Fatty Acid Methyl Ester Analysis Service - Lipidomics|Creative Proteomics

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Fatty Acid Methyl Ester Analysis Service

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Overview of Fatty Acid Methyl Ester

Fatty Acid Methyl Esters (FAMEs) are methylated derivatives of fatty acids formed through transesterification of triglycerides or phospholipids with methanol. They serve as essential indicators for lipid metabolism, membrane composition, and energy storage dynamics across biological systems. FAME profiling has become a cornerstone analytical method for assessing lipid functionality in food sciences, microbial classification, algal biofuel development, and agricultural biotechnology.

Why Analyze Fatty Acid Methyl Esters?

FAME analysis provides a powerful analytical platform to:

Identify and quantify individual fatty acid species.
Monitor metabolic shifts in lipid pathways.
Evaluate feedstock composition for biofuel production.
Authenticate food origin and quality.
Investigate microbial taxonomy and ecology via lipid biomarkers.
Optimize lipid content in genetically modified organisms.

Fatty Acid Methyl Esters Analysis in Creative Proteomics

Comprehensive Fatty Acid Profiling

Quantitative and qualitative analysis of saturated, monounsaturated, and polyunsaturated fatty acids across C10–C26 chain lengths.

Targeted FAME Quantification

Absolute quantification of specific fatty acid methyl esters using certified internal standards (e.g., C16:0, C18:1n-9, C20:4n-6).

ω-3 and ω-6 Fatty Acid Analysis

Detailed profiling of essential fatty acids such as EPA, DHA, ALA, and ARA with relevance to nutritional and metabolic studies.

Branched-Chain and Odd-Carbon FAME Identification

Identification of microbial signature fatty acids including iso, anteiso, and odd-carbon chains.

Trans Fatty Acid Characterization

Detection and quantification of industrial and naturally occurring trans fatty acids (e.g., elaidic acid, trans-vaccenic acid).

Lipid Class-Specific FAME Analysis

Separation of lipid fractions (e.g., phospholipids, triglycerides) prior to methylation for class-specific fatty acid distribution.

FAME Fingerprinting for Microbial or Botanical Classification

Chemotaxonomic identification based on fatty acid biomarker patterns, suitable for microbial ecology or plant origin studies.

Comparative FAME Profiling Across Samples

Statistical analysis and visualization of FAME variation between experimental conditions, treatment groups, or time points.

FAME Pathway Integration

Integration of FAME data with known metabolic pathways using KEGG, LipidMaps, and HMDB databases.

Detected Fatty Acid Methyl Esters and Related Metabolites We Can Identify

Saturated
Monounsaturated
Polyunsaturated
Branched-Chain
Trans-Fatty Acid
Conjugated

Saturated Fatty Acid Methyl Esters (SFAs)

Compound Name	Abbreviation	Carbon Chain	Typical Origin / Function
Methyl caproate	C6:0	C6	Medium-chain triglyceride (MCT); energy metabolism
Methyl caprylate	C8:0	C8	Coconut oil; antimicrobial properties
Methyl caprate	C10:0	C10	Goat milk; energy metabolism
Methyl laurate	C12:0	C12	Palm kernel oil
Methyl myristate	C14:0	C14	Butterfat; membrane structure
Methyl pentadecanoate	C15:0	C15	Dairy marker; odd-chain biomarker
Methyl palmitate	C16:0	C16	Common FA in animals and plants
Methyl margarate	C17:0	C17	Dairy and bacterial marker
Methyl stearate	C18:0	C18	Animal fats; membrane function
Methyl arachidate	C20:0	C20	Peanut oil
Methyl heneicosanoate	C21:0	C21	Experimental lipidomics
Methyl behenate	C22:0	C22	Rapeseed oil
Methyl lignocerate	C24:0	C24	Myelin sheath; sphingolipids
Methyl hexacosanoate	C26:0	C26	Nervous system; peroxisomal disorders
Methyl octacosanoate	C28:0	C28	Plant waxes
Methyl triacontanoate	C30:0	C30	Industrial waxes

Monounsaturated Fatty Acid Methyl Esters (MUFAs)

