Contact Info

Chenfei WANG, Ph. D.

Professor of Bioinformatics

School of Life Science and Technology, Tongji University

1239 Siping Road, Yangpu District, Shanghai 200092, China

Fax:(+86)-02165981195

Email:08chenfeiwang@tongji.edu.cn

Google Scholar:scholar.google.com.hk/citations?user=ZwKmcpYAAAAJ&hl=zh-CN

General Info

Appointments

2023.12 – PresentProfessor, School of Life Science, Tongji University, Shanghai, P.R. China

2020.6 – 2023.11Principal Investigator, School of Life Science, Tongji University, Shanghai, P.R. China

2018.10 – 2020.6Postdoctoral Research Fellow, Dana-Farber Cancer Institute, Harvard   School of Public Health, Boston, MA, USA

2017.6 - 2018.10Postdoctoral Research Fellow, School of Life Science, Tongji University, Shanghai, P.R. China

2013.3 - 2013.12Visiting Scholar, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA

Education

2012.9 - 2017.6PhD in Bioinformatics, School of Life Science, Tongji University, Shanghai, P.R. China

2008.9 - 2012.7 B.A. in Bioinformatics, School of Life Science, Tongji University, Shanghai, P.R. China

Professional experience

Independent reviewer for Nat. Rev. Genet.、Nat. Methods、Nat. Cell Biol.、Nat. Commun.、Cell Genom.、Cell Syst.、Genome Biol.、Nucleic Acids Res.、Mol. Syst. Biol.、Genom. Proteom. Bioinform.、Brief. Bioinformatics

Research Interests

My research interests lie in developing computational methods and mathematical models to quantitatively elucidate the regulatory mechanisms of cell identity transitions and uncover cellular heterogeneities and transitions that lead to disease. Recent achievements are summarized as follows:

I. Develop intelligent algorithms for single-cell and spatial multi-omics

Single-cell and spatial multi-omic technologies have revolutionized our understanding of cellular heterogeneity in complex biological systems. However, corresponding analyses currently face various challenges, including inadequate resolution, coverage, and difficulty integrating and generating heterogeneous multi-modal data. To address these issues, we have developed a series of intelligent algorithms. STRIDE can improve the resolution of Visium-based ST (Nucleic Acids Res. 2022). CELLIST enables accurate cell segmentation in high-resolution ST datasets. SCRIP (Nucleic Acids Res. 2022) and SCRIPro construct gene regulation networks (GRNs) using single-cell and spatial multiomic data. EvaCCI evaluates cell-cell interactions (Genome Biol. 2022), and SCREE analyzes multi-modal single-cell CRISPR screening data (Brief. Bioinfor. 2023). These algorithms have enhanced the resolution of single-cell spatial omics data and provided systematic solutions for challenges such as single-cell multi-omic integration, signal enhancement, and the construction of cell-fate-specific GRNs.

II. Modeling cell identities using generative AI and large-scale multi-modal data

Cell identities in a multicellular system are regulated by both intrinsic factors, including gene regulation, and extrinsic factors, such as cellular crosstalk. We have demonstrated the tight connection between intrinsic epigenetic regulations and cell-fate determination in mouse IVF and SCNT embryo development (Nature 2016, Nat. Cell Biol. 2018, Cell Stem Cell 2018, 2022, Cell Res. 2022). Currently, we are developing generative AI models that are pretrained on large-scale single-cell multi-modal datasets. These models aim to uncover the collaborative effects between gene regulation, cellular crosstalk, and other environmental factors, such as metabolites and mechanics, on cell identity regulation. As a pioneering work, we have introduced SELINA (Cell Rep. Methods. 2023), which utilizes a multi-adversarial domain adaptation network to automatically annotate cell types using a large-scale pretrained human scRNA-seq reference. We hope to use generative cell identity models to provide valuable insights into the mechanisms driving cell identity transitions and to further guide and remodel the transition process.

