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 LiPengyu NiXiao Zhounull nullTrygve E BakkenJaroslav BendlLucy BicksTanima ChatterjeeLijun ChengYuyan ChengYi DaiZiheng DuanMary FlahertyJohn F FullardMichael GanczDiego Garrido-MartínSophia Gaynor-GillettJennifer GrundmanNatalie HawkenElla HenryGabriel E HoffmanAo HuangYunzhe JiangTing JinNikolas L JorstadRiki KawaguchiSaniya KhullarJianyin LiuJunhao LiuShuang LiuShaojie MaMichael MargolisSamantha MazariegosJill MooreJennifer R MoranEric NguyenNishigandha PhalkeMilos PjanicHenry PrattDiana QuinteroAnanya S RajagopalanTiernon R RiesenmyNicole SheddManman ShiMegan SpectorRosemarie TerwilligerKyle J TravagliniBrie WamsleyGaoyuan WangYan XiaShaohua XiaoAndrew C YangSuchen ZhengMichael J GandalDonghoon LeeEd S LeinPanos RoussosNenad SestanZhiping WengKevin P WhiteHyejung WonMatthew J GirgentiJing ZhangDaifeng WangDaniel GeschwindMark GersteinPublished in: bioRxiv : the preprint server for biology (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, multi-omics datasets into a resource comprising >2.8M 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 >550K cell-type-specific regulatory elements and >1.4M 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.
Keyphrases
- single cell
- rna seq
- gene expression
- high throughput
- poor prognosis
- transcription factor
- genome wide
- cell therapy
- endothelial cells
- prefrontal cortex
- stem cells
- emergency department
- dna damage
- mesenchymal stem cells
- wastewater treatment
- oxidative stress
- high resolution
- mass spectrometry
- adverse drug
- functional connectivity
- resting state
- network analysis