Compound Name	Abbreviation	Carbon Chain	Typical Origin / Function
Methyl palmitoleate	C16:1n-7	C16	Mammalian tissue lipid
Methyl sapienate	C16:1n-10	C16	Human skin lipid
Methyl oleate	C18:1n-9	C18	Olive oil; common MUFA
Methyl vaccenate (cis)	C18:1n-7	C18	Ruminant milk fats
Methyl elaidate (trans)	C18:1n-9t	C18	Trans fat; industrial processing
Methyl gondoate	C20:1n-9	C20	Plant oils
Methyl erucate	C22:1n-9	C22	Mustard, rapeseed
Methyl cetoleate	C22:1n-11	C22	Marine fish oils
Methyl nervonate	C24:1n-9	C24	Nervous tissue lipid
Methyl 10-heptadecenoate	C17:1	C17	Microbial lipid marker
Methyl 10-nonadecenoate	C19:1	C19	Bacterial lipid

Polyunsaturated Fatty Acid Methyl Esters (PUFAs)

Compound Name	Abbreviation	Carbon Chain	Omega Family	Biological Role
Methyl linoleate	C18:2n-6	C18	ω-6	Essential FA; inflammation
Methyl α-linolenate	C18:3n-3	C18	ω-3	Precursor to EPA/DHA
Methyl γ-linolenate	C18:3n-6	C18	ω-6	Skin health; inflammation
Methyl stearidonate	C18:4n-3	C18	ω-3	Marine oils
Methyl eicosadienoate	C20:2n-6	C20	ω-6	Lipid signaling
Methyl eicosatrienoate (n-6)	C20:3n-6	C20	ω-6	Intermediate in eicosanoid pathway
Methyl eicosatrienoate (n-3)	C20:3n-3	C20	ω-3	PUFA biosynthesis marker
Methyl arachidonate	C20:4n-6	C20	ω-6	Inflammatory mediator precursor
Methyl EPA	C20:5n-3	C20	ω-3	Anti-inflammatory; cardiovascular relevance
Methyl DPA	C22:5n-3	C22	ω-3	DHA biosynthesis intermediate
Methyl DHA	C22:6n-3	C22	ω-3	Neural and retinal development
Methyl tetracosapentaenoate	C24:5n-3	C24	ω-3	Nervous system fatty acid
Methyl tetracosahexaenoate	C24:6n-3	C24	ω-3	Very-long-chain PUFA research

Branched-Chain and Odd-Carbon FAMEs

Compound Name	Type	Carbon Chain	Origin / Application
Methyl iso-C13:0	Branched	C13	Microbial lipid
Methyl anteiso-C13:0	Branched	C13	Low-temperature bacteria
Methyl iso-C15:0	Branched	C15	Bacterial biomarker
Methyl anteiso-C15:0	Branched	C15	Soil bacteria marker
Methyl iso-C17:0	Branched	C17	Cell membrane FA in microbes
Methyl anteiso-C17:0	Branched	C17	Listeria/Bacillus species
Methyl C15:0	Odd-chain	C15	Dairy fat biomarker
Methyl C17:0	Odd-chain	C17	Ruminant lipid metabolism
Methyl cyclopropane C17:0	CFA	C17	Gram-negative bacteria marker
Methyl cyclopropane C19:0	CFA	C19	Environmental microbe tracking
Methyl cyclopropane C21:0	CFA	C21	Bacterial stress response
Methyl 10-methyl hexadecanoate	Me-C17:0	C17	Actinomycete marker

Trans-Fatty Acid Methyl Esters (TFAs)

Compound Name	Configuration	Carbon Chain	Source / Significance
Methyl elaidate	trans-C18:1n-9	C18	Industrial hydrogenation
Methyl trans-vaccenate	trans-C18:1n-7	C18	Ruminant lipid
Methyl trans-linoleate	trans-C18:2	C18	Processed food marker
Methyl trans-palmitoleate	trans-C16:1	C16	Fermented food marker

Conjugated Fatty Acid Methyl Esters (CLA and derivatives)

Compound Name	Isomer	Carbon Chain	Function / Source
Methyl CLA (c9,t11)	CLA1	C18:2	Dairy products
Methyl CLA (t10,c12)	CLA2	C18:2	Metabolic research
Methyl conjugated linolenate	CLA3	C18:3	PUFA isomer studies
Methyl conjugated eicosatrienoate	CLA4	C20:3	Bioactive FA analogs

Why Choose Our Fatty Acid Methyl Ester Services?

>98% Accuracy in FAME quantification through isotopically labeled internal standards.
LOD as low as 0.1 ng/μL, ensuring detection of ultra-trace fatty acids.
>300+ identifiable FAME species, covering all major lipid classes.

