Tiannan received training of clinical medicine (1999-2006) in Tongji Medical College, Huazhong University of Science and Technology, and learned biology (2001-2005) in Wuhan University, before he moved to Singapore for PhD training in cancer proteomics (2008-2012) in the laboratories of Dr. Newman Sze in Nanyang Technological University and Dr. Oi Lian Kon in National Cancer Centre Singapore. In 2012, Tiannan started his postdoctoral training in the laboratory of Dr. Ruedi Aebersold in ETH Zurich. Tiannan moved to Sydney as the Scientific Director of ProCan, group leader of Cancer Proteome, Children's Medical Research Institute, and the conjoint senior lecturer in The University of Sydney Medical School, in March 2017.

Tiannan joined the Westlake Institute for Advanced Studies, Westlake University in August 2017 as a Tenure-track Assistant Professor, and promoted to Associate Professor with tenure in Jan 2023. He is the director of Westlake Center for Intelligent Proteomics (WeCIP) at Westlake Laboratory, and an associate faculty member of Research Center for Industries of the Future at Westlake University. He also serves in multiple journals, including Molecular & Cellular Proteomics, Proteomics, Proteomics Clinical Applications, Clinical Proteomics, Genomics Proteomics Bioinformatics, Cell Reports Medicine, and Scientific Data.

2023

Associate editor, Molecular Cellular Proteomics

2022

Young Leading Expert Award for Science and Technology Innovation, Ministry of Science of Technology

2022

Overseas Chinese Contribution Award, Second Prize

2022

Standing Committee member, Chinese Human Health Science and Technology Promotion Socieity

2022

Member, Distinguished Expert Committee, All-China Federation of Returned Overseas Chinese

2021

Council member of Human Proteome Organization (HUPO)

2021

Co-chair of HPP Cancer Committee, HUPO

2021

Secretary General of Chinese Proteome Organization (CN-HUPO)

2011

Ray Wu Prize

2021

Outstanding Individual Achievement Award in the Science and Technology Community for the Fight Against COVID-19, the Ministry of Science and Technology

2021

Academic member, Mass Spectrometry Sub-committee, Chinese Physics Society

2021

Council member, permanent members, The Chinese Society of Biochemistry and Molecular Biology

The Guomics team is dedicated to advancing proteomics methodologies to generate comprehensive proteomic datasets. Their main goal is to enhance our understanding of biology and disease. To accomplish this, the team is building an intelligent engine that can analyze the data and provide unparalleled holistic insights into previously unseen molecular underpinnings.

We are dedicated to the advancement of next-generation mass spectrometry-based proteomics techniques, aimed at rapidly analyzing minute quantities of biomedical samples while maximizing proteomic depth and ensuring quantitative accuracy. Through the utilization of pressure-cycling technology in conjunction with DIA mass spectrometry (Guo et al., Nature Medicine, 2015; Cai et al., Nature Protocols, 2022), we have successfully facilitated effective proteomic analysis of tissue specimens at the biopsy level. In recent times, we have made significant strides in the field of expansion proteomics (Li et al., Nature Communications, 2022), which involves physically expanding tissues to enable spatial and single cell proteomics. Additionally, we are actively delving into subcellular proteomics. Leveraging the extensive proteomic big data amassed within our lab and by the scientific community, we are actively exploring the feasibility of constructing a foundational model for proteomics.

A major research focus of the Guomics team is to advance disease diagnosis and treatment. Through collaborations with external partners, the team has achieved significant breakthroughs in the field of COVID-19. In 2020, they published pioneering research in Cell, presenting the first-ever proteomic and metabolic profiles of COVDI-19 sera (Shen, et al. Cell, 2020). This groundbreaking study provided invaluable insights into the disease. Subsequently, in 2021, the team published another paper in Cell, unveiling the first proteomic landscape for COVID-19 autopsies (Nie, et al. Cell, 2021). This comprehensive analysis further deepened our understanding of the virus and opened up potential avenues for diagnosis and treatment. The team's dedication to clinical proteomics technologies is evident in the exploration of AI-empowered proteomic big data for precision medicine, with a specific focus on thyroid nodule diagnosis (Cell Discovery, 2022). Currently, the team is actively involved in the development of innovative diagnostic and therapeutic approaches for lung cancer, pancreatic cancer, and Alzheimer's disease.

