GC-MS-Based Metabolic Flux Analysis Service - Creative Proteomics Metabolomics

Q: What is the primary advantage of GC‑MS over LC‑MS in metabolic flux analysis?

GC‑MS under standard electron ionization (EI, 70 eV) produces predictable, reproducible fragment ions containing specific subsets of the metabolite carbon backbone (e.g. [M‑57] + vs. [M‑159] + in TBDMS derivatives). This provides fragment‑level positional isotope constraints without requiring tandem MS/MS or NMR, while delivering exceptional chromatographic resolution and retention index stability.

Q: Why is TBDMS derivatization preferred over TMS for amino acid flux analysis?

TBDMS (tert‑butyldimethylsilyl via MTBSTFA) produces an extremely stable [M‑57] + fragment via loss of the tert‑butyl radical, yielding a clean, high‑mass ion cluster containing the entire carbon skeleton with minimal secondary fragmentation. Furthermore, the [M‑159] + fragment (loss of C1 carboxyl) provides direct positional information on C2–Cn carbons, whereas TMS derivatives often undergo complex fragmentation rearrangements.

Q: What is protein‑bound amino acid analysis, and when is it used?

Protein‑bound amino acid analysis involves hydrolyzing total cellular biomass in 6 M HCl (105–110 °C, 16–24 h) to release amino acids incorporated into structural proteins. Because protein pools turn over slowly, their isotopic labeling represents a time‑averaged, highly stable reflection of steady‑state metabolic flux, making it the gold standard for microbial fermentations and long‑term cell culture studies.

Q: How do you correct for natural silicon and carbon isotopes in silylated derivatives?

Silylating reagents introduce naturally occurring heavy silicon isotopes (4.7% 29 Si and 3.1% 30 Si) in addition to background 13 C, 15 N, and 18 O. We employ validated computational deconvolution algorithms (IsoCor, AccuCor) that utilize the exact chemical formula of each fragment ion to mathematically subtract all natural isotope contributions before flux modeling.

Q: Can GC‑MS analyze phosphorylated central carbon intermediates like G6P and PEP?

Phosphorylated sugars and nucleotides are highly polar, thermally labile, and difficult to volatilize completely for GC‑MS. While they can be analyzed using extensive two‑step methoximation‑silylation (MOX‑TMS), we frequently combine GC‑MS (for amino acids and organic acids) with our LC‑Orbitrap MS platform (for direct, non‑derivatized analysis of intact phosphorylated hexoses, trioses, and acyl‑CoAs).

Q: What sample biomass is typically required for GC‑MS metabolic flux analysis?

For protein‑bound biomass hydrolysis, 1–5 mg of dry cell pellet is sufficient. For free intracellular metabolite pools, we recommend 1–10 × 10 6 cultured cells or 20–100 mg of wet tissue per biological replicate to ensure high signal‑to‑noise ratios across all mass isotopologue channels.

Q: What computational software and statistical models are used to fit GC‑MS flux data?

We implement Elementary Metabolic Unit (EMU) network modeling in INCA and 13CFLUX2. Flux estimation is validated by Chi‑square (χ²) goodness‑of‑fit testing, residual distribution diagnostics, and 95% profile likelihood confidence intervals to guarantee that all reported reaction rates are mathematically identifiable.

GC-MS-Based Metabolic Flux Analysis — Exploiting Fragment-Level Information

While liquid chromatography-mass spectrometry (LC-MS) excels at measuring intact whole-molecule mass isotopologue distributions (MIDs), gas chromatography-mass spectrometry (GC-MS) under standard electron ionization (EI, 70 eV) produces reproducible, unimolecular fragmentation patterns. These characteristic fragment ions contain specific, well-defined subsets of carbon atoms from the original metabolite backbone (e.g. fragments retaining C1–Cn versus C2–Cn), providing crucial positional isotope constraints without requiring tandem MS fragmentation or nuclear magnetic resonance (NMR).

