysis of LC-MS/MS Data of Intact N-Glycopeptides: Software
2.3.8 Data Analysis of LC-MS/MS Data of De-N-glycopeptides and Non-modified Peptides: Software
3 Methods
3.1 Initial Protein Handling
3.2 N-Glycomics Workflow
3.2.1 Protein Immobilization on PVDF Membrane
3.2.2 Release of N-Glycans
3.2.3 Reduction of N-Glycans
3.2.4 Desalting of N-Glycans Using SCX-C18-SPE
3.2.5 Desalting of N-Glycans Using PGC-C18-SPE (See Note 4.1.7)
3.2.6 PGC-LC-MS/MS
3.2.7 Data Analysis (Manual- and Software-Aided Annotation)
3.3 N-Glycoproteomics Workflows
3.3.1 Tryptic Digestion and Peptide Desalting Using Oligo R3-C18-SPE
3.3.2 TMT-Labeling (Optional)
3.3.3 N-Glycopeptide Enrichment Using ZIC-HILIC-C8-SPE
3.3.4 Peptide De-N-glycosylation
3.3.5 High-pH Prefractionation Using R2-C18-SPE (Optional) (See Note 4.2.5)
3.3.6 Reversed-Phased LC-MS/MS of Intact N-Glycopeptides, De-N-glycopeptides, and Non-modified Peptides
3.3.7 Data Analysis of LC-MS/MS Data of Intact N-Glycopeptides
3.3.8 Data Analysis of LC-MS/MS Data of Non-modified and De-N-glycosylated Peptides
4 Notes
4.1 N-Glycomics Workflow
4.2 N-Glycoproteomics Workflows
References
Chapter 17: The Small-Protein Enrichment Assay (SPEA) for Analysis of Low Abundance Peptide Hormones in Plasma
1 Introduction
2 Materials
2.1 Plasma Precipitation
2.2 Chloroform/Ethanol Delipidation
2.3 SEC Liquid Chromatography Separation
2.4 Sample Preparation for Mass Spectrometry
2.5 DDA Mass Spectrometry Analysis
2.6 DIA Mass Spectrometry Analysis
3 Methods
3.1 Plasma Precipitation
3.2 Chloroform/Ethanol Delipidation
3.3 SEC Liquid Chromatography Separation
3.4 Sample Preparation for Mass Spectrometry
3.5 DDA Mass Spectrometry Analysis
3.6 DIA Mass Spectrometry Analysis
3.7 DDA Data Analysis
3.8 DIA Data Analysis
4 Notes
References
Part V: Analysis of Extracellular Vesicles from Blood
Chapter 18: In-Depth Proteomic Analysis of Blood Circulating Small Extracellular Vesicles
1 Introduction
2 Materials
2.1 Plasma Preparation
2.2 Exosome Isolation and Lysis
2.3 Protein Precipitation and Resuspension
2.4 Immunodepletion of Plasma Contaminants
2.5 Protein Digestion and Peptide Purification
2.6 MS Acquisition (Nano-LC-MS/MS)
2.7 MS Data Analysis and Bioinformatic Tools
3 Methods
3.1 Collection of Blood and Preparation of Plasma
3.2 sEVs Isolation and Lysis
3.3 Immunodepletion of Most Abundant Plasma Proteins
3.4 Protein Digestion and Peptide Purification
3.5 MS Acquisition (Nano-LC-MS/MS)
3.6 MS Data Analysis
3.7 Bioinformatic Analysis
4 Notes
References
Chapter 19: Protocol for Plasma Extracellular Vesicle and Particle Isolation and Mass Spectrometry-Based Proteomic Identificat...
1 Introduction
2 Materials
2.1 Blood Collection and Plasma EVPs Isolation
2.2 LC-MS/MS
2.3 Quantification and Bioinformatic Analysis
3 Methods
3.1 EVPs Isolation
3.2 Preparation of EVPs Samples for LC-MS/MS Processing
3.3 LC-MS/MS Analysis of EVPs Peptides
3.4 Bioinformatic Analysis of Proteomic Data
4 Notes
References
Chapter 20: Analysis of Extracellular Vesicle and Contaminant Markers in Blood Derivatives Using Multiple Reaction Monitoring
1 Introduction
2 Materials
2.1 Blood Serum/Plasma Collection
2.2 Isolation of Extracellular Vesicles
2.3 Isolation of Platelets and Red Blood Cells (RBCs) from Whole Blood
2.4 EV Protein Quantification, Extraction, and Digestion
2.5 Targeted Peptide Analysis by LC-MRM-MS
3 Methods
3.1 Collection and Storage of Blood Serum/Plasma
3.2 Isolation of Extracellular Vesicles
3.3 Isolation of Platelet and Red Blood Cells from Whole Blood
3.4 Protein Digestion
3.5 SIL Peptides
3.6 Analytical Method
4 Notes
References
Chapter 21: Generation of Red Blood Cell Nanovesicles as a Delivery Tool
1 Introduction
2 Materials
2.1 Isolation of Erythrocytes from Whole Blood
2.2 RBC Ghost Generation by Hypotonic Hemolysis
2.3 Erythrocyte-Derived Nanovesicle (edNV) Generation and Purification
2.4 Biophysical Characterization
2.4.1 Nanoparticle Tracking Analysis (NTA) Using ZetaView Particle Metrix
2.4.2 Cryoelectron Microscopy
2.5 Drug Loading and Detection
2.5.1 Active Curcumin Loading by Extrusion
2.5.2 Passive Curcumin Loading by Diffusion
2.5.3 Drug Detection by Spectrophotometry
2.6 MS Sample Preparation
2.6.1 Protein Reduction and Alkylation
2.6.2 Single-Pot, Solid-Phase-Enhanced Sample Preparation
2.6.3 Nano-LC MS/MS
2.7 Data Analysis
3 Methods
3.1 Isolation of Erythrocytes from Whole Blood
3.2 Erythrocyte Ghost Generation by Hypotonic Hemolysis
3.3 Erythrocyte-Derived Nanovesicle Generation and Purification (See Note 3)
3.4 Biophysical Characterization (See Note 9)
3.4.1 Nanoparticle Tracking Analysis (NTA) Using Zetaview Particle Metrix
3.4.2 Cryoelectron Microscopy (See Note 12)
3.5 Drug Loading and Detection (See Note 13)
3.5.1 Active Loading by Extrusion
3.5.2 Passive Loading by Diffusion
3.5.3 Drug Detection by UV-Visible Spectrophotometry
3.6 Proteomic Profiling of Erythrocyte-Derived Nanovesicles
3.6.1 Single-Pot, Solid-Phase-Enhanced Sample Preparation
3.6.2 Nano-LC MS/MS
3.7 Data Analysis
4 Notes
References
Part VI: Targeted-Based Mass Spectrometry
Chapter 22: Progress in Targeted Mass Spectrometry (Parallel Accumulation-Serial Fragmentation) and Its Application in Plasma/...
1 Introduction
2 Materials
2.1 Protein Digestion from Human Serum/Plasma
2.2 prmPASEF Assay Generation and Targeted MS Analysis
2.3 MRM Assay Generation and Targeted MS Analysis
2.4 Websites and Software
3 Methods
3.1 Protein Digestion from Human Serum/Plasma
3.2 Generation of the Secreted Protein Library
3.3 Generation of the Peptide Library for prmPASEF
3.4 Generation of prmPASEF Assays
3.5 Validation and Optimization of prmPASEF Assays
3.6 Generation of MRM Assays
3.7 Verification and Optimization of MRM Assays
3.8 Query MRM or PRM Assays from Cancer Serum Atlas
3.9 Perform prmPASEF or MRM Assays in Serum or Plasma Samples
3.10 Quantitative Analysis of Target Proteins in Serum or Plasma
4 Notes
References
Chapter 23: Offline Peptide Fractionation and Parallel Reaction Monitoring MS for the Quantitation of Low-Abundance Plasma Pro...
1 Introduction
2 Materials
2.1 Enzymatic Digestion
2.2 Standard Curve Preparation and Sample Spiking
2.3 Solid-Phase Extraction (SPE)
2.4 High-pH Reversed-Phase Fractionation
2.5 Determine Plasma Digest Protein Concentration
2.6 Nano-LC-MS/MS
2.7 MS Data Analysis: Software Tools
3 Methods
3.1 Human Plasma
3.2 Enzymatic Digestion of Human Plasma and BSA
3.3 Preparation of NAT and SIS Peptide Dilutions
3.4 Spiking Plasma and BSA Digests with Synthetic Peptides
3.5 SPE of Standard Curve Samples and Plasma Samples
3.6 Reversed-Phase High-pH Peptide Fractionation
3.7 Quantitation of Fractionated Plasma Peptide Concentration
3.8 Nano-LC-MS/MS Analysis by PRM on Q Exactive Plus
3.9 MS Data Analysis
4 Notes
