Peptidomics: Methods and Strategies (Methods in Molecular Biology, 2758)

Preface
Contents
Contributors
Part I: Foundations of Peptidomics
	Chapter 1: Origins, Technological Advancement, and Applications of Peptidomics
		1 Introduction
		2 Development of the Technological Basis
			2.1 Early Discovery of Peptide Hormones Was the Origin
			2.2 Technological Basis in Instrumental Analytics
			2.3 Mass Spectrometry as the Central Tool for Peptide Identification
			2.4 Peptide Profiling as Forerunner of Peptidomics
			2.5 Precision Adjustments of Peptidomic Technology
		3 Technological Advancements and Widening
			3.1 Current Separation and Mass Spectrometry Technology
			3.2 Current Mass Spectrometric Technology
			3.3 Current Developments in Sequencing and Data Processing
			3.4 Current Developments in Quantification
		4 Applications on Model Systems
			4.1 Studies of Peptide Hormones, Neuropeptides, and Other Bioactive Peptides
			4.2 Biochemical Assays for the Discovery of Bioactive Peptides
			4.3 Food Peptidomics
			4.4 Plant Peptidomics
		5 Applications with Clinical Focus
			5.1 Know-How and Challenges in Clinical and Preclinical Peptidomics
			5.2 Peptide Biomarker Discovery from Clinical Samples
			5.3 Immunopeptidomics
		6 Concluding Remarks on the Current Status of Peptidomics
		References
	Chapter 2: Two Different Strategies for Stabilization of Brain Tissue and Extraction of Neuropeptides