As a foundational methodology within our comprehensive metabolic flux analysis service, GC-MS-based MFA provides high chromatographic peak capacity, baseline separation of critical isomers, and deep spectral reproducibility. This platform enables you to:

(i) Resolve carbon transition pathways across glycolysis, the pentose phosphate pathway (PPP), and the tricarboxylic acid (TCA) cycle

(ii) Quantify protein-bound amino acid isotopologue distributions representing integrated, time-averaged steady-state fluxes in microbial and mammalian cultures

(iii) Differentiate anaplerotic flux through pyruvate carboxylase (PC) versus pyruvate dehydrogenase (PDH) using fragment-specific mass shifts in malate, aspartate, and citrate

(iv) Calculate reversible transamination and exchange velocities across amino acid biosynthesis pathways.

What Analytical Challenges Do We Solve in GC-MS Fluxomics?

Accurate GC-MS flux analysis demands robust chemical derivatization, elimination of thermal degradation artifacts, and precise multi-element isotopic correction. Creative Proteomics solves these technical bottlenecks with a dedicated analytical workflow that is:

Fragment-Resolved: Utilizing well-characterized tert-butyldimethylsilyl (TBDMS) and trimethylsilyl (TMS) fragmentation channels (such as [M-57]⁺, [M-159]⁺, and [M-85]⁺) to isolate specific carbon sub-skeletons.
Correction-Rigorous: Correcting raw mass isotopologue signals for natural abundances of carbon (¹³C), silicon (²⁹Si, ³⁰Si from silyl tags), nitrogen (¹⁵N), and oxygen (¹⁸O) using matrix deconvolution routines (IsoCor, AccuCor).
Quantitatively Constrained: Coupling intracellular labeling measurements with extracellular glucose uptake, lactate secretion, and amino acid exchange rates measured via targeted metabolomics panels to establish absolute molar flux boundaries.
Model-Validated: Implementing Elementary Metabolic Unit (EMU) regression algorithms in INCA and 13CFLUX2, validated by Chi-square (χ²) goodness-of-fit testing and non-linear 95% profile likelihood confidence intervals.

Derivatization Strategies: Optimizing Analyte Volatility and Fragmentation

GC-MS requires chemical derivatization to replace active hydrogen atoms (in -COOH, -OH, -NH2, -SH groups) with volatile, thermally stable silyl or alkyl moieties. We select chemistries optimized for specific metabolite classes and target fragmentation channels.

1. Silylation & Alkylation Decision Matrix

Derivatization Chemistry	Target Metabolite Classes	Key Diagnostic Fragments & Carbon Constraints
TBDMS Silylation (MTBSTFA + 1% TBDMCS)	Proteinogenic amino acids (Ala, Val, Leu, Ile, Pro, Gly, Ser, Thr, Met, Asp, Glu, Phe, Tyr) and TCA organic acids (succinate, fumarate, malate, citrate)	Loss of tert-butyl radical yields abundant [M-57]⁺ (intact carbon backbone) and [M-159]⁺ (loss of C1-alpha carboxyl group, retaining C2–Cn carbons). Highly stable with minimal secondary fragmentation.
Two-Step MOX-TMS (Methoximation + MSTFA / BSTFA)	Keto acids (pyruvate, alpha-ketoglutarate, oxaloacetate), phosphorylated sugars (glucose-6-phosphate, 3PG, PEP), and polyols	Methoximation protects carbonyl groups against decarboxylation/enolization, followed by trimethylsilylation. Yields molecular ions and characteristic fragment series for central carbon intermediates.
Chloroformate Alkylation (MCF / ECF / PCF)	Free intracellular and extracellular amino acids, non-proteinogenic amines, and short-chain organic acids	Rapid aqueous-phase reaction converting amino and carboxyl groups into alkyl carbamates and esters within seconds without heating, preserving thermally labile species.
Acid Hydrolysis (6 M HCl) for Protein-Bound Pools	Total cellular biomass hydrolysate	Hydrolyzes structural proteins (105–110 °C, 16–24 h) into constituent amino acids for long-term time-averaged steady-state flux determination in slow-growing systems.

