🧪Metabolomics and Systems Biology Unit 3 – Metabolomics: Analytical Techniques

Key Concepts in Metabolomics

• Metabolomics studies the complete set of small molecule metabolites in a biological system (metabolome) provides a snapshot of the physiological state
• Metabolites are the end products of cellular processes include sugars, amino acids, organic acids, and lipids
• Metabolomics data complements genomics, transcriptomics, and proteomics offers a comprehensive understanding of biological systems
• Untargeted metabolomics aims to detect and quantify as many metabolites as possible without prior knowledge of the specific compounds
  • Generates a global metabolic profile useful for hypothesis generation and discovering novel biomarkers
• Targeted metabolomics focuses on a specific subset of known metabolites often involved in a particular pathway or biological process
  • Provides accurate quantification and validation of key metabolites
• Metabolic fingerprinting rapidly classifies samples based on metabolite patterns without necessarily identifying individual compounds
• Metabolic profiling characterizes and quantifies a predefined set of metabolites related to a specific metabolic pathway or class of compounds
• Metabolic flux analysis measures the rates of metabolic reactions and the flow of metabolites through pathways using stable isotope labeling techniques

Analytical Techniques Overview

• Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) are the two primary analytical techniques used in metabolomics
• NMR spectroscopy measures the magnetic properties of atomic nuclei (commonly $^1$H, $^13$C, and $^31$P$) provides structural information and quantitative data
  • Non-destructive, requires minimal sample preparation, and is highly reproducible
  • Lower sensitivity compared to MS limits its ability to detect low-abundance metabolites
• Mass spectrometry measures the mass-to-charge ratio ($m/z$) of ionized molecules highly sensitive and can detect a wide range of metabolites
  • Often coupled with separation techniques such as liquid chromatography (LC) or gas chromatography (GC) to reduce sample complexity and improve resolution
• Fourier-transform infrared (FTIR) and Raman spectroscopy are complementary techniques that provide information on the vibrational modes of molecules
  • Useful for rapid, non-destructive analysis of solid or liquid samples
• Capillary electrophoresis (CE) separates metabolites based on their charge and size in a narrow capillary offers high resolution and efficiency
• Supercritical fluid chromatography (SFC) uses a supercritical fluid (e.g., CO2) as the mobile phase separates compounds based on their polarity and molecular weight
• Comprehensive two-dimensional chromatography (GC×GC or LC×LC) combines two orthogonal separation techniques for enhanced resolution and peak capacity
• Imaging mass spectrometry (IMS) techniques, such as MALDI-IMS and DESI-IMS, enable spatial mapping of metabolites in tissue sections providing insights into metabolite distribution and localization

Sample Preparation Methods

• Sample preparation is a critical step in metabolomics ensures the extraction and stabilization of metabolites while minimizing matrix effects and interferences
• Quenching rapidly stops enzymatic activity and preserves the metabolic state of the sample common methods include cold methanol, liquid nitrogen, or acid treatment
• Liquid-liquid extraction (LLE) partitions metabolites between two immiscible solvents based on their relative solubility
  • Commonly used solvent systems include methanol-water, chloroform-methanol-water (Bligh and Dyer method), and methyl tert-butyl ether (MTBE)-methanol-water
• Solid-phase extraction (SPE) uses a solid sorbent to selectively retain and elute metabolites based on their physicochemical properties
  • Reversed-phase (RP), normal-phase (NP), and ion-exchange (IEX) SPE materials are available for different classes of metabolites
• Protein precipitation removes proteins from the sample matrix using organic solvents (acetonitrile, methanol) or acids (trichloroacetic acid, perchloric acid)
• Derivatization modifies the chemical structure of metabolites to improve their chromatographic separation, volatility, or ionization efficiency
  • Common derivatization reactions include silylation (TMS), alkylation (methyl or ethyl esters), and oximation (methoxyamine)
• Lyophilization (freeze-drying) removes water from the sample by sublimation under vacuum concentrates the metabolites and improves stability
• Ultrafiltration separates metabolites from proteins and other macromolecules based on molecular weight cutoff (MWCO) using a semipermeable membrane
• Sample normalization accounts for variations in sample amount, extraction efficiency, and instrument response using internal standards or total ion current (TIC)