References
Chapter 24: Profiling Serum Intact N-Glycopeptides Using Data-Independent Acquisition Mass Spectrometry
1 Introduction
2 Materials
2.1 Example Data
2.2 Software Tools
3 Methods
3.1 Spectral Library Building
3.1.1 DDA Database Searching
3.1.2 Preparation for Library Building
3.1.3 Library Building from DDA Results
3.1.4 Library Generation for OpenSWATH
3.2 DIA Data Analysis
3.2.1 Raw Data Conversion to mzML
3.2.2 Targeted Data Extraction
3.2.3 Statistical Control
3.2.4 Multi-run Alignment
3.2.5 Post-analysis Data Visualization
4 Notes
References
Part VII: Assay Development in Biomarker Discovery and Translational Proteomics
Chapter 25: Semi-Automated Lectin Magnetic Bead Array (LeMBA) for Translational Serum Glycoprotein Biomarker Discovery and Val...
1 Introduction
2 Materials
2.1 Lectin Conjugation to Magnetic Beads
2.2 Serum Preparation
2.3 Lectin Pulldown
3 Method
3.1 Lectin Conjugation to Tosyl-Activated Dynabeads MyOne
3.1.1 Day 1: Lectin Coupling to Dynabeads
3.1.2 Day 2: Blocking the Conjugated Beads
3.1.3 Day 3: Lectin-Bead Conjugate Storage
3.2 Serum Sample Preparation
3.2.1 Plate Layout and Controls
3.2.2 Serum Protein Denaturation (See Note 5)
3.3 Lectin Magnetic Bead Array (LeMBA) Pulldown
3.3.1 AssayMAP Bravo System Start-Up
AssayMAP Bravo Deck Layout
Serum Glycoprotein Capture with Lectin-Conjugated Beads
On-Bead Trypsin Digestion
Collect Digested Peptides
AssayMAP Bravo System Shutdown
3.3.2 Overview of the Integra VIAFLO 96 GripTip and Tecan HydroFlex Microplate Washer
Programming of the Tecan HydroFlex Microplate Washer
Programming of the Integra VIAFLO 96 GripTip
Procedures to Work with the Tecan HydroFlex Microplate Washer and the Integra VIAFLO 96 GripTip Systems for LeMBA
3.4 Mass Spectrometry
3.5 Data Quality Control Analysis
4 Notes
References
Chapter 26: Accessing Antibody Reactivities in Serum or Plasma to (Auto-)antigens Using Multiplexed Bead-Based Protein Immunoa...
Abbreviations
1 Introduction
2 Materials
2.1 Coupling of Proteins to MagPlex Beads
2.2 Coupling Confirmation Assay
2.3 Multiplexed Bead-Based Protein Immunoassay
3 Methods
3.1 Preparations for Protein Coupling to MagPlex Beads
3.1.1 Bead Preparation
Selection of Bead Types
Calculation of Required Bead Amounts
Coupling Plate Preparation
3.1.2 Protein Preparation
3.1.3 Control Antigens
3.2 Coupling of Proteins to MagPlex Beads
3.2.1 Bead Activation
3.2.2 Protein Immobilization on Activated MagPlex Beads
3.3 Preparing the Flexmap 3D Instrument
3.4 Coupling Confirmation Assay
3.4.1 Preparations of the Multiplexed Bead Mix
3.4.2 Detection of Tagged Proteins with Penta-HIS-Specific Antibody
3.4.3 Analysis and Interpretation of Results
3.5 Multiplexed Bead-Based Protein Immunoassay for the Determination of Antibody Reactivities
3.5.1 Selection of Sample Matrix
Serum or Plasma Dilution Without IgG Purification
Purification and Dilution of IgG Samples from Serum or Plasma
3.5.2 Preparation of the Bead Mix
3.5.3 Sample Processing and Detection
3.5.4 Data Export and Analysis
4 Notes
References
Chapter 27: Absolute Quantitative Targeted Proteomics Assays for Plasma Proteins
1 Introduction
2 Materials
2.1 Samples
2.2 Sample Preparation Chemicals and Reagents
2.3 Peptide Synthesis
2.4 Equipment and Liquid Chromatography-Mass Spectrometry Instrumentation
2.5 Software Tools and Data Analysis
3 Methods
3.1 Protein Assay Design
3.2 Peptide Synthesis
3.3 Sample Preparation
3.4 LC Separation and MRM-MS Acquisition
3.5 Data Evaluation
4 Notes
References
Part VIII: Plasma-Based Peptide, Lipid, and Metabolite Assays
Chapter 28: Rapid and Quantitative Enrichment of Peptides from Plasma for Mass Spectrometric Analysis
1 Introduction
2 Materials
2.1 Peptide Extraction from Human Plasma
2.1.1 Protein Precipitation
2.1.2 Solid Phase Extraction (SPE)
2.1.3 Reduction and Alkylation
2.2 Peptidomics Characterization
3 Methods
3.1 Peptide Extraction from Human Plasma
3.1.1 Protein Precipitation
3.1.2 Solid Phase Extraction (SPE)
3.1.3 Reduction and Alkylation
3.2 Peptidomics Characterization
3.3 PEAKS Peptidomics Data Analysis
4 Notes
References
Chapter 29: Comprehensive Targeted Lipidomic Profiling for Research and Clinical Applications
1 Introduction
2 Materials
2.1 General
2.2 Chromatography and Mass Spectrometry Analysis
3 Method
3.1 Lipid Extraction
3.2 Mass Spectrometry Setup
3.2.1 Instrument Setup (Chromatography, Agilent 6495C)
3.2.2 Instrument Setup (Mass Spectrometry, Agilent 6495C)
3.2.3 Retention Time Adjustment
3.2.4 Sample Thawing and Preparation
3.2.5 Data Acquisition, Quality Control Monitoring, and Sample Changeover
3.3 Data Integration and Reporting (Agilent)
4 Notes
References
Chapter 30: Multiplexed Bead-Based Peptide Immunoassays for the Detection of Antibody Reactivities
1 Introduction
2 Materials
2.1 Coupling of Peptides to MagPlex Beads
2.2 Multiplexed Immunoassay and Coupling Confirmation Assay with Peptide-Coupled Microspheres
3 Methods
3.1 Peptide Design and Modifications for Coupling
3.2 Coupling of Peptides to MagPlex Beads
3.2.1 Preparation of MagPlex Beads
3.2.2 Preparation of the Antigenic Peptides
3.2.3 Preparation of Control Antigens
3.2.4 Preparation of 2-Azidoethan-1-Amine Hydrochloride
3.2.5 Coupling of 2-Azidoethan-1-Amine Hydrochloride to MagPlex Beads
3.2.6 Coupling of Propargylglycine-Tagged Peptides to Azide-Coupled MagPlex Beads
3.3 FLEXMAP 3D Instrument Preparation
3.4 Coupling Confirmation Assay
3.4.1 Preparation of the Bead Mix
3.4.2 Detection of Tagged Peptides with FLAG-Tag-Specific Antibody
3.4.3 Data Export and Analysis
3.5 Multiplexed Bead-Based Peptide Immunoassay for the Detection of Antibody Reactivities
3.5.1 Preparation of Purified IgG Samples from Serum or Plasma
3.5.2 Preparation of the Bead Mix
3.5.3 Processing of IgG Samples
3.5.4 Data Export and Analysis
4 Notes
References
Chapter 31: Array-Based Multiplex and High-Throughput Serology Assays
1 Introduction
2 Materials
2.1 Bead Coupling and Coupling Efficiency Test
2.2 Serology Assay
2.3 Data Analysis
3 Methods
3.1 Bead Coupling and Coupling Efficiency Test
3.2 Serology Assay
3.3 Data Analysis
4 Notes
References
Part IX: Software and Bioinformatics for the Plasma Proteome
Chapter 32: Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 33: Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection
1 Introduction
2 Materials
2.1 Immunoaffinity Depletion
2.2 Protein Digestion and Sample Cleanup
2.3 LC-SRM Data Acquisition
2.4 SRM Data Analysis and Longitudinal Data Analysis: Software Tools
3 Methods
3.1 Randomization and Batching
3.2 Immunoaffinity Depletion
3.3 Protein Digestion and Sample Cleanup
3.4 LC-SRM Data Acquisition
3.5 LC-SRM Data Analysis and Processing
3.6 Classifier Development and Evaluation Using Random Forest
4 Notes
References
Index

