1. MS-based proteomics method and technology development:

MS-based proteomics technology has become one of the most mainstream omics analysis techniques for system-level characterization of protein composition and dynamics. Current proteomics technology has been able to identify tens of thousands of proteins in a single day of MS analysis, enabling large-scale profiling of important post-translational modifications such as phosphorylation, glycosylation, ubiquitination, acetylation and methylation; systematic characterization of the composition and dynamics of thousands of protein complexes.
Based on our original development of the simple and integrated spintip-based proteomics technology (SISPROT) and a variety of cutting-edge liquid chromatography-mass spectrometry technologies, the team is committed to developing proteomics technologies with integrated, automated and miniaturized features and for significantly improving the analytical sensitivity, analytical throughput, and quantitative analysis accuracy. These efforts enabled large-scale quantitative proteomics analysis of as low as 1000 human stem cells, the human dental stem cell proteome (the largest data set), the human gut microbiome proteome (the largest data set), and pancreatic cancer proteome, etc.

Representative articles:

(1)  Ruilian Xu*, Jun Tang*, Quantong Deng, Wan He, Xiujie Sun, Ligang Xia, Zhiqiang Cheng, Lisheng He, Shuyuan You, Jintao Hu, Yuxiang Fu, Jian Zhu, Yixin Chen, Weina Gao, An He, Zhengyu Guo, Lin Lin, Hua Li, Chaofeng Hu, and Ruijun Tian, Spatial-Resolution Cell Type Proteome Profiling of Cancer Tissue by Fully Integrated Proteomics Technology, Anal. Chem. 2018, 90, 5879−5886

(2)  Xu Zhang*, Wendong Chen*, Zhibin Ning, Janice Mayne, David R. Mack, Alain Stintzi, Ruijun Tian, and Daniel Figeys, Deep Metaproteomics Approach for the Study of Human Microbiomes, Anal. Chem., 2017, 89, 9407-9415

(3)  Wendong Chen, Shuai Wang, Subash Adhikari, Zuhui Deng, Lingjue Wang, Lan Chen, Mi Ke, Pengyuan Yang, Ruijun Tian, Simple and Integrated Spintip-based Technology Applied for Deep Proteome Profiling, Anal. Chem., 2016, 88, 4864-4871

2. Chemical proteomics-based dynamic protein complex analysis:

In human cells, there are more than 100,000 protein-protein interactions at any time. These large numbers of interactions assemble proteins into a wide variety of functional complexes that perform and regulate almost all cellular processes and functions, such as protein translation, cell cycle, and protein synthesis and degradation. Various functional protein complexes often form higher-latitude, extremely complex cellular signaling networks through spatiotemporal interactions (Science, 2009, 326, 1220-1224). Therefore, the systematic identification and characterization of protein complexes and related signaling networks will be of great importance for understanding the nature of life and diseases.
Tumor microenvironment is one of the critical hallmarks of cancer. This is mainly because other "normal" cells accompanying the growth of cancer cells interact closely with cancer cells through intercellular signaling, thereby precisely regulating the initiation, development, migration and drug resistance mechanisms of tumor. These "normal" cells are called stromal cells, mainly including: immune cells, fibroblasts, vascular endothelial cells, and the like. Systematic analysis of these intercellular signaling pathways will help to resolve the molecular mechanisms of the tumor microenvironment and assist in the design of next-generation anticancer drugs and the discovery of related diagnosis biomarkers.
This research direction is devoted to the comprehensive use of the above-mentioned proteomics, chemical biology and biochemistry techniques to develop combinatory proteomic approaches for studying various dynamic protein complexes and intercellular interaction systems in the tumor microenvironment. At present, the team has developed such approaches for studying direct and indirect cancer cell-immune cells and cancer cell-fibroblast interactions in the tumor microenvironment.

Representative articles:

(1)  Yu Shi, Weina Gao, Nikki K. Lytle, Peiwu Huang, Xiao Yuan, Amanda M. Dann, Maya Ridinger, Kathleen E. DelGiorno, Galina Erikson, Huaiyu Sun, Jill Meisenhelder, Elena Terenziani, Puifai Santisakultarm, Uri Manor, Ruilian Xu, Mathias Leblanc, Sarah E. Umetsu, Eric A. Collisson, Andrew M. Lowy, Tannishtha Reya, Timothy R. Donahue, Michael Downes, Geoffrey M. Wahl, Ronald M. Evans, Tony Pawson, Ruijun Tian, Tony Hunter, Targeting LIF-mediated paracrine interaction for pancreatic cancer therapy and monitoring, Nature, accepted

(2)  Bizhu Chu*, An He*, Yeteng Tian, Wan He, Peizhong Chen, Jintao Hu, Ruilian Xu, Wenbin Zhou, Mingjie Zhang, Pengyuan Yang, Shawn S. C. Li, Ying Sun, Pengfei Li, Tony Hunter, Ruijun Tian. Photoaffinity-engineered protein scaffold for systematically exploring native phosphotyrosine signaling complexes in tumor samples. Proc. Natl. Acad. Sci. U. S. A., 2018, 115, E8863-E8872

(3) Ruijun Tian*, Haopeng Wang*, Gerald D. Gish, Evangelia Petsalaki, Adrian Pasculescu, Yu Shi, Marianne Mollenauer, Richard D. Bagshaw, Nir Yosef, Tony Hunter, Anne-Claude Gingras, Arthur Weiss, Tony Pawson, A combinatorial proteomic analysis of intercellular signaling applied to the CD28 T cell co-stimulatory receptor, Proc. Natl. Acad. Sci. U. S. A., 2015, 112, E1594–E1603


1. China State Key Basic Research Program – Protein Machinary (PI, 2016YFA0501403, 2016-2021)
2. National Natural Science Foundation of China (PI, 21575057, 2015-2019)
3. Guangdong Provincial Team Grant (co-PI, 2017B030301018, 2016-2021)
4. Shenzhen Innovation of Science and Technology Commission (PIs, JCYJ20150901153557178, JSGG20160301103415523 and JCYJ20160229153100269, 2015-2020)