Chromatography and Separation

• Chromatography separates metabolites based on their differential partitioning between a stationary phase and a mobile phase
• Gas chromatography (GC) separates volatile compounds using a gaseous mobile phase (carrier gas) and a solid or liquid stationary phase
  • Requires derivatization of non-volatile metabolites (silylation, alkylation) to increase volatility
  • Coupled with electron ionization (EI) mass spectrometry for robust compound identification using mass spectral libraries
• Liquid chromatography (LC) separates metabolites using a liquid mobile phase and a solid stationary phase
  • Reversed-phase LC (RPLC) uses a non-polar stationary phase (C18, C8) and a polar mobile phase separates metabolites based on hydrophobicity
  • Hydrophilic interaction liquid chromatography (HILIC) uses a polar stationary phase (silica, amino, zwitterionic) and a less polar mobile phase separates polar and ionic metabolites
• Ultra-high performance liquid chromatography (UHPLC) employs sub-2-micron particle columns and high pressures (>400 bar) for enhanced separation efficiency and faster analysis times
• Supercritical fluid chromatography (SFC) uses a supercritical fluid (CO2) as the mobile phase separates non-polar to moderately polar compounds
• Capillary electrophoresis (CE) separates charged metabolites based on their electrophoretic mobility in a narrow capillary under an applied electric field
  • Capillary zone electrophoresis (CZE) and micellar electrokinetic chromatography (MEKC) are common CE modes used in metabolomics
• Multidimensional chromatography (e.g., GC×GC, LC×LC) combines two orthogonal separation techniques for enhanced resolution and peak capacity
  • Comprehensive two-dimensional gas chromatography (GC×GC) employs two GC columns with different stationary phases connected by a modulator
  • Two-dimensional liquid chromatography (2D-LC) couples two LC separations, such as RPLC and HILIC, for improved coverage of the metabolome

Mass Spectrometry in Metabolomics

• Mass spectrometry (MS) measures the mass-to-charge ratio ($m/z$) of ionized molecules provides structural information and quantitative data
• Ionization techniques convert metabolites into gas-phase ions soft ionization methods, such as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI), are commonly used in LC-MS
  • ESI produces ions by applying a high voltage to a liquid sample forming charged droplets that evaporate and release ions
  • APCI uses a corona discharge to ionize the sample in the gas phase after vaporization
• Hard ionization techniques, such as electron ionization (EI) and chemical ionization (CI), are used in GC-MS
  • EI bombards the sample with high-energy electrons (70 eV) causing extensive fragmentation and generating reproducible mass spectra
  • CI uses a reagent gas (methane, ammonia) to ionize the sample through proton transfer or adduct formation resulting in less fragmentation
• Mass analyzers separate ions based on their $m/z$ common types include quadrupole (Q), time-of-flight (TOF), and ion trap (IT)
  • Quadrupole mass filters use oscillating electric fields to selectively transmit ions of a specific $m/z$
  • Time-of-flight analyzers measure the time it takes for ions to travel a fixed distance provides high mass accuracy and resolution
  • Ion traps (linear or 3D) confine ions using electric fields and selectively eject them for detection
• Tandem mass spectrometry (MS/MS) involves multiple stages of mass analysis provides structural information through fragmentation of selected precursor ions
  • Collision-induced dissociation (CID) and higher-energy collisional dissociation (HCD) are common fragmentation techniques
• Data acquisition modes in MS include full scan (MS1), selected ion monitoring (SIM), and multiple reaction monitoring (MRM)
  • Full scan mode acquires a complete mass spectrum over a defined $m/z$ range useful for untargeted metabolomics
  • SIM mode monitors specific $m/z$ values for targeted analysis and improved sensitivity
  • MRM mode monitors specific precursor-product ion transitions provides high selectivity and sensitivity for quantitative analysis
• High-resolution mass spectrometry (HRMS) techniques, such as Fourier-transform ion cyclotron resonance (FT-ICR) and Orbitrap, offer high mass accuracy (<1 ppm) and resolving power (>100,000) enabling accurate mass measurements and elemental composition determination

Data Processing and Analysis

• Data preprocessing converts raw MS data into a format suitable for statistical analysis involves noise reduction, peak detection, alignment, and normalization
  • Noise reduction removes background noise and baseline drift using techniques such as smoothing and baseline correction
  • Peak detection identifies and integrates chromatographic peaks representing metabolite features
  • Alignment corrects for retention time shifts across samples ensuring that the same metabolite is compared accurately
  • Normalization adjusts for variations in sample amount, extraction efficiency, and instrument response using internal standards or total ion current (TIC)
• Feature extraction and grouping identifies unique metabolite features across samples based on their $m/z$ and retention time
  • Isotope and adduct grouping assigns related features to a single metabolite
  • Missing value imputation estimates the intensity of undetected features using statistical methods (e.g., k-nearest neighbors, random forest)
• Statistical analysis identifies significant differences in metabolite levels between sample groups and explores patterns in the data
  • Univariate methods, such as t-tests and ANOVA, compare individual metabolite levels between groups
  • Multivariate methods, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), reveal patterns and relationships among metabolites and samples
  • Hierarchical clustering and heatmaps visualize metabolite abundance patterns and sample groupings
  • Pathway analysis maps metabolite changes onto biological pathways to identify affected processes and mechanisms
• Data visualization techniques communicate complex metabolomics data in a clear and informative manner
  • Volcano plots display fold changes and statistical significance of metabolite differences between groups
  • Scores and loadings plots from PCA and PLS-DA show sample clustering and metabolite contributions
  • Network and correlation maps illustrate relationships and co-regulation among metabolites
• Quality control (QC) and validation ensure the reliability and reproducibility of metabolomics data
  • QC samples, such as pooled samples or standard mixtures, are analyzed throughout the run to assess instrument performance and data quality
  • Validation using targeted quantitative analysis or orthogonal techniques confirms the identity and abundance of key metabolites

