Single-cell multi-omic integration compares and contrasts features of brain cell identity. Single-cell multi-omic integration compares and contrasts features of brain cell identity

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. Overall design: Single nuclei were extracted and isolated from 1mm biopsy punches from the BNST of 15 mouse brains (8 male and 7 female replicates), and sequenced using the 10X Chromium system (V3). In total, 204,737 BNST nuclei were recovered. Single nuclei were extracted and isolated from manually dissected samples of the SN of seven de-identified postmortem human donors, and sequenced using the 10X chromium system (V2). In total, 44,274 SN nuclei were recovered.

为实现单细胞数据集的灵活建模，我们开发了LIGER算法与软件包，其可精准刻画细胞身份的共享特征与数据集特异性特征。我们将其应用于四项针对人类与小鼠脑细胞的多样化且极具挑战性的分析，其中前两项包含了针对小鼠与人类脑区的全新单细胞转录组数据集。
首先，我们明确了小鼠终纹床核（bed nucleus of the stria terminalis，BNST）中脑区特异性与性别二态性的基因表达模式。其次，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（GEO: GSE116470））。本次鉴定得到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。
第三，我们联合利用原位表达数据与单细胞表达数据，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甲基化谱数据，揭示了细胞类型特异性基因调控的机制。基于LIGER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。
整体实验设计：我们从15只小鼠（8只雄性、7只雌性生物学重复）的终纹床核中，通过1mm活检打孔获取组织标本并提取、分离单个细胞核，采用10X Chromium系统（V3版本）进行测序，最终共回收得到204737个终纹床核细胞核。
我们从7名已完成身份脱敏的死后人类供体的手动解剖黑质组织样本中提取并分离单个细胞核，采用10X Chromium系统（V2版本）进行测序，最终共回收得到44274个黑质细胞核。