Single cell transcriptome analysis of human embryonic stem cell-derived neurons spanning the rostrocaudal and dorsoventral axes

Our inability to derive the neuronal diversity that comprises the posterior central nervous system (pCNS) using human pluripotent stem cells (hPSCs) poses an impediment to understanding human neurodevelopment and disease in the hindbrain and spinal cord. We established a modular, monolayer differentiation paradigm that recapitulates both rostrocaudal (R/C) and dorsoventral (D/V) patterning, enabling derivation of diverse pCNS neurons with discrete regional specificity. Expansive single-cell RNA-sequencing (scRNAseq) analysis coupled with a novel computational pipeline allowed us to detect hundreds of transcriptional markers within region-specific phenotypes, enabling discovery of gene expression patterns across R/C and D/V developmental axes. Processed data matrix: For each of the 6 samples from direct differentiation (GSE186696) and 14 samples from modular differentiation (GSE186697), We merged the gene expression matrices from each sample into a single matrix while taking the union of the genes from each matrix. The combined matrix is [12,543 cells x 20,598 genes] for the direct differentiation dataset and [49,959 cells x 23,941 genes] for the multiple generation dataset. We transformed the values of these matrices by taking their square root and standardizing each cell’s expression profile by dividing by the mean expression of a gene in each cell for the subsequent clustering analysis. Clusters - HOX profile clusters: We applied Louvain clustering (k=13) for the visualization of the simultaneous expression of HOX genes in our data set (GSE186697), which revealed inter- and intra-sample HOX profile heterogeneity. Clusters - primary clusters: We applied sparse non-negative matrix factorization (NMF) (Kim and Park, 2008) based clustering to define cardinal cell groups within our data set (GSE186697) in an unbiased manner and identified 25 primary clusters. Clusters - subpopulation subclusters: We organized related primary clusters into 17 different groups, and then developed and applied a consensus clustering based approach with the goal of defining robust subclusters representing subtypes of known cardinal populations. Consensus_graph_matrix: We regrouped our 25 primary cell clusters into 17 subgroups based on similarity of the cell types assigned to each cluster, and created a consensus graph of cell co-clustering relationship per subgroup. For every pair of cells in a subgroup, we counted the proportion of times the two cells were in the same cluster (across multiple clustering approaches), and generated a weighed graph of cells with weights corresponding to this proportion.我们无法利用人类多能干细胞（human pluripotent stem cells, hPSCs）获取构成后中枢神经系统（posterior central nervous system, pCNS）的神经元多样性，这一局限阻碍了我们对人类后脑与脊髓的神经发育及相关疾病的研究理解。 我们建立了一种模块化单层分化范式，该范式可同时复刻前后轴（rostrocaudal, R/C）与背腹轴（dorsoventral, D/V）的模式化发育过程，能够高效获取具有明确区域特异性的多种pCNS神经元。 通过大规模单细胞RNA测序（single-cell RNA-sequencing, scRNAseq）分析结合全新的计算流程，我们得以在区域特异性表型中检测到数百个转录标志物，进而发现了沿前后轴与背腹轴发育轴的基因表达特征模式。 处理后的数据矩阵：针对直接分化组的6个样本（GSE186696）与模块化分化组的14个样本（GSE186697），我们将每个样本的基因表达矩阵进行合并，并取所有样本基因的并集作为合并后的基因全集。直接分化数据集的合并矩阵规模为[12543个细胞 × 20598个基因]，多轮分化数据集的合并矩阵规模为[49959个细胞 × 23941个基因]。为开展后续聚类分析，我们对这两个矩阵的数值进行平方根变换，并通过将每个细胞的表达谱除以该细胞内单个基因的平均表达量，完成细胞表达谱的标准化。 聚类——HOX特征聚类：我们对数据集（GSE186697）应用Louvain聚类（k=13）以可视化HOX基因的共表达情况，该分析揭示了样本间与样本内的HOX特征异质性。 聚类——主聚类：我们基于稀疏非负矩阵分解（sparse non-negative matrix factorization, NMF）（Kim与Park, 2008）开展聚类，以无偏方式定义数据集（GSE186697）中的核心细胞群，并鉴定出25个主聚类。 聚类——亚群子聚类：我们将相关性较高的主聚类划分为17个不同组别，随后开发并应用基于共识聚类的分析方法，旨在定义可代表已知核心种群亚型的稳健子聚类。 共识图矩阵：我们根据每个聚类所分配细胞类型的相似性，将25个主细胞聚类重新划分为17个亚组，并为每个亚组构建细胞共聚类关系的共识图。对于亚组内的每一对细胞，我们统计这两个细胞在多次不同聚类方法中被分配至同一聚类的比例，并据此生成权重对应该比例的细胞加权图。