How Are Short-Chain Fatty Acids Measured?

Why Analyze Short-Chain Fatty Acids (SCFAs)?

Short-chain fatty acids (SCFAs) are the products of fermentation of dietary fiber by microorganisms in the intestine and mainly include acetic acid, propionic acid, butyric acid, valeric acid, isovaleric acid, capric acid and isohexanoic acid. The substance is mostly present in the human intestine in the form of ions and is absorbed and utilized by the intestine mainly through the transporter. The substance can promote cell growth, improve the function of the intestine, influence cardiovascular metabolism, and have anti-inflammatory, anti-tumor and immunomodulatory effects.

Biological Importance of SCFAs

SCFAs, such as acetate, propionate, and butyrate, are crucial for maintaining gut barrier integrity, serving as energy sources for colonocytes, and regulating immune responses. Butyrate, for instance, is the primary fuel for colonic epithelial cells and has anti-inflammatory properties, while propionate and acetate influence cholesterol metabolism and immune modulation.

SCFAs as Biomarkers

The levels of SCFAs can be used as biomarkers to assess gut microbiota function, gut permeability, and metabolic conditions. Alterations in SCFA levels are linked to disorders such as irritable bowel syndrome (IBS), obesity, type 2 diabetes, and inflammatory bowel disease (IBD). The ability to measure SCFAs enables researchers to monitor the effects of dietary interventions, prebiotic and probiotic treatments, and the efficacy of microbiome-based therapies.

Methods of SCFA Analysis

The accurate measurement of short-chain fatty acids (SCFAs) is a crucial aspect of microbiome and metabolic research. SCFAs, due to their volatile and small molecular structure, present unique challenges for detection and quantification. Several advanced analytical techniques have been developed to ensure precise measurement, each with specific advantages and limitations. Here we focus on the most established methodologies: gas chromatography (GC), liquid chromatography (LC), mass spectrometry (MS), and nuclear magnetic resonance (NMR). Each of these techniques is selected based on the sensitivity required, the sample matrix, and the specific SCFAs of interest.

Gas Chromatography (GC)

Gas chromatography (GC) is one of the most widely applied techniques for SCFA analysis due to its high resolution, sensitivity, and specificity in detecting volatile organic compounds. In particular, GC coupled with flame ionization detection (GC-FID) has become a gold standard for SCFA measurement in various biological matrices.

GC-FID: In this configuration, the gas chromatograph is equipped with a flame ionization detector (FID), which is highly sensitive to carbon-containing compounds such as SCFAs. The FID generates a signal proportional to the quantity of carbon atoms, making it especially suitable for quantifying organic acids like SCFAs.

Sample Preparation: Before analysis, SCFAs must be extracted from biological samples (e.g., feces, blood, or urine) through techniques such as solvent extraction or solid-phase microextraction (SPME). This step is critical to isolate SCFAs from other matrix components that may interfere with the GC analysis. Derivatization of SCFAs, typically via methylation or silylation, is often required to enhance volatility and stability, reducing the risk of thermal degradation during the chromatographic process.

Chromatographic Process: In GC, the sample is introduced into a heated injection port where it vaporizes. The volatile SCFAs are then carried by an inert gas (commonly helium or nitrogen) through a capillary column coated with a stationary phase. SCFAs are separated based on their volatility and interactions with the column's stationary phase—more volatile acids like acetic acid elute first, while less volatile compounds such as butyric or valeric acid elute later.

The FID detects the separated SCFAs by burning them in a hydrogen-air flame. The ions generated by the combustion process produce an electrical current, which is proportional to the amount of SCFA present in the sample. The resulting chromatogram provides distinct peaks for each SCFA, with peak area corresponding to the concentration of the analyte.

Advantages of GC-FID: The GC-FID method offers high sensitivity and quantification accuracy, particularly for small volatile compounds like SCFAs. The technique is capable of detecting SCFAs at concentrations as low as nanomolar levels, making it ideal for applications requiring precise quantification. Additionally, the FID is highly specific for carbon atoms, minimizing interference from non-carbon-based compounds. The GC-FID also benefits from robust and well-established protocols, making it widely accessible for research and clinical laboratories.