Metabolite Identification Strategies

• Metabolite identification assigns a chemical identity to the detected metabolite features a critical step in interpreting metabolomics data
• Mass spectral library searching compares experimental mass spectra against reference spectra in databases, such as NIST, Metlin, and MassBank
  • Requires comprehensive and well-annotated libraries for the specific sample type and analytical platform
  • Provides putative identifications based on spectral similarity scores and retention time matching
• Accurate mass and isotope pattern matching compares the measured mass and isotope distribution of a metabolite to theoretical values
  • High-resolution mass spectrometry (HRMS) enables accurate mass measurements and elemental composition determination
  • Isotope pattern matching helps to confirm the elemental formula and resolve isobaric compounds
• Fragmentation and MS/MS matching compares experimental MS/MS spectra to reference spectra or in silico fragmentation patterns
  • Collision-induced dissociation (CID) and higher-energy collisional dissociation (HCD) are common fragmentation techniques
  • In silico fragmentation tools, such as MetFrag and MS-FINDER, predict fragmentation patterns based on chemical structure
• Retention time and chromatographic behavior provide additional confirmation of metabolite identity
  • Requires reference standards analyzed under identical chromatographic conditions
  • Retention time index (RTI) and relative retention time (RRT) are used for GC and LC, respectively
• Nuclear magnetic resonance (NMR) spectroscopy provides structural information for metabolite identification
  • $^1$H and $^13$C NMR spectra reveal the chemical environment and connectivity of atoms in the molecule
  • Two-dimensional NMR techniques, such as COSY, HSQC, and HMBC, provide additional structural information
• Orthogonal analytical techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, offer complementary structural information
• Metabolite identification confidence levels range from putative identification to confirmed identification based on the available evidence
  • Level 1: Confirmed identification using reference standards and multiple orthogonal techniques
  • Level 2: Putative annotation based on spectral and physicochemical properties
  • Level 3: Putative characterization based on compound class or chemical formula
  • Level 4: Unknown compounds with no structural information available

Applications and Case Studies

• Biomarker discovery identifies metabolites that are significantly altered in a disease state or in response to a treatment
  • Example: Serum metabolomics revealed elevated levels of branched-chain amino acids (BCAAs) in individuals with insulin resistance and type 2 diabetes
• Drug discovery and development uses metabolomics to assess drug efficacy, toxicity, and mechanism of action
  • Example: Metabolomics analysis of urine samples from rats treated with acetaminophen identified novel biomarkers of liver toxicity, such as N-acetyl-p-benzoquinone imine (NAPQI) and its metabolites
• Plant metabolomics investigates the metabolic diversity and stress responses in plants for crop improvement and natural product discovery
  • Example: Metabolomics profiling of tomato fruits revealed changes in amino acids, sugars, and organic acids during ripening and in response to environmental stress
• Environmental metabolomics studies the impact of environmental factors, such as pollution and climate change, on living organisms
  • Example: Metabolomics analysis of marine mussels exposed to polycyclic aromatic hydrocarbons (PAHs) identified alterations in energy metabolism and oxidative stress pathways
• Microbiome and host-microbe interaction studies use metabolomics to investigate the metabolic crosstalk between microbes and their host
  • Example: Fecal metabolomics revealed differences in the gut metabolome of individuals with inflammatory bowel disease (IBD) compared to healthy controls, including altered levels of short-chain fatty acids (SCFAs) and bile acids
• Precision medicine and personalized nutrition employ metabolomics to tailor interventions based on an individual's metabolic profile
  • Example: Metabolomics analysis of plasma samples from a weight loss intervention study identified metabolic signatures associated with successful weight loss and maintenance, such as increased levels of glycine and serine
• Food and beverage analysis uses metabolomics for quality control, authenticity assessment, and flavor profiling
  • Example: Metabolomics profiling of coffee beans from different geographic origins and processing methods revealed distinct metabolite patterns related to quality and sensory attributes
• Forensic applications of metabolomics include drug testing, toxicological analysis, and postmortem interval estimation
  • Example: Metabolomics analysis of postmortem blood samples identified metabolic markers, such as hypoxanthine and xanthine, that correlate with the time since death