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 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, up to ~2000 metabolites for biological samples. MetDNA also supports metabolite annotation acquired with data independent acquisition (DIA) MS technology (Anal. Chem., 2019). We have futher developed a multi-layer networking approach, namely,  knowledge-guided multi-layer metabolic networking (KGMN), to support large-scale unknown metabolite annotation within MetDNA2 (Nature Commun., 2022a). For tracing 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., 2022b). 

        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)(Nature Commun., 2020; Anal. Chem., 2023), which enables confident metabolite annotation, 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, Anal. Chem, 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). Very recently, we have developed a mass spectrum-oriented computational method, namely, Met4DX, for efficiently processing ion mobility-resolved 4D untargeted metabolomics with high coverage (Nature Commun., 2023). 

With our further developments, Met4DX has evolved into a fast, robust, and convenient mass spectrometry data processing tool for metabolomics and lipidomics. The versatile tool facilitates the processing of both 3-dimensional LC-MS data and 4-dimensional LC-IM-MS data, encompassing main functions such as data conversion, peak detection, retention time correction, peak grouping, assignment of MS/MS spectra, metabolite identification and others. Met4DX is freely available at our website (http://met4dx.zhulab.cn/).

LC-MS based metabolomics

• Targeted and untargeted metabolomics
• Metabolic reaction network (MRN)-based metabolite annotation (known and unknown)
• Global stable-isotope tracing metabolomics
• Applications of new techniques like DIA and IM-MS for metabolomics

Ion mobility-mass spectrometry for metabolomics and lipidomics

• Large-scale ion mobility CCS atlas and      machine-learning driven CCS calculation
• 4D untargeted metabolomics
• 4D untargeted lipidomics
• 4D untargeted analysis of sterols and other bioactive metabolites/lipids

Metabolomics for investigating aging metabolism

• Systematic investigation of metabolic remodeling/homeostasis during aging
• Metabolism regulation of aging in Drosophila melanogaster and mice

Clinical metabolomics

• Clinical metabolomics for Alzheimer's disease (AD)
• Clinical metabolomics for cancers
• Clinical metabolomics for cardiovascular diseases