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Methods in Molecular Biology 2628

David W. Greening Richard J. Simpson Editors

Serum/Plasma Proteomics Methods and Protocols Third Edition

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Serum/Plasma Proteomics Methods and Protocols Third Edition

Edited by

David W. Greening Molecular Proteomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

Richard J. Simpson Department of Biochemistry and Chemistry, School of Agriculture, Biomedicine and Environment, La Trobe Institute for Molecular Science, Melbourne, VIC, Australia

Editors David W. Greening Molecular Proteomics Baker Heart and Diabetes Institute Melbourne, VIC, Australia

Richard J. Simpson Department of Biochemistry and Chemistry, School of Agriculture Biomedicine and Environment La Trobe Institute for Molecular Science Melbourne, VIC, Australia

ISSN 1064-3745 ISSN 1940-6029 (electronic) ISBN 978-1-0716-2977-2 ISBN 978-1-0716-2978-9 (eBook) https://doi.org/10.1007/978-1-0716-2978-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2011, 2017, 2023 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, expressed 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 Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Cartography of the plasma proteome remains technically challenging. Despite extensive efforts to interrogate the plasma proteome for its composition and disease markers, relatively few new candidate biomarkers have been accepted as clinically useful. This lack of success is largely due to the complexity and unknown size of the plasma proteome which has a concentration range exceeding 10 orders of magnitude, including low-abundant tissuederived proteins in the pg/mL range. Despite these technical challenges, recent developments in sensitive sample preparation (enrichment, multiplexing, fractionation) and enhanced data-processing pipelines for the comprehensive, deep, and unbiased mass spectrometry-based proteomic analysis of plasma herald a bright future for plasma-based disease biomarker discovery. Over the past several years, we have witnessed technological advances in mass spectrometry, developments in anti-protein or anti-peptide immunodepletion, sample preparation workflows, chromatographic fractionation, targeted analytespecific monitoring assays, sample multiplexing, and processing and analytical throughput. Software analysis workflows and informatic interrogation and digitalization pipelines are also providing rapid and comprehensive insights for discovery and large-scale understanding of the plasma proteome. This updated volume describes recent developments in these above-mentioned areas of plasma proteomics. This third edition of Serum/Plasma Proteomics provides a detailed insight into topics and methods covering blood processing and handling strategies (Part I), discovery-based mass spectrometry (Part II), and targeted-based mass spectrometry (Part VI). We implement new areas including developments in technologies for plasma proteomics (Part III) and assay development in biomarker discovery and translational proteomics (Part VII). Several new areas have been expanded to integrate enrichment and detection strategies to understand the plasma proteome (Part IV), isolation and use of extracellular vesicles from blood (Part V), and peptide, lipid, and metabolite targeted assays (Part VIII). Workflows to aid in discovery and targeted data analysis and interpretation we have included Software and Bioinformatics for the Plasma Proteome (Part IX). 2023: Serum/Plasma Proteomics is a comprehensive resource of 33 chapters and protocols across the areas of enrichment and fractionation for sensitive sample preparation, multiplexing, advanced mass spectrometry acquisition strategies, and sophisticated data processing pipelines. We introduce developments in microsampling, glycoprotein biology, immunoassay design, high-throughput quantitative analyses, lipidomics, biomarker discovery and validation, and analysis of circulating factors including extracellular vesicles and platelets. This updated volume, contributed by leading experts in the field, complements the initial volumes of Serum/Plasma Proteomics (2011) and An Updated Serum/Plasma Proteomics (2017), and provides a valuable foundation for the development and application of

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blood-based proteomics. We would like to thank all authors and coauthors for sharing their experience, knowledge, and time to make this new edition possible. We hope this resource will catalyze further advances in the deep and scalable quantitative analysis of proteomic studies in plasma and blood products. Melbourne, VIC, Australia

David W. Greening Richard J. Simpson

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

BLOOD PROCESSING AND HANDLING STRATEGIES

1 Protocols for the Isolation of Platelets for Research and Contrast to Production of Platelet Concentrates for Transfusion . . . . . . . . . . . . . . . . . . . . . . Rosemary L. Sparrow, Richard J. Simpson, and David W. Greening 2 Collection of Plasma Samples in Areas with Limited Healthcare Access . . . . . . . . ˜ es Padilha, Alicia Johnson, Camila Braga, Pedro de Magalha and Jiri Adamec 3 Blood Collection Processing and Handling for Plasma and Serum Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conor McCafferty, Natasha Letunica, Ella Swaney, Cai Tengyi, Paul Monagle, Vera Ignjatovic, and Chantal Attard 4 Preparation of Cryoprecipitate and Cryo-depleted Plasma for Proteomic Research Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rosemary L. Sparrow, Richard J. Simpson, and David W. Greening

PART II

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DISCOVERY-BASED MASS SPECTROMETRY

5 High-Throughput Proteome Profiling of Plasma and Native Plasma Complexes Using Native Chromatography . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Aleksandr Gaun, Niclas Olsson, John C. K. Wang, Dan L. Eaton, and Fiona E. McAllister 6 High-Throughput and In-Depth Proteomic Profiling of 5 μL Plasma and Serum Using TMTpro 16-Plex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Yan Zhou, Rui Sun, Sainan Li, Xiao Liang, Liujia Qian, Liang Yue, and Tiannan Guo 7 An Optimized Data-Independent Acquisition Strategy for Comprehensive Analysis of Human Plasma Proteome . . . . . . . . . . . . . . . . . . . . . . . 93 Haoyun Fang and David W. Greening 8 In-Depth Blood Proteome Profiling by Extensive Fractionation and Multiplexed Quantitative Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Xue Zhang, Huan Sun, Zhen Wang, Suiping Zhou, Yingxue Fu, High A. Anthony, and Junmin Peng 9 Early Cancer Biomarker Discovery Using DIA-MS Proteomic Analysis of EVs from Peripheral Blood. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Camila Espejo, Bruce Lyons, Gregory M. Woods, and Richard Wilson

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PART III

DEVELOPMENTS IN TECHNOLOGIES FOR PLASMA PROTEOMICS

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Strategies to Enrich, Identify, and Characterize Glycoproteome in Blood Plasma Using Liquid Chromatography High-Resolution Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Saravanan Kumar 11 Proteome Analysis of Whole Blood Collected by Volumetric Absorptive Microsampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Mark P. Molloy, Cameron Hill, Matthew J. McKay, and Ben R. Herbert 12 Proteomic Applications and Considerations: From Research to Patient Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Natasha Letunica, Conor McCafferty, Ella Swaney, Tengyi Cai, Paul Monagle, Vera Ignjatovic, and Chantal Attard

PART IV

ENRICHMENT & DETECTION STRATEGIES

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Immunoaffinity Mass Spectrometry Diagnostic Tests for Multi-Biomarker Assays. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scott Bringans, Tammy Casey, Jun Ito, Tasha Lumbantobing, Ronan O’Neill, and Richard Lipscombe 14 Secretome Profile of Leukocyte-Platelet-Rich Fibrin (L-PRF) Membranes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ´ ngel Garcı´a Lidia Hermida-Nogueira, Juan Blanco, and A 15 Quantification of Proteins in Blood by Absorptive Microtiter Plate-Based Affinity Purification Coupled to Liquid Chromatography-Mass Spectrometry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Frank Klont, Oladapo Olaleye, and Rainer Bischoff 16 Glycomics-Assisted Glycoproteomics Enables Deep and Unbiased N-Glycoproteome Profiling of Complex Biological Specimens . . . . . . . . . . . . . . . The Huong Chau, Anastasia Chernykh, Julian Ugonotti, Benjamin L. Parker, Rebeca Kawahara, and Morten Thaysen-Andersen 17 The Small-Protein Enrichment Assay (SPEA) for Analysis of Low Abundance Peptide Hormones in Plasma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dylan James Harney and Mark Larance

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PART V ANALYSIS OF EXTRACELLULAR VESICLES FROM BLOOD 18

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In-Depth Proteomic Analysis of Blood Circulating Small Extracellular Vesicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Veronica De Giorgis, Elettra Barberis, Marco Falasca, and Marcello Manfredi Protocol for Plasma Extracellular Vesicle and Particle Isolation and Mass Spectrometry-Based Proteomic Identification . . . . . . . . . . . . . . . . . . . . . . . . . 291 Amirmohammad Nasiri Kenari, Linda Bojmar, Søren Heissel, Henrik Molina, David Lyden, and Ayuko Hoshino

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Analysis of Extracellular Vesicle and Contaminant Markers in Blood Derivatives Using Multiple Reaction Monitoring . . . . . . . . . . . . . . . . . . . . . 301 Lauren A. Newman, Zivile Useckaite, Ting Wu, Michael J. Sorich, and Andrew Rowland 21 Generation of Red Blood Cell Nanovesicles as a Delivery Tool . . . . . . . . . . . . . . . 321 Auriane Drack, Alin Rai, and David W. Greening

PART VI

TARGETED-BASED MASS SPECTROMETRY

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Progress in Targeted Mass Spectrometry (Parallel Accumulation-Serial Fragmentation) and Its Application in Plasma/Serum Proteomics . . . . . . . . . . . . 339 Anqi Hu, Jiayi Zhang, and Huali Shen 23 Offline Peptide Fractionation and Parallel Reaction Monitoring MS for the Quantitation of Low-Abundance Plasma Proteins . . . . . . . . . . . . . . . . 353 Claudia Gaither, Robert Popp, Vincent R. Richard, Rene´ P. Zahedi, and Christoph H. Borchers 24 Profiling Serum Intact N-Glycopeptides Using Data-Independent Acquisition Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Yi Yang and Liang Qiao

PART VII

ASSAY DEVELOPMENT IN BIOMARKER DISCOVERY AND TRANSLATIONAL PROTEOMICS

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Semi-Automated Lectin Magnetic Bead Array (LeMBA) for Translational Serum Glycoprotein Biomarker Discovery and Validation. . . . . . . . 395 Mriga Dutt, Marisa N. Duong, Scott Bringans, Rene´e S. Richards, Richard Lipscombe, and Michelle M. Hill 26 Accessing Antibody Reactivities in Serum or Plasma to (Auto-)antigens Using Multiplexed Bead-Based Protein Immunoassays . . . . . . . . . . . . . . . . . . . . . . 413 Jasmin Huber, Silvia Scho¨nthaler, Manuela Hofner, Yasmin Gillitschka, Regina Soldo, Lisa Milchram, Klemens Vierlinger, Christa No¨hammer, and Andreas Weinh€ a usel 27 Absolute Quantitative Targeted Proteomics Assays for Plasma Proteins . . . . . . . . 439 Yassene Mohammed, David Goodlett, and Christoph H. Borchers

