Xin Li, jointly appointed researcher and doctoral supervisor at the Institute of Zoology, Chinese Academy of Sciences and the Beijing Institute for Stem Cell and Regenerative Medicine; selected into the National Outstanding Young Scholars Program (Overseas) in 2022. Received the Ph.D. degree in Developmental Biology in 2015 from the eight-year National Science Base Class of Northeast Agricultural University, under joint training with the Institute of Zoology, Chinese Academy of Sciences. Conducted postdoctoral research at the University of California, San Diego, and in the laboratory of the prominent cancer biologist Robert Weinberg at the Massachusetts Institute of Technology. Joined the Institute of Zoology, Chinese Academy of Sciences, full-time in December 2021. Research focuses on artificial intelligence biology, stem cells and development, aging, cancer metastasis, and related translational applications. Published a series of high-impact papers as first author (including co-first author) or corresponding author in leading journals including Cell, Science, Cell Research, Nature Cell Biology, and Cell Stem Cell. Currently serves as doctoral supervisor at Northeast Agricultural University, member of the Anti-Aging Branch of the Gerontological Society of China, and member of the Aging Genetics Branch of the Chinese Genetics Society.

Biography

2007-2010 B.S. Northeast Agricultural University Harbin, China

2010–2015 PhD. Developmental Biology NEAU and Institute of Zoology- joint training Beijing, China

2016–2018 Postdoctoral Fellow University of California, San Diego San Diego, USA

2018- 2021 Postdoctoral Fellow Whitehead Institute for Biomedical Research, MIT Boston, USA

2021-present Professor Institute of Zoology, Chinese Academy of Sciences Beijing, China

Our research group focuses on understanding how stem cells coordinate intracellular dynamics—including epigenetic modifications, cell polarity, cell adhesion, and cytoskeletal changes—in response to diverse signals from various tissue microenvironments. These processes are critical for regulating normal tissue development, maintaining tissue homeostasis, facilitating injury repair, and driving cancer initiation and metastasis. We will extensively employ a range of cellular, molecular, and developmental biology techniques to elucidate the intricate dynamic changes of stem cells during tissue development and homeostasis. This knowledge will establish a foundation for further understanding and intervening in tissue aging, injury repair, and cancer initiation and metastasis.

1. AI and Big Data-Driven Analysis: Utilizing algorithms based on biological big data and artificial intelligence to reveal the mechanisms and principles governing intracellular gene expression regulation, and to infer changes in cell fate. This aims to provide predictive models for understanding developmental mechanisms, maintaining organ homeostasis, and enabling early disease diagnosis, treatment, and intervention.
2. Microenvironment and Stem Cell Fate: Investigating the mechanisms by which the tissue microenvironment regulates stem cell behavior and fate decisions during tissue regeneration, aging, and disease, using human stem cells and diverse model organisms.