III. Discovering the diversity and plasticity of cell identities in the TME

Cancer arises from the evasion of immune surveillance, and the immunosuppressive tumor microenvironment has a strong impact on tumor development and therapy resistance. Our objective is to integrate single-cell and spatial multi-omics data with comprehensive bioinformatics data analysis to investigate the effects of intrinsic gene regulation and extrinsic environmental factors on altering immune cell identities in the tumor microenvironment (TME). Moreover, we aim to develop potential methods for remodeling the TME in cancer treatment. As a preliminary step, we have developed a comprehensive scRNA-seq data resource TISCH (Nucleic Acids Res. 2021, 2023) for analyzing gene expression and cell-type composition in the TME. Additionally, we have constructed a pan-cancer cell identity tabula TabulaTIME and discovered widespread profibrotic ecotypes that regulate tumor immunity. Currently, we are collaborating closely with oncologists and immunologists to study the mechanisms of TME evolution and immunotherapy resistance in different types of cancer (Cell 2024, Genome Med. 2023, and EMBO J. 2023).

Research Publications

#: co-first author $: co-corresponding author

^{1. Liu Q#, Zhang J#, Guo C#, Wang M#, Wang C#, Yan Y, Sun L, Wang D, Zhang L, Yu H, Hou L, Wu C, Zhu Y, Jiang G, Zhu H, Zhou Y, Fang S, Zhang T, Hu L, Li J, Liu Y, Zhang H, Zhang B, Ding L, Robles A, Rodriguez H, Gao D$, Ji H$, Zhou H$, Zhang P$. Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies. Cell 2024; 187 (1), 184-203.}

^{2. Han T#, Wang X#, Shi S, Zhang W, Wang J, Wu Q, Li Z, Fu J, Zheng R, Zhang J, Tang Q, Zhang P$, Wang C$. Cancer Cells Resistance to IFN-γ via Enhanced Double-Strand Break Repair Pathway. Cancer Immunol. Res. 2023; 11(3), 381–398.}

^{3. Shi X#, Yu Z#, Ren P, Dong X, Ding X, Song J, Zhang J, Li T$, Wang C$. HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses. Nucleic Acids Res. 2023; 51 (D1), D1029-D1037.}

^{4. Han Y#, Wang Y#, Dong X#, Sun D, Liu Z, Yue J, Wang H, Li T$, Wang C$. TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res. 2023; 51 (D1), D1425-D1431.}

^{5. Wei H#, Han T, Li T, Wu Q$, Wang C$. SCREE: a comprehensive pipeline for single-cell multi-modal CRISPR screen data processing and analysis. Brief. Bioinformatics 2023; 24 (3), bbad123}

^{6. Ren P#, Shi X#, Dong X, Yu Z, Ding X, Wang J, Sun L, Yan Y, Hu J, Zhang P, Chen Q, Zhang J$, Li T$, Wang C$. SELINA: Single-cell Assignment using Multiple-Adversarial Domain Adaptation Network with Large-scale References. Cell Rep. Methods 2023; 3 (9)}

^{7. Hu J#, Zhang L#, Xia H#, Yan Y#, Zhu X, Sun F, Sun L, Li S, Li D, Wang J, Han Y, Zhang J, Bian D, Yu H, Chen Y, Fan P, Ma Q, Jiang G, Wang C$, Zhang P$. Tumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing. Genome Med. 2023; 15(1), 1-14.}

^{8. Cao G#, Yue J#, Ruan Y#, Han Y, Zhi Y, Lu J, Liu M, Xu X, Wang J, Gu Q, Wen X, Gao J, Kang J, Zhang Q, Wang C$, Li F$. Single-cell Dissection of Cervical Cancer Reveals Key Subsets of the Tumor Immune Microenvironment. EMBO J. 2023; 42 (16), e110757.}

^{9. Liu Z, Sun D, Wang C$. Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information. Genome Biol. 2022; 23 (1), 1-38.}

^{10. Sun D, Liu Z, Li T, Wu Q$, Wang C$. STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing. Nucleic Acids Res. 2022; 50 (7), e42-e42.}

^{11. Dong X#, Tang K#, Xu Y, Wei H, Han T, Wang C$. Single-cell Gene Regulation Network Inference by Large-scale Data Integration. Nucleic Acids Res. 2022; 50 (21), e-126-e126.}

^{12. Xu R#, Li S#, Wu Q#, Li C#, Jiang M#, Guo L, Chen M, Yang L, Dong X, Wang H, Wang C$, Liu X$, Ou X$, Gao S$. Stage-specific H3K9me3 occupancy ensures retrotransposon silencing in human preimplantation embryos. Cell Stem Cell 2022; 29 (7), 1051-1066. (Cover Story)}