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PLASMA-BASED PEPTIDE, LIPID, AND METABOLITE ASSAYS

Rapid and Quantitative Enrichment of Peptides from Plasma for Mass Spectrometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477 Amy L. George, Rachel E. Foreman, Mariwan H. Sayda, Frank Reimann, Fiona M. Gribble, and Richard G. Kay Comprehensive Targeted Lipidomic Profiling for Research and Clinical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 Kevin Huynh, Thy Duong, Natalie A. Mellett, Michelle Cinel, Corey Giles, and Peter J. Meikle

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Multiplexed Bead-Based Peptide Immunoassays for the Detection of Antibody Reactivities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Silvia Scho¨nthaler, Jasmin Huber, Manuela Hofner, Yasmin Gillitschka, Regina Soldo, Lisa Milchram, Klemens Vierlinger, Christa No¨hammer, and Andreas Weinh€ a usel 31 Array-Based Multiplex and High-Throughput Serology Assays . . . . . . . . . . . . . . . 535 Jennie Olofsson, Ceke Hellstro¨m, Eni Andersson, Jamil Yousef, Lovisa Skoglund, Ronald Sjo¨berg, Anna Ma˚nberg, Peter Nilsson, and Elisa Pin

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SOFTWARE AND BIOINFORMATICS FOR THE PLASMA PROTEOME

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Yassene Mohammed, David Goodlett, and Christoph H. Borchers Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 Rashmi Madda, Vladislav A. Petyuk, Yi-Ting Wang, Tujin Shi, Craig D. Shriver, Karin D. Rodland, and Tao Liu

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors JIRI ADAMEC • Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA; Redox Biology Center, University of Nebraska-Lincoln, Lincoln, NE, USA ENI ANDERSSON • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden HIGH A. ANTHONY • Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN, USA CHANTAL ATTARD • Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia; The Royal Children’s Hospital, Parkville, VIC, Australia ELETTRA BARBERIS • Biological Mass Spectrometry Lab, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy; Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale, Novara, Italy RAINER BISCHOFF • Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands JUAN BLANCO • Periodontology Unit, Medical-Surgical Dentistry Research Group (OMEQUI), Faculty of Medicine and Odontology, Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain LINDA BOJMAR • Children’s Cancer and Blood Foundation Laboratories, Departments of Pediatrics, and Cell and Developmental Biology, Drukier Institute for Children’s Health, Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA CHRISTOPH H. BORCHERS • Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada; Division of Experimental Medicine, McGill University, Montreal, QC, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada; Department of Pathology, McGill University, Montreal, QC, Canada; Segal Cancer Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada; Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC, Canada CAMILA BRAGA • Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA; Redox Biology Center, University of Nebraska-Lincoln, Lincoln, NE, USA SCOTT BRINGANS • Proteomics International, QEII Medical Centre, Nedlands, Perth, WA, Australia TENGYI CAI • Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia TAMMY CASEY • Proteomics International, QEII Medical Centre, Nedlands, Perth, WA, Australia THE HUONG CHAU • School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW, Australia

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ANASTASIA CHERNYKH • School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW, Australia MICHELLE CINEL • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia VERONICA DE GIORGIS • Biological Mass Spectrometry Lab, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy; Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale, Novara, Italy PEDRO DE MAGALHA˜ES PADILHA • Institute of Biosciences, Sa˜o Paulo State University (UNESP), Botucatu, Sa˜o Paulo, Brazil AURIANE DRACK • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Department of Biochemistry and Chemistry, La Trobe University, Melbourne, VIC, Australia MARISA N. DUONG • Proteomics International, QEII Medical Centre, Nedlands, Perth, WA, Australia THY DUONG • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia MRIGA DUTT • Precision and Systems Biomedicine Laboratory, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia DAN L. EATON • Calico Life Sciences LLC, South San Francisco, CA, USA CAMILA ESPEJO • Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia MARCO FALASCA • Metabolic Signalling Group, Curtin Medical School, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia HAOYUN FANG • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia RACHEL E. FOREMAN • Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK YINGXUE FU • Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, USA CLAUDIA GAITHER • MRM Proteomics Inc, Montreal, QC, Canada ´ NGEL GARCI´A • Platelet Proteomics Group, Center for Research in Molecular Medicine and A Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, and Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain ALEKSANDR GAUN • Calico Life Sciences LLC, South San Francisco, CA, USA AMY L. GEORGE • Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK COREY GILES • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, VIC, Australia YASMIN GILLITSCHKA • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria DAVID GOODLETT • University of Victoria - Genome BC Proteomics Centre, Victoria, BC, Canada; Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada; University of Gdansk, International Centre for Cancer Vaccine Science, Gdansk, Poland

Contributors

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DAVID W. GREENING • Molecular Proteomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Central Clinical School, Monash University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia; Department of Biochemistry and Chemistry, La Trobe University, Melbourne, VIC, Australia FIONA M. GRIBBLE • Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK TIANNAN GUO • iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China DYLAN JAMES HARNEY • Charles Perkins Centre, School of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, NSW, Australia SØREN HEISSEL • Proteomics Resource Center, The Rockefeller University, New York, NY, USA CEKE HELLSTRO¨M • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden BEN R. HERBERT • Sangui Bio Pty Ltd, St. Leonards, NSW, Australia LIDIA HERMIDA-NOGUEIRA • Platelet Proteomics Group, Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, and Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain CAMERON HILL • Sangui Bio Pty Ltd, St. Leonards, NSW, Australia MICHELLE M. HILL • Precision and Systems Biomedicine Laboratory, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia; Centre for Clinical Research, The University of Queensland, Faculty of Medicine, Herston, QLD, Australia MANUELA HOFNER • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria AYUKO HOSHINO • School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan ANQI HU • Institutes of Biomedical Sciences and Minhang Hospital, Fudan University, Shanghai, China JASMIN HUBER • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria KEVIN HUYNH • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, VIC, Australia VERA IGNJATOVIC • Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia; Institute for Clinical and Translational Research, Johns Hopkins All Children’s Hospital, St. Petersburg, FL, USA; Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA JUN ITO • Proteomics International, QEII Medical Centre, Nedlands, Perth, WA, Australia

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ALICIA JOHNSON • Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA; Redox Biology Center, University of Nebraska-Lincoln, Lincoln, NE, USA REBECA KAWAHARA • School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW, Australia RICHARD G. KAY • Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK AMIRMOHAMMAD NASIRI KENARI • School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan FRANK KLONT • Unit of PharmacoTherapy, -Epidemiology & -Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands; Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands SARAVANAN KUMAR • Proteomics Lab, Thermo Fisher Scientific India, First Technology Place, Bangalore, Karnataka, India MARK LARANCE • Charles Perkins Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia NATASHA LETUNICA • Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia XIAO LIANG • iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China RICHARD LIPSCOMBE • Proteomics International, QEII Medical Centre, Nedlands, Perth, WA, Australia SAINAN LI • iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China TAO LIU • Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA TASHA LUMBANTOBING • Proteomics International, QEII Medical Centre, Nedlands, Perth, WA, Australia DAVID LYDEN • Children’s Cancer and Blood Foundation Laboratories, Departments of Pediatrics, and Cell and Developmental Biology, Drukier Institute for Children’s Health, Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA BRUCE LYONS • Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia RASHMI MADDA • Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA ANNA MA˚NBERG • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden MARCELLO MANFREDI • Biological Mass Spectrometry Lab, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy; Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale, Novara, Italy

Contributors

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FIONA E. MCALLISTER • Calico Life Sciences LLC, South San Francisco, CA, USA CONOR MCCAFFERTY • Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia MATTHEW J. MCKAY • Bowel Cancer and Biomarker Laboratory, School of Medical Science, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia PETER J. MEIKLE • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, VIC, Australia NATALIE A. MELLETT • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia LISA MILCHRAM • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria YASSENE MOHAMMED • Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands; University of Victoria - Genome BC Proteomics Centre, Victoria, BC, Canada; Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada HENRIK MOLINA • Proteomics Resource Center, The Rockefeller University, New York, NY, USA MARK P. MOLLOY • Bowel Cancer and Biomarker Laboratory, School of Medical Science, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia PAUL MONAGLE • Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia; Department of Clinical Haematology, Royal Children’s Hospital, Melbourne, VIC, Australia; Kids Cancer Centre, Sydney Children’s Hospital, Randwick, NSW, Australia LAUREN A. NEWMAN • College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia PETER NILSSON • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden CHRISTA NO¨HAMMER • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria RONAN O’NEILL • Proteomics International, QEII Medical Centre, Nedlands, Perth, WA, Australia OLADAPO OLALEYE • Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands JENNIE OLOFSSON • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden NICLAS OLSSON • Calico Life Sciences LLC, South San Francisco, CA, USA BENJAMIN L. PARKER • Department of Anatomy and Physiology, School of Biomedical Sciences, Faculty of Medicine Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia JUNMIN PENG • Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, USA; Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN, USA VLADISLAV A. PETYUK • Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

