News Release

Brigham researchers develop SCENT for genetic mapping of autoimmune diseases

Peer-Reviewed Publication

Brigham and Women's Hospital

Genetic studies of diseases map segments of the genome driving disease. But to understand how those changes contribute to disease progression, it is important to understand how they may alter gene regulation of disease genes in cell populations assumed to be driving disease. “Enhancer-gene maps” link genomic regulatory regions to genes and are essential for understanding disease. But constructing them poses challenges due to limitations in current experimental methods, that make it difficult to apply the technique to rare cell populations and genes that only regulate specific cell types.

Researchers from Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, have developed a statistical method called SCENT (single-cell enhancer target gene mapping). This method uses multimodal single-cell data to establish links between regulatory elements and genes, allowing them to pinpoint probable causal gene loci for both common and rare diseases. These insights might assist the development of treatments for various conditions.

The research team applied SCENT to nine multimodal single-cell datasets representing various human tissues, including immune, neuronal, and pituitary cells, aiming to understand the intricacies of DNA regulation in each specific cell type. With these data, they developed 23 distinct gene-enhancer maps, to investigate genetic variants and expression patterns associated with 1,143 diseases and traits. Notably, they discovered that, for immune diseases, crucial insights emerged not only from immune cells but also from cells within the affected tissues themselves.

“For most autoimmune diseases, people assume that we need a general map of immune cells. But we find that the enhancer-gene maps of immune cells are different in affected disease tissues,” said Soumya Raychaudhuri, MD, PhD, of the Brigham’s Division of Rheumatology, Immunology and Inflammation. “We demonstrate how such a map can be used to interpret genetic data from rheumatoid arthritis and other autoimmune diseases.”

Read More in Nature Genetics.


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