Batch-to-batch CV < 10%, ensuring exceptional reproducibility.
Scalable throughput: Capable of processing 96–384 samples in parallel for high-throughput studies.
Bioinformatics Support: Integrated metabolic pathway interpretation and statistical analysis (PCA, PLS-DA, Volcano plots).

What Methods are Used for Our Fatty Acid Methyl Ester Analysis?

Feature	GC-FID	GC-MS
Sensitivity	High sensitivity for fatty acid quantification	Very high sensitivity and specificity for both identification and quantification
Quantification	Excellent for quantitative analysis	Accurate for both quantitative and qualitative analysis
Identification	Primarily for quantification; limited to fatty acid methyl esters	Excellent for identifying individual fatty acids and their isomers
Complexity	Simpler, faster, and more cost-effective	More complex, time-consuming, but offers detailed analysis
Applications	Routine fatty acid profiling, food and oil analysis	Advanced fatty acid and lipidomic profiling, metabolomics, environmental studies

Agilent 7890A GC System (Figure from Agilent)

Thermo Fisher Q Exactive (Figure from Thermo Fisher)

Workflow of FAME Analysis

Workflow of Fatty Acid Methyl Ester Analysis

Demo Results of Fatty Acid Methyl Ester Analysis

Fatty Acid Methyl Ester (FFA) Quantification Report

Results we provide:

Total FAME Concentration (e.g., µg/mg dry weight or µmol/L) to determine global lipid methylation content.
Absolute Quantification of individual fatty acid methyl esters (e.g., Methyl Palmitate C16:0, Methyl Oleate C18:1, Methyl Arachidonate C20:4).
Relative Abundance (%) to express the proportion of each FAME in the total lipid profile.
Statistical Comparison (t-test/ANOVA) between experimental and control groups for significance analysis.

Format:

Detailed concentration tables for each sample and FAME analyte.
Pie charts showing distribution of major FAME classes (SFAs, MUFAs, PUFAs).
Bar plots highlighting top 10 differential FAMEs across conditions.
Summary sheet with interpretation notes, trend highlights, and quality assessment metrics.

Overlayed GC-MS total ion chromatograms illustrating FAME profiles across samples

GC-MS TICs of different sample profiles

Representative chromatograms from FAME analysis, including a total ion chromatogram (TIC), extracted ion chromatograms (EICs) for C16:0, C18:1, and C20:4, and overlayed TICs comparing three biological samples.

Lipid Class Profiling Report

Contents:

Class-wise Distribution: Saturated (SFA), Monounsaturated (MUFA), and Polyunsaturated (PUFA) fatty acid methyl esters.
Desaturation and Elongation Indexes to assess lipid processing and membrane fluidity implications.
Essential Fatty Acid Markers: n-3 and n-6 derived FAMEs to trace dietary sources or physiological imbalance.

Format:

Bar graphs comparing SFA, MUFA, and PUFA proportions across sample groups.
Heatmaps to visualize changes in FAME categories across time points or treatments.
Radar charts to intuitively compare sample lipid fingerprints.

Comparative & Multivariate Analysis Report

Contents:

Group-wise Comparison using statistical tests (e.g., Welch's t-test, one-way ANOVA).
Significant Biomarker Discovery: Fold change analysis of altered FAMEs, with p-values and effect sizes.
Pathway Enrichment Interpretation: Mapping detected FAMEs to fatty acid biosynthesis, beta-oxidation, and elongation pathways.

Format:

Summary tables including: | FAME | Mean Concentration (µg/mg) | SD | Fold Change | p-value |
Volcano plots for rapid identification of significantly regulated FAMEs.
Principal Component Analysis (PCA) to visualize sample clustering and variation.
Hierarchical Clustering Analysis (HCA) to group samples by lipidomic similarity.

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

Download Brochure

What Our Fatty Acid Methyl Ester Analysis Used For

Food Quality Control

Determination of fatty acid composition in edible oils and processed foods.

Microbial Lipid Profiling

Characterization of bacterial or algal lipid content for strain differentiation or bioengineering.

Agricultural Product Evaluation

Assessment of lipid profiles in crops and seeds to support breeding or nutritional research.

Environmental Monitoring

Analysis of fatty acid biomarkers in sediments or biofilms to trace pollution or microbial activity.

Industrial Fermentation Optimization

Monitoring of lipid accumulation in engineered strains for biodiesel or bioproduct production.

Nutritional Research

Investigation of dietary fat metabolism and fatty acid intake in controlled feeding studies.

