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Single nucleus multi-omics identifies human cortical cell regulatory genome diversity.

Single-cell technologies measure unique cellular signatures but are typically limited to a single modality. Computational approaches allow the fusion of diverse single-cell data types, but their efficacy is difficult to validate in the absence of authentic multi-omic measurements. To comprehensively assess the molecular phenotypes of single cells, we devised single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) and applied it to postmortem human frontal cortex tissue. We developed a cross-validation approach using multi-modal information to validate fine-grained cell types and assessed the effectiveness of computational data fusion methods. Correlation analysis in individual cells revealed distinct relations between methylation and gene expression. Our integrative approach enabled joint analyses of the methylome, transcriptome, chromatin accessibility, and conformation for 63 human cortical cell types. We reconstructed regulatory lineages for

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Single nucleus multi-omics identifies human cortical cell regulatory genome diversity.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

  • 引用：Luo C, Liu H, Xie F, Armand EJ, Siletti K, Bakken TE, Fang R, Doyle WI, Stuart T, Hodge RD, Hu L, Wang BA, Zhang Z, Preissl S, Lee DS, Zhou J, Niu SY, Castanon R, Bartlett A, Rivkin A, Wang X, Lucero J, Nery JR, Davis DA, Mash DC, Satija R, Dixon JR, Linnarsson S, Lein E, Behrens MM, Ren B, Mukamel EA, Ecker JR. (2022). Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell genomics. PMID 35419551 · PMC9004682 · DOI 10.1016/j.xgen.2022.100107
  • 证据类型：PRIMARY_RESEARCH
  • 主题：single-cell、multi-omics
  • 被引次数（采集时）：145
  • 原始记录：[Europe PMC](https://europepmc.org/article/MED/35419551)
  • 来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

    • 作者摘要（按来源许可复用）

    Single-cell technologies measure unique cellular signatures but are typically limited to a single modality. Computational approaches allow the fusion of diverse single-cell data types, but their efficacy is difficult to validate in the absence of authentic multi-omic measurements. To comprehensively assess the molecular phenotypes of single cells, we devised single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) and applied it to postmortem human frontal cortex tissue. We developed a cross-validation approach using multi-modal information to validate fine-grained cell types and assessed the effectiveness of computational data fusion methods. Correlation analysis in individual cells revealed distinct relations between methylation and gene expression. Our integrative approach enabled joint analyses of the methylome, transcriptome, chromatin accessibility, and conformation for 63 human cortical cell types. We reconstructed regulatory lineages for cortical cell populations and found specific enrichment of genetic risk for neuropsychiatric traits, enabling the prediction of cell types that are associated with diseases.

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    Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. · GeniOmics