Single-cell eQTL models reveal dynamic T cell state dependence of disease loci.
Single-cell eQTL models reveal dynamic T cell state dependence of disease loci.
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DOI：
10.1038/s41586-022-04713-1
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发表时间：
2022-06
期刊：
影响因子：
64.8
通讯作者：
中科院分区：
文献类型：
--
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非编码遗传变异可能通过调节基因表达而引起疾病。然而，这些表达的数量性状基因座（eQTL）的鉴定是复杂的跨流体功能细胞状态的细胞类型内的基因调控的差异。这些状态-例如，星形胶质细胞或血管周围成纤维细胞分化中的神经递质驱动程序-在聚集细胞的eQTL研究中被掩盖。在这里，我们在一种复杂的细胞类型中以单细胞分辨率模拟eQTL：记忆T细胞。使用来自259名秘鲁人的超过50万个未受刺激的记忆T细胞，我们发现6，511个顺式eQTL中约有三分之一具有由连续多模式定义的细胞状态介导的效应，例如细胞毒性和调节能力。在某些位点，独立的eQTL变异体具有相反的细胞状态关系。自身免疫变体富集在细胞状态依赖性eQTL中，包括ORMDL3和CTLA4附近的类风湿性关节炎的风险变体;这表明细胞状态背景对于理解潜在eQTL致病性至关重要。此外，连续的细胞状态比传统的离散类别（如CD4+与CD8+）解释了更多的eQTL变异，这表明在单细胞分辨率下对eQTL和细胞状态进行建模可以扩展对功能异质细胞类型中基因调控的了解。
Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states–for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation–are obscured in eQTL studies that aggregate cells. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.
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DOI：
10.1073/pnas.1615590114
发表时间：
2017-08-01
影响因子：
11.1
作者：
Eizenberg-Magar, Inbal;Rimer, Jacob;Friedman, Nir
通讯作者：
Friedman, Nir
影响因子：
64.8
作者：
Heintzman, Nathaniel D.;Hon, Gary C.;Hawkins, R. David;Kheradpour, Pouya;Stark, Alexander;Harp, Lindsey F.;Ye, Zhen;Lee, Leonard K.;Stuart, Rhona K.;Ching, Christina W.;Ching, Keith A.;Antosiewicz-Bourget, Jessica E.;Liu, Hui;Zhang, Xinmin;Green, Roland D.;Lobanenkov, Victor V.;Stewart, Ron;Thomson, James A.;Crawford, Gregory E.;Kellis, Manolis;Ren, Bing
通讯作者：
Ren, Bing
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
影响因子：
30.8
作者：
Calderon, Diego;Nguyen, Michelle L. T.;Pritchard, Jonathan K.
通讯作者：
Pritchard, Jonathan K.
影响因子：
16
作者：
Heinz S;Benner C;Spann N;Bertolino E;Lin YC;Laslo P;Cheng JX;Murre C;Singh H;Glass CK
通讯作者：
Glass CK