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Short Chain Fatty Acids Analysis Service

Creative Proteomics offers precise Short Chain Fatty Acids (SCFAs) analysis using high-sensitivity techniques such as GC-MS and LC-MS/MS. We provide quantitative detection of straight-chain, branched-chain, and hydroxy/keto SCFAs in plasma, feces, and tissue, covering 20+ metabolites. Our analysis reveals SCFA roles in metabolic health, gut microbiota interactions, and immune regulation. Supporting micro-sample analysis (50 µL), we achieve ultra-low detection limits (0.1 µmol/L) with high reproducibility (CV <5%). Integrating stable isotope tracing and metabolic pathway analysis, we deliver customized solutions for nutrition, drug development, and biomedical research.

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Service We Provide
List of Short Chain Fatty Acids
Advantages
Methods
Results and Data Analysis
Sample Requirements
FAQ

What are Short Chain Fatty Acids?

Short Chain Fatty Acids (SCFAs) are saturated aliphatic monocarboxylic acids with fewer than six carbon atoms, primarily produced through microbial fermentation of dietary fibers in the gut. SCFAs play essential roles in host metabolism, immune modulation, and gut health. Common SCFAs include acetate, propionate, and butyrate, which influence energy homeostasis, inflammation, and microbial balance. Their precise quantification is critical in research fields such as nutrition, microbiome studies, and metabolic health.

Short Chain Fatty Acids Analysis in Creative Proteomics

Targeted Quantitative SCFA Analysis

Accurate quantification of key SCFAs, including acetate, propionate, and butyrate, in biological samples such as plasma, serum, feces, and tissue.

Lipidomics Profiling

Comprehensive lipidomics analysis of SCFAs and related metabolites, enabling detailed insight into lipid metabolism and related pathways.

SCFA Metabolic Pathway Analysis

Identification of SCFA-related metabolic pathways to explore their biological functions in metabolic and immune processes.

Microbiome-SCFA Interaction Studies

Investigating the relationship between gut microbiota composition and SCFA production.

Stable Isotope Tracing Analysis

Advanced isotope labeling techniques to track SCFA metabolic flux, providing in-depth insights into metabolic dynamics and pathway activity.

Customized SCFA Analysis Solutions

Flexible and tailored analysis services designed to meet specific research goals, including high-throughput screening, specialized sample processing, and customized reporting.

List of Detected Short Chain Fatty Acids

Straight-Chain
Branched-Chain
Hydroxy and Keto

Straight-Chain SCFAs and Related Metabolites

SCFA Name	Molecular Formula	Related Metabolites	Metabolic Pathways
Acetate	C₂H₄O₂	Acetyl-CoA, Ethanol, Formate	Acetate Pathway, TCA Cycle
Propionate	C₃H₆O₂	Propionyl-CoA, Succinyl-CoA	Propionate Metabolism, Gluconeogenesis
Butyrate	C₄H₈O₂	Butyryl-CoA, Crotonate	Butyrate Pathway, Fatty Acid Oxidation
Valerate	C₅H₁₀O₂	Valeryl-CoA, Isovalerate	Fatty Acid Metabolism
Caproate	C₆H₁₂O₂	Caproyl-CoA, Hexanoic Acid	Fatty Acid β-Oxidation

Branched-Chain SCFAs (BSCFAs) and Related Metabolites

SCFA Name	Molecular Formula	Related Metabolites	Metabolic Pathways
Isobutyrate	C₄H₈O₂	Isobutyryl-CoA, Methylmalonate	Branched-Chain Amino Acid Metabolism
Isovalerate	C₅H₁₀O₂	Isovaleryl-CoA, 3-Hydroxyisovalerate	Leucine Metabolism, Valine Degradation
2-Methylbutyrate	C₅H₁₀O₂	2-Methylbutyryl-CoA, Succinyl-CoA	Isoleucine Metabolism, Fatty Acid Synthesis

Hydroxy and Keto SCFAs and Related Metabolites

SCFA Name	Molecular Formula	Related Metabolites	Metabolic Pathways
3-Hydroxybutyrate	C₄H₈O₃	Acetoacetate, β-Hydroxybutyryl-CoA	Ketogenesis, Fatty Acid Oxidation
4-Hydroxybutyrate	C₄H₈O₃	Succinic Semialdehyde, GABA	GABA Shunt, Succinic Acid Pathway
Acetoacetate	C₄H₆O₃	Acetone, 3-Hydroxybutyrate	Ketogenesis, Fatty Acid Degradation

Why Choose Our Short Chain Fatty Acids Assay?