Most of our current efforts are focused on constructing foundational models for characterizing the proteome and comprehending its network behavior in various contexts. In this highly demanding research endeavor, we have introduced the concept of "perturbation proteomics" and are actively generating perturbation proteomics datasets to gain insights into biological networks. Additionally, we are organizing proteomic datasets generated within our laboratory and by the broader scientific community, and have named this comprehensive database as ProtTalks-DB (db.prottalks.com). Moreover, we are developing an advanced AI engine called ProtTalks, which is designed to unravel the enigmatic interactions within the proteome.

1. Bo Shen #, Xiao Yi #, Yaoting Sun #, Xiaojie Bi #, Juping Du #, Chao Zhang #, Sheng Quan #, Fangfei Zhang, Rui Sun, Liujia Qian, Weigang Ge, Wei Liu, Shuang Liang, Hao Chen, Ying Zhang, Jun Li, Jiaqin Xu, Zebao He, Baofu Chen, Jing Wang, Haixi Yan, Yufen Zheng, Donglian Wang, Jiansheng Zhu, Ziqing Kong, Zhouyang Kang, Xiao Liang, Xuan Ding, Guan Ruan, Nan Xiang, Xue Cai, Huanhuan Gao, Lu Li, Sainan Li, Qi Xiao, Tian Lu, Yi Zhu*, Huafen Liu*, Haixiao Chen*, Tiannan Guo*. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. Cell. 2020.182(1): 59-72

2. Xiu Nie#, Liujia Qian#, Rui Sun#, Bo Huang#, Xiaochuan Dong#, Qi Xiao#, Qiushi Zhang#, Tian Lu, Liang Yue, Shuo Chen, Xiang Li, Yaoting Sun, Lu Li, Luang Xu, Yan Li, Ming Yang, Zhangzhi Xue, Shuang Liang, Xuan Ding, Chunhui Yuan, Li Peng, Wei Liu, Xiao Yi, Mengge Lyu, Guixiang Xiao, Xia Xu, Weigang Ge, Jiale He, Jun Fan, Junhua Wu, Meng Luo, Xiaona Chang, Huaxiong Pan, Xue Cai, Junjie Zhou, Jing Yu, Huanhuan Gao, Mingxing Xie, Sihua Wang, Guan Ruan, Hao Chen, Hua Su, Heng Mei, Danju Luo, Dashi Zhao, Fei Xu, Yan Li, Yi Zhu*, Jiahong Xia*, Yu Hu*, Tiannan Guo*. Multi-organ Proteomic Landscape of COVID-19 Autopsies. Cell. 2021.184(3): 775-791

3. Yaoting Sun#, Sathiyamoorthy Selvarajan#, Zelin Zang#, Wei Liu#, Yi Zhu#, Hao Zhang#, Wanyuan Chen, Hao Chen, Lu Li, Xue Cai, Huanhuan Gao, Zhicheng Wu, Yongfu Zhao, Lirong Chen, Xiaodong Teng, Sangeeta Mantoo, Tony Kiat-Hon Lim, Bhuvaneswari Hariraman, Serene Yeow, Syed Muhammad Fahmy bin Syed Abdillah, Sze Sing Lee, Guan Ruan, Qiushi Zhang, Tiansheng Zhu, Yifan Hu, Zhen Dong, Weigang Ge, Qi Xiao, Weibin Wang, Guangzhi Wang, Junhong Xiao, Yi He, Zhihong Wang, Wei Sun, Yuan Qin, Jiang Zhu, Xu Zheng, Linyan Wang, Xi Zheng, Kailun Xu, Yingkuan Shao, Shu Zheng, Kexin Liu, Ruedi Aebersold, Haixia Guan, Xiaohong Wu, Dingcun Luo, Wen Tian, Stan Z. Li*, Oi Lian Kon*, N. Gopalakrishna Iyer*, Tiannan Guo*. Artificial intelligence defines protein-based classification of thyroid nodules. Cell Discovery. 2022. 8:85