2. Comparison of Standard 13C Tracer Configurations for GC-MS-MFA

Tracer Substrate	Target Metabolic Decision Node	GC-MS Fragment Readout & Analytical Value
[1,2-¹³C₂]Glucose	Glycolysis vs. Oxidative Pentose Phosphate Pathway (PPP) split ratio	Direct glycolysis yields [M-57]⁺ M+2 lactate and alanine (C1-C2-C3). Oxidative PPP decarboxylation of carbon-1 as ¹³CO₂ yields M+1 lactate and alanine. Quantified via our pentose phosphate pathway metabolic flux analysis.
[U-¹³C₆]Glucose	Glycolytic rate, PDH vs. PC entry into TCA, and amino acid biosynthesis	PDH entry generates M+2 acetyl-CoA → M+2 citrate, glutamate (C4-C5), and alpha-KG; PC entry yields M+3 oxaloacetate → M+3 malate, aspartate, and citrate in our glycolytic pathway metabolic flux analysis.
[U-¹³C₅]Glutamine	Oxidative TCA cycle cycling vs. IDH-mediated reductive carboxylation	Oxidative metabolism yields M+4 alpha-KG, succinate, malate, and aspartate; reverse reductive carboxylation generates M+5 citrate and M+3 malate for lipid synthesis within our TCA cycle metabolic flux analysis.
Parallel [1-¹³C]Glucose + [U-¹³C₆]Glucose	Comprehensive central network identifiability	Simultaneously fitted in a unified stoichiometric model to resolve non-oxidative PPP reversibility (transketolase/transaldolase) and anaplerotic flux within our 13C metabolic flux analysis framework.

Why Choose Our GC-MS-Based Metabolic Flux Analysis Service?

Fragment-Level Positional Information without MS/MS Complexity
Predictable unimolecular EI fragmentation generates distinct fragment ions corresponding to specific carbon backbone segments, providing position-specific labeling constraints without costly tandem MS methods.
High Chromatographic Resolution & Retention Index Consistency
Ultra-inert capillary GC columns (DB-5ms UI) deliver sharp chromatographic peaks, resolving isomeric amino acids (leucine vs. isoleucine) and organic acids with retention time repeatability (CV < 0.2%).
Protein-Bound Biomass Hydrolysis Capability
Direct measurement of protein-bound amino acids averages out short-term fluctuations, capturing the true time-integrated metabolic steady state of microbial fermentations and bioreactor cultures.
Multi-Element Natural Abundance Deconvolution
Our automated computational pipeline rigorously corrects for natural isotopic contributions of 13C, 29Si, 30Si, 15N, 18O, and 34S, ensuring unbiased mass isotopologue inputs for EMU network regression.

Instrumentation & Method Parameters for GC-MS Flux Profiling

Analytical Platform

Gas Chromatography-Mass Spectrometry (GC-MS) System

Gas Chromatograph: Agilent 7890B / 8890 GC System with 7693A Autosampler

Mass Spectrometer: Agilent 5977B / 7000D Mass Selective Detector (MSD)

Ionization Source: Electron Ionization (EI, 70 eV), Ion Source Temperature: 230 °C, Quadrupole: 150 °C

GC Column: DB-5ms Ultra Inert Capillary Column (30 m × 0.25 mm × 0.25 μm, 5% phenyl methylpolysiloxane)

Carrier Gas: Ultra-high purity Helium (≥ 99.999%), constant flow at 1.0–1.2 mL/min

Inlet & Injection: Split/Splitless inlet operating at 250–280 °C, split ratio 1:5 to 1:20 (1 μL injection volume)

Method Performance

Parameter	Typical Specification
Ionization Energy	Standard 70 eV EI
Acquisition Modes	Selected Ion Monitoring (SIM) & Full Scan (m/z 50–650)
Retention Time Precision	CV < 0.2% across sequence batches
MID Analytical Precision	CV ≤ 1.0% across replicate injections
Linear Dynamic Range	3.5–4 orders of magnitude
Derivatization Reproducibility	CV ≤ 3.0% with internal standard normalization

Orthogonal High-Resolution LC-MS & NMR Capabilities

When studying intact non-volatile phosphorylated hexoses (G6P, PEP), nucleotides, and acyl-CoAs that resist GC volatilization, our platform seamlessly integrates complementary NMR-based metabolic flux analysis and LC-Orbitrap MS workflows.