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Contributors

ELISA PIN • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden ROBERT POPP • MRM Proteomics Inc, Montreal, QC, Canada LIUJIA QIAN • iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China LIANG QIAO • Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China ALIN RAI • Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, Australia FRANK REIMANN • Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK RENE´E S. RICHARDS • Precision and Systems Biomedicine Laboratory, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia VINCENT R. RICHARD • Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada KARIN D. RODLAND • Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA; Department of Cell Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA ANDREW ROWLAND • College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia MARIWAN H. SAYDA • Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK SILVIA SCHO¨NTHALER • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria HUALI SHEN • Institutes of Biomedical Sciences and Minhang Hospital, Fudan University, Shanghai, China TUJIN SHI • Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA CRAIG D. SHRIVER • Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA; John P. Murtha Cancer Center, Uniformed Services University of the Health Sciences, Walter Reed National Military Medical Center, Bethesda, MD, USA RICHARD J. SIMPSON • Department of Biochemistry and Chemistry, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, VIC, Australia RONALD SJO¨BERG • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden LOVISA SKOGLUND • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden REGINA SOLDO • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria MICHAEL J. SORICH • College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia ROSEMARY L. SPARROW • Transfusion Science, Melbourne, VIC, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia

Contributors

xvii

HUAN SUN • Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, USA RUI SUN • iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China ELLA SWANEY • Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia CAI TENGYI • Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia MORTEN THAYSEN-ANDERSEN • School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW, Australia; Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Aichi, Japan JULIAN UGONOTTI • School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW, Australia ZIVILE USECKAITE • College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia KLEMENS VIERLINGER • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria JOHN C. K. WANG • Calico Life Sciences LLC, South San Francisco, CA, USA YI-TING WANG • Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA ZHEN WANG • Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, USA ANDREAS WEINHA€ USEL • Austrian Institute of Technology GmbH, Center for Health and Bioresources, Competence Unit Molecular Diagnostics, Vienna, Austria RICHARD WILSON • Central Science Laboratory, University of Tasmania, Hobart, TAS, Australia GREGORY M. WOODS • Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia TING WU • College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia YI YANG • Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China JAMIL YOUSEF • Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden LIANG YUE • iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China

xviii

Contributors

RENE´ P. ZAHEDI • Manitoba Centre for Proteomics and Systems Biology, Winnipeg, MB, Canada; Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada; Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada JIAYI ZHANG • Institutes of Biomedical Sciences and Minhang Hospital, Fudan University, Shanghai, China XUE ZHANG • Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, USA SUIPING ZHOU • Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN, USA YAN ZHOU • iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China

Part I Blood Processing and Handling Strategies

Chapter 1 Protocols for the Isolation of Platelets for Research and Contrast to Production of Platelet Concentrates for Transfusion Rosemary L. Sparrow, Richard J. Simpson, and David W. Greening Abstract Platelets are specialized cellular elements of blood and play a central role in maintaining normal hemostasis, wound healing, and host defense but also are implicated in pathologic processes of thrombosis, inflammation, and tumor progression and dissemination. Transfusion of platelet concentrates is an important treatment for thrombocytopenia (low platelet count) due to disease or significant blood loss, with the goal being to prevent bleeding or to arrest active bleeding. In blood circulation, platelets are in a resting state; however, when triggered by a stimulus, such as blood vessel injury, become activated (also termed procoagulant). Platelet activation is the basis of their biological function to arrest active bleeding, comprising a complex interplay of morphological phenotype/shape change, adhesion, expression of signaling molecules, and release of bioactive factors, including extracellular vesicles/microparticles. Advances in high-throughput mRNA and protein profiling techniques have brought new understanding of platelet biological functions, including identification of novel platelet proteins and secreted molecules, analysis of functional changes between normal and pathologic states, and determining the effects of processing and storage on platelet concentrates for transfusion. However, because platelets are very easily activated, it is important to understand the different in vitro methods for platelet isolation commonly used and how they differ from the perspective for use as research samples in clinical chemistry. Two simple methods are described here for the preparation of research-scale platelet samples from human whole blood, and detailed notes are provided about the methods used for the preparation of platelet concentrates for transfusion. Key words Platelet, Blood, Activation, Isolation, Transfusion, Proteomics

1

Introduction Platelets, the smallest of the human blood cellular elements (~3.6
0.7 μm), are central players in maintaining normal hemostasis, including surveillance of blood vessel wall integrity, clot formation at sites of blood vessel injury, blood vessel repair, clot retraction, and host defense. On the other hand, pathological platelet activity, referred to as thrombosis, is implicated in blood vessel constriction, atherosclerosis, inflammation, and promoting

David W. Greening and Richard J. Simpson (eds.), Serum/Plasma Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2628, https://doi.org/10.1007/978-1-0716-2978-9_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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tumor growth/metastases [1–3]. Platelets are formed following maturation of megakaryocytes in the bone marrow and are released as anucleated fragments into the circulation [4]. Being anuclear, platelets are not cells in the strict meaning of the word, although they are often referred to as blood cells. Circulating platelets have a discoid shape; are the second most numerous cellular element in blood, normally circulating at concentrations between 150 and 450
109 platelets/L; and have the lowest specific gravity of formed blood cells [1, 2]. Their shape and small size, combined with blood flow rheology, allow platelets to access the edge of blood vessels, thereby enabling them constantly to survey vascular integrity. Complex, regulated reactions occur between platelets, von Willebrand factor, collagen, and soluble coagulation factors in regions of disturbed vasculature. These changes induce platelet adherence to vessel walls and platelet activation, which leads to platelet aggregation, procoagulant activity, spreading, microparticle release, and formation of a primary hemostatic plug [1, 2]. Since platelets lack nuclear DNA and their genome consists of a subset of megakaryocyte-derived mRNA transcripts, they represent a simplified biological model when investigating cell function [4]. While valuable information may be gathered from studies of platelet mRNA [5, 6], the rapid signaling and regulatory events in platelets are not governed by, or dependent on, alterations in gene expression. On the other hand, characterization of the platelet proteome by proteomic techniques provides powerful insight into the vicissitudes of platelet function [7, 8]. Platelet proteomic studies reported to date can be grouped into three distinct purposes: (i) cataloging the spectrum of proteins that comprise the normal, resting platelet proteome, (ii) characterizing proteins expressed by or released from activated platelets, and (iii) identifying specific platelet sub-proteomes. For example, proteomic studies have detailed the proteins expressed at the platelet membrane [9, 10] and in granules [11], changes in phosphorylation patterns [12, 13], and functional endpoints in response to external stimuli (i.e., extracellular vesicles [14, 15] and releasate [16, 17]), as well as effects of processing and storage on platelet concentrates (PCs) for transfusion [18–20] and in pathological conditions [21–23]. The separation of platelets from whole blood is based on the differential densities of the various cellular elements when blood is subjected to defined centrifugation forces. Platelets, being the smallest and lightest cellular elements of blood, remain suspended in the liquid plasma when whole blood is centrifuged at a low centrifugal force. Protocols for the preparation of platelets rely on this characteristic, including those used by blood processing facilities for the preparation of PCs for clinical use. Transfusion of PCs is indicated for the treatment of thrombocytopenia caused by hematological disease or the effects of chemotherapy or bleeding related to surgery or trauma injury. PCs for

Platelet Isolation for Research and Transfusion

5

transfusion can be prepared by three different methods: (i) plateletrich plasma-platelet concentrates (PRP-PC), (ii) buffy coat-platelet concentrates (BC-PC), and (iii) apheresis-platelet concentrates (apheresis-PC). All three processing methods are fully closed sterile systems. For the preparation of PRP-PC, an initial soft centrifugation produces PRP, which is separated from white cells and red cells, and the PRP is centrifuged at a higher g force to pellet the platelets. The PRP method is typically used for preparing research platelet samples from smaller volumes of whole blood. Disadvantages of this method are that it is difficult to avoid aspirating some white cells and red cells with the PRP [9] and the platelets are hardspun against the surface of the container, which increases the risk of platelet activation and/or damage. In contrast, platelets prepared by the BC-PC method are not subjected to being pelleted, and the levels of other cellular contaminants tend to be lower [24]. For apheresis-PC, a specialized automated blood cell separator is used whereby the blood donor is inline directly and whole blood is drawn, immediately mixed with anticoagulant, and separated into components; the target component (e.g., platelets) is collected into a separate bag, while other components (i.e., plasma, red cells, white cells) are returned to the donor [25]. In developed countries, blood for transfusion is collected, processed, and distributed by licensed blood processing facilities that operate in a highly regulated environment governed by strict codes of practice similar to those that apply to the manufacturers of medicinal products (i.e., the code of good manufacturing practice (cGMP)). These regulations are designed to ensure standardization of all procedures to maximize quality and safety of blood transfusion components, such as PCs [26]. For this protocol, two research-scale methods for the preparation of platelets from whole blood are described that yield a minimally manipulated platelet specimen suitable for use in proteomic analysis and other research applications [27, 28]. Because many researchers source platelets from a supplier of blood transfusion products, an overview of the methods used by blood processing facilities to prepare PCs from whole blood donations and by singledonor apheresis collection is also provided.