Ultra-Low Detection Limits: Achieve precise quantification with detection limits as low as 0.1 µmol/L using GC-FID and GC-MS platforms.
Comprehensive Metabolite Coverage: Analyze over 20 SCFAs and related metabolites in a single run, providing a complete metabolic profile.

Exceptional Reproducibility: Ensure reliable data with a coefficient of variation (CV) of less than 5%, delivering highly reproducible results across multiple samples.
Minimal Sample Requirements: Perform comprehensive analysis with only 50 µL of plasma, serum, or feces, conserving precious samples.

What Methods are Used for Our Short Chain Fatty Acids Analysis?

Method	Detector/Platform	Sensitivity (LOD)	Sample Type	Derivatization Required?	Strengths	Limitations	Applications
GC-FID	Flame Ionization Detector	0.1–1.0 µM 8	Feces, serum, plasma	Yes (for low conc.)	Cost-effective; High reproducibility for volatile SCFAs	Poor resolution for isomers; Limited sensitivity for polar SCFAs	Routine QC in fermentation, biogas monitoring
GC-MS	Quadrupole MS	0.05–0.5 µM 8	Feces, colonic content	Yes	High specificity; Identifies branched-chain SCFAs	Requires derivatization; Matrix interference in complex samples	Research on microbiota metabolites
LC-MS/MS	Triple Quadrupole MS	0.01–0.1 µM	Plasma, serum, tissues	Optional (O-BHA derivatization improves sensitivity)	High sensitivity; Quantifies non-volatile SCFAs (e.g., lactate, succinate)	Matrix effects require SIL-IS	Preclinical studies, pharmacokinetics
UHPLC-QE-Orbitrap	High-Resolution MS	0.001–0.01 µM	Serum, complex matrices	No	Ultra-high resolution; Detects trace isomers (e.g., isobutyrate vs. butyrate)	Data complexity requires expertise	Aging/metabolic disease research
Static Headspace-GC-FID	FID with SHS	0.1–10 ppm	Pharmaceutical residues, food	No	Minimal sample prep; Avoids solvent interference	Limited to volatile SCFAs; Lower sensitivity	Residual solvent analysis in drugs

Key Considerations for Method Selection

Sample Complexity:

For fecal or colonic content (high matrix interference), GC-MS or LC-MS/MS with derivatization is preferred .
For plasma/serum (low SCFA levels), LC-MS/MS or UHPLC-Orbitrap offers superior sensitivity .

Throughput vs. Cost:

GC-FID is ideal for high-throughput industrial QC with moderate sensitivity.
LC-MS/MS balances speed and precision for preclinical/research labs.

Isomer Differentiation:

UHPLC-Orbitrap resolves structural isomers (e.g., isovalerate vs. valerate) critical for mechanistic studies .

Agilent 7890A GC System (Figure from Agilent)

Thermo Fisher Q Exactive (Figure from Thermo Fisher)

Short Chain Fatty Acids Analysis Service: Results and Data Analysis

Short Chain Fatty Acids Composition

Results we provide:

Comprehensive profiling of SCFAs, including acetate, propionate, butyrate, and branched-chain SCFAs.
Quantification of SCFAs based on carbon chain length and functional groups.
Distribution of SCFAs across different biological matrices (e.g., plasma, feces, tissue, fermentation media).

Data analysis:

Visual representation of SCFA profiles (e.g., bar graphs, heatmaps).
Comparative analysis of SCFA distribution across experimental conditions.

