ENGLISH

Separation Techniques Applied to Omics Sciences: From Principles to Relevant Applications (Advances in Experimental Medicine and Biology, 1336)

Book information

Publisher
Springer
Year
2021
ISBN
3030772519, 9783030772512
Language
english
Format
PDF
Filesize
12 MB (12632293 bytes)
Edition
1st ed. 2021
Pages
285\277
Time added
2021-11-02 14:58:29

Description

Preface Acknowledgments Contents The New Omics Era into Systems Approaches: What Is the Importance of Separation Techniques? 1 Systems Biology 2 Omics Sciences and the Systems Biology Era 2.1 Genomics to Systems Biology, Post-Genomics Era 2.1.1 First-Generation Sequencing (Gel-Based Sequencing) 2.1.2 Second-Generation Sequencing 2.1.3 Third-Generation Sequencing 2.2 Proteomics and the Advance in Systems Biology Studies 2.3 Metabolomics 3 Perspectives References Biological Applications for LC-MS-Based Proteomics 1 Introduction 2 Proteomics via LC-MS 3 Targeted Proteomics 4 Untargeted Proteomics 5 Quantitation 6 Applications for Proteomics 6.1 Pure and Applied Research 6.2 Standardized Laboratory Tests 6.3 Personalized Medicine 6.4 Other Sectors 6.5 Applications Under Development 7 Conclusion References The Role of Chromatographic and Electromigration Techniques in Foodomics 1 Introduction 2 Proteomic Approaches in Foodomics 3 Metabolomic Approaches in Foodomics 4 Genomic and Transcriptomic Approaches in Foodomics 5 Conclusions and Trends References CE-MS for Proteomics and Intact Protein Analysis 1 Introduction 2 Technological Considerations 2.1 Sample Introduction and Preconcentration 2.1.1 Electrophoretic Sample Concentration 2.1.2 Chromatographic Sample Concentration 2.2 Background Electrolyte 2.3 Preventing Analyte-Wall Interactions 2.4 CE-MS Interfacing 2.5 Mass Analyzers and Fragmentation 3 Applications 3.1 PTM Detection and Characterization 3.2 Biopharmaceutical Development and QC 3.3 Biomarker Discovery and Clinical Application 4 Conclusions and Future Perspectives References Peptidomics and Capillary Electrophoresis 1 Introduction 2 Sample Preparation 3 CE-MS 3.1 Basic Characteristics 3.2 Peptide Identification 3.3 Peptide Quantification 4 Applications 4.1 Determination of Biomarkers 4.2 Food Peptidomics 5 Conclusions References Discovery of Native Protein Complexes by Liquid Chromatography Followed by Quantitative Mass Spectrometry 1 Introduction 2 Liquid Chromatography to Fractionate Native Protein Complexes 2.1 Size Exclusion Chromatography (SEC) 2.2 Ion Exchange Chromatography (IEX) 3 Mass Spectrometry to Quantify Protein Complexes 3.1 The Protein Inference Problem 3.2 Label-Based Quantification 3.3 Label-Free Quantification 3.4 Generation of Spectral Library 4 Generation of Protein/Peptide Quantification Dataset 4.1 Extraction of PPI Features from Experimental Dataset(s) 4.2 Extraction of PPI Features from Literature(s) 4.3 Noise Modeling and Missing Value Imputation 5 Application of Machine Learning to Predict PPI 5.1 Denoising a Predicted PPI Matrix 5.2 Methods to Cluster the Denoised PPI Matrix (R) 5.3 Using Weka to Train ML Classifier 5.4 Network Analysis Using Cytoscape 6 Conclusion References Capillary Electrophoresis-Based N-Glycosylation Analysis in the Biomedical and Biopharmaceutical Fields 1 Introduction 2 The Biochemical Background of Glycosylation 3 Glycobiomarker Discovery 4 Glycosylated Biopharmaceuticals 5 Glycan Analysis Options 6 Summary References Practical Considerations in Method Development for Gas Chromatography-Based Metabolomic Profiling 1 Introduction to Modern Gas Chromatography 2 Rate Theory 3 Column Chemistry 4 Derivatization 5 Liquid Sample Introduction 6 Sample Preparation for GC Analysis 6.1 Liquid–Liquid Extraction (LLE) 6.2 Solid-Phase Extraction (SPE) 6.3 Solid-Phase Microextraction (SPME) 7 Comprehensive Two-Dimensional Gas Chromatography 8 Coupling to Mass Spectrometry 9 Perspectives References Capillary Electrophoresis-Mass Spectrometry for Metabolomics: Possibilities and Perspectives 1 Introduction 2 CE-MS Methodology 2.1 CE Systems for Metabolomics 2.2 Interfacing Designs and Their Implications for Metabolomics 3 Applications 3.1 Targeted Applications 3.2 Untargeted Applications 4 Conclusions and Perspectives References Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview 1 Introduction 2 Liquid Chromatography-Mass Spectrometry: Practical Approaches 2.1 LC Separation 2.2 Optimization in LC 2.2.1 Design of Experiments (DoE) 3 Sample Preparation Strategies for LC-MS-Based Metabolomics 3.1 Immunocapture for Targeted and Semi-targeted Metabolomics: New Perspectives 4 LC-MS/MS-Based Metabolomics and Data Acquisition 4.1 Mass Analyzers 4.1.1 Triple Quadrupole (QqQ) 4.1.2 Time of Flight (TOF) and Hybrid Quadrupole Time of Flight (QqTOF) 4.1.3 Orbitrap 5 Typical HRMS Data Acquisition 5.1 Full-Scan MS HRMS Data 5.1.1 Data-Dependent Acquisition (DDA) 5.1.2 Data-Independent Acquisition (DIA) 5.2 MSE Data Acquisition Using QTOF Systems 5.3 MS/MSALL with SWATH™ Acquisition Using TripleTOF™ Systems 5.4 All-Ion Fragmentation Using Orbitrap 6 Informatic Tools and Databases 7 Metabolomics for Biomarkers Discovery and Clinical Applications 8 Applications in Metabolic Flux Analysis (MFA) 9 Conclusions and Perspectives References Analytical Platforms for Mass Spectrometry-Based Metabolomics of Polar and Ionizable Metabolites 1 Introduction 2 Gas Chromatography 3 Hydrophilic Interaction Liquid Chromatography 4 Capillary Electrophoresis 5 Ion Pairing Chromatography 6 Ion Chromatography 7 Conclusion and Future Trends References Metabolomics Data Treatment: Basic Directions of the Full Process 1 Introduction 2 Data Processing 2.1 Data Preprocessing 2.1.1 Data Acquisition and Data Compression 2.1.2 Noise Filtering and Baseline Correction 2.1.3 Peak Detection and Deconvolution 2.1.4 Peak Alignment 2.1.5 Missing Values 2.2 Normalization 2.3 Data Pretreatment 3 Statistical Data Analysis 3.1 Univariate Statistical Analysis 3.2 Multivariate Statistical Analysis 3.2.1 Principal Components Analysis (PCA) 3.2.2 Partial Least Squares-Discriminant Analysis, PLS-DA 3.2.3 Orthogonal Partial Least-Square Discriminant Analysis, OPLS-DA 3.2.4 Hierarchical Cluster Analysis (HCA) 3.2.5 Other Statistical Analysis Methods 3.2.6 Model Validation 4 Software 5 Minimum Standards for Reporting on Data Processing in Metabolomics Studies References Index

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