2

Materials

2.1 Blood Collection and Platelet Sample Preparation

1. Acid citrate dextrose (ACD) blood collection tubes (e.g., Whole Blood Glass Vacutainer® tube with anticoagulant, ACD Sol A, 8.5 mL, BD Biosciences #364606) (see Note 1). 2. Sterile hypodermic needles compatible with the blood collection tubes, e.g., BD Vacutainer® Safety-Lok™ Blood Collection Set #367281; 21-G butterfly needle with attached sterile tubing.

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3. Tourniquet, alcohol venipuncture site.

swabs

for

disinfection

of

the

4. Personnel protective equipment—examination/surgical gloves, gown, eye safety glasses. 5. Waste container for biological and sharp hazards. 6. Polypropylene tubes for processing and storage (15 mL). 7. Tube rack. 8. Adhesive labels for blood sample tubes and indelible marker pen. 9. Transfer pipettes, wide aperture. 10. Centrifuge (swing bucket rotor, compatible with 15 mL tubes, programmable temperature setting; set at 22  C). 11. Platelet wash buffer (see Note 2). 12. Platelet activation inhibitors (see Note 2) (optional). 13. Water bath set at 22–24  C (optional).

3 3.1

Methods Blood Collection

1. For the preparation of normal, resting platelets, the blood donor must be healthy, with no signs of infection or inflammation, and must not have taken medications that have antithrombotic (anticlotting) or anti-inflammatory effects, such as aspirin or ibuprofen. 2. Blood collection must only be performed by personnel trained in phlebotomy/venipuncture. Safety precautions for the collection and handling of blood must be employed at all times (see Note 3). Particular care must be taken with insertion of the hypodermic needle into the center midstream of the vein to limit the possibility of activation of the hemostasis/coagulation system, which could compromise the quality of the blood sample. A hypodermic needle of 21-gauge or wider is recommended to minimize shear stress upon the collected blood specimen. 3. It is important to collect the volume of blood specified for the particular blood collection tube to ensure the correct blood/ anticoagulant ratio is achieved (see Note 4). 4. After blood collection, gently invert the tube three to four times to ensure thorough mixing with the ACD anticoagulant. 5. Blood samples must be maintained at temperate conditions (i.e., 20–24  C) and platelet preparations prepared within 4 h of blood collection. To ensure blood samples are held at the correct temperature, place in a water bath set at 22–24  C if necessary.

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7

6. One 8.5 mL tube of whole blood from a donor with a normal platelet count (i.e., 150–450
109 platelets/L) should yield sufficient numbers of platelets to prepare a sample for proteomic analysis and other expression validations as required. 3.2 Platelet-Rich Plasma (PRP) Preparation Method

Precaution: Platelets are extremely labile and are very easily activated during sample preparation. It is important to limit the extent of manipulation of the blood sample to avoid unintentional activation of the platelets. All procedures must be performed at 20–24  C to maintain platelet quality and viability. The temperature of all equipment (e.g., centrifuge) and wash buffers (if used) must be between 20 and 24  C prior to use. At temperatures below 20  C, platelets undergo cold storage-induced activation [29, 30] and must be avoided in order to isolate normal resting platelets: 1. The PRP is separated from whole blood (typical volume 8.5 mL) by light spin centrifugation at 110
g for 15 min at 22  C, without brake. Under these centrifugation conditions, the platelets will remain suspended in the plasma (upper fraction, yellow-colored fluid), while the denser white cells and red cells will softly settle to the lower fraction (Fig. 1a). Using a

Fig. 1 Schematic of methods to isolate platelets for research from whole blood. (a) Platelet-rich plasma (PRP) method, (b) buffy coat (BC) method. The order of the hard and soft centrifugations is different between the two methods. Following the second spin, the platelets are pelleted in the PRP method but are in suspension in the BC method and necessitate a third centrifugation to pellet the platelets. In general, the BC method yields a purer platelet sample with less carryover of contaminant white cells, red cells, and plasma. PPP platelet-poor plasma

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wide-aperture transfer pipette, carefully collect only the upper 40% of the PRP to avoid contamination with white cells and red cells. Do not let the tip of the pipette touch the sides of the tube. Place the PRP into a fresh tube. Consider preloading the tube with a platelet activation inhibitor (see Note 2) to help maintain the platelets in a quiescent state. Allow the platelets to rest for 10 mins at 22  C. Take a small aliquot of the PRP to use for quality assessment, which should be performed as quickly as possible (see Note 5). 2. To sediment the platelets in the PRP sample, centrifuge at a higher centrifugation rate (1700
g for 5 min, 22  C, without brake). A small off-white/beige-colored pellet should be evident at the bottom of the tube. Carefully remove the plasma supernatant (i.e., platelet-poor plasma), taking care not to disturb the platelet pellet. Discard the plasma supernatant if not required; alternatively, the plasma can be stored frozen at 50% of cases), then it is likely to have a strong predictive power. Conversely, if there is no consistency and each iteration results in a different set of selected features, then it is likely that there is no predictive power among the features and hence quite often the 95% confidence interval of the synthetic AUC includes the value of 0.5. Thus, instead of a particular classification model with known AUC, our approach results in recommendation of the predictive features and corresponding estimation of the AUC. For proper testing of the model, it requires an additional set of samples. 8. The Random Forest approach was our method of choice, because the number of features is not large enough to warrant a deep neural network approach, and the approach based on the LASSO logistic regression underperformed RF. Among a number of approaches for feature selection, including out-ofbag estimates, simply selecting top ten features, recursive feature elimination, or Boruta, Boruta was the most effective for the objective.

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Acknowledgments This work was supported by Federal Award No. HU0001-16-20014 (Subaward No. 3879, to K.D. Rodland and T. Liu). The authors thank the clinical and laboratory staff at the Uniformed Services University of the Health Sciences and Pacific Northwest National Laboratory (PNNL). Portions of the research were performed in the Environmental Molecular Sciences Laboratory (grid.436923.9), a US Department of Energy (DOE) Office of Biological and Environmental Research national scientific user facility on the PNNL campus. PNNL is a multiprogram national laboratory operated by Battelle for the DOE under contract no. DE-AC05-76RL01830. The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions, or policies of the Uniformed Services University of the Health Sciences; the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.; the Department of Defense; or the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the US Government. References 1. Allegra E, Trapasso S, La Boria A, Aragona T, Pisani D, Belfiore A et al (2014) Prognostic role of salivary CD44sol levels in the followup of laryngeal carcinomas. J Oral Pathol Med 43(4):276–281 2. Betancourt LH, Pawlowski K, Eriksson J, Szasz AM, Mitra S, Pla I et al (2019) Improved survival prognostication of node-positive malignant melanoma patients utilizing shotgun proteomics guided by histopathological characterization and genomic data. Sci Rep 9:5154 3. Pereira LH, Reis IM, Reategui EP, Gordon C, Saint-Victor S, Duncan R et al (2016) Risk stratification system for oral cancer screening. Cancer Prev Res (Phila) 9(6):445–455 4. Li SX, Yang YQ, Jin LJ, Cai ZG, Sun Z (2016) Detection of survivin, carcinoembryonic antigen and ErbB2 level in oral squamous cell carcinoma patients. Cancer Biomark 17(4): 377–382 5. Hsiao YC, Chi LM, Chien KY, Chiang WF, Chen SF, Chuang YN et al (2017) Development of a multiplexed assay for oral cancer candidate biomarkers using peptide immunoaffinity enrichment and targeted mass spectrometry. Mol Cell Proteomics 16(10):1829–1849 6. Bosley AD, Das S, Andresson T (2013) Chapter 21: A role for protein–protein interaction networks in the identification and

characterization of potential biomarkers. In: Issaq HJ, Veenstra TD (eds) Proteomic and metabolomic approaches to biomarker discovery. Academic Press, Boston, pp 333–347 7. Rifai N, Gillette MA, Carr SA (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24(8):971–983 8. Shi T, Song E, Nie S, Rodland KD, Liu T, Qian WJ et al (2016) Advances in targeted proteomics and applications to biomedical research. Proteomics 16(15–16):2160–2182 9. Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S, Batra SK (2020) Radiomics in stratification of pancreatic cystic lesions: machine learning in action. Cancer Lett 469:228–237 10. Huang S, Yang J, Fong S, Zhao Q (2020) Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett 471:61–71 11. Mucaki EJ, Zhao JZL, Lizotte DJ, Rogan PK (2019) Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning. Signal Transduct Target Ther 4:1 12. Shapanis A, Lai C, Sommerlad M, Parkinson E, Healy E, Skipp P (2020) Proteomic profiling of archived tissue of primary melanoma identifies

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experiments in human plasma using in vitro synthesized proteins as analytical standards. Methods Mol Biol 1410:207–221 18. Anderson L, Hunter CL (2006) Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 5(4):573–588 19. Pino LK, Searle BC, Bollinger JG, Nunn B, MacLean B, MacCoss MJ (2020) The skyline ecosystem: informatics for quantitative mass spectrometry proteomics. Mass Spectrom Rev 39(3):229–244 20. Johnson WE, Li C, Rabinovic A (2006) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1):118–127 21. https://cran.r-project.org/web/packages/ ROCR/index.html 22. Miron Kursa WR (2010) Feature selection with the Boruta package. J Stat Softw 36(11):1–13 23. https://cran.r-project.org/web/packages/ randomForest/index.html