Quality Control & Data Validation

Derivatization Efficiency Audits: Monitoring conversion completeness and absence of unreacted or degraded species
Silicone Background & Silyl Tag Deconvolution: Mathematical subtraction of ²⁹Si (4.7%) and ³⁰Si (3.1%) isotopic contributions from MTBSTFA/MSTFA reagents
Mass Balance & Rate Constraints: Concurrently measured uptake and secretion rates applied as boundary conditions in our central carbon metabolic flux analysis

Agilent 7890B / 5977B GC-MS (Figure from Agilent)

Agilent 8890 / 7000D Triple Quad GC-MS (Figure from Agilent)

Thermo Scientific Orbitrap Exploris High-Resolution LC-MS

Thermo Scientific Orbitrap Exploris (Figure from Thermo Fisher)

Bruker Avance III HD 600 MHz NMR Spectrometer

Bruker Avance III HD 600 MHz NMR (Figure from Bruker)

GC-MS Metabolic Flux Analysis Workflow — A Step-by-Step Guide

Network Formulation & 13C Tracer Selection

We review your biological system and establish stoichiometric network equations with mapped carbon transitions, selecting optimal [1,2-13C2]glucose, [U-13C6]glucose, or co-tracer schemes.

Stable Isotope Labeling & Biomass Harvesting

Cells or microorganisms are cultured under steady-state conditions with 13C tracers. Free intracellular pools are cryogenically quenched (-80 °C), and/or protein pellets are isolated for biomass hydrolysis.

Targeted Derivatization (TBDMS / MOX-TMS)

Lyophilized extracts or amino acid hydrolysates undergo chemical derivatization (MTBSTFA for amino/organic acids; MOX-MSTFA for keto acids) to generate volatile silyl derivatives.

GC-MS Acquisition & Fragment MID Extraction

Isotopologue ion clusters for key diagnostic fragments ([M-57]⁺, [M-159]⁺) are acquired in SIM or full-scan EI mode. Extracellular media are analyzed for substrate uptake/secretion rates.

Natural Abundance Deconvolution & EMU Flux Fitting

Raw MIDs are corrected for natural 13C, 29Si, 30Si, 15N, and 18O isotopes using IsoCor. Elementary Metabolic Unit models in INCA calculate net/exchange fluxes with 95% profile likelihood confidence intervals.

How to Prepare and Submit Samples for GC-MS-MFA

Sample Type	Amount Required	Preparation & Quenching Instructions	Storage & Shipping
Protein Pellets (Biomass Hydrolysis)	1–5 mg dry cell pellet	Centrifuge cultured cells (4 °C, 5 min), wash once with cold saline, decant supernatant, flash-freeze cell pellet in liquid nitrogen for microbial metabolic flux analysis.	Store at −80 °C; ship on dry ice
Free Intracellular Metabolites (Adherent)	1–10 × 10⁶ cells (60/100 mm dish)	Rapid medium aspiration (<3 s), wash with ice-cold saline, add pre-cooled (-80 °C) 80:20 MeOH:H2O, scrape on dry ice. Avoid trypsinization in mammalian cell metabolic flux analysis.	Store at −80 °C; ship on dry ice
Free Intracellular Metabolites (Suspension)	2–10 × 10⁶ cells	Rapid cold centrifugation (4 °C, <2 min) or direct cold-solvent quenching (-40 °C), followed by cell pellet extraction.	Store at −80 °C; ship on dry ice
Microbial Broth / Fermentation Filtrate	2–10 mg dry cell weight	Fast vacuum filtration (<2 s) with immediate liquid nitrogen flash freezing of the filter membrane. Retain supernatant for extracellular rate measurements.	Store at −80 °C; ship on dry ice
Plant & Animal Tissues	20–100 mg wet weight	Instant freeze-clamping in liquid nitrogen upon excision, followed by cryogenic pulverization without thawing for plant metabolic flux analysis.	Store at −80 °C; ship on dry ice
Conditioned Culture Supernatant	≥ 200 μL	Collect from matched labeling time points, centrifuge (10,000 × g, 5 min) to remove cellular debris, aliquot for extracellular glucose, lactate, and amino acid consumption/secretion rates.	Store at −80 °C; ship on dry ice