Sample Requirements for Fatty Acid Methyl Ester Analysis Solutions

Requirement	Specification	Details
Sample Type	Vegetable Oils (Soybean, Canola, Olive), Animal Fats (Beef Tallow, Pork Lard), Dairy Products (Butter, Cheese), Seafood (Fish Oil, Krill Oil), Algal Oil, Biodiesel, Microbial Cultures, Human Body Fluids (Serum, Plasma, Breast Milk), Animal Tissues (Chicken, Beef, Pork)	A wide range of sample types, covering food, biological, bodily fluids, microbial cultures, and animal tissues
Sample Size	5 grams (for solid samples), 10 mL (for liquid samples), 1 mL (for bodily fluids), 5 grams (for animal tissues)	Quantity of sample required for analysis
Extraction Solvent	Methanol or Hexane	Choice of solvents for FAME extraction
Reaction Temperature	70°C	Temperature at which the reaction occurs
Gas Chromatograph (GC) Method	GC-FID or GC-MS	Analysis methods with FID or Mass Spectrometry detection
Column Type	Capillary Column	Type of GC column used for separation
Column Temperature	200°C	Temperature of the GC column during analysis
Detection Limits	≤ 0.05%	Low detection limits to ensure precision

FAQs for Fatty Acid Methyl Ester Analysis Service

How should I prepare and store my samples before submission?

Samples should be homogenized (e.g., freeze-dried for tissues, vortexed for liquids) and stored at -80°C to prevent lipid degradation. For microbial cultures, ensure cells are pelleted and rinsed to remove media residues. Avoid repeated freeze-thaw cycles.

What is the minimum sample quantity required for FAME analysis?

The required amount depends on the sample type:

Microbial biomass: 10–50 mg (wet weight).
Plant/animal tissues: 20–100 mg (dry weight).
Oils/lipid extracts: 1–10 mg. Contact us for low-abundance samples or specialized requests.

Can FAME analysis distinguish between cis/trans or positional isomers of fatty acids?

Yes, GC-MS with specialized columns (e.g., highly polar cyanopropyl phases) can resolve cis/trans isomers (e.g., oleate vs. elaidate) and some positional isomers (e.g., n-3 vs. n-6 PUFAs). However, conjugated isomers (e.g., CLA) may require supplementary methods like silver-ion chromatography.

How long does a typical FAME analysis workflow take?

From sample receipt to report delivery:

Standard profiling: 5–7 business days.

Complex analyses (e.g., lipid class separation or isomer resolution): 7–10 business days. Expedited services are available upon request.

Can I analyze short-chain fatty acids (SCFAs, e.g., C4:0–C8:0) alongside long-chain FAMEs?

SCFAs require separate protocols due to their high volatility. We recommend using dedicated SCFA analysis (e.g., GC-FID with acidification) or providing additional samples for parallel testing.

How are lipid oxidation products or degraded FAMEs handled during analysis?

We include antioxidant additives (e.g., BHT) during lipid extraction and methylation to minimize oxidation. Suspected degradation (e.g., elevated peroxidation markers) will be flagged in the report with recommendations for re-testing.

Do you support custom FAME panels for specific research needs (e.g., focusing on ω-3/ω-6 ratios only)?

Yes, we offer tailored panels to prioritize specific fatty acid classes (e.g., ω-3/ω-6 PUFAs, branched-chain markers). Custom panels reduce costs and streamline data interpretation for focused studies.

How are data normalized (e.g., per sample weight, total lipid content)?

Data can be normalized flexibly:

Absolute quantification (µg/mg sample weight).
Relative abundance (% of total FAMEs).
Per internal standard (e.g., spiked C17:0 for lipid extraction efficiency). Specify your preferred normalization method upon submission.

Can FAME profiles be linked to functional traits (e.g., membrane fluidity, biofuel efficiency)?

Yes, we provide interpretive annotations (e.g., double bond index, chain length averages) to correlate FAME profiles with membrane properties, energy density, or oxidative stability. Ask about our bioinformatics add-ons for deeper functional insights.

What if my sample contains interfering substances (e.g., pigments, detergents)?

We perform pre-analysis cleanup (e.g., solid-phase extraction, solvent partitioning) to remove common interferents. Inform us in advance if your sample contains unusual contaminants (e.g., surfactants, heavy metals) for optimized processing.

How do I interpret "undetected" or trace-level FAMEs in my report?

Values below the method's limit of detection (LOD) are flagged as "undetected." Trace amounts (between LOD and limit of quantification) are reported semi-quantitatively. We provide LOD/LOQ tables for reference in all reports.