INDEX A

Bioinformatics ............................... 54, 73, 215, 280, 286, 292, 293, 295, 297, 326, 331, 340, 344, 345, 434, 526, 528 Biomarker discovery............................. 81, 128–130, 138, 182, 183, 195, 287, 298, 299, 340, 341, 395, 396, 407, 512 Biotinylation ......................................................... 417, 510 Buffy coat .............................................44, 114, 306, 308, 309, 326, 331

Absolute quantification........................61, 182, 204, 303, 443, 446, 503, 561 Acidic/basic pH ....................................... 83–85, 88, 113, 117–119, 258 Activation................................................ 4–11, 14, 15, 43, 145, 158, 168, 171, 252, 286, 293, 296, 306, 344, 408, 417, 424, 434, 528, 538, 540–542, 548, 570 Affimer .................................................222, 223, 225, 228 Affinity proteomics............................................... 548, 568 Affinity purification ................................. 53, 54, 222, 230 Algorithm ............................................. 72–74, 76, 82, 94, 99, 103, 140, 188, 275, 298, 299, 366, 367, 377, 381, 388, 389, 569, 588 Anion-exchange chromatography ............................54, 56 Antibody microarray ..................................................... 414 Antibody selectivity....................................................... 195 Anticoagulant ............................................. 5, 6, 9, 10, 36, 42, 43, 45, 46, 89, 145, 159, 163, 170, 174, 190, 210, 294, 298, 315, 469 Apheresis...........................................................5, 9, 13, 45 Arrays ................................ 181–183, 188, 275, 414, 506, 514, 537, 541, 543, 545 Assay........................................ 15, 19, 21, 22, 26, 38, 46, 61, 63, 69, 76, 94, 98, 99, 110, 112, 114, 120, 121, 132, 135, 137, 146, 148, 149, 185, 195–197, 205, 209, 229, 231, 248, 267, 274, 288, 292, 310, 311, 316, 317, 325, 330, 332, 340–349, 354, 355, 361, 368, 371–379, 381, 384, 395, 397, 398, 410, 415–418, 420, 421, 423, 425–437, 440, 441, 443, 444, 446, 447, 468, 477, 481, 485, 506–509, 511–513, 515–517, 521–531, 535–541, 543–551, 558–560, 563–568, 570, 571, 580, 581, 583, 585, 586 Auto-antibody-profiling ...................................... 414–437 Automated proteomics/automatization....................... 55, 230, 415

Cancer................................. 39, 127–130, 148, 182, 185, 186, 188, 236, 237, 259, 291, 292, 302, 341, 344, 346, 349, 363, 365, 396, 414, 429, 506, 517, 570, 579–582, 586 Cell lysate...............................................54, 230, 239, 275 Cell surface ........................................................... 302, 303 Centrifugal ultrafiltration .......................... 131, 135, 212, 266, 304 Challenges .............................. 33, 48, 93, 173, 186, 229, 237, 265, 396, 439, 478, 489, 490, 500 Citrate ............................... 9, 10, 34–36, 42, 43, 45, 159, 161, 167, 190, 322, 441, 469 Clinical proteomics .............................363, 443, 563, 570 Collection ...................................... 5, 6, 9, 10, 12–14, 19, 20, 24, 25, 30, 33–39, 42, 43, 45, 46, 85, 89, 114, 131, 134, 135, 148, 159, 160, 163, 170, 174, 175, 177, 186, 208, 210, 216, 217, 270, 273, 281–285, 292–294, 298, 302–304, 306, 315, 326, 328, 353, 398, 400, 401, 403, 424, 425, 428, 436, 439, 478, 482, 508, 538, 590 Complexes ................................. 4, 53, 54, 56–62, 72–75, 110, 128, 183, 185, 188, 222, 228, 229, 235, 237, 287, 303, 340, 343, 354, 362, 365, 396, 409, 410, 465, 467, 489, 540, 543, 558 Concentrates.................................... 4, 5, 10, 14, 41, 135, 207, 208, 212, 250, 307, 446 Cryodepletion ...........................................................41–48 Cryoprecipitate..........................................................41–48

B

D

Bead array ......................... 397, 402–407, 537, 539–543, 545, 547–551

Databases ...................................120, 122, 183, 187, 188, 215, 275, 297, 331, 340, 344, 563

C

David W. Greening and Richard Simpson (eds.), Serum/Plasma Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2628, https://doi.org/10.1007/978-1-0716-2978-9, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

593

SERUM/PLASMA PROTEOMICS: METHODS AND PROTOCOLS

594 Index

Data-dependent acquisition (DDA) ...................... 61, 70, 72, 73, 82, 88, 93–95, 97, 99, 102, 105, 119, 128–130, 174, 177, 215, 252, 255, 266–268, 272, 275, 343, 362, 366–369, 371, 373, 386–388, 407, 483, 484 Data-independent acquisition (DIA)..................... 94, 95, 97, 99–103, 105, 129, 130, 140, 147, 174, 266–268, 272, 273, 275, 288, 343, 366–368, 377–387, 389, 407 Degradation....................................... 164, 216, 287, 316, 321, 333, 446, 478, 481 Density-cushion centrifugation .................................... 321 Depletion ....................................... 11, 39, 81–83, 85, 89, 93, 94, 97, 104, 105, 110, 129, 216, 265, 266, 315, 354, 568, 585, 588 Designing targeted assays ............................................. 558 Digestion .....................................68, 86, 90, 96, 99, 104, 114–116, 121, 132, 139, 150, 161, 164, 167, 170, 171, 175–177, 196, 197, 202, 205, 213, 215, 216, 222–224, 226, 227, 230, 244, 252, 253, 258, 266, 268, 271, 272, 274, 285, 286, 288, 296, 304, 307, 309, 325, 330, 333, 342–344, 354, 355, 357, 361, 362, 386, 397, 404, 410, 445, 478, 560, 561, 568, 569, 581, 583, 585 Discovery ....................................... 59, 62, 128–130, 135, 144, 147, 150, 183, 184, 186, 188–190, 221, 237, 266, 275, 281, 285, 299, 395–397, 409, 441, 506, 514, 579, 580 Donor .................................................5–7, 12, 13, 15, 41, 44, 46, 47, 306, 501 Drug delivery ....................................................... 321, 322 Dynamic range .................................. 54, 81, 93, 95, 110, 111, 128, 156, 164, 173, 236, 265, 440, 550

E EDTA.................................. 10, 23, 36, 39, 89, 114, 145, 159, 170, 190, 273, 281, 294, 306, 308, 355 Electron-transfer dissociation (ETD) ................ 158, 162, 167–169, 171, 248, 255, 366 Enrichment..................................31, 48, 75, 81, 82, 156, 157, 159, 161, 164, 166–167, 171, 215, 222, 228, 237, 239, 245, 253, 256, 267, 310, 331, 332, 386, 442, 568 Enzyme-linked immunosorbent assay (ELISA) .... 21, 26, 228, 414, 434, 536, 540 Exosome ............................................... 48, 279–285, 287, 292, 302, 304 Extracellular particles ................................... 48, 129, 134, 148, 279, 301 Extracellular vesicles (EVs)...................... 4, 48, 129–132, 134, 135, 137, 138, 144–149, 185, 186, 279, 280, 291, 301–304, 306, 307, 309–313, 315–317, 324 Ex vivo ........................................................................... 315

F False discovery rate (FDR) ...............................72, 73, 88, 120, 144, 169, 177, 188, 217, 256, 257, 272, 286, 366, 369, 372, 382, 385, 389 Fatty acids ............................................................... 24, 490 Flow cytometry .................................................... 507, 536 Fluorescent beads.......................................................... 530 Fluorescent detection ................................................... 418 Fractionation ........................................ 54–56, 60, 67, 82, 83, 93, 110, 112, 117, 130, 133, 137, 143, 173, 182, 208, 222, 266, 354–356, 358, 359, 361

G Gene ontology (GO) ........................ 280, 298, 340, 343, 344, 563, 564 Glycopeptides ............................ 156, 157, 161, 165–167, 169, 171, 236, 237, 248, 259, 366, 367, 369, 371–374, 377, 378, 380, 382, 383, 385–389, 396 Glycoproteins .................................... 156, 157, 159, 161, 164–166, 169, 236, 237, 239, 240, 257, 332, 340, 389, 396, 403, 404, 406, 448, 459, 467, 588 Glycoproteome.................................. 156, 157, 164, 236, 237, 239, 365, 366

H Handling...........................................6, 11, 33–38, 43, 46, 115, 128, 130, 146, 149, 197, 239–242, 248, 256, 257, 306, 408, 418, 422, 424, 442, 445, 469, 506, 507, 511, 546, 547, 549, 550 Hemolysis ...................................... 46, 97, 282, 298, 315, 322, 323, 326, 332 Heparin ....................................................... 36, 39, 42, 45, 159, 190, 454 High-abundant...............................................24, 479, 483 High resolution ................................. 110, 118, 147, 157, 175, 244, 248, 255, 259, 295, 340, 415, 491, 506 High-throughput ....................................... 53–77, 82, 83, 128, 197, 303, 396, 490, 491, 506, 512, 536–539, 565, 580 HPLC, see Reverse phase liquid chromatography Hydrophilic interaction chromatography (HILIC) ................. 156, 157, 161, 166, 171, 245

