单细胞多组学整合比较与区分脑细胞特征
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To flexibly model single-cell datasets, we developed LIGER, an algorithm and software package that delineates shared and dataset-specific features of cell identity. We applied it to four diverse and challenging analyses of human and mouse brain cells, the first two of which include newly generated single-cell transcriptome datasets for mouse and human regions. First, we determined region-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis (BNST). Second, we analyzed expression in the human substantia nigra (SN), integrating cell types across donors, and relating cell types to those in the mouse (using mouse SN data from Saunders et al., 2018 (GEO: GSE116470)). The main cell types identified include neurons, astrocytes, microglia, oligodendrocytes, polydendrocytes, and endothelial cells. Third, we jointly leveraged in situ and single-cell expression data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we integrated mouse cortical single-cell RNA-seq and DNA methylation profiles, revealing mechanisms of cell-type-specific gene regulation. Integrative analyses using LIGER promise to accelerate investigations of cell-type definition, gene regulation, and disease states.
为实现单细胞数据集的灵活建模，我们开发了LIGER——一款可阐明细胞身份的共享特征与数据集特异性特征的算法与软件包。我们将其应用于四项针对人类与小鼠脑细胞的多样化且具挑战性的分析任务，其中前两项分析包含了全新生成的小鼠与人类脑区单细胞转录组数据集。其一，我们明确了小鼠终纹床核（bed nucleus of the stria terminalis，BNST）中的脑区特异性与性别二态性基因表达模式。其二，我们分析了人类黑质（substantia nigra，SN）中的基因表达情况，整合了不同供体间的细胞类型，并将人类细胞类型与小鼠中的对应细胞类型进行关联，所用小鼠黑质数据来自Saunders等人2018年发表的研究（基因表达汇编数据库（Gene Expression Omnibus，GEO）：GSE116470）。此次研究所鉴定出的主要细胞类型包括神经元、星形胶质细胞、小胶质细胞、少突胶质细胞、多突胶质细胞以及内皮细胞。其三，我们联合利用了原位表达与单细胞表达数据，对小鼠额叶皮层中存在的细胞精细亚型进行空间定位。最后，我们整合了小鼠皮层的单细胞RNA测序与DNA甲基化图谱，揭示了细胞类型特异性基因调控的机制。借助LIGER开展的整合分析有望加速细胞类型定义、基因调控以及疾病状态相关研究的进程。
