Industrial engineers increasingly make use of microprocessors to monitor and control industrial processes. This book provides a comprehensive account of how CAN (controller area network) can be designed and applied in a wide variety of industrial settings. It covers thoroughly: CAN chip implementati
Metabolomics Perspectives: From Theory to Practical Application
β Scribed by Jacopo Troisi
- Publisher
- Academic Press
- Year
- 2022
- Tongue
- English
- Leaves
- 685
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Metabolomics Perspectives: From Theory to Practical Application is an expertly written volume, which provides a thorough description of the current state-of-the-art in the metabolomics field.
The philosophy behind the book is to guide the reader in a step-by-step exploration of metabolomics experiments, ranging from sample preparation to data extraction, analysis and interpretation, and to discuss the main current applications and future perspectives of this emerging science.
Armed with critical insights, coupled with a clear writing, the book consists of three main sections. The first one introduces the pivotal theoretical fundamentals and provides a comprehensive overview of the "wet" laboratory workflow, including protocol instructions and a detailed description of experimental methods and analytical techniques. The second section covers a wide range of topics in the context of data analysis, including guidance in exploratory analysis, supervised and unsupervised machine learning approaches and validation and optimization methods. In addition to the several examples reported in the text, the book features an R package, specifically designed to perform all the described algorithms, which is hosted on a companion website (www.metabolomicsperspectives.com) together with several sets of available metabolomic data. Finally, an extensive dissertation describes the latest advances and the major fields of interest for metabolomics applications, highlighting their crucial potentials for future biomedical research.
Thus, this book represents a must-read for both experienced researchers, interested in metabolomics, and newcomers to the field.
β¦ Table of Contents
Front Cover
Metabolomics Perspectives
Copyright Page
Contents
List of contributors
Foreword
Introduction
1 Fundamentals
1 System biology
Introduction
Genomics
Introduction
Genomic tools
Epigenetics
Introduction
Epigenetic tools
Transcriptomics
Introduction
Transcriptomic tools
Proteomics
Introduction
Proteomic tools
Metabolomics
Introduction
Metabolomic tools
References
2 Experimental design in metabolomics
Introduction
Applications of metabolomic experiments
Biomarker discovery
Detection of altered biochemical pathways
Monitoring of response to stimuli
Untargeted and targeted approaches
Untargeted metabolomics
Targeted metabolomics
Sample types
Metabolically active versus metabolically inactive
Tissue and cells
Tissues
Primary and immortalized cells
Whole blood, plasma, and serum
Urine
Other biofluids
Saliva
Cerebrospinal fluid
Amniotic fluid and breast milk
Sweat and tears
Analytical methodologies
Nuclear magnetic resonance
Mass spectrometry
Gas chromatography
Liquid chromatography
Techniques without sampling
Sample preparation
Quenching
Extraction
Sample clean up
Solvent removal
Solid-phase extraction
Ultrafiltration
Controlling metabolite concentrations
Nuclear magnetic resonance
Gas chromatography-mass spectrometry
Derivatization
Liquid chromatography-mass spectrometry
Identification and quantification of metabolites
Quantification
Calibration curve technique
Internal standard and isotope dilution
Identification
Public libraries and databases
Metabolomics Standards Initiative
Quality control
Conclusion
References
Further reading
3 Separation techniques
The role of the separation processes in metabolomics research
Sample preparation
Sample extraction techniques
Derivatization
Fundamentals of chromatography
Definitions and classifications
Retention
Selectivity
Efficiency of separation
Resolution
Peak capacity
Qualitative and quantitative analysis in chromatography
Liquid chromatography
Instrumentation
Principal separation modes
Detectors
Gas chromatography
Mobile phase and flow control
Temperature zones
Sample introduction and inlets
Column, stationary phases, and separation
Detectors
Multidimensional chromatography
Concept of multidimensionality
Practical and instrumental aspects
Other separation techniques
Capillary electrophoresis
Supercritical fluid chromatography
Chiral chromatography
References
4 Mass spectrometry in metabolomics
Mass spectrometry
Mass spectrum
Isotopes
Resolution and accuracy
Mass spectrometer
System for sample introduction
Ion sources
Mass analyzer
Ion detector
Ion sources
EI ion source
Matrix-assisted laser desorption ionization ion source
Electrospray ion source
Mass analyzers
Quadrupole mass analyzer
Time of flight mass analyzer
Orbitrap mass analyzer
Quadrupole ion trap
Tandem mass spectrometry
Instruments for tandem mass spectrometry analysis
Tandem mass spectrometry scan modes
Product ion scan
Precursor ion scan
Neutral loss scan
Multiple reaction monitoring
