Systematic Analysis of Fatty Acids in Human Cells with a Multiplexed Isobaric Tag (TMT)-Based Method

使用基于多重同位素标签 (TMT) 的方法对人体细胞中的脂肪酸进行系统分析

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作者:Fangxu Sun, Alexander A Choi, Ronghu Wu

Abstract

Fatty acids (FAs) are essential components in cells and are involved in many cellular activities. Abnormal FA metabolism has been reported to be related to human diseases such as cancer and cardiovascular diseases. Identification and quantification of FAs provide insights into their functions in biological systems, but it is very challenging to analyze them due to their structures and properties. In this work, we developed a novel method by integrating FAs tagged with stable isotope labeled aminoxy tandem mass tags (aminoxyTMTs) and mass spectrometric analysis in the positive mode. On the basis of their structures, the aminoxyTMT reagents reacted with the carboxylic acid group of the FAs, resulting in an amine group with high proton affinity covalently attached to the analytes. This enabled the analysis of FAs under the positive electrospray ionization-mass spectrometry (ESI-MS) mode, which is normally more popular and sensitive compared to the negative mode. More importantly, the multiplexed TMT tags allowed us to quantify FAs from several samples simultaneously, which increased the experimental throughput and quantification accuracy. FAs extracted from three types of breast cells, i.e., MCF 10A (normal), MCF7 (minimally invasive) and MDA-MB-231 (highly invasive) cells, were labeled with the six-plexed aminoxyTMTs and quantified by LC-MS/MS. The results demonstrated that the abundances of some FAs, such as C22:5 and C20:3, were markedly increased in MCF7 and MDA-MB-231 cancer cells compared to normal MCF 10A cells. For the first time, aminoxyTMT reagents were exploited to label FAs for their identification and quantification in complex biological samples in the positive MS mode. The current method enabled us to confidently identify FAs and to accurately quantify them from several samples simultaneously. Because this method does not have sample restrictions, it can be extensively applied for biological and biomedical research.

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