1. Feng G^#*, Qin X^#, Zhang J^#, Huang W^#, Zhang Y^#, Cui W^#, Chen Y^#, Li S^#, Liu W, Tian Y, Liu Y, Dong J, Xu P, Man Z, Liu G, Liang Z, Jiang X, Yang X, Wang P, Yang G, Wang H, Wang X, Tong M^*, Zhou Y^*, Zhang S^*, Chen Y^*, Wang Y^* and Li X^*, CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions. Advanced Science, 2026.
2. Guo F^#, He C^#, Xu Y^#, Wu H^#, Li D^#, Jiang S^#, Ma X, Huang C, Gu Q, Du P, Yu L, Feng G, Wang H^*, Li W^* and Li X^*, High-efficiency generation of blastoids with gastrulation potential through Gata4-induced PrE specification in mESCs. Cell Discovery, 2025.
3. Zhang J^#*, Zhang J^#, Han L^#, Wu S, Li J, Eaton EN, Yuan B, Reinhardt F, Li H, Strasser PC, Das S, Liu Donaher J, Khalil MI, Jiang H, Deuschel A, Lin D, Sebastiany C, Maranga M, Shubitidze S, Liu X, Lambert AW, Zhang Y, Liu Y, Sui L, Elmiligy S, Pozza U, Gunsay R, Mishra R, Velarde J, Iyer S, Henry WS, Weiskopf K, Feng G, Oni TE, Watnick RS, Li X^* and Weinberg RA^* , Inflammation awakens dormant cancer cells by modulating the epithelial-mesenchymal phenotypic state. Proc Natl Acad Sci U S A, 2025.
4. Wu W^#, Liu W^#, Liu Z^* and Li X^*, Construction of a Genetic Prognostic Model in the Glioblastoma Tumor Microenvironment. Genes (Basel), 2025.
5. Wang P^#, Liu W^#, Wang J^#, Liu Y^#, Li P^#, Xu P, Cui W, Zhang R, Long Q, Hu Z, Fang C, Dong J, Zhang C, Chen Y, Wang C, Liu G, Xie H, Zhang Y, Xiao M, Chen S, Jiang H, Consortium XC, Chen Y, Yang G, Zhang S, Meng Z^*, Wang X^*, Feng G^*, Li X^* and Zhou Y^*, scCompass: An Integrated Multi-Species scRNA-seq Database for AI-Ready. Adv Sci (Weinh), 2025.
6. Li P^#, Ying S^#, Zou Y^#, Wang X, Zhang R, Huang C, Dai M, Xu K, Feng G, Li X, Jiang H, Li Z, Zhang Y^*, Li W^* and Zhou Q^*, NSun2-Mediated tsRNAs Alleviate Liver Fibrosis via FAK Dephosphorylation. Cell Prolif, 2025.
7. Fang C^#, Cui W^#, Hu Z^#, Liu W, Chen S, Chang S, Long Q, Li C, Liu Y, Jiang H, Wang P, Pan J, Hu G, Liu G, Meng Z^*, Zhou Y^*, Chen L^*, Feng G^* and Li X^*, Cell-GraphCompass: Modeling Single Cells with Graph Structure Foundation Model. National Science Review, 2025.
8. Yang X^#, Liu G^#, Feng G^#, Bu D^#, Wang P^#, Jiang J^#, Chen S^#, Yang Q^#, Miao H, Zhang Y, Man Z, Liang Z, Wang Z, Li Y, Li Z, Liu Y, Tian Y, Liu W, Li C, Li A, Dong J, Hu Z, Fang C, Cui L, Deng Z, Jiang H, Cui W, Zhang J, Yang Z, Li H, He X, Zhong L, Zhou J, Wang Z, Long Q, Xu P, Consortium XC, Wang H, Meng Z, Wang X, Wang Y, Wang Y, Zhang S, Guo J, Zhao Y^*, Zhou Y^*, Li F^*, Liu J^*, Chen Y^*, Yang G^* and Li X^*, GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model. Cell Res, 2024.

-The first cross-species knowledge-embedded single-cell foundation model

-Selected as the cover story of the issue in Cell Research (Vol. 34, Issue 12)

