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 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; 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) mass spectrometry 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 (, 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, 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).

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