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

luan, e.; cheng, k.; long, q.; li, d.; li, z.; liu, b.; wang, y.; li, w.; guo, b.; tan, f.; yi, x.; feng, l.; song, j.; zheng, h.

doi:10.1101/2020.03.26.010769

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bioRxiv · 10.1101/2020.03.26.010769

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

luan, e.·cheng, k.·long, q.·li, d.·li, z.·liu, b.·wang, y.·li, w.·guo, b.·tan, f.·yi, x.·feng, l.·song, j.·zheng, h.

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Abstract

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

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luan, e., cheng, k., long, q., li, d., li, z., liu, b., wang, y., li, w., guo, b., tan, f., yi, x., feng, l., song, j., zheng, h.. 2020-03-29. A Reference Feature based method for Quantification and Identification of LC-MS based untargeted metabolomics. https://doi.org/10.1101/2020.03.26.010769

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