Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With mass spectrometry-based metabolomics technologies, thousands of metabolites can be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, new discoveries linking cellular pathways to biological mechanism are being revealed and shaping our understanding of cell biology, physiology and medicine. Although relatively new compared with its genomic and proteomic predecessors, research in metabolomics has already led to the discovery of biomarkers for disease diagnosis, fundamental insights into cellular biochemistry and clues related to disease pathogenesis.
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 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) MS technology (Anal. Chem., 2019). We have futher developed a multi-layer networking approach, knowledge-guided multi-layer metabolic networking (KGMN), to support large-scale unknown metabolite annotation within MetDNA2 (Nature Commun., 2022a). Recently, we further developed a two-layer interactive networking strategy that integrates data-driven and knowledge-driven networks to enhance metabolite annotation within MetDNA3 (Nature Commun., 2025b).
2) Stable-isotope tracing metabolomics
Stable-isotope tracing metabolomics allows to unravel metabolic activity quantitatively by measuring the isotopically labeled metabolites, but has been largely restricted by coverage. To address this challenge, 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). In addtion, using mass spectrometry-resolved stable-isotope tracing metabolomics, we develop an isotopologue similarity networking strategy, namely IsoNet, to effectively deduce previously unknown metabolites and related metabolic reactions (Nature Commun., 2025a).
3) 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/).
4) Ion mobility-mass spectrometry-resolved single-cell metabolomics
Current single-cell metabolomics approaches are limited by insufficient sensitivity, robustness, and metabolite coverage. We developed an ion mobility–resolved mass cytometry technology that integrates high-throughput single-cell injection with ion mobility–mass spectrometry for multidimensional metabolomic profiling. Combined with our computational tool, MetCell, this technology allows high-throughput analysis while achieving exceptional profiling depth, detecting over 5,000 metabolic peaks and annotating approximately 800 metabolites per cell.
