Chinese Immune Atlas Maps 10 Million Cells to Reveal Aging and Disease Secrets

The human immune system undergoes profound changes with age, sex, and genetic background, yet the molecular mechanisms linking these factors to immune cell behavior have remained poorly understood. The Chinese Immune Multi-Omics Atlas (CIMA) represents a major step forward in characterizing this complexity at unprecedented resolution.

Published in Science, CIMA analyzed more than 10 million circulating immune cells from 428 healthy Chinese adults using integrated multi-omics approaches. This allowed researchers to simultaneously examine gene expression, chromatin accessibility, and genetic variation across diverse immune cell types, capturing how each biological factor — sex, age, and genetics — contributes to immune heterogeneity.

Key discoveries include the identification of 9,600 eGenes (genes whose expression is influenced by nearby genetic variants) and 52,361 chromatin accessibility peaks (caPeaks) resolved at the cell-type level. The team also constructed an enhancer-driven gene regulatory network comprising 237 robust regulons, providing a detailed map of how gene expression is controlled in immune cells. Critically, pleiotropic links were uncovered between immune disease risk loci, cis-expression QTLs, and chromatin accessibility QTLs, illuminating how noncoding genetic variants may drive immune-related disease susceptibility.

To extend the utility of CIMA, the researchers developed CIMA-CLM, a cell language model trained on chromatin sequence data, capable of predicting chromatin accessibility and evaluating the functional impact of noncoding variants — an increasingly important capability as most disease-associated variants lie outside protein-coding regions.

For longevity researchers, CIMA is particularly valuable as a reference for studying age-associated immune remodeling. Caveats include the study's focus on a single ethnic population, which may limit generalizability, and the cross-sectional design, which precludes causal conclusions about aging trajectories.