Enhanced Detection of Short-Chain Fatty Acids Using Gas Chromatography Mass Spectrometry

Abstract

Short-chain fatty acids (SCFAs) are mainly produced by intestinal microbiota and play an important role in many host biological processes such as immune system development, glucose and energy homeostasis, and regulation of immune response and inflammation. In addition, they participate in the regulation of anorectic hormones, which have a role in appetite control, tumor suppression, and regulating central and peripheral nervous systems. As such, there is great interest in monitoring levels of SCFAs in various biological samples. Due to the highly hydrophilic and volatile characteristics of SCFAs, optimizing extraction and sample preparation procedures is often a central component to further improve SCFA quantification. Here, we describe a rapid and highly sensitive analytical method for measuring SCFAs in human serum and feces. Briefly, SCFAs are protected by adding sodium hydroxide, followed by a one-step extraction (pH > 7). Then, SCFAs are quantified with gas chromatography coupled to mass spectrometry (GC-MS) after derivatization by N-tert-Butyldimethylsilyl-N-Methyltrifluoroacetamide (MTBSTFA). This method demonstrates excellent sensitivity, linearity, and derivatization efficiency for simultaneous determination of 14 different SCFAs. Further, this validated method can be successfully applied to quantify SCFAs in micro-scale biological samples. In summary, we describe efficient and advanced sample preparation and detection procedures, which are critically needed for monitoring SCFA concentrations in human biological samples.

Basic Protocol 1:

SCFA Extraction and Detection from Fecal and Serum Samples with Gas Chromatography-Mass Spectrometry

INTRODUCTION

Short-chain fatty acids (SCFAs) are fatty acids with less than six carbon atoms encompassing both saturated aliphatic SCFAs (including acetic acid, propionic acid, butyric acid, valeric acid, among others) and branched chain SCFAs (including isobutyric acid, 2-methylbutyric acid, isovaleric acid, and others). The former are mainly produced from dietary fibers metabolized by anaerobic intestinal microbiota (Ríos-Covián et al., 2016; Cummings, 1981), while the latter are predominantly derived from the catabolism of branched-chain amino acids (Macfarlane and Macfarlane, 2003).

SCFAs provide a valuable opportunity to study the human gut microbiome, which consists of trillions of microbes (Mallick et al., 2019), is involved in numerous physiological processes (Flint et al., 2012a), and is vital to the host’s health (Qin et al., 2010). For instance, development of a competent immune system is strongly influenced by the production of gut-derived metabolites such as SCFAs and lipopolysaccharides (Daliri et al., 2017), while a number of other critical functions have also been ascribed to the intricate cross-talk between host and microbiota, such as synthesis of SCFAs and vitamins, adipose storage, and regulation of angiogenesis, as well as certain aspects of behavior (Cryan and O’Mahony, 2011; Yamashiro, 2018). Interestingly, results have increasingly shown that metabolome-derived ligands play major roles in disease propagation (Dvořák et al., 2020; Scoville et al., 2019) and, conversely, may also modulate toxic effects of environmental contaminants (Lim et al., 2020; Dempsey et al., 2019; Li et al., 2018). Given the enormous metabolic potential of the intestinal microbiota (Holmes et al., 2012), there is great interest in characterizing metabolic interactions between the host and microbes (Nicholson et al., 2012). Metabolomics, the comprehensive study of small molecular-weight metabolites and their dynamic changes in biological systems (Patti et al., 2012; Chong et al., 2018; Wishart, 2008; Gu et al., 2015; Shi et al., 2019; Gowda et al., 2008; Shi et al., 2020; Jasbi et al., 2019c, 2019b) has been increasingly applied to this end (Holmes et al., 2012; Nicholson et al., 2012). Considering the ubiquitous presence of SCFA-activated G-protein-coupled receptors (GPCRs) present on multiple cell types, including intestinal epithelial cells, macrophages, dendritic cells, and mast cells (Valdes et al., 2018; Cox and Blaser, 2013; Kim et al., 2013), research efforts have centered on characterizing SCFAs such as acetate, propionate, and butyrate produced by gut microbes under anaerobic conditions in the large intestine by fermentation of dietary fibers (Flint et al., 2012b). SCFAs have been shown to be associated with health outcomes such as glucose and energy homeostasis, and regulation of immune response and inflammation, in addition to regulation of anorectic hormones, which have a role in appetite control, tumor suppression, and regulating central and peripheral nervous systems (Cox and Blaser, 2013; Kim et al., 2013; Bienenstock et al., 2015; Hamer et al., 2008).

