Optimization of the quantitative protocol for organic acid in fecal samples using gas chromatography-mass spectrometry

Organic acids (OAs) play important roles in a variety of intracellular metabolic pathways, such as the tricarboxylic acid cycle, fatty acid oxidation, glycolysis. The accurate detection of OAs in fecal samples was crucial for comprehending the metabolic changes associated with various metabolic disease. However, the analytical protocol detecting OAs profiling in feces have received scant attention. In this work, an optimized protocol based on chromatography-mass spectrometry for simultaneous quantification of 23 OAs in rat feces was developed. The optimal conditions involved using a 40-mg fecal sample mixed with isopropyl alcohol, acetonitrile, and deionized water (3:2:2 vol ratio) with a total volume of 1500 μL, followed by ultrasonic extraction and a derivatization reaction with an 80 μL derivative agent. The protocol showed an acceptable linearity (R2 ≥ 0.9906), the satisfactory precision (RSD% ≤ 14.87%), the low limits of detection (0.001 to 1 μg/mL) and the limit of quantification (0.005 to 1.5 μg/mL). Moreover, the dried residues of the extracted solution showed the better stability of OAs at -20 °C, which was more suitable for a large-scale sample analysis. Finally, the developed protocol was successfully applied to compare the difference of OAs profiling in fecal samples harvested from normal and nonalcoholic fatty liver disease rats, which was beneficial to find out the metabolic change of OAs profiling and explain the related mechanism of the disease.

Gas chromatography/mass spectrometry analysis of organic acid profiles in human serum: A protocol of direct ultrasound-assisted derivatization

RATIONALE

Low-molecular-weight organic acids that generally contain one to three carboxyl groups are involved in many important biological processes; therefore, it is important to develop a quantitative method for analyzing organic acids in serum in order to allow an evaluation of metabolic changes. In this study, we evaluated a protocol for detecting 26 organic acids in serum based on ultrasound-assisted derivatization by gas chromatography/mass spectrometry (GC/MS).

METHODS

Serum samples were prepared using ultrasound-assisted silane derivatization before GC/MS analysis to quantify concentrations of organic acids. Additionally, we investigated the variables affecting derivatization yields, including the extraction solvent, derivatization reagents, and derivatization conditions (reaction temperature, duration, and sonication parameters). The protocol was ultimately applied to detect organic acid profiles related to obesity.

RESULTS

We used acetone as the extraction solvent and determined suitable derivatization conditions, as follows: BSTFA + 1% TMCS, 50°C, 10 min, and 100% ultrasound power. The protocol showed satisfactory linearity (r = 0.9958-0.9996), a low limit of detection (0.04-0.42 μmol/L), good reproducibility (coefficient of variation (CV) %: 0.32-13.76%), acceptable accuracy (recovery: 82.97-114.96%), and good stability within 5 days (CV%: 1.35-12.01% at room temperature, 1.24-14.09% at 4°C, and 1.01-11.67% at -20°C). Moreover, the protocol was successfully applied to obtain the organic acid profiles from obese and healthy control subjects.

CONCLUSIONS

We identified and validated a protocol for ultrasound-assisted derivatization prior to GC/MS analysis for detecting 26 kinds of organic acids in serum. The results suggest the efficacy of this protocol for clinical applications to determine metabolic changes related to fluctuations in organic acid profiles.

Systematic evaluation of sample preparation strategy for GC-MS-based plasma metabolomics and its application in osteoarthritis

Sample preparation plays a crucial part in plasma metabolomics. In order to obtain an optimal sample extraction method for gas chromatography mass spectrometry (GC-MS)-based plasma metabolomics, five different extraction strategies including protein precipitation, liquid-liquid extraction and solid-phase extraction were evaluated systematically for both plasma untargeted- and targeted-metabolomics. The comprehensive evaluation revealed that the all-in-one sample preparation method, MeOH-MTBE-H₂O (1:5:1.5, v/v/v), was the optimal extraction method for both untargeted- and targeted-metabolomics. Next, the optimal sample preparation protocol was applied in plasma metabolomics of osteoarthritis (OA). A panel containing cholesterol, lactic acid, stearic acid, alpha-tocopherol and oxalic acid was selected as candidate biomarker to distinguish OA patients from healthy controls (HC) based on the support vector machine (SVM) classification model. The discriminating capability of the candidate biomarker panel was further validated successfully with logistic regression and principal components analysis (PCA) analysis. Therefore, the panel could potentially act as diagnostic biomarker for osteoarthritis.