		1 Introduction
		2 Materials
			2.1 Stabilization with Heat-Induced Denaturation
			2.2 Stabilization with Microwave Oven
			2.3 Materials for Extraction of Neuropeptides
		3 Methods
			3.1 Heat Stabilization of Frozen Tissue Samples Using a Heat Stabilizer Apparatus
			3.2 Microwave Irradiation of Mouse Brain
				3.2.1 Calibration of Conventional Microwave Oven with Water
				3.2.2 Calibration of Microwave Oven with Mouse Brain
				3.2.3 Heat Stabilization of Experimental Mice
			3.3 Extraction of Neuropeptides from Heat Stabilized Brain Samples
		4 Notes
		References
	Chapter 3: Mass Spectrometric De Novo Sequencing of Natural Peptides
		1 Introduction
		2 Materials
			2.1 Skin Secretion Obtaining
			2.2 Derivatization
			2.3 LC-MS/MS Analysis
		3 Methods
			3.1 Skin Secretion Obtaining
			3.2 Disulfide Bond Derivatization
				3.2.1 Reduction/Alkylation
				3.2.2 Oxidation
			3.3 LC-MS/MS
			3.4 Manual De Novo Sequencing
				3.4.1 Secondary Fragmentation of Pro-containing Peptides
				3.4.2 The Importance of  HRMS
				3.4.3 Disulfide Bond-Containing Peptides Analysis
				3.4.4 Complementarity of Different Fragmentation Methods
				3.4.5 Different Charge State  Ions
				3.4.6 Distinguishing of Isomeric Leu/Ile
		4 Notes
		References
	Chapter 4: Data-Independent Acquisition Peptidomics
		1 Introduction
		2 Prearrangements of the Pipeline
			2.1 Pipeline Installation
			2.2 Pipeline Settings
			2.3 Pipeline Test Data
		3 Usage of the Pipeline
			3.1 Pipeline Execution
			3.2 Results and Output Format Specifications
			3.3 Limitations and Alternative Approaches
		4 Notes
		References
	Chapter 5: Quantitative Peptidomics: General Considerations
		1 Introduction
			1.1 Label-Free Approaches
			1.2 Isotopic Tags
				1.2.1 Trimethylaminobutyrate (TMAB) Tags
				1.2.2 Reductive Methylation with Formaldehyde
			1.3 Isobaric Tags
			1.4 Summary
		2 Materials
			2.1 General Materials Required for All Approaches
			2.2 Additional Materials for Label-Free Quantitation
			2.3 Additional Materials for Quantitation with Isotopic Labels
		3 Methods
			3.1 What Is the Overall Goal of the Study?
			3.2 What Is the Anticipated Difference Between Levels of Peptides in the Two Samples?
			3.3 What Is Your Knowledge of Chemistry and Access to Standard Organic Chemistry Laboratory Equipment?
			3.4 What Is Your Budget, and Do You Have to Pay for Each LC/MS Run?
			3.5 How Many Peptidomics Studies Do You Plan to Do?
			3.6 Do You Want to See the Primary Data, or Do You Trust Computers to Interpret the Results?
		4 Notes
		References
	Chapter 6: Quantitative Peptidomics Using Reductive Methylation of Amines
		1 Introduction
		2 Materials
			2.1 Fluorescamine Assay
			2.2 Reductive Methylation of Peptides
			2.3 Peptide Purification
		3 Methods
			3.1 Fluorescamine Assay to Determine the Level of Primary Amine in a Sample
			3.2 Reductive Methylation of Peptides
			3.3 Peptide Purification and Analysis
		4 Notes
		References
	Chapter 7: Label-Free Quantitation of Endogenous Peptides
		1 Introduction
		2 Materials
			2.1 Sample Preparation
			2.2 LC-MS Analysis and Peptide Identifications
			2.3 Data Processing and Statistical Analysis
		3 Methods
			3.1 Sample Preparation
			3.2 LC-MS and LC-MS/MS Analysis
			3.3 Peptide Identifications and Quantification
			3.4 Data Processing
			3.5 Statistical Analysis
		4 Notes
		References
	Chapter 8: Bioinformatics for Prohormone and Neuropeptide Discovery
		1 Introduction
		2 Materials
			2.1 Genomic Data of Desired Species
			2.2 Prohormone Protein Sequences of Phylogenetically Close Species
			2.3 Bioinformatic Tools
			2.4 Spreadsheet and Text Editor Applications to Record Findings
		3 Methods
			3.1 Create a List of Putative Prohormones
			3.2 Identification of Putative Prohormones
				3.2.1 Text Search for Prohormone and Neuropeptide Names
				3.2.2 Homology Search Against Protein Databases and Genome Assembly Databases
				3.2.3 Novel Detection Based on Neuropeptide Motifs
				3.2.4 Validation of Predicted Prohormone Protein Sequences
			3.3 Sequence Verification of Predicted Prohormone Proteins
			3.4 Peptide Prediction from Prohormone Protein Sequences
				3.4.1 Signal Peptide Prediction
				3.4.2 Prediction of Putative Peptides
		4 Notes
		References
	Chapter 9: Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain an...
		1 Introduction
		2 Materials
			2.1 Peptidomics Data
			2.2 Python Scripts for Retrieval and Analysis of Peptidomics Data
			2.3 The MultiPep Stand-Alone Program
		3 Methods
			3.1 Retrieving Peptides from mzID Files
			3.2 Predicting with MultiPep
			3.3 Annotating, Ranking, and Comparing Predicted Bioactive Peptides Across Peptidomics Datasets
				3.3.1 Considerations Regarding Prediction Thresholds
				3.3.2 Bioactive Peptides Uniquely Detected in Compared Peptidomics Datasets
				3.3.3 Bioactive Peptides Present in Both Compared Peptidomics Datasets
				3.3.4 Evaluating Ranked Peptides
			3.4 General Considerations When Using Bioactivity Predictors
		4 Notes
		References
Part II: Applications Using Non-human Model Systems
	Chapter 10: Methods for Intracellular Peptidomic Analysis
		1 Introduction
		2 Materials
			2.1 Equipment
			2.2 Reagents
				2.2.1 Peptides Extraction and Acidification
				2.2.2 Peptide Labeling
			2.3 Consumables
		3 Methods
			3.1 Sample Homogenization
				3.1.1 Brain
				3.1.2 Soft Tissues
				3.1.3 Hard Tissues
				3.1.4 Cell Culture
				3.1.5 Body Fluids
				3.1.6 Blood Plasma Preparation
			3.2 Sample Acidification and Filtration
			3.3 Peptide Purification
			3.4 Peptide Quantification
			3.5 Peptide Labeling
			3.6 Mass Spectrometry
			3.7 Data Analysis Using Mascot Software
			3.8 Search and Quantitation with Mascot Daemon
			3.9 Quantitative Analysis of Isotope-Labeled Peptides
			3.10 Using Mascot Wrangler
			3.11 Optional Steps: Subtracting Contaminants
		4 Notes
		References
	Chapter 11: Neuropeptidomics of Genetically Defined Cell Types in Mouse Brain
		1 Introduction
		2 Materials
			2.1 Peptide Extraction
			2.2 Affinity Purification
			2.3 MS Analysis
		3 Methods
			3.1 Mice
			3.2 Stabilization of Tissue and Peptide Extraction
			3.3 Purification of Peptides
			3.4 MS Analysis
		4 Notes
		References
	Chapter 12: Nontargeted Identification of d-Amino Acid-Containing Peptides Through Enzymatic Screening, Chiral Amino Acid Anal...
