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Rong Zeng, PhDProfessor

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Email: zengrong@@shanghaitech.edu.cn

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中文信息English
Clinical Systems Biology and Proteomics

Principal investigator

Name:

Rong ZengProfessor , PhD, Professor

Position:

Director of Clinical Systems Biology Platform/Vice Director of National Facility for Protein Science in Shanghai

Affiliation:

Shanghai Clinical Research and Trial Center

Honor:

Education Background:
  • 1991-1995, Hunan Normal University, B.S
  • 1995-2000, Shanghai Institute of Biochemistry, Chinese Academy of Sciences, Shanghai, China, Ph. D.
Working Experience:
  • 2000-2004, Institutes of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China, Associate Professor
  • 2004-2024, Institutes of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China, Professor
  • 2024-Now, ShanghaiTech University, Professor
  • 2025-Now, National Facility for Protein Science in Shanghai, Vice Director

Group Introduction

Research Area:
Clinical Systems Biology, Quantitative Proteomics, Protein Dynamics
Research Interests:




I think the [21st] century will be the century of complexity

                             - Stephen Hawking

 

Among all the complex systems, life system is undoubtedly one of the most complex, while modern biology based on reductionism does not have a full understanding of the complexity of life, let alone the corresponding technology to reveal the complex performance of life operation. The strategies and methods of systems biology will provide a more perfect and dynamic research path, reconstructing the framework for understanding the basic rules of complex life and complex diseases.


Our work has been continuously concentrated on protein dynamic research, and parallelly advanced in three aspects: the development of new technologies, the research and application in clinical diagnosis and treatment, and the exploration of basic problems in complex systems.

1. We keep working on new technologies for quantitative proteomics and protein dynamic research. At present, we have co-built a laboratory with the National Facility for Protein Science in Shanghai to innovate technologies, combining omics strategies and artificial intelligence to deeply analyze the dynamic behavior of protein-small molecules and protein-protein complex networks.

2. Supported by the Shanghai Clinical Research and Trial Center, we are aiming to create a platform for clinical systems biology. Through the collection and analysis of biomolecule quantitative data from large population, we will construct personalized dynamic molecular network, develop big data and intelligent system to provide precise support for disease diagnosis and treatment.

3. The big data based on population scale will certainly produce new content and diversity that cannot be presented by traditional biomedical research, and at the same time put forward a series of basic questions. We will continue to explore basic theoretical issues of complex systems and molecular networks of life.


 


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Research Achievement

As one of the pioneering teams engaged in proteomics and systems biology, our team has constantly zoomed in on the development of quantitative proteomics methods, and established the earliest proteome research platform based on mass spectrometry in China, supporting a series of high-impact research work. And then we paved the way to the development of systems biology, a new frontier of inter-discipline. In recent years, we have deeply collaborated with clinical research, resulting in achievements in clinical systems biology, such as cancer, diabetes and metabolic diseases, pan-vascular diseases and so on. To date, more than 200 papers have been published, with a total of more than 12,000 citations and H-index 60+. Our team has also been very effective in student training, with more than 60 PhD and MS graduated, including a number of rising stars in the current academic and industrial community.

 

Representative work:

1. Research and development of new technologies for quantitative proteomics and protein dynamic research.

Through wet plus dry lab mode, we created a multi-in-one strategy for the multiple-protease digestion in large-scale analysis applicable, with low time-consuming, high sensitivity, improved coverage, and high reproducibility (Anal Chem, 2020). We presented featured ion-guided stoichiometry (FIGS), a universal method for accurate and robust peptide quantification for the DIA-MS data, dramatically improving the quantification accuracy (JPR, 2021). We also created a chromatography-independent mass spectrometry analysis flow (JPR, 2023). By developing time-series plasma protein-metabolite network as systems signatures, we for the first time revealed that RYGB could generate a unique path for diabetes remission (EBioMedicine, 2018). We established methods to trace the dynamics of proteome and lipidome connectivity as an index to evaluate the quality of lipoproteins and HDL (JMCB, 2022). In 2025, we reported a new Pan-SAP method and its discovery (Cell Genomics,2025). Based on mass spectrometry and new data analysis strategies, we found the variety and abundance of protein variants caused by single amino acid polymorphism.

2. Research and application of clinical systems biology.

In the field of oncology research, we published a work "Integrated Omics of Metastatic Colorectal Cancer " (Cancer Cell, 2020), which integrated the conception of systems biology, omics techniques and clinical basis, completed the first integrated-omics map related to colorectal cancer metastasis, and put forward a new strategy for molecular typing and personalized treatment. Through combining clinical proteome, transcriptome and epigenetic data, we revealed the inhibition and regulation mechanism of lung cancer-related protein DDX56 on target gene WNT2B (Mol Cancer, 2021). By developing lipidomic methods, we collaboratively unveiled a largest number of sphingolipids significantly related to the risk of type 2 diabetes (Plos Med, 2020). Another collaborative study identified food-component association with lipid and their potential effects on risk factors for diabetes (Diabetes Care, 2023).

3. Exploration of the basic problems of the complex system of life.

There are a variety of proteins in the body that perform different functions. According to the traditional rule in database, each protein is regarded as composed of a single amino acid sequence. But the situation is not so simple. As early as in 2011, Zeng Rong's team and Wu Jiarui's team jointly published their research work (JMCB, 2011, 2014), suggesting that there is a single amino acid polymorphism (single amino-acid polymorphism, SAP) caused by single nucleotide polymorphism (SNP). SAP leads to a variety of protein variants. The sequence and abundance of these protein variants determine the total composition and behavior of a protein, and then form protein polymorphism, which is highly related to physiological traits, the occurrence and development of diseases. However, this problem is rarely touched in classical proteome research. In 2025, Based on a new Pan-SAP method (Cell Genomics, 2025), we explored the dark mass hidden in population proteome data, qualitatively and quantitatively analyzed protein polymorphism, revealing the association with cognitive and aging phenotypes. Based on this, we are aiming to build a higher coverage research system, in order to reveal the underlying logic of the operation of life from the perspective of complexity.


Representative Publications (*First Author, # Corresponding Author)

Monograph

Patent

Funding

  • 1. Outstanding Youth Foundation of the National Natural Science Foundation of China (2004), Leader
  • 2. CAS Hundred Talents (2009), CAS, Leader
  • 3. CAS Strategic Priority Project (2020), CAS, Leader

Awards

  • 1. 2005, China Young Female Scientist Award

Research Achievement

Group Member and Photo

  • Name：Qingrun Li
    Position：Research Associate Professor
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  • Name：Xiaojing Gao
    Position：Research Assistant Professor
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  • Name：Liming Sun
    Position：Postdoct
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  • Name：Yuanyuan Yin
    Position：Postdoct
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  • Name：Chengshuang Chu
    Position：Doctoral Student
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  • Name：Yutong Guo
    Position：Doctoral Student
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  • Name：Yuqing Cao
    Position：Doctoral Student
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  • Name：Haoqiang Zhang
    Position：Postgraduate Student
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  • Name：Yiqian Chen
    Position：Postgraduate Student
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  • Name：Jingcheng Shi
    Position：Postgraduate Student
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  • Name：Jing Qu
    Position：Postgraduate Student
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  • Name：Sheng Liu
    Position：Postgraduate Student
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  • Name：Longxing Xu
    Position：Postgraduate Student
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