团队介绍：https://wanglabtongji.github.io/people

项目介绍：https://wanglabtongji.github.io/software/

研究方向为：

一、单细胞空间多组学增强、生成和功能解析智能算法

单细胞和空间多组学技术已经彻底改变了我们对复杂生物系统中细胞异质性的认识。然而，相应的分析目前面临着各种挑战，包括分辨率不足、基因覆盖范围有限、以及难以整合和生成异质性的多模态数据。为了解决这些问题，课题组开发了一系列智能算法。STRIDE（Nucleic Acids Res. 2022）和Cellist能够分别增强低分辨率和高分辨率的空间转录组数据信号，将其提升至单细胞精度。SCRIP（Nucleic Acids Res. 2022）和SCRIPro（Bioinform. 2024）利用单细胞和空间多组学数据构建基因调控网络。EvaCCI用于评估细胞间相互作用（Genome Biol. 2022），SCREE可分析多模态单细胞CRISPR筛选数据（Brief. Bioinformatics 2023）。这些算法提高了单细胞空间组学数据的可用性，并为单细胞时空多模态数据整合分析以及生理、病理状态的细胞计算拟合奠定了基础。

二、基于生成式人工智能和大规模时空多模态数据的细胞表型建模

多细胞系统中的细胞表型受到内在因素（如基因表达调控）和外在因素（如细胞间相互作用）的调控。课题组前期在小鼠及人类胚胎发育中的工作已经证明了内在表观遗传调控可以用来准确的预测细胞命运（Nature 2016; Nat. Cell Biol. 2018; CellStem Cell 2018, 2022; Cell Res. 2022）。目前，课题组正在开发基于大规模单细胞多模态数据预训练的生成式人工智能模型。这些模型旨在揭示细胞内多模态调控、细胞间相互作用等其他环境因素对细胞表型的影响。课题组开发了基于多对抗领域自适应网络的SELINA算法（Cell Rep. Methods., 2023），使用大规模预训练的人类单细胞RNA测序参考数据集自动注释细胞类型。我们希望利用生成式人工智能模型，深入了解驱动细胞表型的分子机制，并进一步指导和重塑这一转变过程。

三、复杂疾病微环境中细胞表型的多样性、可塑性和与个体表型的关联

癌症是免疫监视逃逸的结果，免疫抑制性肿瘤微环境对肿瘤的发展和治疗耐药性具有强烈影响。课题组基于深度生物信息学数据分析，结合单细胞时空多组学数据，研究内在基因表达调控和外在环境因素对肿瘤微环境中疾病相关细胞表型的影响，以及细胞表型和个体表型的关联，从而发展重塑肿瘤微环境的方法用于癌症治疗。课题组已经开发了一个全面的单细胞RNA测序数据资源TISCH （Nucleic Acids Res. 2021, 2023），用于分析肿瘤微环境中的基因表达和细胞类型组成。课题组也构建了全癌种的细胞表型图谱TabulaTIME（Nat. Cancer 2024），并发现广泛存在的调节肿瘤免疫的纤维化生态型。目前，课题组正在与肿瘤学家和免疫学家紧密合作，研究不同类型癌症中肿瘤微环境演变和免疫治疗耐药性的机制（Cell 2024; Nat. Genet. 2024; CancerImmunol. Res. 2023; Genome Med. 2023; EMBO J. 2023）。

代表文章：

#: 共同一作 *: 通讯作者

2024：

1. Han Y, Zhang L,Sun D, Cao G, Wang Y, Yue J, Hu J, Dong Z, Li F, Li T, Zhang P, Wu Q*,Wang C*. Spatiotemporal analyses ofthe pan-cancer single-cell landscape reveal widespread profibrotic ecotypesassociated with tumor immunity. Nat. Cancer. 2024; Inpress.
2. Yan Y#,Sun D#, Hu J#, Sun L, Yu H, Xiong Y, Huang Z, Xia H, ZhuX, Bian D, Sun F, Chen Y, Hou L, Wu C, Fan R, Zeng A*, Zhang L*,Sun Y*, Wang C*, Zhang P*.Multi-omic profiling highlights factors associated with resistance toimmuno-chemotherapy in non-small cell lung cancer. Nat. Genet. 2024; In press.
3. Liu S#*, Feng C#, Tan L#,Zhang D, Li Y, Han Y*, Wang C*. Single-celldissection of multifocal bladder cancer reveals malignant and immune cellsvariation between primary and recurrent tumor lesions. Commu.Biol. 2024; In press.
4. Chang Z#, Xu Y#, Dong X#,Gao Y, Wang C*. Single-cell and spatialmultiomic inference of gene regulatory networks using SCRIPro. Bioinform.2024; In press.
5. Sun F#, Li H#, Sun D#,Fu S#, Gu L#, Shao X#, Wang Q#,Dong X#, Duan B#, Xing F#, Wu J#,Xiao M*, Zhao F*, Han J*, Liu Q*,Fan X*, Li C*, Wang C*, Shi T*.Single-Cell Omics: experimental workflow, data analyses and applications. Sci.China Life Sci. 2024;  Inpress.
6. 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 identifiesbiological insights and subtype-specific therapeutic strategies. Cell 2024; 187 (1),184-203.

