Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree.

A wealth of clustering algorithms are available for single-cell RNA sequencing (scRNA-seq) data to enable the identification of functionally distinct subpopulations that each possess a different pattern of gene expression activity. Implementation of these methods requires a choice of resolution parameter to determine the number of clusters, and critical judgment from the researchers is required to determine the desired resolution. This supervised process takes significant time and effort. Moreover, it can be difficult to compare and characterize the evolution of cell clusters from results obtained at one single resolution. To overcome these challenges, we built Multi-resolution Reconciled Tree (MRtree), a highly flexible tree-construction algorithm that generates a cluster hierarchy from flat clustering results attained for a range of resolutions. Because MRtree can be coupled with most scRNA-seq clustering algorithms, it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. The constructed trees from multiple scRNA-seq datasets effectively reflect the extent of transcriptional distinctions among cell groups and align well with levels of functional specializations among cells. Importantly, application to fetal brain cells identified subtypes of cells determined mainly by maturation states, spatial location and terminal specification.

A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation.

We performed RNA sequencing on 40,000 cells to create a high-resolution single-cell gene expression atlas of developing human cortex, providing the first single-cell characterization of previously uncharacterized cell types, including human subplate neurons, comparisons with bulk tissue, and systematic analyses of technical factors. These data permit deconvolution of regulatory networks connecting regulatory elements and transcriptional drivers to single-cell gene expression programs, significantly extending our understanding of human neurogenesis, cortical evolution, and the cellular basis of neuropsychiatric disease. We tie cell-cycle progression with early cell fate decisions during neurogenesis, demonstrating that differentiation occurs on a transcriptomic continuum; rather than only expressing a few transcription factors that drive cell fates, differentiating cells express broad, mixed cell-type transcriptomes before telophase. By mapping neuropsychiatric disease genes to cell types, we implicate dysregulation of specific cell types in ASD, ID, and epilepsy. We developed CoDEx, an online portal to facilitate data access and browsing.