Generally, SCFAs are produced and absorbed in the large intestine (Sakata, 1987). In addition to providing energy for enterocytes, they play an important role in physiological activity and biological homeostasis. For example, SCFAs can activate 5’ adenosine monophosphate-activated protein kinase to regulate metabolic homeostasis and inhibit lipolysis in adipocytes through GPCRs (Samuel et al., 2008). They are also involved in the interaction between host microbiota and brain function, given their inherent ability to cross the blood-brain barrier (Erny et al., 2017). Recently, numerous studies have indicated SCFAs to be associated with colorectal cancers, diarrheal disorders, inflammatory bowel diseases, autism spectrum disorder (ASD), Rett syndrome, and many metabolic diseases such as obesity, hypertension, and diabetes (Pingitore et al., 2017; Natarajan et al., 2016; Roelofsen et al., 2010; Strati et al., 2016; Zhao et al., 2017; Sun et al., 2017; Roy et al., 2006). Moreover, some SCFAs such as propionic acid and butyric acid show specific functions in many tissues and organs. For example, propionic acid is an energy source of intestinal cells (Schwarz et al., 2017; Wolever et al., 1991). Given the demonstrated importance of SCFAs in physiological and pathological processes, quantitative and qualitative analysis of these volatile fatty acids in biological samples is of increasing interest to scientific researchers of various disciplines (Pluznick, 2016; Miller, 2012; Wong et al., 2006).

The protocol described here (Basic Protocol 1) expounds a highly sensitive, rapid, accurate, and applicable GC-mass spectrometry (GC-MS) method for quantification of multiple SCFAs in biological samples (such as serum and fecal) in micro-scale (Jasbi et al., 2019a). Due to their production in the large intestine and absorption into the bloodstream, fecal and serum samples are often used as typical specimens for the measurement of SCFAs, and are used here to illustrate the protocol. In this approach, sodium hydroxide is added to the samples at the beginning to reduce the volatility of SCFAs under basic conditions, and then, SCFAs are extracted and derivatized by N-tert-Butyldimethylsilyl-N-Methyltrifluoroacetamide (MTBSTFA) (See Figure 1 for a summary of the fecal sample preparation method). The advantage of using MTBSTFA is that the derivatization reaction can smoothly proceed under 60 °C without any assistance such as ultrasonication (Lotti et al., 2017), and the derivatives can be directly injected into GC-MS without further extraction (Lotti et al., 2017; He et al., 2018; Hoving et al., 2018; Zhao et al., 2006).

Figure 1.

Overview of the fecal sample preparation method described in Basic Protocol 1 to detect SCFAs using GC-MS.

STRATEGIC PLANNING

Fecal and serum samples for use in this protocol can be obtained using previously described human sample extraction methods (Lotti et al., 2017; Zheng et al., 2013). After the subjects fast overnight (12 h), serum and fecal samples should be collected the subsequent morning, and prepared at 4 °C or stored at −20 °C (short-term) or −80 °C (long-term). During preparation, the samples should remain at 4 °C until dried in the vacuum concentrator. Due to drying time lengths, the volume of samples prepared at a given time should not exceed the vacuum concentrator capacity. If the entire protocol cannot be completed on a single run, a suggested stopping point is right before sample derivatization.

All solutions should be prepared daily before sample preparation. Any additional materials should be arranged prior to the protocol or during hands-off time.

The retention time and quantification mass for each SCFA are determined using its chemical standard (Table 1). Users should prepare calibration curves with the 14 SCFAs to determine their concentrations. They can do this by preparing a series of standard solutions across a range of concentrations (after normalization to the Internal Standard (hexanoic acid-6,6,6-d₃ (Sigma-Aldrich, cat. no. 498727) and measuring signal response; operators can plot the relationship to interpolate concentrations of the analytes. Standards are commercially available (Acetic acid (Thermo Fisher Scientific, cat. no. MFCD00036152), Propionic acid (Sigma-Aldrich, cat. no. 402907), Isobutyric acid (Sigma-Aldrich, cat. no. I1754), Butyric acid (Sigma-Aldrich, cat. no. B103500), 2-Methylbutyric acid (Sigma-Aldrich, cat. no. W269514), Isovaleric acid (Sigma-Aldrich, cat. no. 129542), Valeric acid (Sigma-Aldrich, cat. no. 240370), 2-Methylpentanoic acid (Sigma-Aldrich, cat. no. W275409), 3-Methylpentanoic acid (Sigma-Aldrich, cat. no. W343706), Isocaproic acid (Sigma-Aldrich, cat. no. 277827), Caproic acid (Sigma-Aldrich, cat. no. 153745), 2-Methylhexanoic acid (Sigma-Aldrich, cat. no. 338273), 4-Methylhexanoic acid (Sigma-Aldrich, cat. no. 559016), Heptanoic acid (Sigma-Aldrich, cat. no. 75190)).