Are statistical comparisons (e.g., ANOVA, PCA) included in standard reports?

Basic statistical tests (e.g., fold change, t-tests) are included. Advanced multivariate analyses (PCA, hierarchical clustering) are optional add-ons. Specify your needs during project setup.

Can I integrate FAME data with other omics datasets (e.g., transcriptomics)?

Absolutely! We export data in compatible formats (Excel, CSV) for integration with tools like MetaboAnalyst or pathway mappers (KEGG, LipidMaps). Ask about multi-omics correlation services.

Publication

White matter lipid alterations during aging in the rhesus monkey brain. Dimovasili, C., Vitantonio, A. T., Conner, B., Vaughan, K. L., Mattison, J. A., & Rosene, D. L. GeroScience, 2024. https://doi.org/10.1007/s11357-024-01353-3.
Evidence for phosphate-dependent control of symbiont cell division in the model anemone Exaiptasia diaphana. Faulstich, N. G., Deloach, A. R., Ksor, Y. B., Mesa, G. H., Sharma, D. S., Sisk, S. L., & Mitchell, G. C. mBio, 2024. https://doi.org/10.1128/mbio.01059-24.
The olfactory receptor Olfr78 promotes differentiation of enterochromaffin cells in the mouse colon. Dinsart, G., Leprovots, M., Lefort, A., Libert, F., Quesnel, Y., Veithen, A., ... & Garcia, M. I. EMBO reports, 2024. https://doi.org/10.1038/s44319-023-00013-5.
Annexin A2 modulates phospholipid membrane composition upstream of Arp2 to control angiogenic sprout initiation. Sveeggen, T. M., Abbey, C. A., Smith, R. L., Salinas, M. L., Chapkin, R. S., & Bayless, K. J. The FASEB Journal, 2023. https://doi.org/10.1096/fj.202201088R.
Lipid Membrane Engineering for Biotechnology (Doctoral dissertation, Aston University). Gomes Almeida, A. C. Lipid Membrane Engineering for Biotechnology, 2023. https://doi.org/10.48780/publications.aston.ac.uk.00046663.
Loss of G0/G1 switch gene 2 (G0S2) promotes disease progression and drug resistance in chronic myeloid leukaemia (CML) by disrupting glycerophospholipid metabolism. Gonzalez, M. A., Olivas, I. M., Bencomo‐Alvarez, A. E., Rubio, A. J., Barreto‐Vargas, C., Lopez, J. L., ... & Eiring, A. M. Clinical and Translational Medicine, 2022. https://doi.org/10.1002/ctm2.1146.
Characterising Chinese Hamster Ovary cell extracellular vesicle production in biopharmaceutical manufacturing (Doctoral dissertation, University of Sheffield). O'Donnell, F. Plant Biotechnology Journal, 2022. https://etheses.whiterose.ac.uk/33062/.
Summative and ultimate analysis of live leaves from southern US forest plants for use in fire modeling. Matt, F. J., Dietenberger, M. A., & Weise, D. R. Energy & Fuels, 2020. https://doi.org/10.1152/ajpgi.00184.2023.
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. Nature communications, 2020. https://doi.org/10.1038/s41467-019-13603-6.
Laboratory evaluation of larvicidal and oviposition deterrent properties of edible plant oils for potential management of Aedes aegypti (Diptera: Culicidae) in drinking water containers. Njoroge, T. M., & Berenbaum, M. R. Journal of medical entomology, 2019. https://doi.org/10.1093/jme/tjz021.
Experimental microbial dysbiosis does not promote disease progression in SIV-infected macaques. Alexandra M. Ortiz et al. Nature Medicine, 2018. https://doi.org/10.1038/s41591-018-0132-5.
Multi‐omics identify xanthine as a pro‐survival metabolite for nematodes with mitochondrial dysfunction. Anna Gioran et al. EMBO Journal, 2019. https://doi.org/10.15252/embj.201899558.

References

Feng, Saixiang, et al. "Either fadD1 or fadD2, which encode acyl-CoA synthetase, is essential for the survival of Haemophilus parasuis SC096." Frontiers in Cellular and Infection Microbiology 7 (2017): 72. https://doi.org/10.3389/fcimb.2017.00072
Kokotou, Maroula G., Christiana Mantzourani, and George Kokotos. "Development of a liquid chromatography–high resolution mass spectrometry method for the determination of free fatty acids in milk." Molecules 25.7 (2020): 1548. https://doi.org/10.3390/molecules25071548

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

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