		1 Introduction
		2 Materials
			2.1 Aminopeptidase M Digestion
			2.2 Chiral Amino Acid Analysis
		3 Methods
			3.1 Aminopeptidase M Digestion
			3.2 Chiral Amino Acid Analysis
			3.3 LC-MS for Structure Confirmation
		4 Notes
		References
	Chapter 13: Albumen and Yolk Plasma Peptidomics for the Identification and Quantitation of Bioactive Molecules and the Quality...
		1 Introduction
		2 Materials
			2.1 Peptide Extraction and Sample Preparation
			2.2 Mass Spectrometry
				2.2.1 MALDI-TOF-MS
				2.2.2 Liquid Chromatography-Mass Spectrometry
			2.3 Data Processing and Peptide Identification/Quantitation
		3 Methods
			3.1 Egg Sample Collection, Albumen-Yolk Separation, and Individual Compartment Handling
			3.2 Peptide Extraction and Sample Preparation
			3.3 MALDI-TOF-MS Analysis of Egg Peptides
			3.4 NanoLC-Q-Orbitrap-MS/MS Analysis of Egg Peptides
			3.5 Peptide Identification and Label-Free Untargeted Quantitation
			3.6 Label-Free Targeted Quantitation with Parallel Reaction Monitoring (PRM)/Skyline
		4 Notes
		References
	Chapter 14: An Updated Guide to the Identification, Quantitation, and Imaging of the Crustacean Neuropeptidome
		1 Introduction
		2 Materials
			2.1 Chemicals and Equipment
				2.1.1 Chemicals
				2.1.2 Equipment
				2.1.3 Other Materials
			2.2 Instrumentation and Software
		3 Methods
			3.1 Identification and Quantitation (Fig. 3)
			3.2 Localization by Mass Spectrometric Imaging (Fig. 6)
		4 Notes
		References
	Chapter 15: Strategy for the Identification of Host-Defense Peptides in Frog Skin Secretions with Therapeutic Potential as Ant...
