Xin, Q. | Search bioRxiv

bioRxiv2020

Dissecting human skeletal stem cell ontogeny by single-cell transcriptomic and functional analyses

Human skeletal stem cells (SSCs) have been discovered in fetal and adult bones. However, the spatiotemporal ontogeny of human SSCs during embryogenesis has been elusive. Here we map the transcriptional landscape of human embryonic skeletogenesis at single-cell resolution to address this fundamental question. We found remarkable heterogeneity within human limb bud mesenchyme and epithelium, as well as the earliest osteo-chondrogenic progenitors. Importantly, embryonic SSCs (eSSCs) were found in the perichondrium of human long bones, which self-renew and generate osteochondral lineage cells, but not adipocytes or hematopoietic stroma. eSSCs are marked by the adhesion molecule CADM1 and highly enrich FOXP1/2 transcriptional network. Interestingly, neural crest-derived cells with similar phenotypic markers and transcriptional network were also found in the sagittal suture of human embryonic calvaria. Taken together, this study revealed the cellular heterogeneity and lineage hierarchy during human embryonic skeletogenesis, and identified distinct skeletal stem/progenitor cells that orchestrate endochondral and intramembranous ossification.

developmental biology↗

bioRxiv2020

TIPS: Trajectory Inference of Pathway Significance through Pseudotime Comparison for Functional Assessment of single-cell RNAseq Data

Recent advances in bioinformatics analyses have led to the development of novel tools enabling the capture and trajectory mapping of single-cell RNA sequencing (scRNAseq) data. However, there is a lack of methods to assess the contributions of biological pathways and transcription factors to an overall developmental trajectory mapped from scRNAseq data. In this manuscript, we present a simplified approach for trajectory inference of pathway significance (TIPS) that leverages existing knowledgebases of functional pathways and transcription factor targets to enable further mechanistic insights into a biological process. TIPS returns both the key pathways whose changes are associated with the process of interest, as well as the individual genes that best reflect these changes. TIPS also provides insight into the relative timing of pathway changes, as well as a suite of visualizations to enable simplified data interpretation of scRNAseq libraries generated using a wide range of techniques. The TIPS package can be run through either a web server, or downloaded as a user-friendly GUI run in R, and may serve as a useful tool to help biologists perform deeper functional analyses and visualization of their single-cell and/or large cohort RNAseq data.

bioinformatics↗