^{13. Wang C#, Chen C#, Liu X#, Li C#, Wu Q, Chen X, Yang L, Kou X, Zhao Y, Wang H, Gao Y$, Zhang Y$, Gao S$. Dynamic nucleosome organization after fertilization reveals regulatory factors for mouse zygotic genome activation. Cell Res. 2022; 32 (9), 801-813. (Cover Story)}

^{14. Sun D#, Wang J#, Han Y#, Dong X, Zheng R, Ge J, Shi X, Wang B, Li Z, Ren P, Sun L, Yan Y, Zhang P, Zhang F$, Li T$, Wang C$. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acid Res. 2021; 49 (D1), D1420-D1430.}

^{15. Wang C#, Sun D#, Huang X, Wan C, Li Z, Han Y, Qin Q, Fan J, Qiu X, Xie Y, Meyer CA, Brown M, Tang M, Long H, Liu T$, and Liu XS$. Integrative analyses of single-cell transcriptome and regulome using MAESTRO. Genome Biol. 2020; 21(1), 1-28.}

^{16. Wang C#, Liu X#, Gao Y#$, Yang L#, Li C, Liu W, Chen C, Kou X, Zhao Y, Chen J, Wang Y, Le R, Wang H, Duan T, Zhang Y$, Gao S$. Reprogramming of H3K9me3-dependent heterochromatin during mammalian embryo development. Nat. Cell Biol. 2018; 20(5), 620-631.}

^{17. Gao R#, Wang C#, Gao Y#, Bai D, Liu X, Kou X, Zhao Y, Zang R, Liao Y, Jia Y, Chen J, Wang H, Wan X, Liu W$, Zhang Y$, Gao S$. Inhibition of aberrant DNA re-methylation improves the development of nuclear transfer embryos. Cell Stem Cell 2018; 23(3), 426-435.}

^{18. Liu X#, Wang C#, Liu W#, Li J#, Li C, Kou X, Chen J, Zhao Y, Gao H, Wang H, Zhang Y$, Gao Y$, Gao S$. Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature 2016; 537(7621), 558-562.}

^{19. Liu W#, Liu X#, Wang C#, Gao Y#, Gao R, Kou X, Zhao Y, Li J, Wu Y, Xiu W, Wang S, Yin J, Liu W, Cai T, Wang H, Zhang Y$, Gao S$. Identification of key factors conquering developmental arrest of somatic cell cloned embryos by combining embryo biopsy and single-cell sequencing. Cell Discov. 2016; 2(1), 1-15.}

Positions

The laboratory of Professor Chenfei Wang at Tongji University invites applicants for the positions listed below. The research in the laboratory focuses on developing computational algorithms to decipher mechanisms of phenotypic-driven cell identity changes using single-cell spatial multi-omics.

Computational biology postdoc position

Computational biology postdocs will be working on developing AI-based algorithms and data mining projects to model cell identity changes in cancer immunology, aging, and developmental system. The postdoc fellow will be working on single-cell multi-omics, spatial multi-omics, single-cell CRISPR screens dataset analyses. The full-time postdoctoral compensation is 300K-420K RMB/year, with additional performance reward up to 90K.

Requirements:

1. Ph.D. degree in bioinformatics, computation science, physics or statistics.

2. Strong programming skills (Python or C or C++ or Java and R).

3. Strong computational genomics experiences or machine learning background.

4. At least one first-authored English paper published in Bioinformatics or above.

5. Interested applicants should submit CV, a letter of interest with a one-page proposal for a project to be conducted in the Wang Lab to Dr. Wang with the subject line Computational postdoctoral application.

Experimental biology postdoc position

Experimental biology postdocs will be working on adopting or developing cutting-edge genomics technologies like single-cell multi-omics, spatial multi-omics, and single-cell CRISPR screens to understand mechanisms of cell identity changes in cancer immunology, aging, and developmental system. The full-time postdoctoral compensation is 300K-420K RMB/year, with additional performance reward up to 90K.

Requirements:

1. Ph.D. degree in biology.

2. Strong molecular, cell biology, and genomics skills.

3. At least one first-authored English paper published in the Journal of Biological Chemistry or above.   

4. Motivation, independence, and creativity.

5. Interested applicants should submit CV, a letter of interest with a one-page proposal for a project to be conducted in the Wang Lab to Dr. Wang with the subject line Experimental postdoctoral application".