Chinese Immune Multi-Omics Atlas

Human peripheral blood exhibits molecular and cellular heterogeneity across populations, yet the underlying mechanisms remain unclear. We present the Chinese Immune Multi-Omics Atlas (CIMA), characterizing molecular variations linked to sex, age, and genetic variants through multi-omics analysis of more than 10 million circulating immune cells from 428 Chinese adults. CIMA established an enhancer-driven gene regulatory network comprising 237 robust regulons; identified 9600 eGenes and 52,361 caPeaks at cell type resolution; and revealed pleiotropic associations among immune-related disease risk loci, cis-expression quantitative trait loci (QTLs), and chromatin accessibility QTLs. Furthermore, the cell language model CIMA-CLM predicted chromatin accessibility and evaluated the effects of noncoding variants from chromatin sequences and gene expression. CIMA provides a comprehensive reference for immune-related disease research.

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Publication Details

Journal
Science
Published
2026-01-08
DOI
https://doi.org/10.1126/science.adt3130
Citations
5
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
123.71
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article

Chinese Immune Multi-Omics Atlas

Xiaoya Chen, W H Zhang, Y. B. Zhao, J Wang et al.
5 citations
Science
Genetic Associations and Epidemiology
123.71
article

Chinese Immune Multi-Omics Atlas

Xiaoya Chen, W H Zhang, Y. B. Zhao, J Wang, Yilin Wei, Panhong Liu, X. F. Wang, Jin Xh, Guodan Zeng, Peng Gao, Xiaoping Xu, H.W. Wang, Zhewei Xu, Yang Liu, Jianhua Yin, Zhongjin Zhang, Yong Zhang, Zhiyong Huang, Jingzhi Lv, Shanshan Duan, Yiwei Lai, Guixue Hou, Runsheng Chen, Yanan Cao, Miguel A. Esteban, Shijie Hao, Ying Gu, Xinyu Zhang, Yue Gao, Wen Ma, Yong Bai, Weikai Wang, Yue Yuan, Shichen Yang, Xin Liu, Jian Yang, Pengfei Cai, Hua Wang, Tao Yang, Ye Yin, Yang Wang, Chang Liu, Ting Qi, Jun Xie, Xiru Zhang, Xiao Yang, Yaling Huang, Wenwei Zhang, Wenwen Zhou, Chuanyu Liu, Yuxiang Li, Yuhui Zheng, Fupeng Li
article en
5 citations

Abstract

Human peripheral blood exhibits molecular and cellular heterogeneity across populations, yet the underlying mechanisms remain unclear. We present the Chinese Immune Multi-Omics Atlas (CIMA), characterizing molecular variations linked to sex, age, and genetic variants through multi-omics analysis of more than 10 million circulating immune cells from 428 Chinese adults. CIMA established an enhancer-driven gene regulatory network comprising 237 robust regulons; identified 9600 eGenes and 52,361 caPeaks at cell type resolution; and revealed pleiotropic associations among immune-related disease risk loci, cis-expression quantitative trait loci (QTLs), and chromatin accessibility QTLs. Furthermore, the cell language model CIMA-CLM predicted chromatin accessibility and evaluated the effects of noncoding variants from chromatin sequences and gene expression. CIMA provides a comprehensive reference for immune-related disease research.

ScienceVol. 391(6781)
BGI Group (China) (CN), Agency for Science, Technology and Research (SG), Fujian Medical University (CN), Ministry of Education of the People's Republic of China (CN), National University of Singapore (SG), Shanxi Medical University (CN), Chinese University of Hong Kong (HK), Anhui Medical University (CN), Chinese Academy of Sciences (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Fudan University (CN), Peking Union Medical College Hospital (CN), Ruijin Hospital (CN), Westlake University (CN), Northwest University (CN), Institute of Computing Technology (CN), Beijing Academy of Artificial Intelligence (CN), Zhangzhou Municipal Hospital of Fujian Province (CN), Institute of Biophysics (CN), Lishui Central Hospital (CN), Zhongshan Hospital (CN), First Affiliated Hospital of Anhui Medical University (CN), State Key Laboratory of Chemical Engineering (CN), University of Chinese Academy of Sciences (CN), BGI Research (CN), Genome Institute of Singapore (SG), South China University of Technology (CN)
Openalex Percentile: Top 0%
Genetic Associations and Epidemiology
123.71
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