Table 1.

Retention time (RT) and quantification mass (m/z) of 14 SCFA chemical standards.

Compound	Retention time (min)	Quantitation Ion (m/z)
Acetic acid	5.86	117
Propionic acid	7.32	131
Isobutyric acid	7.53	145
Butyric acid	7.92	145
Isovaleric acid	8.03	159
2-Methylbutyric acid	8.45	159
Valeric acid	9.00	159
2-Methylpentaboic acid	9.35	173
3-Methylpentanoic acid	9.67	173
Isocaproic acid	9.72	173
Caproic acid	10.23	173
2-Methylhexanoic acid	10.55	187
4-Methylhexanoic acid	11.14	187
Heptanoic acid	11.55	187

BASIC PROTOCOL 1: SCFA EXTRACTION AND DETECTION FROM FECAL AND SERUM SAMPLES WITH GAS CHROMATOGRAPHY-MASS SPECTROMETRY

The protocol described here, employed in our previous work (Jasbi et al., 2019a), is a rapid and highly sensitive method for quantifying the presence of 14 SCFAs, namely acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, 2-methylbutyric acid, valeric acid, 2-methylpentanoic acid, 3-methylpentanoic acid, isocaproic acid, caproic acid, 2-methylhexanoic acid, 4-methylhexanoic acid, and heptanoic acid, in either serum or fecal samples through GC-MS. The sample preparation method described below has shown to reliably detect branched chain and saturated aliphatic SCFAs with good intra- and inter-day precision.

We describe sample preparation steps for either fecal (Steps 1a to 16a) or serum (Steps 1b to 12b) samples, before outlining the steps required for GC-MS analysis and data extraction. During preparation, SCFAs are preserved through the use of sodium hydroxide (NaOH), extracted with the addition of methanol (MeOH), and derivatized with MTBSTFA. Optimal conditions include using extracting solutions with a pH = 9, and a derivatization time and temperature of 30 minutes and 60 °C, respectively.

All reagents should be of analytical grade, handled with care, and capped when not in use. All aqueous solutions should be prepared with ultrapure water (filtered deionized water) and stored at room temperature unless otherwise noted. Follow all standard institutional waste disposal protocols.

MATERIALS

Human fecal sample or Human serum sample (see Strategic Planning)

Methanol (MeOH; Sigma-Aldrich, cat. no. 34860)

Pyridine (Sigma-Aldrich, cat. no. 360570)

0.5 M Sodium hydroxide (NaOH; Thermo Fisher Scientific cat. no. 60-014-41) in water

0.1 M Sodium hydroxide (NaOH; Thermo Fisher Scientific cat. no. 60-014-41) in water

Internal Standard solution (200 μM hexanoic acid-6,6,6-d3 in water)

Derivatization solution (see Reagents and Solution)

N-tert-Butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA; Sigma-Aldrich, cat. no. 394882)

1.5 mL Eppendorf tubes

Pipet and pipet tips

2 mL Glass vials

100 mL Graduated cylinder

Vortex mixer (Fisher Scientific, cat. no. 14955151)

Centrifuge with speed, time, and temperature control (Thermo Scientific, cat. no. 75004270)

Vacuum concentrator (CentriVap Concentrator, cat. no. 7810010)

0.5 mm diameter Stainless Steel beads (Next Advance, Troy, NY, cat. no. SSB05)

0.10 g measuring spoon (Next Advance, Troy, NY, cat. no. MSP01)

Tissue homogenizer (Next Advance, Troy, NY, cat. no. BBY5M)

Digital dry bath (Thermo Scientific, cat. no. 88870002)

Scientific balance with mg readability (VWR-64B)