9. Hao M^#, Wei L, Yang F, Yao J^*, Theodoris CV^*, Wang B^*, Li X^*, Yang G^* and Zhang X^*, Current opinions on large cellular models. Quantitative Biology, 2024.
10. Fang C^#, Wang Y^#, Song Y, Long Q, Lu W, Chen L, Feng G, Zhou Y^* and Li X^*, How do large language models understand genes and cells. ACM Transactions on Intelligent Systems and Technology, 2024.
11. 李鑫^# and 于汉超^*, 人工智能驱动的生命科学研究新范式. 中国科学院院刊, 2024.
12. 江海平^#, 高纯纯, 刘文豪, 杨运桂 and 李鑫^*, 数据驱动的生命科学研究进展. 中国科学院院刊, 2024.
13. Zhang Y^#*, Donaher JL, Das S, Li X, Reinhardt F, Krall JA, Lambert AW, Thiru P, Keys HR, Khan M, Hofree M, Wilson MM, Yedier-Bayram O, Lack NA, Onder TT, Bagci-Onder T, Tyler M, Tirosh I, Regev A, Lees JA and Weinberg RA^*, Genome-wide CRISPR screen identifies PRC2 and KMT2D-COMPASS as regulators of distinct EMT trajectories that contribute differentially to metastasis. Nat Cell Biol, 2022.
14. Li X^#, Wang J^#, Wang L^#, Gao Y^#, Feng G, Li G, Zou J, Yu M, Li YF, Liu C, Yuan XW, Zhao L, Ouyang H, Zhu JK^*, Li W^*, Zhou Q^* and Zhang K^*, Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. Signal Transduct Target Ther, 2022.
15. Zhang Y^#, Zhang X^#, Shi J^#, Tuorto F^#, Li X^#, Liu Y, Liebers R, Zhang L, Qu Y, Qian J, Pahima M, Liu Y, Yan M, Cao Z, Lei X, Cao Y, Peng H, Liu S, Wang Y, Zheng H, Woolsey R, Quilici D, Zhai Q, Li L, Zhou T, Yan W, Lyko F, Zhang Y^*, Zhou Q^*, Duan E^* and Chen Q^*, Dnmt2 mediates intergenerational transmission of paternally acquired metabolic disorders through sperm small non-coding RNAs. Nat Cell Biol, 2018.
16. Xia H^#, Li X^#, Gao W^#, Fu X^#, Fang RH, Zhang L^* and Zhang K^*, Tissue repair and regeneration with endogenous stem cells. Nature Reviews Materials, 2018.
17. Wang J^#, Li X^#, Wang L^#, Li J^#, Zhao Y, Bou G, Li Y, Jiao G, Shen X, Wei R, Liu S, Xie B, Lei L, Li W, Zhou Q^* and Liu Z^*, A novel long intergenic noncoding RNA indispensable for the cleavage of mouse two-cell embryos. EMBO Rep, 2016.
18. Li X^#, Cui XL^#, Wang JQ^#, Wang YK, Li YF, Wang LY, Wan HF, Li TD, Feng GH, Shuai L, Li ZK, Gu Q, Hao J, Wang L, Zhao XY, Liu ZH, Wang XJ, Li W^* and Zhou Q^*, Generation and Application of Mouse-Rat Allodiploid Embryonic Stem Cells. Cell, 2016.
19. Chen Q^#, Yan M^#, Cao Z^#, Li X^#, Zhang Y^#, Shi J^#, Feng GH, Peng H, Zhang X, Zhang Y, Qian J, Duan E^*, Zhai Q^* and Zhou Q^*, Sperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder. Science, 2016.

-Top Ten Advances in Life Sciences in China for 2016, Selected by the Chinese Academy of Sciences and the Chinese Academy of Engineering Members

20. Wang J^#, Li X^#, Zhao Y, Li J, Zhou Q^* and Liu Z^*, Generation of cell-type-specific gene mutations by expressing the sgRNA of the CRISPR system from the RNA polymerase II promoters. Protein & cell, 2015.
21. Shi J^#, Chen Q^#, Li X^#, Zheng X^#, Zhang Y, Qiao J, Tang F, Tao Y^*, Zhou Q^* and Duan E^*, Dynamic transcriptional symmetry-breaking in pre-implantation mammalian embryo development revealed by single-cell RNA-seq. Development, 2015.
22. Li X^#, Wang JQ^#, Wang LY^#, Wan HF, Li YF, Li TD, Wang YK, Shuai L, Mao YH, Cui XL, Wang L, Liu ZH, Li W and Zhou Q^*, Co-participation of paternal and maternal genomes before the blastocyst stage is not required for full-term development of mouse embryos. J Mol Cell Biol, 2015.
23. Li X^#, Xia B-l, Li W and Zhou Q^*, Assessing reprogramming by chimera formation and tetraploid complementation. Nuclear Reprogramming: Methods and Protocols, 2014.
24. Li W^#, Li X^#, Li T^#, Jiang MG^#, Wan H, Luo GZ, Feng C, Cui X, Teng F, Yuan Y, Zhou Q, Gu Q, Shuai L, Sha J, Xiao Y, Wang L, Liu Z, Wang XJ, Zhao XY and Zhou Q^*, Genetic modification and screening in rat using haploid embryonic stem cells. Cell Stem Cell, 2014.