May 19, 2022
Genetic Variation of Immune Cells
Natural variation between human immune systems at the genetic level has been historically difficult to characterize, particulary at single-cell resolution, making it challenging to determine the genetic factors that contribute to autoimmune disease.
The biggest barrier to single cell immune analyses has been sample size- having sequence information from enough individuals, and a high number of cells from each individual. A new dataset called the OneK1K cohort, published and analyzed by Yazar et. al. in Science, solves these challenges, with scRNA-seq of 1.27 immune cells from 982 donors.
Single Cell Analysis Uncovers Underpinnings of Autoimmune Disease
Combining the scRNA-seq data with genotype data, the authors stratified the samples into 14 immune cell types. Interestingly, loci implicated in variation between individuals affected gene expression in a cell type-specific manner, meaning that the SNPs and associated loci could be present in all cell types but only cause measurable changes in some.
The author termed these cis-expression quantitative trait loci (eQTLs), and further found that 19% of their identified eQTLs shared the same causal locus as GWAS risk association studies for autoimmune diseases. This combination of single cell analysis data and population genetics provides a robust list of independently identified genetic variants that contribute to autoimmune diseases, which can then be further examined to understand the underlying molecular mechanism.
For example, 57 risk loci were identified for multiple sclerosis (MS) in this study. Thanks to the single cell analysis data, it could be pinpointed that for one risk locus, 3q12, acts through changes in EAF2 expression in a B cell-specific manner, highlighting a new causal pathway for this disease and potentially many others.
Outsourcing Bioinformatics Analysis
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Jane Cook, Journalist & Content Writer, Bridge Informatics
Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email daniel.dacey@old.bridgeinformatics.com or dan.ryder@old.bridgeinformatics.com.
Sources:
https://www.science.org/doi/10.1126/science.abf3041
