Metabolomics Perspectives: From Theory to Practical Application

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
			Naïve 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
			Boruta’s 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 metabolites’ selection strategies
		Introduction
		Low-level variable selection
			Unsupervised low-level variable selection
				Percentage observed
				Variance based
			Supervised low-level variable selection
				Quantitative response
					Pearson’s 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
		Kolmogorov–Smirnov 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 MALDI–mass 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
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