Notes

Always submit parallel uncultured medium blanks and time-matched conditioned supernatants for boundary rate quantification
Avoid using plastic tubes or tips with leaching plasticizers that interfere with GC-MS chromatographic baseline
Maintain an uninterrupted cold chain (−80 °C or dry ice) during sample storage and transport

Deliverables: What You Receive from GC-MS-Based MFA

Deconvoluted Fragment MID Matrix: Natural-abundance-corrected fractional labeling (M+0 to M+n) for [M-57]⁺, [M-159]⁺, and [M-85]⁺ fragment ions across amino acids and organic acids (.csv / Excel).
Extracellular Consumption & Secretion Rates: Quantitative boundary uptake and production rates (mmol/gDW/h or nmol/10⁶ cells/h).
Reaction Network & EMU Model Files: Complete stoichiometric equations, carbon atom transition matrices, and executable project files (.xml / INCA / 13CFLUX2).
Estimated Metabolic Flux Values & Confidence Intervals: Calculated net (v_net) and exchange (v_xch) fluxes with non-linear 95% profile likelihood confidence intervals.
Statistical Diagnostics & Residuals: Chi-square (χ²) goodness-of-fit statistics, sum of squared residuals (SSR), and measured vs. simulated MID correlation plots.
Publication-Ready Pathway Flux Maps: High-resolution vector graphics (PDF/SVG) illustrating central carbon routing with flux-scaled arrow widths, supported by comprehensive fluxomics data analysis.

$GC-MS Mass Isotopologue Distribution of TBDMS-derivatized amino acid fragments showing fractional 13C labeling$

Representative GC-MS mass isotopologue distribution (MID) bar plot showing natural-abundance-corrected fractional labeling across [M-57]⁺ and [M-159]⁺ fragments of TBDMS-amino acids.

Quantitative Metabolic Flux Map of Central Carbon and Amino Acid Networks

Quantitative central carbon and amino acid biosynthetic flux map with reaction arrow widths scaled to absolute in vivo flux velocities and exchange rates.

Profile likelihood confidence interval analysis demonstrating nonlinear 95% statistical boundaries and parameter identifiability for key branch-point fluxes.

Statistical Goodness-of-Fit and Chi-Square Residual Diagnostics Plot for GC-MS Flux Data

Statistical goodness-of-fit diagnostic plot showing measured versus simulated GC-MS MID correlation and weighted residual distribution with chi-square threshold.

Applications of GC-MS-Based Metabolic Flux Analysis

Our GC-MS metabolic flux analysis service supports research across academic, biotechnology, and industrial bioprocess programs:

Microbial Bioprocess & Metabolic Engineering

Quantify carbon yield, eliminate byproduct overflow (acetate, ethanol), and optimize amino acid/organic acid secretion in engineered microbial hosts

Cancer Metabolism & Anaplerosis

Differentiate pyruvate dehydrogenase (PDH) vs. pyruvate carboxylase (PC) carbon entry and measure glutamine-driven TCA anaplerotic kinetics

Biomass & Protein Turnover Studies

Analyze protein-bound amino acid hydrolysates to capture time-averaged steady-state fluxes in continuous cultures and slow-growing systems

Plant & Crop Central Carbon Partitioning

Map carbon allocation between glycolysis, TCA cycle, and photorespiratory amino acid synthesis under abiotic stress conditions

Industrial Fermentation & Strain Evolution

Evaluate metabolic rewiring in adaptively evolved bacterial and fungal production strains to guide rational pathway re-engineering

Targeted Metabolomic Profiling Resolves Neonate-Specific TCA Cycle and Ketogenesis Perturbations Induced by Volatile Anesthetics

Journal: eLife

Published: 2021

DOI: https://doi.org/10.7554/eLife.65400

Background

Volatile anesthetics (VAs, including isoflurane and sevoflurane) are widely utilized in pediatric surgery, yet their off-target metabolic effects in neonates remain incompletely understood. Neonatal mammals depend critically on milk-derived fatty acids and hepatic ketogenesis to generate circulating β-hydroxybutyrate (β-HB), supplying over 25% of basal energy and supporting rapid brain development. When exposed to sub-anesthetic doses of volatile anesthetics, neonates experience rapid metabolic disruption and ketone depletion.

