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 (typically equivalent in cost to 10 samples). Note that Solid samples will require an additional sample preparation step, typically bead blasting. For more details see our pricing page.
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 Name||Abbreviation||Carbon Number|
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.
Please carefully follow the recommendations below when submitting samples to the Duke Proteomics and Metabolomics Shared Resource (DPMSR) for Short Chain Fatty Acid (SCFA) analysis of fecal, cecal, or tissue samples. Please contact firstname.lastname@example.org or email@example.com you have any questions.
1. Samples need to be submitted in the tubes used for homogenization. (Precellys Lysing Kit, https://www.bertin-corp.com/life-sciences/30-precellys-lysing-kits-ck14.html#/capacity-2ml). In rare cases of very low sample size (i.e. < 10 mg) the 0.5 mL tubes are recommended.
2. Tare the individual tubes, recording the weight. (All tubes vary somewhat in mass.)
3. Add a mass of fecal material between 50-100 mg to each weighed tube. Seal the tube. Record the weight of the tube containing the sample, and when submitting the sample, include the weight of the sample – corrected for the tube weight. IMPORTANT: Submission of samples with fecal weight above this range will result in an additional charge, as we will need to homogenize the sample, transfer, then re-homogenize. Sample weights below this range will adversely impact the Limit of Quantitation for those samples. Also, remember that error in sample weight is directly proportional to the error in the SCFA quantitative measurement.
4. Label each tube with the identifier to be used in the Sample Submission System. Instructions for online submission can be found at: https://genome.duke.edu/cores-and-services/proteomics-and-metabolomics/sample-submission.
1. Always use gloves when weighing the tubes including during the process of weighing them before addition of the sample.
2. Record the weight of the tube and the tube plus sample in milligrams.
3. Freeze the tubes at -80oC once they are prepared. If the tube are to be shipped, dry ice must be used to maintain the samples at low temperature.
4. Be aware that the beads in the tubes can react to static electricity. Handle the tubes carefully to keep the beads from flying out of the tube.
(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.