A Reference Feature based method for Quantification and Identification of LC-MS based untargeted metabolomics

Abstract

2Batch inconsistency is a major problem when applying LC-MS based untargeted 1 3 metabolomics in real-time analysis situation such as clinical diagnosis or health monitoring. 4And inefficiency of collecting MS2 is a major problem for metabolite identification. Here, we 1 5 developed a reference-feature based quantification and identification strategy (RFQI). In 1 6 RFQI, samples are individually profiled using a pre-fixed reference feature table. 7Quantification results show that RFQI improves features＇overlap rate and reduce variance 1 8 across batches significantly in real-time-analysis mode, and can find more than 4-fold 1 9 numbers of features. Besides, RFQI collects MS2 from consecutive increasing samples for 2 0 metabolite identification of pre-fixed features, thus it can effectively compensate for the poor 2 1 efficiency of MS2 collection in data-dependent acquisition mode. In summary, RFQI can 2 2 make full advantage of consecutive increasing samples in real-time analysis situation, both for 2 3 quantification and identification. 2 4 Key words: batch effect, LC-MS based untargeted metabolomics, metabolite identification 2 5 2 6 2 7