Challenge:

Determine whether volatile anesthetics acutely impair mitochondrial fatty acid oxidation, induce TCA cycle cataplerosis, or alter intermediate metabolite pools in neonatal liver and plasma compared to adult controls.

Findings (from the published study)

Statistical goodness-of-fit diagnostic plot showing measured versus simulated GC-MS MID correlation and weighted residual distribution with chi-square threshold.

Rapid Hepatic Ketone Depletion: Exposure to 1% and 0.2% isoflurane for 30 minutes rapidly and potently depleted β-hydroxybutyrate (β-HB) in whole liver and plasma of P7 neonatal mice, demonstrating an acute disruption uncoupled from sedation.
TCA Cycle Cataplerosis & Citrate Accumulation: Targeted metabolomics revealed marked TCA cycle backup, evidenced by a ~100% accumulation of hepatic citrate and isocitrate, elevated blood lactate, and cataplerotic transamination into alanine and aspartate pools.
ACC Activation & CPT1 Inhibition: Citrate accumulation combined with acetyl-CoA availability stimulated acetyl-CoA carboxylase (ACC), producing malonyl-CoA and inhibiting carnitine palmitoyltransferase 1 (CPT1), thereby blocking mitochondrial fatty acid import and depleting acylcarnitines.
Neonate-Specific Metabolic Sensitivity: Unlike neonatal animals, adult mice demonstrated resistance to malonyl-CoA-mediated fatty acid oxidation shutdown, confirming an age-specific metabolic vulnerability to volatile anesthetics.

Figure 4. Targeted metabolomic analysis of whole liver β-HB and fatty acid intermediate depletion in isoflurane-exposed neonates

Figure 5. Schematic model of mitochondrial glucose and fatty acid metabolism, TCA cycle cataplerosis, and citrate-driven ACC regulation

Process Insight

The study illustrates how high-precision quantitative metabolomics of TCA organic acids, amino acids, and acylcarnitines provides the empirical foundation to resolve pathway-level regulatory rheostats, demonstrating that mitochondrial intermediate accumulation can trigger feedback inhibition of upstream fatty acid oxidation.

Where Our GC-MS-Based Metabolic Flux Analysis Service Fits

Creative Proteomics supported the metabolomics workflow for this investigation, providing end-to-end analytical and data processing capabilities:

Targeted Organic Acid & TCA Profiling: Precise quantification of TCA cycle intermediates, pyruvate, lactate, and ketone bodies across liver tissues and plasma.
Acylcarnitine & Amino Acid Analysis: Quantitative profiling of free carnitine, acetylcarnitine (C2), and acylcarnitine species reflecting mitochondrial transport status.
Rigorous Method Validation & Quality Control: High-reproducibility batch QC ensuring robust statistical distinction across age groups and anesthetic exposure conditions.

Reference

Stokes, J., Freed, A., Bornstein, R., Su, K. N., Snell, J., Pan, A., Liu, G. X., & Johnson, S. C. Mechanisms underlying neonate-specific metabolic effects of volatile anesthetics. eLife. 2021;10:e65400.

What is the primary advantage of GC-MS over LC-MS in metabolic flux analysis?

GC-MS under standard electron ionization (EI, 70 eV) produces predictable, reproducible fragment ions containing specific subsets of the metabolite carbon backbone (e.g. [M-57]⁺ vs. [M-159]⁺ in TBDMS derivatives). This provides fragment-level positional isotope constraints without requiring tandem MS/MS or NMR, while delivering exceptional chromatographic resolution and retention index stability.

Why is TBDMS derivatization preferred over TMS for amino acid flux analysis?

TBDMS (tert-butyldimethylsilyl via MTBSTFA) produces an extremely stable [M-57]⁺ fragment via loss of the tert-butyl radical, yielding a clean, high-mass ion cluster containing the entire carbon skeleton with minimal secondary fragmentation. Furthermore, the [M-159]⁺ fragment (loss of C1 carboxyl) provides direct positional information on C2–Cn carbons, whereas TMS derivatives often undergo complex fragmentation rearrangements.

