INTRODUCTORY PARAGRAPHCharacterization of the cardiac cellulome—the network of cells that form the heart—is essential for understanding cardiac development and normal organ function, and for formulating precise therapeutic strategies to combat heart disease. Recent studies have challenged assumptions about both the cellular composition1 and functional significance of the cardiac non-myocyte cell pool, with unexpected roles identified for resident fibroblasts2 and immune cell populations3,4. In this study, we characterized single-cell transcriptional profiles of the murine non-myocyte cardiac cellular landscape using single-cell RNA sequencing (scRNA-Seq). Detailed molecular analyses revealed the diversity of the cardiac cellulome and facilitated the development of novel techniques to isolate understudied cardiac cell populations such as mural cells and glia. Our analyses also revealed networks of intercellular communication as well as extensive sexual dimorphism in gene expression in the heart, most notably demonstrated by the upregulation of immune-sensing and pro-inflammatory genes in male cardiac macrophages. This study offers new insights into the structure and function of the mammalian cardiac cellulome and provides an important resource that will stimulate new studies in cardiac cell biology.

Recent technological advances in single cell capture and nano-scale reactions have led to a major revolution in single cell transcriptomics 1,2,3 . Single cell datasets are analyzed using computational and statistical frameworks that enable feature (gene) selection, dimensionality reduction, clustering and differential gene expression. Multiple software packages exist that allow researchers well versed in computational analysis to perform this analysis [4][5][6] . However, identifying the exact parameters required for cell type identification is an iterative process greatly improved when informed by biology. In addition, interactive exploration of single cell datasets incorporating a biologist's knowledge greatly improves data interpretation, yet often such experts do not have big data handling skills.Advances in web application frameworks and visualization methods for dense datasets facilitate the development of interactive applications to allow easy and intuitive exploration of single cell data. Here, we introduce an R Shiny 7 web application, CellView, that allows knowledge-based and hypothesis-driven exploration of processed single cell transcriptomic data. The input into CellView is an R dataset (. . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/123810 doi: bioRxiv preprint first posted online with three pre-computed data frames containing expression, clustering, and gene symbol information.This file is agnostic of upstream computational approaches providing flexibility in algorithms used to calculate these data frames. This .Rds file can be shared with the end user, eliminating the need for hosting datasets, thereby decreasing the size of a virtual machine or cloud instance required to host and use CellView. Multiple tabs allow for easy access to the data and visualization of gene expression across and within clusters, aiding cell type identification.To illustrate the utility and power of CellView, we generated and analyzed single cell transcriptome data from peripheral blood mononuclear cells (PBMCs) using the 10X Genomics Chromium 8 . As defined by the CellRanger 8 pipeline, this data consisted of 6,554 single cells sequenced to 90.1% saturation with, on average, 824 genes and 2,077 molecules detected per cell. Dimensionality reduction using tSNE 9 was applied to genes selected by normalized dispersion, and with clustering by DBSCAN 10 . CellView automatically determines cluster numbers, updates the user interface, and renders a 3D scatter plot displaying cells clustered in tSNE space (Fig 1b) (Fig 1c), a marker of B-cells, and CD3D, a marker of T-cells (Fig 1d), provide representative views of the 'Explore' tab.The 'Co-expression' tab enables the generation of heatmaps to visualize expression of multiple genes either across all clusters, in the 'AllClusters' sub-menu, or on selected cells wi...