Limitations: The primary drawback of GC-FID is the requirement for derivatization. Derivatization adds steps to sample preparation, increasing time and the potential for error. Moreover, some SCFAs, particularly those with higher boiling points, may degrade at the high temperatures used during GC analysis, leading to potential inaccuracies in quantification. Another limitation is that FID, while highly sensitive for quantification, does not provide structural information about the analytes. For this reason, GC-FID is often combined with mass spectrometry (GC-MS) when molecular identification is also required.

Procedure for the determination of short-chain fatty acids in human serum by gas chromatography-mass spectrometry (Wang et al., 2020)

Liquid Chromatography (LC)

Liquid chromatography (LC) is another method used to analyze SCFAs, especially when the derivatization required by GC is not feasible or desired. Unlike GC, LC does not rely on the volatility of the analytes, making it suitable for non-volatile or thermally labile SCFAs.

In LC, SCFAs are separated based on their interactions with a liquid mobile phase and a stationary phase within the column. Reverse-phase LC (RP-LC) is the most common type used for SCFA analysis. The mobile phase, typically a mixture of water and organic solvents like methanol, carries the SCFAs through the column, where they are separated based on polarity and hydrophobicity. SCFAs are then detected using either UV detection or, more commonly, mass spectrometry (LC-MS).

Advantages: LC eliminates the need for derivatization, simplifying sample preparation. It is also more suitable for complex biological matrices such as blood or feces, where non-volatile components may interfere with GC analysis. Additionally, LC-MS enables both quantification and structural identification of SCFAs, providing a more comprehensive analysis than GC-FID.

Limitations: LC, however, generally has lower sensitivity compared to GC-FID, especially when UV detection is used. The method also struggles with detecting very short-chain SCFAs (e.g., formic acid) due to their poor retention and separation in typical LC columns. Additionally, LC equipment and reagents can be more expensive, making it less accessible for routine analysis.

Mass Spectrometry (MS)

Mass spectrometry, particularly when coupled with either GC or LC, offers an exceptionally sensitive and specific method for SCFA detection and quantification. The combination of GC-MS or LC-MS provides both qualitative and quantitative information, allowing for precise identification of SCFAs based on their molecular mass.

Process: In GC-MS, SCFAs are first separated by gas chromatography, as described earlier, and then introduced into a mass spectrometer. The SCFAs are ionized (typically by electron impact ionization), and the resulting ions are separated based on their mass-to-charge ratio (m/z). This produces a mass spectrum that is unique to each SCFA, enabling both identification and quantification.

Similarly, in LC-MS, SCFAs are separated via liquid chromatography before being ionized and detected by the mass spectrometer. LC-MS is particularly valuable for non-volatile or thermally labile SCFAs that cannot be analyzed by GC-MS.

Advantages: MS, whether coupled with GC or LC, provides unparalleled sensitivity and specificity. It can detect SCFAs at trace levels (pico- to nanomolar concentrations) and allows for the identification of unknown or unexpected SCFAs, which might be overlooked by other techniques. Additionally, MS provides structural information, which is critical in distinguishing isomers or closely related compounds.

Limitations: The main disadvantage of MS-based methods is their complexity and cost. Mass spectrometers are expensive to purchase and maintain, and the method requires a high level of technical expertise. Additionally, MS can suffer from ion suppression, particularly in complex biological samples, which may reduce the accuracy of quantification unless carefully controlled.

Sample Preparation and Challenges of SCFA Analysis

Accurate measurement of SCFAs requires careful sample preparation to minimize losses and ensure consistency. SCFAs are volatile and can easily degrade or be contaminated, presenting several challenges.

Sample Types

• Fecal Samples: Feces is the most common source for SCFA analysis, as it directly reflects gut microbiota activity. Fecal SCFAs are particularly useful for investigating the role of the gut microbiome in health and disease.
• Blood/Serum Samples: Systemic SCFA levels, measured in blood or serum, can provide insights into the metabolic impact of SCFAs beyond the gut.
• Urine and Other Fluids: SCFAs can also be measured in urine and saliva, though at lower concentrations than in feces or blood.

