Uncovering the Cellular Mechanisms of Immune Disease Genetic Variants via Single-Cell Transcriptome Analysis of 2,000 Individuals
Background
For decades, genome-wide association studies (GWAS) have identified numerous genetic variants that increase the risk of disease. However, more than 90% of the discovered variants are located in non-coding DNA regions that do not directly specify protein amino acids. It was no small task to interpret how these variants lead to actual diseases through specific molecular pathways. This is because non-coding region variants primarily serve as switches that finely tune the expression levels of neighboring genes.
Gene expression quantitative trait loci (eQTL) analysis has become a key methodology for identifying these regulatory switches. Past studies have largely relied on analyzing bulk tissue, where tens of thousands of cells are mixed together. In tissues where numerous immune cells are intertwined like blood, a fatal limitation is revealed. This is because significant expression changes occurring in rare cells within the blood were drowned out and lost amidst the signals of the majority of other cells.
It is common for the same genetic variant to perform opposite functions depending on the cell type or to regulate gene expression only in specific immune cells. Previous bulk analysis methods could not isolate these cell-type-specific characteristics. To understand where individual genetic differences create waves within the immune system, large-scale analysis at single-cell resolution was essential.
Key Findings
An international research team, led by the Garvan Institute of Medical Research and the Murdoch Children's Research Institute in Australia, has released the results of the first phase of the 'TenK10K' project in the international journal Nature. The study involved precisely profiling the whole genome and single-cell transcriptomes of over 5 million individual cells isolated from the Peripheral Blood Mononuclear Cells (PBMC) of 1,967 donors.
By integrating single-cell RNA sequencing (scRNA-seq) technology, the researchers classified immune cells in the blood into 28 distinct subtypes and rigorously analyzed the correlation between genetic variants and gene expression. Through this process, they achieved the feat of mapping risk loci associated with 16 complex immune diseases and 44 hematological phenotypes at the cellular level. As many as 1,500 disease candidate genes are attributed to specific cell subtypes.
A notable achievement is the demonstration that the effects of disease-related genetic variants are observed exclusively during specific differentiation stages or activation states of certain immune cells. Variants that increase the risk of rheumatoid arthritis or systemic lupus erythematosus selectively disrupted gene expression only in specific micro-subtypes of helper T cells, rather than in all lymphocytes. Faint signals that were dismissed as statistical noise in past bulk tissue analyses transformed into clear biological pathways upon reaching single-cell resolution. This clearly demonstrates that dysregulation of micro-immune cells is the molecular mechanism determining disease susceptibility.
Significance and Outlook
The completed single-cell eQTL map serves as a robust stepping stone to narrow the gap between genomic information and actual pathogenesis. This is because it provides precise coordinates for the cellular components in the body that malfunction due to risk factors of complex genetic diseases. In new drug research, it is expected that the search for targets that selectively attack only the cell subtypes causing the disease will become much easier. This opens a path to reducing the side effects of existing immunotherapies that increase infection risk by indiscriminately suppressing systemic immunity.
However, clear challenges remain. Since this data was primarily collected from blood cells of healthy individuals, it has limitations in fully representing the cellular states within chronic inflammatory tissues where disease is progressing. While the sample of approximately 2,000 people is a leap forward compared to previous single-cell studies, the statistical power is still lacking to capture the influence of all rare variants. Broadening the cohort to include diverse racial groups and integrating it with spatial transcriptomics technology, which captures cellular positional information within tissues, will be the key determinant of future completeness.
Nature, Published online: 28 October 2026; doi:10.1038/d41586-026-03067-2Single-cell profiling of nearly 2,000 people brings scientists closer to understanding the link between genetic variation, gene expression and health.
The pharmaceutical and biotech industries can use this map as an accelerator in the early stages of drug discovery. If it is known that a specific autoimmune disease variant causes overexpression of a target protein only in activated B cells or helper T cell subtypes, a strategy of designing small molecule compounds or antibody-drug conjugates (ADCs) that selectively target those cells from the outset is effective. This approach can reduce unnecessary immunosuppressive side effects and increase the success rate of clinical trials.
In the field of clinical genomic diagnostics, it contributes to establishing personalized treatment strategies. When a disease-risk variant is discovered in a patient's whole-genome sequence, it becomes possible to practically implement personalized medicine by predicting which immune cell pathways will be primarily damaged and taking targeted preventive measures even before symptoms appear.