Dpath software reveals hierarchical haemato-endothelial lineages of Etv2 progenitors based on single-cell transcriptome analysis

Abstract

Developmental, stem cell and cancer biologists are interested in the molecular definition of cellular differentiation. Although single-cell RNA sequencing represents a transformational advance for global gene analyses, novel obstacles have emerged, including the computational management of dropout events, the reconstruction of biological pathways and the isolation of target cell populations. We develop an algorithm named dpath that applies the concept of metagene entropy and allows the ranking of cells based on their differentiation potential. We also develop self-organizing map (SOM) and random walk with restart (RWR) algorithms to separate the progenitors from the differentiated cells and reconstruct the lineage hierarchies in an unbiased manner. We test these algorithms using single cells from Etv2-EYFP transgenic mouse embryos and reveal specific molecular pathways that direct differentiation programmes involving the haemato-endothelial lineages. This software program quantitatively assesses the progenitor and committed states in single-cell RNA-seq data sets in a non-biased manner.

A novel algorithm for the collective integration of single cell RNA-seq during embryogenesis

“…Next, we used scNCA to integrate 3,387 single cells from six published temporal scRNA-seq datasets focused on mouse cardiovascular development from the epiblast (E6.5) to the four-chambered heart (E9.5) (Fig 3C). These datasets included the single cells from the whole epiblast[3–5], Mesp1 + cardiovascular progenitors[4], Flk1 + mesodermal progenitors[5], Etv2 + hemato-endothelial progenitors[6], as well as two scRNA-seq datasets obtained from distinct cardiac regions at E8.5 and E9.5[7,26]. We also added 264 Nkx2-5-EYFP + single cells from the E7.75 and E8.5 mouse embryo.…”

“…The raw reads of the public datasets used in this study were downloaded from NCBI SRA (Deng et al: GSE45719[1]; Mohammed et al: GSE100597[3]; Lescroart et al: GSE100471[4]; Gong et al: PRJNA350294[6]; Li et al: GSE76118[35]), EBI ArrayExpress (Goolam et al: E-MTAB-3321[2]; Scialdone et al: E-MTAB-4079[5];) and GNomEx databases (DeLaughter: 272R, 274R, 275-292R, 439R, and 440R[7]). The raw read counts for each gene were obtained with TopHat (v2.0.13) and HTSeq (v0.6.0) with default parameters[36,37].…”

Pathologic Stimulus Determines Lineage Commitment of Cardiac C-kit + Cells

“…Based on acquired images, we selected single live cells to proceed with library preparation, according to the Fluidigm protocol. 15 Raw single cell RNA sequencing data was plotted in a matrix consisting of 23425 genes and 405 cells. We used t-SNE to visualize the data and clustered cells into four clusters using Partitioning Around Medoids (PAM) clustering algorithm.…”

Single-cell transcriptional profiling: a window into embryonic cell-type specification

“…Another case is the transcription factor ETS translocation variant 2 (Etv2), which is required for endothelial, endocardial and blood development 95,96 . Using scRNA-seq, Etv2-expressing cells were shown to be broadly transcriptionally divided into mesodermal, blood, endocardial and endothelial subpopulations, suggesting that, in addition to immature multipotent mesodermal progenitors, unipotent progenitors may also exist 97 .…”