Sample Collection and Storage

Samples should be collected in airtight containers to prevent exposure to oxygen, which can degrade SCFAs. Immediate freezing or refrigeration is recommended to preserve SCFA integrity. The use of preservatives, such as acids, can also help prevent bacterial contamination and SCFA degradation.

Extraction Techniques

• Solvent Extraction: SCFAs are typically extracted from biological samples using organic solvents like ether or methanol. This step isolates the SCFAs from other compounds present in the sample.
• Filtration and Centrifugation: Before analysis, samples are often filtered and centrifuged to remove particulate matter and concentrate the SCFAs.
• Internal Standards: Internal standards, compounds that behave similarly to SCFAs but are not present in biological samples, are used to correct for sample loss or variability during extraction and analysis.

Challenges in SCFA Analysis

SCFA Stability: SCFAs are volatile and easily degrade if not handled properly. Ensuring stability during sample preparation and storage is one of the most critical challenges.

Matrix Effects: Biological samples, particularly fecal matter, are complex and contain many compounds that can interfere with SCFA detection.

Contamination Risk: SCFAs are naturally present in many environments, making cross-contamination a concern during both sample collection and analysis.

Data Interpretation and Quantification of SCFA Detection

The detection of short-chain fatty acids (SCFAs) in biological samples is only the first step in understanding their biological significance. Accurate data interpretation and quantification are essential to translate raw analytical data into meaningful biological insights. SCFA quantification requires rigorous calibration, precise data processing, and advanced statistical analysis to ensure that the results are reliable, reproducible, and relevant to the underlying biological or clinical question.

Calibration and Standard Curves

Quantitative analysis of SCFAs relies on establishing a robust calibration curve. Calibration involves analyzing known concentrations of SCFA standards to create a reference curve, which allows for the accurate conversion of the detector response (e.g., peak area or intensity) into SCFA concentration in unknown samples.

To construct a calibration curve, a series of SCFA standards of known concentrations are analyzed under the same conditions as the biological samples. The response from the detector (such as the flame ionization detector in GC-FID or the mass spectrometer in GC-MS/LC-MS) is plotted against the concentration of the standards. The resulting curve is used to calculate the concentration of SCFAs in the sample by interpolation of the response values.

A critical aspect of calibration is ensuring linearity across the range of SCFA concentrations of interest. Inaccurate calibration curves can lead to systematic errors in quantification, especially for SCFAs present at very low or very high concentrations. Therefore, multiple concentration points are typically used to ensure the accuracy of the curve, and internal standards are often added to correct for variations in sample preparation and instrument response.

Internal Standards and Quality Control

To ensure accuracy and reproducibility, internal standards are commonly used in SCFA quantification. These internal standards are compounds structurally similar to SCFAs but not present in the sample. They are added at a known concentration to the sample prior to extraction and analysis. Since internal standards undergo the same extraction, processing, and analytical steps as the SCFAs of interest, they help control for potential losses during sample preparation or variability in instrument response.

Internal standards also enable the correction of matrix effects—interactions between the sample matrix (e.g., feces or plasma) and the detection system that can alter the signal strength. For instance, biological matrices often contain a wide range of other organic compounds that can interfere with SCFA detection, particularly in chromatographic methods. By comparing the response of the internal standard with that of the SCFAs in the sample, these effects can be normalized, ensuring more accurate and reliable quantification.

In addition to internal standards, quality control (QC) measures, such as running blank samples and duplicate measurements, are essential to validate the analytical method's precision and sensitivity. Reproducibility is tested by analyzing QC samples at various points during the analysis, ensuring that any drift in instrument performance is detected and corrected.

Sensitivity, Limits of Detection (LOD), and Limits of Quantification (LOQ)

Sensitivity is a key factor in the interpretation of SCFA data, particularly because SCFAs are often present at low concentrations in biological samples. Sensitivity is defined as the ability of the method to detect small quantities of SCFAs, and it is typically evaluated by determining the limits of detection (LOD) and limits of quantification (LOQ).

The LOD refers to the lowest concentration of an SCFA that can be reliably distinguished from the background noise but not necessarily quantified with precision. In contrast, the LOQ is the lowest concentration at which the SCFA can be quantified with acceptable accuracy and precision. The LOD and LOQ are determined during method validation and depend on factors such as the signal-to-noise ratio and the sensitivity of the detector used.