Untargeted metabolomics in complex samples
Analytical techniques in mass spectrometry -based metabolomics
Gas chromatography-mass spectrometry
Liquid chromatography-tandem mass spectrometry
Imaging mass spectrometry
Data analysis
Applications
Metabolomic analysis for clinical biomarker discovery
Metabolomics in drug development
Metabolomics in nutrition science
Metabolomics in toxicology
Metabolomics in forensic science
References
Further reading
5 Nuclear magnetic resonance in metabolomics
Introduction
Nuclear magnetic resonance spectroscopy
1D nuclear magnetic resonance
1D 1H nuclear magnetic resonance spectroscopy
1H 1D nuclear magnetic resonance in metabolomic studies
1D 1H nuclear magnetic resonance examples
1D 13C nuclear magnetic resonance in metabolomic studies
1D 15N nuclear magnetic resonance in metabolomics
31P nuclear magnetic resonance in metabolomic studies
19F in metabolomic studies
2D nuclear magnetic resonance spectroscopy
High-resolution magic-angle spinning nuclear magnetic resonance spectroscopy
Pure shift nuclear magnetic resonance
Recent advances
Improvements in nuclear magnetic resonance hardware and techniques and additional tools to aid in metabolomics studies
Nuclear magnetic resonance magnets
Nuclear magnetic resonance probes
Flow probes
Metabolomics databases and nuclear magnetic resonance software programs
Databases for nuclear magnetic resonance-based metabolomics
Use of software to analyze metabolite nuclear magnetic resonance data
Advantages of nuclear magnetic resonance spectroscopy
Reproducibility
Challenges and limitations
Sample preparation
Summary and future perspectives
References
6 Targeted metabolomics
Targeted metabolomics
Inborn errors of metabolism
Application of targeted metabolomics to the newborn screening of inborn errors of metabolism
Examples of inborn error of metabolism diagnosed by the newborn screening
Methylmalonic acidemias
Propionic acidemia
Glutaric acidemia
Isovaleric acidemia
Phenylketonuria
Hereditary tyrosinemias
Maple syrup urine disease
Conclusion
References
7 Approaches in untargeted metabolomics
Introduction
Local and nonlocal metabolomics effects
Untargeted metabolomics application
Metabolomics profiling
Cardiovascular disease
Neurodegenerative disease
Limitations
Sources of metabolome variability
Key trends in untargeted metabolomics
Metabolome coverage
Moving metabolomics from laboratories to clinics
Metabolomics pipeline standardization
Sample size
Independent cohort to validate the results
Cause/effects disambiguation
Conclusion
References
2 Data analysis
8 Techniques for converting metabolomic data for analysis
Introduction
Data preprocessing
Mass spectrometry-based experiments
Peak picking and Smoothing
Deconvolution
Alignment
Gap filling
Nuclear magnetic resonance
Water signal elimination
Chemical shift calibration
Binning
Normalization
Internal standard normalization
Probabilistic quotient normalization
Quantile normalization
Data pretreatment
Centering
Scaling
Transformation
Conclusion
References
9 Data analysis in metabolomics: from information to knowledge
Introduction
Exploratory analysis
Univariate approach
Tests to investigate metabolite concentration differences
Multivariate approach
Loadings and scores in principal components analysis
Significative components
Conclusion
Unsupervised machine learning analysis
Introduction
Cluster analysis
Hierarchical clustering
Agglomerative hierarchical methods
Divisive hierarchical methods
Nonhierarchical clustering
K-means method
Jarvis-Patrick method
Conclusion
Supervised machine learning
Introduction
Decision trees
NaΓ―ve Bayesian
Discriminant analysis
Artificial neural network
Introduction
Artificial neural networks training
Conclusions
Support vector machine
Nonlinear separable data
Regressive models
Partial least square regression
Geometric interpretation of the partial least square regression
The prediction error in partial least square regression
RIDGE regression
Least absolute shrinkage and selection operator regression
Partial least square discriminant analysis
Latent variables
The variables important in the projection
Geometric interpretation of the partial least square discriminant analysis
Orthogonal partial least squares discriminant analysis
Classification model validation
Leave-one-out cross-validation
Leave-k-out cross-validation
k-fold cross validation
Permutation test
Class imbalance
Metrics to estimate the classification performances
Sampling strategies
Machine learning algorithms modification
Ensemble machine learning
Bagging
Boosting
Features selection
Features filtering
Borutaβs algorithm
Genetic algorithm
Genetic algorithm operators
Features generation
Embedded methods
Conclusions
Hyperparameters optimization
Parameters and hyperparameters in machine learning
Hyperparameters tuning
Grid search
Random search
Bayesian optimization
Appendix
References
10 Relevant metabolitesβ selection strategies
Introduction
Low-level variable selection
Unsupervised low-level variable selection
Percentage observed
Variance based
Supervised low-level variable selection
Quantitative response
Pearsonβs correlation coefficient