		1 Introduction
		2 Materials
			2.1 Preparative HPLC
			2.2 Cell Culture
			2.3 MALDI-ToF Mass Spectrometry
		3 Methods
			3.1 HPLC Fractionation
			3.2 BRIN-BD11 Cell Culture
			3.3 Insulin-Release Assay
			3.4 Cytotoxicity Assay
			3.5 MALDI Matrix Solution
			3.6 Analyte and Peptide Calibration Mixtures
			3.7 Mass Range Determination
			3.8 Accurate Mass Determination
			3.9 Conclusions
		4 Notes
		References
	Chapter 16: Peptidomics of Zebrafish Brain in a 6-OHDA-Induced Neurodegeneration Model
		1 Introduction
		2 Materials
			2.1 Zebrafish Maintenance
			2.2 Anesthetization and 6-OHDA Microinjection of Zebrafish
			2.3 Locomotor Assessment
			2.4 Peptide Extraction
			2.5 Reductive Methylation of Amines Labeling
			2.6 Liquid Chromatography and Mass Spectrometry
		3 Methods
			3.1 Zebrafish
				3.1.1 Zebrafish Maintenance and Pre-6-OHDA Microinjection Preparations
				3.1.2 Anesthetization and 6-OHDA Microinjection of Zebrafish
			3.2 Locomotor Assessment
			3.3 Whole Brain Peptide Extraction
			3.4 Reductive Methylation of Amines Labeling (See Notes 9-11)
			3.5 Liquid Chromatography and Mass Spectrometry
			3.6 Peptide Identification and Relative Quantitation Analysis
		4 Notes
		References
	Chapter 17: Analysis of the Snake Venom Peptidome
		1 Introduction
		2 Materials
			2.1 Venom Collection
			2.2 Solid-Phase Extraction (SPE) of Peptides
			2.3 Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)
			2.4 RP-HPLC Fractionation
			2.5 ESI-Q-TOF Analysis of the Peptide Fractions
			2.6 MALDI-Q-TOF Analysis of the Peptide Fractions
			2.7 Data Analysis
		3 Methods
			3.1 Venom Collection and Processing
			3.2 Peptide Fraction Enrichment
			3.3 LC-MS/MS Analysis
			3.4 Database Search
			3.5 RP-HPLC Fractionation
			3.6 ESI-Q-TOF Analysis of the Peptide Fractions
			3.7 MALDI-Q-TOF Analysis of the Peptide Fractions
			3.8 De Novo Peptide Sequencing
		4 Notes
		References
	Chapter 18: Identification of Peptides in Spider Venom Using Mass Spectrometry
		1 Introduction
		2 Materials
			2.1 Quantification
			2.2 Solid Phase Extraction
			2.3 Digestion
			2.4 Mass Spectrometry
			2.5 Bioinformatics
		3 Methods
			3.1 Venom Milking
			3.2 Quantification
			3.3 Solid Phase Extraction (SPE)
			3.4 Peptide Digestion
			3.5 Mass Spectrometry
			3.6 Bioinformatics of Native Peptides
			3.7 Bioinformatics of Digested Peptides
		4 Notes
		References
	Chapter 19: Identification and Targeted Quantification of Endogenous Neuropeptides in the Nematode Caenorhabditis elegans Usin...
		1 Introduction
			1.1 Peptidomics
			1.2 Peptidomics in Caenorhabditis elegans
			1.3 Targeted Peptidomics
		2 Materials
			2.1 Culturing C. elegans
				2.1.1 For Culturing C. elegans on Solid Medium
				2.1.2 For Culturing C. elegans in Liquid
			2.2 Sample Preparation
			2.3 Peptidomics Analysis
				2.3.1 Example Instrument Setup for Data-Dependent Neuropeptide Discovery
				2.3.2 Example Instrument Setup for Relative Neuropeptide Quantification with Parallel Reaction Monitoring
			2.4 Data Processing and Peptide Identification
		3 Methods
			3.1 Maintenance of C. elegans Cultures
				3.1.1 Growth on Solid NGM Plates
				3.1.2 Growth in Liquid Cultures
			3.2 Sample Preparation
			3.3 Liquid Chromatography-Mass Spectrometry
			3.4 Data Processing and Peptide Identification
		4 Notes
		References
	Chapter 20: Intracellular and Extracellular Peptidomes of the Model Plant, Physcomitrium patens
		1 Introduction
		2 Materials
		3 Methods
			3.1 Calibration of Size Exclusion Chromatography (SEC) Column
			3.2 Sample Preparation for Intracellular Peptides (see Note 8)
