Smith, J. R. | bioRxiv Science

bioRxiv2020

Single cell RNA sequencing redefines the mesenchymal cell landscape of mouse endometrium

The endometrium is a dynamic tissue that exhibits remarkable resilience to repeated episodes of differentiation, breakdown, regeneration and remodelling. Endometrial physiology relies on a complex interplay between the stromal and epithelial compartments with the former containing a mixture of fibroblasts, vascular and immune cells. There is evidence for rare populations of putative mesenchymal progenitor cells located in the perivascular niche of human endometrium, but the existence of an equivalent cell population in mouse is unclear. In the current study we used the Pdgfrb-BAC-eGFP transgenic reporter mouse in combination with bulk and single cell RNA sequencing (scRNAseq) to redefine the endometrial mesenchyme. Contrary to previous reports we show that CD146 is expressed in both PDGFR{beta}+ perivascular cells as well as CD31+ endothelial cells. Bulk RNAseq revealed cells in the perivascular niche which express high levels of Pdgfrb as well as genes previously identified in pericytes and/or vascular smooth muscle cells (Acta2, Myh11, Olfr78, Cspg4, Rgs4, Rgs5, Kcnj8, Abcc9). scRNAseq identified five subpopulations of cells including closely related pericytes/vascular smooth muscle cells and three subpopulations of fibroblasts. All three fibroblast populations were PDGFR+/CD34+ but were distinct in their expression of Spon2/Angptl7 (fibroblast 1), Smoc2/Rgs2 (fibroblast 2) and Clec3b/Col14a1/Mmp3 (fibroblast 3), with potential functions in regulation of immune responses, response to wounding and organisation of extracellular matrix respectively. In conclusion, these data are the first to provide a single cell atlas of the mesenchymal cell landscape in mouse endometrium. By identifying novel markers for subpopulations of mesenchymal cells we can use mouse models investigate their contribution to endometrial function, compare with other tissues and apply these findings to further our understanding of human endometrium. HighlightsO_LIGFP expression in the mouse endometrium, under the control of the Pdgfrb promoter, is restricted to two cell populations based on the intensity of GFP with GFPbright cells close to the vasculature C_LIO_LISingle cell RNAseq identified five subpopulations of GFP+ mesenchymal cells: pericytes, vascular smooth muscle cells (vSMC) and three closely related but distinct populations of fibroblasts C_LIO_LIBioinformatics revealed that pericytes and vSMC share functions associated with the circulatory system, actin-filament process and cell adhesion, and an apparent role for pericytes in smooth muscle cell migration and response to interferons C_LIO_LIComparisons between the fibroblast subpopulations suggest distinct roles in regulation of immune response, response to wound healing and collagen organisation. C_LI Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

physiology↗

bioRxiv2020

Gene Embeddings of Complex network (GECo) and hypertension disease gene classification

Complex diseases such as hypertension, cancer, and diabetes cause nearly 70% of the deaths in the U.S. and involve multiple genes and their interactions with environmental factors. Therefore, identification of genetic factors to understand and decrease the morbidity and mortality from complex diseases is an important and challenging task. With the generation of an unprecedented amount of multi-omics datasets, network-based methods have become popular to represent the multilayered complex molecular interactions. Particularly node embeddings, the low-dimensional representations of nodes in a network are utilized for gene function prediction. Integrated network analysis of multi-omics data alleviates the issues related to missing data and lack of context-specific datasets. Most of the node embedding methods, however, are unable to integrate multiple types of datasets from genes and phenotypes. To address this limitation, we developed a node embedding algorithm called Node Embeddings of Complex networks (NECo) that can utilize multilayered heterogeneous networks of genes and phenotypes. We evaluated the performance of NECo using genotypic and phenotypic datasets from rat (Rattus norvegicus) disease models to classify hypertension disease-related genes. Our method significantly outperformed the state-of-the-art node embedding methods, with AUC of 94.97% compared 85.98% in the second-best performer, and predicted genes not previously implicated in hypertension. Availability and implementationThe source code is available on GitHub at https://github.com/bozdaglab/NECo.

bioinformatics↗