A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

Mingdu Luo, Yandong Yin, Zhiwei Zhou, Haosong Zhang, Xi Chen, Hongmiao Wang and Zheng-Jiang Zhu ()
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Mingdu Luo: Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Yandong Yin: Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Zhiwei Zhou: Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Haosong Zhang: Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Xi Chen: Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Hongmiao Wang: Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Zheng-Jiang Zhu: Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

Date: 2023
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DOI: 10.1038/s41467-023-37539-0

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