北京大学生物医学前沿创新中心

联系方式

E-mail：zemin@pku.edu.cn
办公电话：+86-010-62768190
办公地点：北京大学综合科研楼生物医学前沿创新中心310室
邮件地址：北京市海淀区颐和园路5号北京大学综合科研楼，100871

研究组主页：http://cancer-pku.cn

张泽民

BIOPIC 副主任、研究员；ICG 研究员

个人履历

2016.8-现在，北京大学未来基因诊断高精尖创新中心，研究员

2014-现在，北京大学BIOPIC，中心副主任

2014-现在，北京大学生命科学院，长聘教授

2014-现在，北大-清华生命科学中心，高级研究员

2014-现在，北京大学BIOPIC，研究员

1998-2014, 美国GENENTECH、ROCHE公司科研部，生物信息首席科学家

1995-1998，美国旧金山加州大学，博士后

1995，哲学博士，美国宾夕法尼亚州立大学

1988，理学学士，南开大学

主要研究方向

张泽民实验室致力于用前沿的基因组学和生物信息学技术来解决癌症生物学中的重要问题，结合计算（干）和实验（湿）方法来揭示肿瘤发生过程、微环境和对药物响应中的系统变化和具体遗传因素，以推进癌症免疫治疗和靶向治疗的发展。首先，我们应用单细胞测序技术来研究肿瘤微环境，特别是浸润肿瘤免疫细胞的精确组成和功能状态，应用单细胞测序技术来研究肿瘤的异质性以及各种异质性对癌细胞的功能和药敏的影响。第二，我们将尖端生物信息学方法应用到癌症基因组学大数据中，以揭示癌症的亚型、驱动基因以及其他致癌因素的遗传基础，如基因融合、等位基因差异表达、肿瘤特有的转录异构体等，从而发现新型癌症靶点和标记物。第三，我们开发原创性的生物信息学工具来进行单细胞基因组数据和大规模癌症基因组学数据的分析、整合和可视化，为对这些数据的有效挖掘提供基础。

获奖及荣誉

2019年中国生物信息十大进展

细胞出版社2019中国年度论文

2019年拜耳研究者奖

2018年中国生物信息十大进展

细胞出版社2017中国年度论文

2017年度科学中国人

2017年度中国十大医学科技新闻

2017年度中国生命科学十大进展

2017, The Boehringer Ingelheim Investigator Award

2017, 北京大学转化研究个人奖

2014, 长江学者（教育部）

2014, 千人计划（中组部）

2012 Genentech Billboard (形象科学家“To us, science is personal”)

2005 Genentech 年度报告科学家

Genentech 特殊贡献奖 (multiple times)