Ultrasonic liquid processor (VWR 97043-996)

Agilent 7820A GC-5977B MSD system (Agilent Technologies, Santa Clara, CA)

Agilent HP-5 ms capillary column (30 m x 250 μm x 0.25 μm)

Agilent MassHunter Workstation Software Quantitative Analysis (B.09.00)

Protocol steps

Fecal Sample Preparation (Figure 1)

• 1a.Weigh out 20 mg ± 1.5 mg of each biological sample in a 1.5 mL Eppendorf tube.
• 2a.Add 20 μL of a 0.5 M NaOH solution, 20 μL of Internal Standard solution, and 460 μL of MeOH to each sample.
• 3a.Add 0.6 g of stainless-steel beads to the tube and homogenize for 2 min in the tissue homogenizer.
• 4a.Prepare an 80% v/v solution of MeOH in water and add 400 μL of it to the sample.
• 5a.Vortex mixture for 30 s and store at −20 °C for 30 min.
• 6a.Sonicate in ultrasonic liquid processor in ice bath for 10 min at 4 °C.

Prepare ice bath for use prior to sonication.

• 7a.Centrifugate at 14,000 RPM for 10 min at 4 °C.
• 8a.Transfer 700 μL of supernatant into a new 1.5 mL Eppendorf tube.

The remaining solution may be stored long-term at −80 °C for future analysis of total protein concentration.

• 9a.Dry the supernatant in the vacuum concentrator at 37 °C for 240 min.

Drying time is dependent upon total sample size and vacuum concentrator capacity. Fecal sample sizes greater than the vacuum concentrator capacity will result in additional drying time. If it cannot be immediately dried, samples should be stored at −20 °C short-term or at −80 °C long-term. This is a preferred stopping point if the protocol cannot be completed in a single run.

• 10a.Derivatize samples by adding 40 μL of derivatization solution and placing the samples in the digital dry bath for 90 min at 60 °C.
• 11a.Add 60 μL of MTBSTFA and incubate the mixture at 60 °C for 30 min.
• 12a.Vortex mixture for 30 s
• 13a.Centrifuge at 14,000 RPM for 10 min at 4 °C.
• 14a.Transfer 70 μL of the supernatant into a new 2 mL glass vial and dilute with 140 μL of pyridine.
• 15a.Proceed to GC-MS Analysis and Data Extraction.

Serum Sample Preparation (Figure 2)

Figure 2.

Overview of the serum sample preparation method described in Basic Protocol 1 to detect SCFAs using GC-MS.

• 1b.Transfer 20 μL of each serum sample to a 1.5 mL Eppendorf tube
• 2b.Add 30 μL of 0.1 M aqueous NaOH, 20 μL of Internal Standard solution, and 430 μL of MeOH.
• 3b.Vortex mixture for 10 s and store at −20 °C for 20 min.

Check pH to confirm the mixture is at ~9.

• 4b.Centrifuge at 14,000 RPM for 10 min at 4 °C.
• 5b.Transfer 450 μL of the supernatant into a new 1.5 mL Eppendorf tube.
• 6b.Dry the supernatant under vacuum at 37 °C for 120 min.

Drying time is dependent upon total sample size and vacuum concentrator capacity. Serum sample sizes greater than the vacuum concentrator capacity will result in additional drying time. If it cannot be immediately dried, samples should be stored at −20 °C short-term or at −80 °C long-term. This is a preferred stopping point if the protocol cannot be completed in a single run.

• 7b.Derivatize samples with 40 μL of derivatization solution in the digital dry bath for 90 min at 60 °C.
• 8b.Add 60 μL of MTBSTFA and incubate the mixture at 60 °C for 30 min.
• 9b.Vortex mixture for 30 s
• 10b.Centrifuge at 14,000 RPM for 10 min at 4 °C.
• 11b.Transfer 70 μL of supernatant into a new 2 mL glass vial and dilute with 140 μL of pyridine.
• 12b.Proceed to GC-MS Analysis and Data Extraction

GC-MS Analysis and Data Extraction

• 16.Set injection volume to 1 μL and solvent delay time to 5 min for each sample.
• 17.Hold initial oven temperature at 60 °C for 1 min.
• 18.Increase temperature to 325 °C at a rate of 10 °C/min and hold at 325 °C for 10 min.
• 19.Hold helium at a constant flow rate of 20 mL/min through the column.
• 20.Set the temperatures of the front inlet, transfer line, and electron impact (EI) ion source to 250 °C, 290 °C, and 230 °C, respectively.
• 21.Set electron energy to −70 eV and collect the mass spectral data in full scan mode (m/z 50-600).
• 22.Transfer data to the Agilent Quantitative Analysis software.
• 23.Create a new batch with all of the appropriate samples selected and the desired method applied.
• 24.After designating the method, select to integrate the data.