$High-performance liquid chromatography electrospray ionization tandem mass spectrometry analysis of plasma lipoproteins and plasma lipoprotein free fraction$

Determination of short-chain fatty acids by GC-MS (Wang et al., 2020)

$High-performance liquid chromatography electrospray ionization tandem mass spectrometry analysis of plasma lipoproteins and plasma lipoprotein free fraction$

The TIC of standards of 14 SCFAs by GC–MS (Wang et al., 2020)

$High-performance liquid chromatography electrospray ionization tandem mass spectrometry analysis of plasma lipoproteins and plasma lipoprotein free fraction$

GC/MS detection for analyzing microbial metabolites short chain fatty acids in fecal and serum samples (Zhang et al., 2019)

Short Chain Fatty Acids Quantification and Profiling

Results provided:

Absolute quantification of SCFAs with detection limits as low as 0.1 µmol/L using GC-FID and GC-MS.
Profiling of SCFA metabolic intermediates and related lipid metabolites.
Assessment of SCFA variations under different physiological and experimental conditions.

Data insights:

Statistical analysis (e.g., ANOVA, PCA) to identify significant SCFA variations.
Correlation analysis between SCFA concentrations and metabolic pathways.

Comparative Analysis and Statistical Insights

Results provided:

Differential analysis of SCFA levels across control and experimental groups.
Identification of key SCFAs associated with environmental, dietary, or microbial influences.

Data insights:

Pathway enrichment analysis to connect SCFA changes with metabolic networks.
Multivariate analysis to uncover trends and correlations in SCFA metabolism.

Lipidome Data Analysis Workflow Workflow of Lipidome Data Analysis

Explore our Lipidomics Solutions brochure to learn more about our comprehensive lipidomics analysis platform.

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What Our Short Chain Fatty Acids Analysis Used For

Metabolic Pathway Analysis

Identify SCFA-related biochemical pathways in various biological systems.

Biotechnology and Bioenergy

Study SCFA production in microbial fermentation for industrial applications.

Nutritional Studies

Assess dietary effects on SCFA metabolism and energy utilization.

Agricultural and Animal Science

Analyze SCFAs in soil, feed, and livestock metabolism.

Food and Beverage Industry

Optimize fermentation processes and assess SCFA composition in fermented products.

Environmental Monitoring

Detect SCFAs in wastewater, soil, and fermentation systems.

Sample Requirements for Short Chain Fatty Acids Analysis Solutions

Sample Type	Minimum Volume/Amount Required	Storage Conditions	Notes
Plasma/Serum	50-100 µL	-80°C, avoid freeze-thaw cycles	Collect in EDTA or heparin tubes, centrifuge, and store supernatant.
Feces	50-100 mg	-80°C	Freeze immediately after collection, avoid prolonged exposure to air.
Urine	500 µL	-80°C	Preferably first-morning urine, centrifuge if necessary.
Tissue	50 mg	-80°C	Snap freeze in liquid nitrogen, store in airtight tubes.
Cell Culture Media	500 µL	-80°C	Collect supernatant, filter to remove debris, and freeze.
Fermentation Broth	1 mL	-80°C	Centrifuge, remove particulates, and store supernatant.

FAQs for Short Chain Fatty Acids Analysis Service

Should samples be processed immediately after collection? How to prevent SCFA degradation?

SCFAs are volatile and susceptible to microbial metabolism, so prompt processing is essential.

Feces/Intestinal Contents: Freeze at -80°C within 30 minutes of collection. If immediate freezing is not possible, use a stabilizing solution (e.g., phosphoric acid-based) to inhibit enzymatic activity, allowing storage at room temperature for up to 36 hours.
Blood/Serum: Centrifuge immediately after collection to separate plasma/serum and store at -80°C, avoiding repeated freeze-thaw cycles.

How do SCFA extraction methods differ for various sample types (feces, serum, tissue)?

Feces/Intestinal Contents: Homogenize and centrifuge to collect the supernatant. Extraction is commonly performed using 60% acetonitrile or acidified water, with derivatization (e.g., 3-nitrophenylhydrazine) to enhance detection sensitivity.
Serum/Plasma: Protein removal is required, typically using methanol precipitation or solid-phase extraction (SPE). Dilution before analysis helps minimize matrix effects.
Tissue Samples: Cryogenic grinding followed by homogenization is recommended, with extraction in a 1:9 (w/v) buffer-to-tissue ratio.

Why is derivatization necessary? What are the advantages of different derivatization reagents?

