Automated Trimethyl Sulfonium Hydroxide Derivatization Method for High-Throughput Fatty Acid Profiling by Gas Chromatography-Mass Spectrometry.

Paul Gries,Alessia Lodi,Yen Bao Huynh,Atul Singh Rathore,Xiyuan Lu,Stefano Tiziani,Jennifer Chiou

doi:10.3390/molecules26206246

Paul Gries, Alessia Lodi + Show 5 more

Open Access

https://doi.org/10.3390/molecules26206246

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Abstract

Fatty acid profiling on gas chromatography–mass spectrometry (GC–MS) platforms is typically performed offline by manually derivatizing and analyzing small batches of samples. A GC–MS system with a fully integrated robotic autosampler can significantly improve sample handling, standardize data collection, and reduce the total hands-on time required for sample analysis. In this study, we report an optimized high-throughput GC–MS-based methodology that utilizes trimethyl sulfonium hydroxide (TMSH) as a derivatization reagent to convert fatty acids into fatty acid methyl esters. An automated online derivatization method was developed, in which the robotic autosampler derivatizes each sample individually and injects it into the GC–MS system in a high-throughput manner. This study investigated the robustness of automated TMSH derivatization by comparing fatty acid standards and lipid extracts, derivatized manually in batches and online automatically from four biological matrices. Automated derivatization improved reproducibility in 19 of 33 fatty acid standards, with nearly half of the 33 confirmed fatty acids in biological samples demonstrating improved reproducibility when compared to manually derivatized samples. In summary, we show that the online TMSH-based derivatization methodology is ideal for high-throughput fatty acid analysis, allowing rapid and efficient fatty acid profiling, with reduced sample handling, faster data acquisition, and, ultimately, improved data reproducibility.

Highlights

Fatty acid profiling is a commonly applied analytical methodology in academic research, health care, and industrial production and is used in a variety of applications that span from analyzing metabolic biomarkers to tracking environmental pollutants [1,2,3,4,5,6,7,8].Advances in robotic platforms and associated software have enabled the automation of many tasks that previously required repetitive and extensive manual labor [9,10,11,12,13]
Gas chromatography–mass spectrometry (GC–MS) platforms are well established for fatty acid analysis and are widely used in lipidomic and metabolomic research [8,17,18,19,20,21,22,23,24,25,26,27,28]
The advent of method editor software enables the rapid adaptation of simple sample preparation methods, such as the trimethyl sulfonium hydroxide (TMSH) derivatization method detailed in this study

Summary

Introduction

Fatty acid profiling is a commonly applied analytical methodology in academic research, health care, and industrial production and is used in a variety of applications that span from analyzing metabolic biomarkers to tracking environmental pollutants [1,2,3,4,5,6,7,8].Advances in robotic platforms and associated software have enabled the automation of many tasks that previously required repetitive and extensive manual labor [9,10,11,12,13]. The development of automated metabolite preparation and profiling methodologies that reduce researcher handling while maintaining or improving data quality are in high demand. GC–MS analysis of fatty acids requires the derivatization of these analytes into non-polar derivatives, such as fatty acid methyl esters (FAMEs) [29]. This transformation improves sample volatility and subsequent chromatographic separation of the individual fatty acids [30,31,32]. Many derivatization processes involve lengthy incubations at high temperatures that can potentially induce metabolite degradation [33]. Fatty acids and their representative FAMEs have been reported to be stable when exposed to temperatures higher than 300 ◦ C [33,37,38]

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