  The appropriate peaks should be integrated according to their chemical standard retention times (see Table 1).

  Figure 3 shows the total ion chromatogram of 14 SCFA standards. Agilent MassHunter Workstation Software Quantitative Analysis (B.09.00) can be used to process the GC-MS data for compound identification, peak selection, and quantification. The concentrations of SCFAs in biological samples should be calculated using the calibration curves constructed from the corresponding signals of SCFAs standards (see Strategic Planning).

Figure 3.

Total ion chromatogram of 14 SCFA standards.

REAGENTS AND SOLUTIONS

All aqueous solutions should be prepared using ultrapure water. The reagents should be at least of analytical grade. Abide by all necessary waste disposal protocols as they apply. Inspect all pipettes for readiness prior to use.

Derivatization Solution

MeOX (Sigma-Aldrich, cat. no. 226904)

Pyridine (Sigma-Aldrich, cat. no. 360570)

Add 20 mg of MeOX per 1 mL of pyridine solution. Prepare daily (i.e., prepare fresh each time) and at room temperature. The solution may require minor shaking to dissolve MeOX, and should be prepared in a fume hood. Handle reagents with care and cap tightly when not in use.

COMMENTARY

Background Information

Some techniques such as high-performance liquid chromatography, nuclear magnetic resonance, and capillary electrophoresis have been applied in quantitative and qualitative analyses of SCFAs, although gas chromatography (GC) is still the most common choice for analysis, due to the high volatility of short chain fatty acids (Nusbaum et al., 2018; Primec et al., 2017; Huda-Faujan et al., 2010; Gu et al., 2013). In 1952, A.T. James et al. first used GC to separate volatile fatty acids (James and Martin, 1952), leading to a variety of GC-based methods that have been explored for the analysis of SCFAs in biological samples (Hoving et al., 2018; He et al., 2018; Lotti et al., 2017; Zheng et al., 2013; Zhao et al., 2006; Moreau et al., 2003). Most of the extraction procedures reported in the literature describe using acidic phase separation (Lotti et al., 2017; He et al., 2018; Zhao et al., 2006) and a two-step extraction (Zheng et al., 2013; Moreau et al., 2003) with highly volatile organic solvents, including hexane or diethyl ether. Due to their highly hydrophilic and volatile characteristics, complex sample preparation procedures under acidic conditions could affect method precision during determination of SCFAs, especially for a large batch of samples. Therefore, optimization of extraction and GC derivatization procedures is a key factor to further improve quantification of SCFAs in biological samples.

There are significant advantages conferred by the SCFA detection method described here compared to previous methods, including decreased analytical burden and sample analysis time. We extract SCFAs under basic conditions, which reduces volatility of SCFAs. This extraction approach allows us to dry and concentrate the samples, further improving the SCFA detection sensitivity. The advantage of using MTBSTFA compared to many other derivatization reagents is that the derivatization reaction can smoothly proceed under 60 °C without any assistance such as ultrasonication (Lotti et al., 2017), and the derivatives can be directly injected into GC-MS without further extraction (Lotti et al., 2017; He et al., 2018; Hoving et al., 2018; Zhao et al., 2006). This is the first description of a protocol in which SCFAs are extracted under an alkaloid environment (pH > 7) and derivatized by MTBSTFA to enhance GC-MS detection of SCFAs. The 14 SCFAs monitored in the current protocol are highly preserved in human serum and fecal samples and are of high biological relevance. Notably, these 14 SCFAs just serve as a proof-of-concept; in theory, the method may be expanded to detect other SCFAs of interest.

