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Shou-Wen Wang, Ph.D.

▎Physics    Biology            Website：https://www.shouwenwang-lab.com/

▎Email：wangshouwen@westlake.edu.cn

Biography

Dr. Shou-Wen Wang received his bachelor’s degree in Engineering Physics from Tsinghua University in 2013. He went on to obtain a PhD in Physics from Tsinghua University in 2018. During 2018-2022, Dr. Wang worked as a postdoctoral researcher in the Department of Systems Biology at Harvard Medical School, where he developed methods for analyzing single-cell lineage tracing and multi-omic data to better understand the fundamental principles of cell differentiation and embryonic development. In 2023, he joined Westlake University as an assistant professor in the School of Life Sciences, with a joint appointment in the physics department at the School of Science.


2020 the Quantitative Biology Award from the Damon Runyon Cancer Research Foundation

Research

The division and differentiation of cells are two intertwined fundamental biological processes. By understanding the history of cell division, we can better understand cell differentiation and gain deeper understanding of embryonic development, tissue homeostasis, disease occurrence, and other related issues. Through the analysis of the history of cell division (lineage), many important discoveries have been made in the past, including cell apoptosis and the existence of stem cells in different tissues.

Lineage tracing traditionally relies on dyes or fluorescent proteins, which has a very low throughput. In the past decade, the rapid development of single-cell genomics has enabled us to analyze cell lineages in high throughput and simultaneously obtain the genomic state of individual cells (including transcriptomes and epigenomes). Dr. Wang has made a series of contributions in the field of single-cell genomic lineage tracing: 1) the discovery of stem cell regulatory factors through modeling (2020, Nature, third author); 2) the prediction of cell fate through the combination of cell lineage and transcriptome (2022, Nature Biotechnology, first author + co-corresponding author); 3) the development of next-generation lineage tracing mice, single-cell multi-omics technologies, and corresponding analytical methods (2023, Cell, co-corresponding author).

The field of single-cell genomic lineage tracing is growing rapidly. Our lab is dedicated to combining lineage tracing with single-cell multi-omics technologies to gain a deep understanding of cell fate choice in relevant biological context. We have a strong foundation in data analysis and algorithm development. We develop state-of-the-art lineage tracing analysis methods and collaborate closely with other experimental research groups to investigate specific biological questions. At the same time, we have established our own experimental platform to conduct single-cell multi-omics lineage tracing experiments, in an attempt to answer more general questions related to cell lineages and fates.

Our lab has already assembled a team with friendly, passionate, and interdisciplinary members. Our lab has multiple positions available for postdoctoral researchers, doctoral students, and research assistants. We welcome individuals with backgrounds in computational biology, experimental biology, physics, computer science and other disciplines to join us. We provide a great environment for you to learn new skills and start a great career at the cutting edge! 

Representative Publications (*Corresponding authors)

1. J. Gao+, Z. Zhang+, D. Chen, S. Diao, S. Liu, S-W. Wang*, & L. Li*, Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution, Nature Methods (2026).

2. Z. Kang, H. Chen, S. Li, S-W. Wang* & B. Zhou*, Evolving strategies for lineage tracing: Genetic markers, synthetic barcodes, and natural variants, Cell Stem Cell (2026).

3. S-W. Wang*, Reading cell division histories from the methylome, Nature Reviews Genetics (2026).

4. R. Fu, M. Chen, S-W. Wang*, DNA methylation meets lineage tracing: History, recent progress, and future directions, Quantitative Biology (2025)

5. L. Li+, S. Bowling+, H. Lin, D. Chen, S-W. Wang* & F. D. Camargo*, DARLIN mouse for in vivo lineage tracing at high efficiency and clonal diversity, Nature Protocols (2025)

6. S-W. Wang*, MethylTree: exploring epimutations for accurate and non-invasive lineage tracing, Nature Methods (2025)

7. M. Chen+, R. Fu+, Y. Chen, L. Li* & S-W Wang*, High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations, Nature Methods (2025)

8. L. Li, S. Bowling, S. E. McGeary, Q. Yu, B. Lemke, K. Alcedo, Y. Jia, X. Liu, M. Ferreira, A. M. Klein, S.-W. Wang*, F. D. Camargo*, A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells, Cell (2023).

9. S.-W. Wang*, M. J. Herriges, K. Hurley, D. N. Kotton, A. M. Klein*, CoSpar identifies early cell fate biases from single cell transcriptomic and lineage information, Nature Biotechnology (2022).

10. A. E. Rodriguez-Fraticelli, C. S. Weinreb, S.-W. Wang, R. P. Migueles, M. Jankovic, M. Usart, A. M. Klein, S. Lowell, F. D. Camargo*, Combined single cell lineage and transcriptome sequencing reveals Tcf15 as a master regulator of hematopoietic stem cell fate, Nature (2020).

11. S.-W. Wang*, L.-H. Tang*, Emergence of collective oscillations in adaptive cells, Nature Communications (2019).

12. S.-W. Wang, A.-F. Bitbol*, N. S. Wingreen*, Revealing evolutionary constraints on proteins through sequence analysis, PLOS Computational Biology (2019).

13. S.-W. Wang, K. Kawaguchi, S.-i. Sasa and L.-H. Tang, Entropy production of nanosystems with time scale separation, Physical Review Letters (2016).