bioRxiv Science⌕ Search

Biology subjects

Myung, P.

Publications and source records attributed to Myung, P..

3 recordsLinked to original sources

Sharp cell type boundaries emerge from coordinated morphogen signaling

Classic models of the French flag problem depict sharp cell-type boundaries emerging from threshold responses to morphogen gradients. How discrete cell-type boundaries arise from morphogen signals that vary continuously across developing tissues is not completely understood. We use hair follicle dermal condensate formation to study a sharp developmental transition in which proliferative progenitors undergo cell-cycle exit concurrent with molecular differentiation. Using genetic and genomic approaches, we show that Wnt and Hedgehog signaling interact to coordinate the timing of these two processes. We identify a division of labor between the pathways: Wnt signaling promotes cell-cycle exit by regulating chromatin binding of the Hedgehog mediator GLI3, while Hedgehog signaling induces differentiation genes in a Wnt-dependent manner and simultaneously elevates Wnt activity. When Wnt and Hedgehog activities are temporally aligned, differentiation and cell-cycle exit occur within the same developmental window, restricting both the duration and abundance of intermediate states and producing a sharp cell-type boundary. When these signals are misaligned, intermediate states persist and expand, producing fuzzy boundaries. These findings reveal a mechanism in which interacting morphogen signals regulate the duration and abundance of intermediate states during a developmental transition, thereby controlling how continuous cell-state progression is translated into discrete tissue patterning.

developmental biology↗

Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics

Single-cell RNA-sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal-transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here, we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates discovery of gene programs that control the changes and activities of biological processes.

bioinformatics↗

Inference and analysis of cell-cell communication using CellChat

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applications of CellChat to several mouse skin scRNA-seq datasets for embryonic development and adult wound healing shows its ability to extract complex signaling patterns, both previously known as well as novel. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build a cell-cell communication atlas in diverse tissues.

bioinformatics↗