Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation

During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets. This dataset comprises of summary stats for eQTLs identified in the study (parquet files) and the ones which passed significance threshold (tensor_out.tar.gz archive). Files are described by cell subset (CD4 Naive, CD 4 Memory, TEMRA, TCM, etc.), time since activation (16h, 4h, 5days) as described in the publication (preprint https://doi.org/10.1101/2021.12.06.470953)T细胞在激活过程中会发生广泛的基因表达变化，这些变化会重塑细胞特性以发挥其效应功能。因此，解析T细胞激活过程中基因表达的遗传调控机制，能够帮助我们深入理解遗传变异如何影响机体对感染与免疫性疾病的应答反应。 本研究构建了覆盖T细胞激活时间进程的单细胞表达数量性状位点（expression quantitative trait loci, eQTL）图谱。我们对119名健康个体的655349个CD4+初始T细胞及记忆T细胞进行了测序分析，涵盖了未受刺激细胞的转录状态，以及三个细胞激活时间节点的转录特征。 本研究共鉴定出38个细胞簇，其中既包括中枢记忆T细胞、效应记忆T细胞这类稳定细胞簇，也包含仅在特定激活时间点出现的瞬时细胞簇，例如干扰素应答细胞。 我们借助T细胞激活轨迹进行eQTL定位分析，共鉴定得到6407个eQTL基因，其中三分之一（2265个基因）在T细胞激活过程中呈现动态表达调控特征。 我们将上述分析结果与免疫介导性疾病的全基因组关联研究（Genome-Wide Association Study, GWAS）变异位点进行整合分析，共观察到127处共定位事件，且动态eQTL在其中呈现显著富集现象。 免疫疾病位点与参与T细胞激活调控的基因发生共定位，且功能相似的基因往往会被疾病风险等位基因以相同方向的方式产生表达扰动。 本研究结果凸显了绘制特定情境下基因表达调控图谱的重要性，为解析免疫性疾病的遗传易感机制提供了新视角，同时有助于优先筛选新型治疗靶点。 本数据集包含本研究中鉴定得到的eQTL汇总统计数据（Parquet文件），以及通过显著性阈值筛选的结果文件（tensor_out.tar.gz压缩包）。 数据集文件按照细胞亚群（CD4初始T细胞、CD4记忆T细胞、TEMRA、TCM等）以及激活后时间（16小时、4小时、5天）进行分类，详细说明可参考已发表的预印本文章（https://doi.org/10.1101/2021.12.06.470953）