What is protein-bound amino acid analysis, and when is it used?

Protein-bound amino acid analysis involves hydrolyzing total cellular biomass in 6 M HCl (105–110 °C, 16–24 h) to release amino acids incorporated into structural proteins. Because protein pools turn over slowly, their isotopic labeling represents a time-averaged, highly stable reflection of steady-state metabolic flux, making it the gold standard for microbial fermentations and long-term cell culture studies.

How do you correct for natural silicon and carbon isotopes in silylated derivatives?

Silylating reagents introduce naturally occurring heavy silicon isotopes (4.7% ²⁹Si and 3.1% ³⁰Si) in addition to background ¹³C, ¹⁵N, and ¹⁸O. We employ validated computational deconvolution algorithms (IsoCor, AccuCor) that utilize the exact chemical formula of each fragment ion to mathematically subtract all natural isotope contributions before flux modeling.

Can GC-MS analyze phosphorylated central carbon intermediates like G6P and PEP?

Phosphorylated sugars and nucleotides are highly polar, thermally labile, and difficult to volatilize completely for GC-MS. While they can be analyzed using extensive two-step methoximation-silylation (MOX-TMS), we frequently combine GC-MS (for amino acids and organic acids) with our LC-Orbitrap MS platform (for direct, non-derivatized analysis of intact phosphorylated hexoses, trioses, and acyl-CoAs).

What sample biomass is typically required for GC-MS metabolic flux analysis?

For protein-bound biomass hydrolysis, 1–5 mg of dry cell pellet is sufficient. For free intracellular metabolite pools, we recommend 1–10 × 10⁶ cultured cells or 20–100 mg of wet tissue per biological replicate to ensure high signal-to-noise ratios across all mass isotopologue channels.

What computational software and statistical models are used to fit GC-MS flux data?

We implement Elementary Metabolic Unit (EMU) network modeling in INCA and 13CFLUX2. Flux estimation is validated by Chi-square (χ²) goodness-of-fit testing, residual distribution diagnostics, and 95% profile likelihood confidence intervals to guarantee that all reported reaction rates are mathematically identifiable.

Mechanisms underlying neonate-specific metabolic effects of volatile anesthetics

Stokes, J., Freed, A., Bornstein, R., Su, K. N., Snell, J., Pan, A., Liu, G. X., & Johnson, S. C.

Journal: eLife

Year: 2021

DOI: https://doi.org/10.7554/eLife.65400

B cell-intrinsic epigenetic modulation of antibody responses by dietary fiber-derived short-chain fatty acids

Sanchez, H. N., Moroney, J. B., Gan, H., Shen, T., Im, J. L., Li, T., ... & Casali, P.

Journal: Nature Communications

Year: 2020

DOI: https://doi.org/10.1038/s41467-019-13603-6

Impaired ketogenesis ties metabolism to T cell dysfunction in COVID-19

Karagiannis, F., Peukert, K., Surace, L., Michla, M., Nikolka, F., Fox, M., ... & Kurts, C.

Journal: Nature

Year: 2022

DOI: https://doi.org/10.1038/s41586-022-05128-8

Elongating porcine conceptuses can utilize glutaminolysis as an anaplerotic pathway to maintain the TCA cycle

Seo, H., Kramer, A. C., McLendon, B. A., Fang, X., Satterfield, M. C., Wu, G., ... & Johnson, G. A.

Journal: Biology of Reproduction

Year: 2022

DOI: https://doi.org/10.1093/biolre/ioac097

Hierarchical glycolytic pathways control the carbohydrate utilization regulator in human gut Bacteroides

Kabonick, S. G., Verma, K., Modesto, J. L., Pearce, V. H., Winokur, K. M., Groisman, E. A., & Townsend, G. E.

Journal: Nature Communications

Year: 2025

DOI: https://doi.org/10.1038/s41467-025-59704-3

Macrophage-Associated Lipin-1 Promotes beta-Oxidation in Response to Proresolving Stimuli

Schilke, R. M., Blackburn, C. M. R., Rao, S., Krzywanski, D. M., Finck, B. N., & Woolard, M. D.

Journal: ImmunoHorizons