Flavell, R. | Search bioRxiv

bioRxiv2019

Map3k2-Regulated Intestinal Stromal Cells (MRISC) Define a Distinct Sub-cryptic Stem Cell Niche for Damage Induced Wnt Agonist R-spondin1 Production

Intestinal stem cell propagation and differentiation are essential for rapid repair of tissue damage in the gut. While intestinal stromal cells were recently identified as key mediators of this process, the cellular and molecular mechanisms by which this diverse population induces tissue repair remains poorly understood. Here we show that Map3k2 has a colon stromal cell specific function critically required for maintenance of Lgr5+ intestinal stem cells and protection against acute intestinal damage. This Map3k2-specific function is mediated by enhancing Wnt agonist R-spondin1 production. We further reveal a unique novel cell population, named Map3k2-regulated intestinal stromal cells (MRISC), as the primary cellular source of R-spondin1 following intestinal injury. Together, our data identify a novel intestinal stem cell niche organized by MRISC, which specifically dependent on the Map3k2-signaling pathway to augment the production of Wnt agonist R-spondin1 and promote regeneration of the acutely damaged intestine.\n\nHighlightsO_LIMap3k2 protects mice from DSS-induced colitis by promoting intestinal stem cell regeneration.\nC_LIO_LIMap3k2-MAPK pathway cross-talks with Wnt signaling pathway via upregulation of R-spondin1.\nC_LIO_LIMap3k2-Regulated Intestinal Stromal Cells (MRISC) marked by co-expression of CD90, CD34 and CD81 defines a novel colonic stem cell niche.\nC_LI

cell biology↗

bioRxiv2019

Detecting regions of differential abundance betweenscRNA-Seq datasets

Traditional cell clustering analysis used to compare the transcriptomic landscapes between two biological states in single cell RNA sequencing (scRNA-seq) is largely inadequate to functionally identify distinct and important differentially abundant (DA) subpopulations between groups. This problem is exacerbated further when using unsupervised clustering approaches where differences are not observed in clear cluster structure and therefore many important differences between two biological states go entirely unseen. Here, we develop DA-seq, a powerful unbiased, multi-scale algorithm that uniquely detects and decodes novel DA subpopulations not restricted to well separated clusters or known cell types. We apply DA-seq to several publicly available scRNA-seq datasets on various biological systems to detect differences between distinct phenotype in COVID-19 cases, melanomas subjected to immune checkpoint therapy, embryonic development and aging brain, as well as simulated data. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies, but also reveals new DA subpopulations that were not described before. Analysis of these novel subpopulations yields new biological insights that would otherwise be neglected.

bioinformatics↗