In SCFA analysis, particularly for low-abundance SCFAs such as valerate or caproate, the LOD and LOQ are critical metrics. Highly sensitive techniques like GC-MS or LC-MS typically offer lower LOD and LOQ values, making them preferable for detecting SCFAs in plasma or tissues where concentrations are much lower than in fecal samples.

Data Processing and Peak Integration

Once SCFAs have been detected, the next critical step is processing the raw data, typically in the form of chromatograms (for GC or LC) or spectra (for MS). Accurate peak integration is necessary for quantifying SCFAs, as the area under each peak is directly proportional to the concentration of the SCFA in the sample.

Peak integration requires precise identification of SCFA peaks based on their retention time (in chromatography) or mass-to-charge ratio (in mass spectrometry). Misidentification or poor resolution between overlapping peaks can lead to significant errors in quantification. Advanced software algorithms are often employed to automate peak detection and integration, though manual inspection of the chromatograms or spectra is still necessary to correct for baseline drift or irregularities in peak shape.

It is also important to differentiate SCFA peaks from those of other compounds present in the sample. In complex biological matrices, such as feces or blood, co-eluting compounds may interfere with SCFA detection. The use of tandem MS (MS/MS) can enhance specificity by providing additional fragmentation patterns that confirm the identity of the SCFA, reducing the likelihood of false positives.

Normalization and Comparative Analysis

After quantifying SCFAs, it is often necessary to normalize the data to account for variations in sample volume, extraction efficiency, or biological variability. Common normalization techniques include adjusting SCFA concentrations relative to total sample mass (e.g., per gram of feces) or to the concentration of a specific reference compound (e.g., creatinine in urine or serum).

Once normalized, SCFA data are typically compared across different groups (e.g., healthy vs. diseased populations) or experimental conditions (e.g., pre- and post-intervention). Statistical analyses, such as t-tests, ANOVA, or non-parametric tests, are used to determine whether observed differences in SCFA concentrations are statistically significant.

For more complex datasets, multivariate analyses such as principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA) are employed. These methods can reveal patterns in the data, such as correlations between SCFA profiles and clinical outcomes or microbiome composition. PCA, for example, is useful for visualizing the variability in SCFA concentrations across multiple samples and identifying which SCFAs are most associated with specific health conditions or treatments.

Statistical Considerations in SCFA Quantification

Statistical rigor is essential for drawing meaningful conclusions from SCFA data. Sample size, data variability, and the choice of statistical tests all play crucial roles in determining the reliability of the results. For instance, the biological variability in SCFA production—due to factors such as diet, microbiome composition, and host metabolism—can be significant. Therefore, large sample sizes and replication are necessary to capture the full range of SCFA concentrations and reduce the impact of outliers.

Furthermore, SCFA data often do not follow a normal distribution, particularly in heterogeneous populations. In such cases, non-parametric tests like the Mann-Whitney U test or Kruskal-Wallis test may be more appropriate than traditional parametric tests like the t-test or ANOVA. Correcting for multiple comparisons is also critical when analyzing a broad range of SCFAs, as the likelihood of false positives increases with the number of statistical tests performed.

Biological Interpretation and Relevance

The ultimate goal of SCFA quantification is to link measured concentrations to biological function and health outcomes. SCFA levels can reflect the metabolic activity of the gut microbiota, dietary patterns, or disease states. For example, elevated levels of butyrate in feces are often associated with improved gut barrier function and anti-inflammatory effects, while decreased acetate levels in plasma might indicate metabolic dysregulation in conditions like obesity or type 2 diabetes.

Interpreting SCFA data requires an understanding of both the local (e.g., in the gut) and systemic (e.g., in blood) roles of these fatty acids. While fecal SCFAs provide insights into gut microbial activity, systemic SCFAs (measured in blood or urine) can inform on their metabolic effects throughout the body, including their influence on lipid metabolism, glucose homeostasis, and immune function.

Reference:

1. Wang, Rui, et al. "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 (2020): 273-286.