			3.3 SEC Fractionation of the Intracellular Peptide Samples
			3.4 Redissolving and Disulfide Reduction in Peptide Samples
			3.5 Desalting Peptide Samples
			3.6 Sample Preparation for Extracellular Peptides
			3.7 Solid Phase Extraction of Extracellular Peptides
			3.8 LC-Coupled Tandem Mass Spectrometry Analysis
			3.9 MS Data Processing and Peptide Identification
		4 Notes
		References
Part III: Clinical Applications and Outlook
	Chapter 21: The Strategy for Peptidomic LC-MS/MS Data Analysis: The Case of Urinary Peptidome Study
		1 Introduction
		2 Materials
			2.1 Sample Preparation
			2.2 Liquid Chromatography/Mass Spectrometry Analysis (LC-MS)
			2.3 Data Processing
		3 Methods
			3.1 Extraction of Endogenous Urinary Peptides
			3.2 Liquid Chromatography/Mass Spectrometry
			3.3 Custom Urine-Specific Protein Database Development
				3.3.1 Urinary Proteins Trypsinolysis
				3.3.2 Isoelectric Focusing (IEF)
			3.4 Data Analysis
				3.4.1 Peptide Identification and Statistical Analysis
				3.4.2 Grouping of Peptides with Overlapping Sequences
		4 Notes
		References
	Chapter 22: Peptidomics Strategies to Evaluate Cancer Diagnosis, Prognosis, and Treatment
		Abbreviations
		1 Peptidomics in Cancer Disease: From Discovery to Clinical Application
		2 Exploring Peptidomics for Cancer Diagnosis, Prognosis, and Treatment Application
			2.1 Diagnosis
			2.2 Prognosis
			2.3 Treatment
		3 Methods Used to Identify and Verify Peptides with Potential Role in Cancer Diagnosis, Prognosis, and Treatment
		4 Shortcomings of Implementing Peptidomics in Clinical Practice
		References
	Chapter 23: Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins
		1 Introduction
		2 Materials
			2.1 Sample Collection and Cell Culture
			2.2 HLA Proteins Immunoprecipitation and Peptide Purification
			2.3 Peptide Sequencing Using Mass Spectrometry
			2.4 Data Analysis
		3 Methods
			3.1 Experimental Planning and Sample Collection
			3.2 HLA Proteins Immunoprecipitation and Peptide Purification
			3.3 Peptide Sequencing Using Mass Spectrometry
			3.4 Data Analysis
		4 Notes
		References
	Chapter 24: Profiling Human Cerebrospinal Fluid (CSF) Endogenous Peptidome in Alzheimer´s Disease
		1 Introduction
		2 Materials
			2.1 Chemicals and Equipment
			2.2 Instrumentation and Software
		3 Methods
			3.1 CSF Sample Collection
			3.2 CSF Sample Processing
			3.3 Peptide Desalting
			3.4 LC-MS/MS Analysis
			3.5 Data Analysis
		4 Notes
		References
	Chapter 25: Deep Learning-Assisted Analysis of Immunopeptidomics Data
		1 Introduction
		2 Materials
			2.1 Online Services USE and Oktoberfest
			2.2 Local Installation of Oktoberfest
			2.3 Supported Input Files for Oktoberfest
		3 Methods
			3.1 Deep Learning-Assisted Manual Spectrum Validation Using the Universal Spectrum Viewer (USE)
			3.2 Deep Learning-Assisted Analysis Via an Online Interface to Oktoberfest
			3.3 Deep Learning-Assisted Analysis Using Oktoberfest Locally
			3.4 Example of Running Oktoberfest Locally
		4 Notes
		References
	Chapter 26: Current Challenges and Future Directions in Peptidomics
		1 Introduction
		2 Trends in the Technological Basis and Synergy with Other `-Omics´ Technologies
			2.1 Expected Trends Related to Analytical Instrumentation
			2.2 Current Developments from Data Processing and Artificial Intelligence
		3 Expected Evolutions within Fields of Applications
			3.1 Basic Science Applications
				3.1.1 Peptide Hormones and Neuropeptides
				3.1.2 Intracellular Peptides
				3.1.3 Emergence of Plant Peptidomics
			3.2 Food Peptidomics
			3.3 Clinical Applications
		4 Concluding Remarks About the State of Peptidomics
		References
Index