1989 CUSBEA 学者(China-US Biochemistry Exchange and Application Program)

代表性论文及论著

B. Liu, C. Li, Z. Li, D. Wang, X. Ren, and Z. Zhang*. (2020) An entropy-based metric for assessing the purity of single cell populations, Nature Communications, 11:3155
X. Ren*, G. Zhong, Q. Zhang, L. Zhang, Y. Sun and Z. Zhang*. (2020) Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly, Cell Research, (doi.org/10.1038/s41422-020-0353-2)
Q. Zhang and Z. Zhang. (2020) Stepwise immune alterations in multiple myeloma progression. Nature Cancer, 1:477-479
L. Zhang, Z. Li, K. M. Skrzypczynska, Q. Fang, W. Zhang, S. A. O’Brien, Y. He, L. Wang, Q. Zhang, A. Kim, R. Gao, J. Orf, T. Wang, D. Sawant, J. Kang, D. Bhatt, D. Lu, C-M Li, A. Rapaport, K. Perez, Y. Ye, S. Wang, X. Hu, X. Ren, W. Ouyang, Z. Shen*, J. G. Egen*, Z Zhang*, and X. Yu*. (2020) Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell, 181:442-459
C. Li, B. Liu, B. Kang, Z. Liu, Y. Liu, C. Chen, X. Ren*, and Z. Zhang*. (2020) SciBet as a portable and fast single cell type identifier. Nature Communications, 11:1818
PCAWG Transcriptome Core Group, C. Calabrese, N. R. Davidson, D. Demircioğlu, N. A. Fonseca, Y. He, A. Kahles, K-V Lehmann, F. Liu, Y. Shiraishi, C. M. Soulette, L. Urban, L. Greger, S. Li, D. Liu, M. D. Perry, Q. Xiang, F. Zhang, J. Zhang, P. Bailey, S. Erkek, K. A. Hoadley, Y. Hou, M. R. Huska, H. Kilpinen, J. O. Korbel, M. G. Marin, J. Markowski, T. Nandi, Q. Pan-Hammarström, C. S. Pedamallu, R. Siebert, S. G. Stark, H. Su, P. Tan, S. M. Waszak, C. Yung, S. Zhu, P. Awadalla, C. J. Creighton, M. Meyerson, B. F. Ouellette, K. Wu, H. Yang, PCAWG Transcriptome Working Group, A. Brazma*, A. N. Brooks*, J. Göke*, G. Rätsch*, R. F. Schwarz*, O. Stegle*, Z. Zhang* & PCAWG Consortium. (2020) Genomic basis for RNA alterations in cancer. Nature, 578:129-136
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. (2020) Pan-cancer analysis of whole genomes. Nature, 578:82–93
R. Bernards, E. Jaffee, J. A. Joyce, S. W. Lowe, E. R. Mardis, S. J. Morrison, K. Polyak, C. L. Sears, K. H. Vousden, and Z. Zhang. (2020) A roadmap for the next decade in cancer research. Nature Cancer, 1:12-17
R. Yang, S. Cheng, N. Luo, R. Gao, K. Yu, B. Kang, L. Wang, Q. Zhang, Q. Fang, L. Zhang, C. Li, A. He, X. Hu, J. Peng*, X. Ren*, and Z. Zhang*. (2020) Distinct epigenetic features of tumor-reactive CD8+ T cells in colorectal cancer patients revealed by genome-wide DNA methylation analysis. Genome Biology, 21:2
X. Wang, Y. He, Q. Zhang, X. Ren, and Z. Zhang. (2019) Direct comparative analysis of 10X Genomics Chromium and Smart-seq2. Genome, Proteome and Bioinformatics, (accepted)
F. Liu, Y. Zhang, L. Zhang, Z. Li, Q. Fang, R. Gao, and Z. Zhang. (2019) Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data. Genome Biology, 20:242
Q. Zhang, Y. He, N. Luo, S. J. Patel, Y. Han, R. Gao, M. Modak, S. Carotta, C. Haslinger, D. Kind, G. W. Peet, G. Zhong, S. Lu, W. Zhu, Y. Mao, M. Xiao, M. Bergmann, X. Hu, S. P. Kerkar, A. B. Vogt, S. Pflanz, K. Liu*, J. Peng*, X. Ren*, and Z. Zhang* (2019) Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell, 179:829-845.
X. Ren and Z. Zhang. (2019) Understanding tumor-infiltrating lymphocytes by single cell RNA sequencing. Advances in Immunology, 144:217-245
Z. Tang, B. Kang, C. Li, T. Chen, and Z. Zhang. (2019) Identification of transcriptional isoforms associated with survival in cancer patients. Journal of Genetics and Genomics, 46: 413-421.
Y. Zhang, L. Zheng, L. Zhang, X. Hu, X. Ren, and Z. Zhang (2019) Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients. Scientific Data, 6:131.
Z. Tang, B. Kang, C. Li, T. Chen, and Z. Zhang. (2019) GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Research, 47:W556-W560
L. Zhang and Z. Zhang. (2019) Recharacterizing tumor-infiltrating lymphocytes by single-cell RNA sequencing. Cancer Immunology Research, 7:1040-1046.
X. Ren*, L. Zheng, and Z. Zhang*. (2019) SSCC: a computational framework for rapid and accurate clustering of large-scale single cell RNA-seq data. Genome, Proteome and Bioinformatics, 17:201-210
L. Zhang, X. Yu, L. Zheng, Y. Zhang, Y. Li, Q. Fang, R. Gao, B. Kang, Q. Zhang, J.Y. Huang, H. Konno, X. Guo, Y. Ye, S. Gao, S. Wang, X. Hu, X. Ren, Z. Shen*, W. Ouyang*, and Z. Zhang*. (2018) Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature, 564:268-272
X. Guo, Y. Zhang, L. Zheng, C. Zheng, J. Song, Q. Zhang, B. Kang, Z. Liu, L. Jin,R. Xing, R. Gao, L. Zhang, M. Dong, X. Hu, X. Ren, D. Kirchhoff, H. G. Roider, T. Yan*, and Z. Zhang*. (2018) Global characterization of T cells in non-small cell lung cancer by single-cell sequencing. Nature Medicine, 24:978-985
X. Ren*, B. Kang, and Z. Zhang*. (2018) Understanding tumor ecosystems by single-cell sequencing: promises and limitations. Genome Biology, 19:211
S. Mizuno, R. Yamaguchi, T. Hasegawa, S. Hayashi, M.i Fujita, F. Zhang, Y. Koh, S-Y Lee, S-S Yoon, E. Shimizu, M. Komura, A. Fujimoto, M. Nagai, M. Kato, H. Liang, S. Miyano, Z. Zhang*, H. Nakagawa*, and S. Imoto*. (2018) Immuno-genomic Pan-cancer Landscape Reveals Diverse Immune Escape Mechanisms and Immuno-Editing Histories. bioRxiv, doi: https://doi.org/10.1101/285338
C. Zheng, L. Zheng, J.-K. Yoo, H. Guo, Y. Zhang, X. Guo, B. Kang, R. Hu, J. Y. Huang, Q. Zhang, Z. Liu, M. Dong, X. Hu, W. Ouyang*, J. Peng*, and Z. Zhang*. (2017) Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell, 169(7), 1342–1356
Z. Tang, C. Li, B. Kang, G. Gao, C. Li, and Z. Zhang. (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Research, 45(W1):W98-W102
N. A. Fonseca, Y. He, L. Greger, A. Brazma*, and Z. Zhang*. (2017) Comprehensive genome and transcriptome analysis reveals genetic basis for gene fusions in cancer. bioRxiv, doi: https://doi.org/10.1101/148684
X. Hu and Z. Zhang. (2016) Understanding the genetic mechanisms of cancer drug resistance using genomic approaches. Trends in Genetics, 32(2):127-37