Short Chain Fatty Acid Analysis

The short-chain fatty acids (SCFAs) are the major class of metabolites produced in the large bowel by the anaerobic gut microbiome (1), and they play an essential and incompletely understood role in a wide variety of human diseases, including autoimmune diabetes (2), non-alcoholic liver disease (3), cirrhosis (4), neurodevelopmental disorders (5-7), atherosclerosis (8), vaccine response (9), graft vs. host disease (10), obesity (11), cardiovascular disease (12), and kidney disease (13). Twelve of these referenced SCFA-disease studies are recent review articles written in 2017, thus research in SCFAs produced by the microbiome is of current interest in a variety of disease states.

The Duke Proteomics and Metabolomics Shared Resource utilizes a UPLC-MS/MS method (14, 15) to analyze short chain fatty acids (SCFA’s), including 12 acids from C-2 to C-8 (Table 1).  The SCFA method utilizes an Acquity UPLC coupled to a Xevo TQ-S triple quadrupole mass spectrometry by Waters Corporation to perform quantitative multiplexed analysis of up to 12 SCFA’s in fecal samples. Fecal, serum and plasma samples can be analyzed with this SCFA method; other sample matrices may be compatible but will need to be considered on a case-by-case basis. In a typical fecal or plasma sample from a healthy donor, 6-10 SCFA’s are present in amounts greater than the lower limit of quantitation. Pricing is performed on a per sample basis, plus the cost of running calibration curves. Note that Solid samples will require an additional sample preparation step, typically bead blasting. For more details see our pricing page or contact George Dubay, Will Thompson or Arthur Moseley.

Technical Information:

A 96 well plate is used for analysis, which includes calibration standards and isotopically labeled internal standards, along with quality control samples and blanks. Up to 80 experimental samples can be analyzed on one plate. Only 50 milligrams of fecal material is required per sample analysis. A typical chromatogram for separation of SCFA’s from a fecal sample is shown in Figure 1A., and the reproducibility of the chromatography for the analysis of > 100 samples is illustrated in Figure 1B.

Negative electrospray ionization allows for the SCFA’S to be readily and specifically detected by MS/MS instrumentation. The typical concentration range detected with the method is 0.1 – 200 uM, although this range can vary depending on the specific acid to be analyzed. Quantitation of SCFA’s is performed using the calibration curve generated as part of the standard analysis. 

Table 1 – List of Targeted SCFAs. The table above provides abbreviations used in the chromatogram shown in Figure 1, along with the full acid name and carbon number

SCFA NameAbbreviationCarbon Number
Acetic acidAAC-2
Propionic acidPAC-3
i-Butyric acidi-BAC-4
Butyric acidBAC-4
2-Me-Butyric acid2-Me-BAC-5
Valeric acidi-VAC-5
3-Me-Valeric acid3-Me-VAC-6
i-Caproic acidi-CAC-6
Caproic acidCAC-6
Heptanoic acidHAC-7
Octanoic acidOAC-8

(A) Chromatogram of separation of short chain fatty acid NPH derivatives by LC-MS/MS and (B) the reproducibility in retention time observed over >100 injections.

Figure 1.  (A) Chromatogram of separation of short chain fatty acid NPH derivatives by LC-MS/MS and (B) the reproducibility in retention time observed over >100 injections.

References

(1) Short-chain fatty acids: ready for prime time?  C.C. Roy, C.L. Kien, L. Bouthillier, E. Levy, Nutr. Clin. Pract. 2006, 21: 351–366

(2) Early-Life Nutritional Factors and Mucosal Immunity in the Development of Autoimmune Diabetes, Xiao L, Van't Land B, van de Worp WRPH, Stahl B, Folkerts G, Garssen J. Front Immunol. 2017, 8:1219

(3) Fructose: A Dietary Sugar in Crosstalk with Microbiota Contributing to the Development and Progression of Non-Alcoholic Liver Disease, Lambertz J, Weiskirchen S, Landert S, Weiskirchen R., Front Immunol. 2017, 8:1159

(4) Gut Microbiome-based Therapeutics in Liver Cirrhosis: Basic Consideration for the Next Step., Fukui H., J Clin Transl Hepatol. 2017, 5(3):249-260

(5) Cross Talk: The Microbiota and Neurodevelopmental Disorders., Kelly JR, Minuto C, Cryan JF, Clarke G, Dinan TG., Front Neurosci. 2017, 11:490

(6) Microbiome, inflammation, epigenetic alterations, and mental diseases, Alam R, Abdolmaleky HM, Zhou JR., Am J Med Genet B Neuropsychiatr Genet. 2017, 174(6):651-660

(7) Microbiome, probiotics and neurodegenerative diseases: deciphering the gut brain axis., Westfall S, Lomis N, Kahouli I, Dia SY, Singh SP, Prakash S., Cell Mol Life Sci. 2017

(8) Gut Microbiota and Atherosclerosis., Li DY, Tang WHW., Curr Atheroscler Rep. 2017, 19(10):39

(9) The potential of the microbiota to influence vaccine responses. Lynn DJ, Pulendran B., J Leukoc Biol. 2017

(10) Gut microbiota and acute graft-versus-host disease., Yoshioka K, Kakihana K, Doki N, Ohashi K., Pharmacol Res. 2017, 122:90-95

(11) Obesity and the gastrointestinal microbiota: a review of associations and mechanisms. Graham C, Mullen A, Whelan K. Nutrition Reviews, 73(6), 376–385;

(12) Gut Microbiota in Cardiovascular Health and Disease., Tang WH, Kitai T, Hazen SL., Circ Res. 2017, 120(7):1183-1196

(13) Intestinal Microbiota in Type 2 Diabetes and Chronic Kidney Disease., Sabatino A, Regolisti G, Cosola C, Gesualdo L, Fiaccadori E., Curr Diab Rep. 2017, 17(3):16

(14) An isotope-labeled chemical derivatization method for the quantitation of short-chain fatty acids in human feces by liquid chromatography-tandem mass spectrometry, Han J, Lin K, Sequeira C, Borchers CH, Anal Chim Acta. 2015, 854:86-94

(15) LC-MS/MS Method for Quantification of Short Chain Fatty Acids in Biological Samples, Christoffersen  https://www.ucviden.dk/student-portal/files/39110634/Final_Report.pdf.