Single-cell genomics and regulatory networks for 388 human brains.
Prashant S EmaniJason J LiuDeclan ClarkeMatthew JensenJonathan WarrellChirag GuptaRan MengChe Yu LeeSiwei XuCagatay DursunShaoke LouYuhang ChenZhiyuan ChuTimur GaleevAhyeon HwangYunyang LiZhengchang SuXiao Zhounull nullTrygve E BakkenJaroslav BendlLucy K BicksTanima ChatterjeeLijun ChengYuyan ChengYi DaiZiheng DuanMary FlahertyJohn F FullardMichael GanczDiego Garrido-MartínSophia C Gaynor-GillettJennifer GrundmanNatalie HawkenElla HenryGabriel E HoffmanAo HuangYunzhe JiangTing JinNikolas L JorstadRiki KawaguchiSaniya KhullarJianyin LiuJunhao LiuShuang LiuShaojie MaMichael MargolisSamantha MazariegosJill E MooreJennifer R MoranEric NguyenNishigandha PhalkeMilos PjanicHenry E PrattDiana QuinteroAnanya S RajagopalanTiernon R RiesenmyNicole SheddManman ShiMegan SpectorRosemarie TerwilligerKyle J TravagliniBrie WamsleyGaoyuan WangYan XiaShaohua XiaoAndrew C YangSuchen ZhengMichael J GandalDonghoon LeeEd S LeinPanagiotis RoussosNenad SestanNishigandha PhalkeKevin P WhiteHyejung WonMatthew J GirgentiJing ZhangDaifeng WangDaniel H GeschwindMark B Gersteinnull nullPublished in: Science (New York, N.Y.) (2024)
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.