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Dr. Xiaotao Shen
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Prof. Zheng-Jiang Zhu

Professor

Chinese Academy of Sciences

The research of Dr. Zhu group focuses on the development of mass spectrometry-based metabolomics and lipidomics technologies, and their applications in investigating the mechanisms of aging and aging-dependent diseases. In the past five years, the major academic achievements include the following two aspects.

    1) Metabolite annotation in untargeted metabolomics

We have developed a metabolic reaction network (MRN)-based recursive algorithm (MetDNA; http://metdna.zhulab.cn) that expands metabolite annotations without the need for a comprehensive standard spectral library (Nature Commun., 2019). We demonstrated that MetDNA enables to identify 5-10 folds more metabolites than other tools from one experiment. MetDNA also supports metabolite annotation acquired with data independent acquisition (DIA) technology (Anal. Chem., 2019). We have also developed an integrated strategy using ion mobility-mass spectrometry (IM-MS) for known and unknown metabolite annotation in various biological samples (Nature Commun., 2020). For analysis of stable-isotope labelled metabolites, we have developed a technology, termed MetTracer, leveraging the advantages of untargeted metabolite annotation and targeted extraction to trace the isotope labeled metabolites in complex matrices globally (Nature Commun., 2022).

    2) Ion mobility-mass spectrometry based metabolomics and lipidomics technologies

We have developed a large-scale ion mobility CCS atlas AllCCS (http://allccs.zhulab.cn), which enables confident metabolite annotation (Nature Commun., 2020), and a variety of four-dimensional (4D) metabolomics and lipidomics technologies which support the comprehensive profiling of metabolites and lipids with high accuracy and broad coverage (Bioinformatics., 2019; Anal. Chim. Acta., 2020, 2022). To demonstrate its capability for analyses of isomeric metabolites, we also developed an IM-MS based four-dimensional sterolomics technology by leveraging a machine learning-empowered high-coverage library (>2,000 sterol lipids) for accurate sterol identification (Nature Commun., 2021).

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