Qualitative response
Fold change
Hypothesis testing
Medium-level variable selection
Variable selection or wrapper methods
Stepwise regression
Global optimization algorithms
High-level variable selection
Embedded methods for the selection of variables
Regularization techniques
Latent variable methods
Principal component regression
Partial least squares
Decision trees
Random forests
Support vector machine
Heuristic approach
Bootstrap and stability selection
Cross validation
Concluding remarks
References
11 Pathway analysis
Metabolites ontology
Introduction to ontologies
Ontologies for metabolites
Common metabolite databases
Human metabolome database
LipidMaps
CheBi
Common pathway databases
Kyoto encyclopedia of genes and genomes
Small molecules pathway database
Consensus path database
Reactome
Wikipathways
Metabolic pathway analysis
Overrepresentation
Enrichment
Metabolite set enrichment analysis
KolmogorovβSmirnov test
Wilcoxon signed rank test
Topological methods
Tools for metabolomic pathway analysis
Conclusions
References
3 Application
12 Cell culture metabolomics and lipidomics
Introduction
Sample processing and experimentation for cell culture lipidomics and metabolomics
Methods for optimized metabolite and lipid extractions for cell culture analysis
Analysis of metabolic processes including metabolic flux
Methods and protocols for isolation and metabolomics of small extracellular vesicles from cell culture supernatants
Cell culture for isolation of small extracellular vesicles
Isolation of small extracellular vesicles using ultracentrifugation
Differential ultracentrifugation
Density gradient ultracentrifugation
Isolation of small extracellular vesicles using tangential flow filtration
Characterization of small extracellular vesicles
Metabolite extractions from cells and small extracellular vesicles
Sample preparation and analysis with nuclear magnetic resonance spectroscopy
Cell culture metabolomics and lipidomics data analysis
Cell culture metabolomics and cell modeling for the design and optimization of cell culture applications
Determination of major metabolic pathways or network from metabolomics or fluxomics data
Network analysis in cell culture metabolomics
Mechanistic modeling for cell culture optimization, design, and information gathering
Machine learning and hybrid models and artificial intelligence for cell design
References
13 Single cell metabolomics
Introduction
Single-cell metabolomics in microbial technology
Single-cell metabolomics in plant science and agriculture
Diversified animal applications
Single-cell metabolomics in developmental biology
Single-cell metabolomics in aging and senescence study
Single-cell metabolomics in stem cell biology
Single-cell metabolomics in functional genomics
Single-cell metabolomics in nutrition research
Single-cell metabolomics in environmental biology
Single-cell metabolomics in system biology
Single-cell metabolomics in immunology
Single-cell metabolomics in detection of metabolite dynamicity and pathway modulation
Single-cell metabolomics in clinical metabolism and disease perspective
Conclusion and future prospect
References
14 Gut microbiota-derived metabolites in host physiology
Introduction
Metabolomics methods in host-microbiome studies
Fermentable metabolites and short chain fatty acids
Secondary bile acids
Amino acids- and tryptophan-derived metabolites
Additional microbially derived metabolites
Perspectives and future directions
References
Further reading
15 MALDIβmass spectrometry imaging: the metabolomic visualization
Introduction
Basics of MALDI mass spectrometry imaging
Matrix choice and application
Tissue preparation for MALDI mass spectrometry imaging analysis
MALDI mass spectrometry imaging instrumentation
MALDI mass spectrometry imaging of endogenous metabolites
Metabolite annotation and quantitation in MALDI mass spectrometry imaging
Conclusion and future perspectives
References
16 Metabolomics for oncology
Introduction
Reprogramming of cancer cell metabolism
Glucose and Warburg effect
Lactate shuttle due to tumor hypoxia and Warburg effect
Glutamine metabolism
Serine metabolism
Methionine metabolism
Metabolism of arginine and ornithine involved in linking tricarboxylic acid and urea cycles
Proline metabolism
Lipid synthesis pathway
Nucleotide biosynthesis pathway
Applications and examples of human cancer metabolomics
Serum/plasma metabolomics studies
Urine metabolomics studies
Tissue metabolomics studies
Fecal metabolomics studies
Saliva metabolomics studies
Metabolomics studies on other biological matrices
Conclusion
References
17 Metabolomics as a tool for precision medicine
Systems approaches and systems medicine
Individual phenotyping using nuclear magnetic resonance
Applications
References
18 Metabolomics in public health
Introduction
Data integration
System biology and metabolomics in publich health
Longitudinal and life-long studies in metabolomics
Quantitative methods are necessary
Big data and metabolomics in public health
Policies, training, and resources
Final remarks
References
Index
Back Cover
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