4. Lu Li#, Cuiji Sun#, Yaoting Sun#, Zhen Dong#, Runxin Wu, Xiaoting Sun, Hanbin Zhang, Wenhao Jiang, Yan Zhou, Xufeng Cen, Shang Cai, Hongguang Xia, Yi Zhu, Tiannan Guo* & Kiryl D. Piatkevich*. Spatially resolved proteomics via tissue expansion. Nature Communications. 2022.13: 7242.

5. Tiannan Guo, Petri Kouvonen, Ching Chiek Koh, Ludovic C Gillet, Witold E Wolski, Hannes L Röst, George Rosenberger, Ben C Collins, Lorenz C Blum, Silke Gillessen, Markus Joerger, Wolfram Jochum, Ruedi Aebersold*. Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps. Nature Medicine. 2015.21(4):407–413

6. Xue Cai#, Zhangzhi Xue#, Fang-Fang Zeng#, Jun Tang#, Liang Yue#, Bo Wang#, Weigang Ge, Yuting Xie, Zelei Miao, Wanglong Gou, Yuanqing Fu, Sainan Li, Jinlong Gao, Menglei Shuai, Ke Zhang, Fengzhe Xu, Yunyi Tian, Nan Xiang, Yan Zhou, Peng-Fei Shan, Yi Zhu*, Yu-ming Chen*, Ju-Sheng Zheng*, Tiannan Guo*. Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome. Cell Reports Medicine. 2023. 4: 101172.

7. Xue Cai, Zhangzhi Xue, Chunlong Wu, Rui Sun, Liujia Qian, Liang Yue, Weigang Ge, Xiao Yi, Wei Liu, Chen Chen, Huanhuan Gao, Jing Yu, Luang Xu, Yi Zhu*, Tiannan Guo*. . High-throughput proteomic sample preparation using pressure cycling technology. Nature Protocols. 2022 Oct;17(10):2307-2325

8. Xiaojie Bi#, Wei Liu#, Xuan Ding#, Shuang Liang#, Yufen Zheng#, Xiaoli Zhu#, Sheng Quan#, Xiao Yi, Nan Xiang, Juping Du, Haiyan Lyu, Die Yu, Chao Zhang Luang Xu, Weigang Ge, Jiale He, Guangjun Zhu, Donglian Wang, Hongguo Zhu, Shiyong Chen, Jun Li, Haihong Zhao, Yi Zhu*, Huafen Liu*, Jiaqin Xu*, Bo Shen*, Tiannan Guo*. Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19. Cell Rep. 2022 Jan 18;38(3):110271.

9. Shaohua Tang#, Rui Sun# , Weigang Ge# , Tingting Mao# , Liujia Qian# , Chongquan Huang# , Zhouyang Kang# , Qi Xiao , Meng Luo , Qiushi Zhang , Sainan Li , Hao Chen , Wei Liu , Bingjie Wang , Shufei Li , Xiaoling Lin , Xueqin Xu , Huanzheng Li , Lianpeng Wu , Jianyi Dai , Huanhuan Gao , Lu Li , Tian Lu , Xiao Liang , Xue Cai , Guan Ruan , Fei Xu , Yan Li , Yi Zhu* , Ziqing Kong*, Jianping Huang *, Tiannan Guo*. Enhanced inflammation and suppressed adaptive immunity in COVID-19 with prolonged RNA shedding. Cell Discovery. 2022.8(1): 70

10. Jianfeng Bao#, Rui Sun#, Jingwen Ai#, Liujia Qian#, Fang Liu#, Hongyu Wang#, Lingling Tan, Xue Cai, Yingqiu Shi, Xiao Liang, Weigang Ge, Jing Wu, Chen Chen, Wenhong Zhang*, Jinsong Huang* & Tiannan Guo*. Proteomic characterization of Omicron SARS-CoV-2 host response. . Cell Discovery. 2022.8(1): 46