On January 8, 2026, a multi-institutional research team led by the State Key Laboratory of Genome and Multi-omics Technologies (initiated by BGI-Research), working with clinical and academic partners including Ruijin Hospital affiliated with Shanghai Jiao Tong University School of Medicine, Shanxi Medical University, and other collaborators, reports in Science the Chinese Immune Multi-Omics Atlas (CIMA).

The Chinese Immune Multi-Omics Atlas was published in Science.

By integrating single-cell transcriptomics, single-cell chromatin accessibility, whole-genome sequencing, and plasma metabolomic, lipidomic, and clinical biochemistry profiles, the study offers a high-resolution reference for how age, sex, and genetic variation shape circulating immune cells in adults. The resource addresses a long-standing bottleneck in large-scale single-cell epigenomic data and expands population representation in mechanistic immune studies.

At the heart of CIMA is a cohort of 428 adults aged 20 to 77 years who self-reported no active disease at the time of sampling. The team profiled more than 10 million peripheral blood immune cells, retaining 6.5 million high-quality cells from single-cell RNA sequencing and 3.8 million from single-cell ATAC sequencing after quality control. This scale enabled the identification of 73 immune cell types, including rare populations below 0.1% frequency, and revealed immune cell subsets and molecular features that vary with age and sex.

Mapping the Chinese Immune Landscape. By integrating multi-omics data from 428 individuals to categorize over 10 million immune cells, this study establishes a high-definition reference atlas for East Asian immune diversity, providing a critical baseline for precision medicine.

Using paired gene expression and chromatin accessibility data, the researchers constructed enhancer-driven gene regulatory networks that capture how transcription factors coordinate immune cell identity. They identified 404 enhancer-linked regulatory units, comprising 84,625 regulatory regions and 13,645 target genes, and systematically mapped the regulatory relationships among key transcription factors, cis-regulatory elements, and target genes across 61 immune cell subtypes. 

To connect regulatory variation to inherited genetics, the team performed whole-genome sequencing and mapped cell type-resolved expression quantitative trait loci (eQTLs) and chromatin accessibility QTLs (caQTLs). The analysis identified 9,600 eGenes and 52,361 caPeaks, with substantial cell type specificity—nearly 30% of eGenes and 55% of caPeaks were specific to a single cell type. This reinforces that many disease-relevant genetic effects are not "blood-wide" but concentrated within particular immune subtypes.

A key demonstration of the atlas's mechanistic value comes from integrative analyses linking variants to molecular traits and disease risk. Using summary-data-based Mendelian randomization across 154 traits, including plasma lipids, metabolites, inflammatory proteins, and immune-related diseases, the researchers identified 1,196 significant pleiotropic associations across 68 immune cell types. One illustrative example involves rs34415530, which is associated with cell-specific regulation of IKZF4 in CD4 Treg-FOXP3 cells and with circulating IL-12B protein levels and asthma susceptibility. These findings provide biologically grounded hypotheses for where and how noncoding risk variants may shape immune-mediated disease processes.

Decoding the Cellular Mechanisms of Disease. Using statistical modeling to trace genetic risks to specific cellular environments, this research transforms vague genetic associations into precise, actionable targets for future therapeutic development.

Beyond the atlas itself, the study introduces CIMA-CLM, a cell language model that integrates chromatin sequence features and single-cell gene expression to predict chromatin accessibility and assess the functional impact of noncoding variants. Across 32 immune cell types, CIMA-CLM achieved high concordance with experimental profiles, with an overall mean Pearson correlation of 0.8951 and a mean AUROC of 0.9560. By enabling in silico mutational analyses, the model offers a new computational route to explore the regulatory consequences of disease-associated variants.

This research framework also demonstrates the potential to link and integrate cell atlas analysis with more general genome-based foundational models (such as Genos). This fusion aims to build a multi-level, interpretable intelligent prediction framework from DNA sequences to cellular functions, paving the way for understanding life regulatory mechanisms and accelerating biomedical discoveries.

AI-Driven Prediction of Immune Regulation. The CIMA Cell Language Model uses artificial intelligence to predict chromatin accessibility from DNA sequences, enabling researchers to simulate the effects of genetic mutations.

This study is a successful demonstration of BGI Group's "133111i" Multi-Omics Precision Health Management System, a new paradigm for precision health research. By integrating multi-dimensional information such as single-cell transcriptomics, epigenomics, plasma metabolomics, lipidomic, and physiological characteristics, CIMA established a multi-omics baseline for immune health. It enables the digital analysis and precise insights into complex life systems, empowering disease prevention and health management.

This systematic research framework and the high-quality data it generated have already driven innovation incubation. Based on this, a BGI AI evaluation model for personal immune capability was developed, enabling high-precision and comprehensive assessments of individual immune states. This achievement marks the effective translation of cutting-edge scientific research into accessible healthcare service.

CIMA also serves as a reference for the international immunology and genomics communities. By anchoring genetic effects in specific cell types and regulatory elements, and by providing open data and interactive tools through the CIMA Portal, the atlas helps researchers refine the biological interpretation of GWAS signals, compare immune regulatory architecture across ancestries, and explore how age and sex interact with genetic regulation in blood.

As noted by Tao Cheng, Academician of the Chinese Academy of Engineering and Director of the Institute of Hematology at the Chinese Academy of Medical Sciences, and Guang Ning, Academician of the Chinese Academy of Engineering at Ruijin Hospital affiliated with Shanghai Jiao Tong University School of Medicine, CIMA's cell type-specific regulatory networks provide crucial insights for immune-metabolic interactions in diseases such as atherosclerosis and type 2 diabetes, and integration with other Chinese population cohorts will advance precision medicine for complex diseases.

Building on this foundation, the larger-scale Phase II CIMA initiative has officially launched. Its research scope will expand from healthy populations to major disease cohorts, including autoimmune diseases, cardiovascular diseases, and infectious diseases. By leveraging advanced technologies such as Stereo-cell and protein multiplex detection, the initiative aims to systematically unravel the immunological mechanisms underlying disease onset and progression, identify new diagnostic and therapeutic targets, and provide high-quality data resources for constructing more precise "virtual cell" models, enabling digital prediction of disease simulation and intervention strategies.

All participants provided written informed consent, and candidates with active disease and pregnant women were excluded. Ethics approval for this study is obtained.

This research can be assessed here: https://www.science.org/doi/10.1126/science.adt3130