SCFAs have high polarity and volatility, making direct detection challenging due to peak overlapping and low sensitivity.

3-Nitrophenylhydrazine (3-NPH): Suitable for UHPLC-QTOF-MS/MS, mild reaction conditions (30°C, 30 min).
Silylation Reagents (e.g., BSTFA): Used in GC-MS, but requires anhydrous conditions and longer reaction times.
Methylation: Effective for long-chain fatty acid analysis but has lower specificity for SCFAs.

Which method is better for SCFA detection: GC-MS or HPLC?

GC-MS: High sensitivity (LOD: 0.02–0.08 μg/mL), ideal for volatile SCFAs (e.g., acetate, propionate).
HPLC (pre-column derivatization): Suitable for non-volatile organic acids (e.g., lactate), offering good separation in complex matrices like feces.
UHPLC-QTOF-MS/MS: High-resolution qualitative and quantitative analysis of 16+ SCFAs, but with higher operational costs.

How to assess the reliability of SCFA analysis results?

Linearity: R² > 0.99, covering physiological concentrations (e.g., fecal acetate: 50-200 μmol/g).
Precision: Intra-/inter-day RSD < 10%, repeatability RSD < 6%.
Recovery Rate: 80-120% is considered acceptable.

Why might low-abundance SCFAs (e.g., isovalerate) not be detected?

Sample Preparation Losses: Ensure proper pH control (pH 2-3) during extraction and optimize derivatization efficiency.
Matrix Interference: Colored compounds or lipids in feces may suppress ionization; additional purification (e.g., solid-phase extraction) is recommended.
Instrument Parameters: Optimize GC-MS split ratio and temperature ramping to reduce background noise.

How can SCFA results be interpreted alongside gut microbiota data?

Functional Correlations: Butyrate is positively correlated with Clostridiales, while propionate is linked to Bacteroides.
Metabolic Pathways: Metagenomic analysis of acetyl-CoA pathway genes can verify SCFA biosynthesis potential.
Biological Implications: Low butyrate levels may indicate gut barrier dysfunction or inflammatory conditions.

How to choose internal standards? What are common issues?

Recommended Internal Standards: Butyrate-d4, 2-methylpentanoic acid, selected based on similar physicochemical properties to SCFAs.

Common Issues:

Peak Overlap: Optimize chromatography conditions or switch internal standards.
Abnormal Recovery: Ensure internal standards are added before derivatization for consistency.

What key information should be included in the data report?

Absolute Quantification Values: Clearly stated units (e.g., μmol/g wet weight or μmol/L plasma).
Methodology Parameters: LOD, LOQ, precision, and other validation metrics.
Reference Ranges: Example—healthy adult fecal butyrate concentration: 20-40 μmol/g.

Publications

White matter lipid alterations during aging in the rhesus monkey brain. GeroScience, 2024. https://doi.org/10.1007/s11357-024-01353-3
Characterization of Dnajc12 knockout mice, a model of hypodopaminergia. bioRxiv, 2024. https://doi.org/10.1101/2024.07.06.602343
Annexin A2 modulates phospholipid membrane composition upstream of Arp2 to control angiogenic sprout initiation. The FASEB Journal, 2023. https://doi.org/10.1096/fj.202201088R
Lipid Membrane Engineering for Biotechnology (Doctoral dissertation, Aston University). Lipid Membrane Engineering for Biotechnology, 2023. https://doi.org/10.48780/publications.aston.ac.uk.00046663
Multi‐omics identify xanthine as a pro‐survival metabolite for nematodes with mitochondrial dysfunction. EMBO Journal, 2019. https://doi.org/10.15252/embj.201899558

References

Wang, R., et al. (2020). A fast and accurate way to determine short chain fatty acids in human serum by GC–MS and their distribution in children with digestive diseases. Chromatographia, 83(2), 273-286.
Zhang, Shuming, Hongbin Wang, and Mei-Jun Zhu. "A sensitive GC/MS detection method for analyzing microbial metabolites short chain fatty acids in fecal and serum samples." Talanta 196 (2019): 249-254.

* Our services can only be used for research purposes and Not for clinical use.

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