Critical Parameters

Samples should be prepared upon collection at 4 °C or stored promptly at −20 °C (short-term) or at −80 °C (long-term). In order to preserve the integrity of the samples, freeze-thaw cycles should be minimized (Wang et al., 2019; Stevens et al., 2019; González-Domínguez et al., 2020). In addition, the recovery of SCFAs is decreased with each day the samples remain at 4 °C or higher. It is thus prudent to only thaw the samples being prepared and analyze them with GC-MS in a timely manner. With the objective of reducing the time in which samples are thawed, all solutions should be prepared prior to starting the procedure. Ultrapure water should be used for all solutions. If the samples cannot be immediately dried after transferring the supernatant, samples should be stored at −20 °C short-term or at −80 °C long-term. Typically, it is best to not exceed the capacity of the vacuum concentrator with the volume of samples prepared at a given time.

Robust calibration curves (R² ≥ 0.95) are essential for calculation of results. Users should prepare a new calibration curve for every batch run.

Troubleshooting

Table 2 describes a few common problems users may face when following the protocol, along with possible causes and recommended solutions.

Table 2.

Troubleshooting guide for SCFA detection with GC-MS.

Problem	Possible Cause(s)	Solution
Vacuum pressure is consistently higher than that recommended for flow rate	Air leaks or other vacuum problems	Consult operation manual
Spurious chromatogram peaks and poor repeatability	Backflash in response to flooding the injection line	Inject less sample volume
	Poor recovery of analyte from sample	Ensure that freeze/thaw cycles have been kept to ≤ 2 and that samples are stored at −80 °C for long-term storage
Shifting retention, baseline disturbances	Column bleed, column contamination, column aging	Replace with new column and condition before use

Understanding Results

This protocol is able to provide good quantitation of 14 SCFAs from biological samples. Figure 3 shows the total ion chromatogram of 14 SCFA standards, and Table 1 shows the retention time and quantification mass (m/z) values of 14 SCFA chemical standards. After the data is extracted from the Quantitative Analysis software, it may be exported for quantitation. Figure 4 shows an excellent calibration curve, using valeric acid as an example. A calibration curve demonstrates concentration (μL/mL) plotted against peak intensity for the SCFA. It may be generated by analyzing different concentrations of a SCFA standard through GC-MS. The previous concentrations are used to generate a curve with their measured intensities. Calibration curves may be used to identify an unknown SCFA concentration (from the test samples) through its peak intensity.

Figure 4.

Calibration curve for valeric acid plotted as concentration vs peak intensity. A calibration curve is created by first preparing a set of standard solutions with known concentrations of the analyte; the instrument response is measured for each and plotted vs. concentration of the standard solution.

Typical concentrations of detected short-chain fatty acids may vary significantly between fecal and serum samples and will most likely be indicative of experimental or conditional factors. Furthermore, normal concentrations of individual SCFAs will vary. For example, as can be determined using established metabolomics databases such as the Human Metabolomics Database (HMDB), normal concentrations of acetic acid in human blood (male and female) may range from 26.8 to 64.2 μM (Psychogios et al., 2011); operators should consult relevant literature when interpreting their obtained results. A reduced yield of SCFAs may be attributed to extended periods of sample thawing and/or preparation at or above 4 °C.

When multiple samples are measured, the data can be grouped and entered into a statistical software. For example, the analyses observed in our unpublished studies in Figure 5, such as box and volcano plots, principal component analysis (PCA), and receiver operating characteristic (ROC) curve can be performed to reveal integral information regarding statistical significance, effect size, variance explained, as well as sensitivity and specificity. In general, it is best practice to run duplicates (i.e., two technical replicates per sample) for data validation.

Figure 5.

Examples of typical data analyses that can be used for the 14 SCFAs from human serum and fecal samples: A) Box plot of one-way ANOVA showing log-transformed concentrations in a conventional three-group study design, B) Volcano plot of significant SCFAs and fold-change magnitude between two experimental groups, C) Principal components analysis (PCA) of two groups (green/blue) between three PC components, and D) Receiver operating characteristic (ROC) curve of multivariate model for discrimination between two experimental groups.

Time Considerations

Quickly performing the extraction and derivatization protocol is paramount to avoid SCFA loss during preparation. Serum sample preparation can be completed in six hours, with two hours of hands-on time for 60 samples. The protocol for fecal samples is longer and may involve up to ten hours, with a hands-on time of three hours for 60 samples. The drying time is dependent upon total sample size and type of sample prepared. With practice and a vacuum concentrator system with the appropriate capacity, 60 fecal samples can be prepared for GC-MS analysis in one day. Due to drying length, it is the preferred stopping point.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.