2023：

1. Han T#, Wang X#, Shi S, ZhangW, Wang J, Wu Q, Li Z, Fu J, Zheng R, Zhang J, Tang Q, Zhang P*, WangC*. Cancer CellsResistance to IFN-γ via Enhanced Double-Strand Break Repair Pathway. CancerImmunol. Res. 2023; 11(3), 381–398.
2. Wei H#, Han T, Li T, Wu Q*, WangC*. SCREE: acomprehensive pipeline for single-cell multi-modal CRISPR screen dataprocessing and analysis. Brief. Bioinformatics 2023; 24 (3),bbad123.
3. 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-cellAssignment using Multiple-Adversarial Domain Adaptation Network withLarge-scale References. Cell Rep. Methods 2023; 3 (9).
4. 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-smallcell lung cancer revealed by single-cell RNA sequencing. GenomeMed. 2023; 15(1),1-14.
5. 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, WangC*, Li F*.Single-cell Dissection of Cervical Cancer Reveals Key Subsets of the TumorImmune Microenvironment. EMBO J. 2023; 42 (16), e110757.
6. Han Y#, Wang Y#, Dong X#,Sun D, Liu Z, Yue J, Wang H, Li T*, WangC*. TISCH2: expanded datasets and new tools for single-cell transcriptomeanalyses of the tumor microenvironment. Nucleic Acids Res. 2023; 51 (D1),D1425-D1431.
7. Shi X#, Yu Z#, Ren P, Dong X,Ding X, Song J, Zhang J, Li T*, WangC*. HUSCH: an integrated single-cell transcriptome atlas for human tissuegene expression visualization and analyses. Nucleic Acids Res. 2023; 51(D1), D1029-D1037.

2022：

1. Xu R#, Li S#, Wu Q#,Li C#, Jiang M#, Guo L, Chen M, Yang L, Dong X, Wang H, WangC*, Liu X*,Ou X*, Gao S*. Stage-specific H3K9me3 occupancy ensuresretrotransposon silencing in human preimplantation embryos. CellStem Cell 2022; 29 (7), 1051-1066. (Cover Story)
2. Sun D, Liu Z, Li T, Wu Q*, WangC*. STRIDE:accurately decomposing and integrating spatial transcriptomics usingsingle-cell RNA sequencing. Nucleic Acids Res. 2022; 50(7), e42-e42.
3. Dong X#, TangK#, Xu Y, Wei H, Han T, WangC*. Single-cell Gene Regulation Network Inference by Large-scale DataIntegration. Nucleic Acids Res. 2022; 50(21), e-126-e126.
4. Liu Z, Sun D, WangC*. Evaluation ofcell-cell interaction methods by integrating single-cell RNA sequencing datawith spatial information. Genome Biol. 2022; 23 (1),1-38.
5. WangC#, 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 factorsfor mouse zygotic genome activation. Cell Res. 2022; 32 (9), 801-813. (CoverStory)

2016-2021:

1. Sun D#,Wang J#, Han Y#, Dong X, Zheng R, Ge J, Shi X, Wang B, LiZ, Ren P, Sun L, Yan Y, Zhang P, Zhang F*, Li T*, WangC*. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acid Res. 2021; 49 (D1), D1420-D1430.
2. 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. GenomeBiol. 2020; 21(1), 1-28.
3. 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, ZhangY*, Gao S*. Reprogramming of H3K9me3-dependent heterochromatin during mammalian embryo development. Nat. Cell Biol. 2018; 20(5), 620-631.
4. 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.
5. Liu X#, Wang C#, Liu W#, Li J#, Li C, Kou X, Chen J, Zhao Y, Gao H, WangH, Zhang Y*, Gao Y*, Gao S*. Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature 2016;537(7621), 558-562.
6. 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.