Principal Investigator, Interdisciplinary Research Center on Biology and Chemistry (IRCBC), Shanghai Institute of Organic Chemistry (SIOC)

·         Postdoctoral Fellow, The Scripps Research Institute, California, US

·         Ph.D., University of Massachusetts-Amherst, Massachusetts, US

·         B.S., Nanjing University, China

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/).

1. M. Luo, Y. Yin, Z. Zhou, H. Zhang, X. Chen, H. Wang, and Z.-J. Zhu*, A Mass Spectrum-oriented Computational Method for Ion Mobility-resolved Untargeted Metabolomics, Nature Communications, 2023, 14: 1813.  Web Link

2. H. Wang†, H. Jia†, Y. Gao, H. Zhang, J. Fan, L. Zhang, F. Ren, Y. Yin, Y. Cai*, J. Zhu*, and Z.-J. Zhu*, Serum Metabolic Traits Reveal Therapeutic Toxicities and Responses of Neoadjuvant Chemoradiotherapy in Patients with Rectal Cancer, Nature Communications, 2022, 13: 7802.  Web Link

3. Z. Zhou†, M. Luo†, H. Zhang, Y. Yin, Y. Cai, and Z.-J. Zhu* , Metabolite Annotation from Knowns to Unknowns through Knowledge-guided Multi-layer Metabolic Networking, Nature Communications, 2022, 13: 6656.   Web Link

4. R. Wang, Y. Yin, J. Li, H. Wang, W. Lv, Y. Gao, T. Wang, Y. Zhong, Z. Zhou, Y. Cai, X. Su, N. Liu*, and Z.-J. Zhu*, Global Stable-isotope Tracing Metabolomics Reveals System-wide Metabolic Alternations in Aging Drosophila, Nature Communications, 2022, 13: 3518. Web Link

5. X. Mei†, Y. Guo†, Z. Xie†, Y. Zhong, X. Wu, D. Xu, Y. Li, N. Liu, and Z.-J. Zhu*, RIPK1 Regulates Starvation Resistance by Modulating Aspartate Catabolism, Nature Communications, 2021, 12: 6144. Web Link

6. T. Li, Y. Yin, Z. Zhou, J. Qiu, W. Liu, X. Zhang, K. He, Y. Cai, and Z.-J. Zhu*, Ion Mobility-based Sterolomics Reveals Spatially and Temporally Distinctive Sterol Lipids in the Mouse Brain, Nature Communications, 2021, 12: 4343. Web Link

7. Z. Zhou, M. Luo, X. Chen, Y. Yin, X. Xiong, R. Wang, and Z.-J. Zhu*, Ion Mobility Collision Cross-Section Atlas for Known and Unknown Metabolite Annotation in Untargeted Metabolomics，Nature Communications, 2020, 11: 4334. Web Link

8. X. Shen, R. Wang, X. Xiong, Y. Yin, Y. Cai, Z. Ma, N. Liu, and Z.-J. Zhu*，Metabolic Reaction Network-based Recursive Metabolite Annotation for Untargeted Metabolomics, Nature Communications, 2019, 10: 1516. Web Link