I Immunoaffinity capture/immunocapture ......... 196, 222, 228–230, 284, 302 Immunoaffinity depletion............................580–582, 585 Immunoassays ................................... 110, 184, 222, 228, 229, 339, 397, 416, 418, 420, 421, 423, 425, 429–435, 437, 478, 479, 507, 508, 510, 511, 513, 516, 517, 521, 522, 526–528, 530, 532, 536 Immuno-mass spectrometry......................................... 195

SERUM/PLASMA PROTEOMICS: METHODS In-gel digestion ............................................................... 56 Intact glycopeptides .......................... 169, 171, 236, 365, 366, 387 Isobaric tag quantitation ....................................... 55, 182 Isotopic labelling........................................................... 305 iTRAQ labelling ................................................... 183, 186

L Label-free proteomics ................................................... 331 Label-free quantitation .......................................... 75, 177 Lectin ................................ 156, 157, 159, 160, 164, 166, 170, 171, 396–410, 463 Library-free...................................................................... 99 Ligand binding assays ................................................... 229 Lipidomics ...........................................440, 490, 500, 502 Low abundant .................................. 58, 75, 93, 208, 478 Low-molecular weight (LMW).................................... 478

M MALDI-TOF ................................................................ 182 MaxQuant ......................................... 76, 82, 97, 99, 147, 175, 177, 248, 257, 272, 275, 326 Membrane ....................................... 4, 90, 129, 148, 207, 208, 210–212, 215, 217, 239, 240, 249, 250, 302, 321–324, 327, 331, 332, 513, 537, 586 Membrane proteome .................................................... 331 Metabolite/s .............................. v, 20, 21, 23–24, 28–29, 31, 185, 274, 279 Metabolomics ............................... 21, 119, 174, 440, 565 Microparticle .........................................4, 14, 15, 48, 287 MicroRNA (miRNA) ........................................... 279, 333 Microsampling...................................................v, 173–178 Microsphere-based assay ............................................... 415 Molecular weight cut-off (MWCO) filter ................... 585 Multiple reaction monitoring (MRM) .............. 183, 196, 203, 303, 310, 311, 339–343, 346, 347, 349, 361, 362, 407, 440, 441, 443, 444, 447, 491, 492, 497, 498, 500, 502, 557, 559, 569, 570 Multiplex assay .............................................................. 522 Multiplex immunoassay ............................. 55, 60, 61, 73, 110, 128, 197, 237, 239, 354, 397, 414, 423

N Nano tracking analysis .................................................. 148 Nanovesicles ............................................... 321–323, 327, 329–331, 333 Native separation.......................................................54, 58 N-glycan .............................................159, 169, 250–252, 256, 258, 369 N-linked glycosylation ......................................... 166, 171

AND

PROTOCOLS Index 595

O 18

O labeling.........................................157, 162, 167, 171

P Parallel accumulation-serial fragmentation.................. 340 Parallel reaction monitoring (PRM) .................. 339–341, 343, 346, 349, 354, 355, 359, 360, 362, 440, 441, 557 Peptide spiking ............................................ 228, 310, 358 Peptidome ..................................................................... 478 Platelet ....................................... 3–15, 44, 110, 114, 145, 207, 208, 294, 302, 304, 306, 308, 309, 315, 326, 448, 463 Platelet-rich plasma (PRP)................................5, 7, 8, 12, 13, 207, 308 Pooling, samples ............................................13, 116, 121 Post-translational modifications (PTMs)............. 75, 156, 184, 188, 248, 365, 485, 562 Pre-analytical variables ....................................... 33, 34, 39 Precipitation ................................ 30, 112, 114–116, 120, 216, 266, 280, 283, 287, 302, 304, 307, 478, 479, 481 Protease .....................................61, 63, 68, 88, 129, 159, 164, 169, 171, 230, 239, 249, 252, 253, 271, 273, 281, 283, 398, 399, 463, 465–467, 469 Protein complexes ................................ 53–60, 72, 74, 75, 396, 409, 410, 465, 467 Protein concentrations............................... 24, 27, 30, 56, 57, 68, 95, 98, 104, 110, 114, 128, 135, 137, 149, 216, 248, 249, 298, 316, 329, 332, 356, 357, 401, 435, 443, 444, 469, 565, 585 Protein-protein interaction............................................. 74 Proteotypic ................................231, 303, 345, 361, 440, 441, 443, 469, 558, 564, 568

Q Quantitation .................................... 68, 94, 96, 112, 129, 132, 135, 137, 144, 146, 147, 150, 174, 183, 212, 213, 217, 244, 252, 256–258, 273–275, 304, 316, 332, 354, 359–361, 440, 441, 478, 479, 481, 558, 560, 583 Quantitative proteomics ............................ 208, 288, 447, 469, 564

R RBC nanovesicles .......................................................... 321 Red blood cells (RBCs) ..................................44, 46, 114, 174, 210, 294, 298, 304, 308, 309, 315, 321–324, 326–328, 331, 332

SERUM/PLASMA PROTEOMICS: METHODS AND PROTOCOLS

596 Index

Replicates ................................ 15, 83, 90, 105, 204, 348, 349, 367, 371, 372, 387, 388, 495, 501, 571 Reproducibility.....................................20, 21, 83, 90, 98, 164, 170, 182, 186, 195, 205, 240, 266, 274, 298, 302, 339, 385, 396, 407, 415, 492, 501, 506, 570, 583 Reverse phase liquid chromatography .................. 82, 386 Reverse phase protein arrays........................................... 82 R programming ............................................................. 297

S Safety............................................5, 6, 10, 14, 21, 43, 45, 48, 56, 145, 306 Secreted protein database .......................... 109, 239, 272, 340, 344, 506 Secretome ...........................................208, 211–213, 215, 216, 340 Selected reaction monitoring (SRM)................. 174, 222, 227, 228, 231, 232, 343, 345, 348, 443, 495, 503, 559, 563, 568, 569, 580–584, 586, 587 Shotgun proteomics...................119, 128, 344, 345, 567 Size-exclusion chromatography (SEC) ...................54–65, 68, 69, 73, 76, 77, 129, 130, 266, 268–270, 273, 274, 280, 284, 287, 302, 306, 307 Small protein enrichment assay (SPEA) ............. 265–275 Solid-phase extraction (SPE)..................... 238, 241–243, 245, 253, 285, 356, 358, 361, 362, 446, 478–482, 486, 582, 585, 586 Solubility............................................................... 321, 485 Spectral library......................................94, 130, 134, 137, 140, 147, 150, 215, 217, 345, 366, 368, 370–379, 381, 384, 386–389 Stable isotope labelling with amino acids in cell culture (SILAC)...................................... 182 Staining/visualization ....................... 137, 249, 295, 299, 305, 369, 385, 386, 447 Standard operating procedures (SOPs) ......................114, 145, 361 Statistics ................................................................ 328, 386 Stepped fractionation........................................... 272, 366 Storage ................................................4, 6, 12–14, 19, 35, 43–45, 63, 64, 85, 89, 105, 114, 134, 136, 159, 163, 170, 205, 239, 241, 242, 275, 283, 306, 316, 333, 348, 398, 401, 408, 435, 436, 530, 538, 540, 542, 545

Strong cation-exchange (SCX)........................... 241, 242, 258, 266, 271, 274 SWATH......................................183, 210, 215, 217, 343, 368, 369, 377, 378, 381

T Tagging.......................................................................... 113 Targeted lipidomics....................................................... 490 Targeted proteomics .................................. 340, 341, 343, 440, 441, 443, 558–560, 563–565, 567, 570, 580, 582, 583 Thaw/refreeze............................... 15, 21, 35, 38, 45, 85, 134, 273, 327, 403 Time-course................................................................... 500 TMT labeling .......................................61, 66, 69, 70, 74, 84, 87–88, 91, 110, 113, 114, 116, 117, 121, 122, 245, 253 Transfusion ........................................... 4, 5, 9, 10, 13–15, 42, 45–48 2-dimensional electrophoresis (2-DE) ........................ 184

U Ultracentrifugation .............................292, 294, 295, 302 UniProt......................................133, 140, 162, 168, 177, 297, 326, 343, 344, 368

V Validation....................................... 7, 129, 130, 135, 144, 169, 183, 184, 186, 189, 340, 341, 346, 361, 396, 397, 407, 409, 421, 506, 515, 516, 563, 580, 581, 590 Variation ................................. 20, 21, 57, 73, 75, 83, 90, 94, 105, 110, 117, 189, 208, 229, 232, 259, 293, 347, 385, 386, 407, 423, 431, 437, 492, 495, 498, 501–503, 526, 564, 580 Venipuncture ................................................ 6, 20, 21, 26, 43, 97, 174 Vesicles .......................................282, 287, 294, 302, 303, 306, 310, 328 Visualization ............................................... 295, 299, 369, 385, 386, 447 Volumetric absorptive microsampling (VAMS) ..................................................... 173–178