The Human-Hocker-2021 dataset comprises of 8,993 nonCM cells from the hearts of two healthy adult human donors. Nine cell types were identified, including fibroblasts, myofibroblasts, endothelial cells, pericytes, adipocytes, smooth muscle cells, nerve cells and two groups of immune cells (macrophages and lymphocytes).

The mouse dataset from Wang et al. (Cardiovascular Research, 2021, PMID: 33839759) consists of 12,779 cells from two adult mice, encompassing six major nonCM cell types, namely fibroblasts, endothelial cells, pericytes, granulocytes and two types of immune cells (macrophages and lymphocytes). Of them, the three major cell types (fibroblasts, endothelial cells and macrophages) are further clustered into three to five subtypes with each subtype representing a distinct functional state.

The mouse dataset from Farbehi et al. (Elife, 2019, PMID: 30912746) is comprised of 12,991 nonCM cells (the total cardiac interstitial cell population (TIP)) from healthy and injured mouse hearts (3 and 7 days post-MI surgery), where 5,658, 3,825 and 3,508 cells are from healthy, 3 days post-MI and 7 days post MI hearts, respectively. Unbiased clustering identified 24 cell populations, including the major cell types fibroblasts, endothelial cells, mural cells and immune cells (macrophages, monocytes, dendritic cells, B cells, T cells and natural killer (NK) cells). Similarly, there are multiple subtypes identified for several major cell types. For example, four subtypes are identified in fibroblasts; three in endothelial cells, and eight in macrophages/monocytes.

The Mouse-McLellan-2020 dataset (PMID: 32795101) comprises of 13,176 nonCM cells from four un-treated, four sham- and eight AngII-treated adult mice, where angiotensin II-treatment could stimulate pathological remodeling of heart. A total of 14 cell types were identified, including fibroblasts, epicardial cells, endothelial cells, lymphatic endothelial cells, endocardial cells, smooth muscle cells, pericytes, schwann cells, proliferating mesenchymal cells, macrophages, dendritic-like cells, granulocytes, B cells and T/NK cells. In addition, nine, three, four and two subtypes were identified for the major cell types, fibroblasts, endothelial cells, macrophages and smooth muscle cells, respectively.

The zebrafish dataset from Ma et al. (unpublished) consists of 25,972 cells from healthy and injured zebrafish hearts. The authors performed scRNA-seq on the nonCMs isolated from the adult zebrafish hearts before and after injury to investigate cellular functions of nonCMs during heart regeneration. The study generated transcriptome profiles for 6,550, 9,373, 7,018 and 3,031 cells before injury (uninjured heart), and at 2, 7 and 14 days post-injury, respectively. Nine clusters were identified in the uninjured hearts , namely fibroblasts, endothelial cells, thrombocytes and six types of immune cells (macrophages, neutrophils, resident mesenchymal cells, T cells, B cells, and NK cells). Integrative analysis was performed for each of the three major cell types, fibroblasts, endothelial cells and macrophages, and further identified four, four and five subtypes, respectively.

ExpressHeart incorporates multiple datasets from three species, thus allowing users to perform the analyses of nonCMs features between different datasets from the same species, as well as across different species. These analyses can provide a comprehensive view of the similarities and differences in the transcriptomic dynamics among different conditions or different species.

We first provide a panel “DEG Query” allowing users to check whether a gene of interests is differentially expressed in any of the four incorporating datasets. For a given gene, if it is a DEG, ExpressHeart would list all the relevant information, including differentially expressing in which cell type and dataset, the proportions of cells the gene detected in the target and background cell groups and the adjusted p-value.

Second, we provide lists of high confidence DEGs. For scRNA-seq data, cluster annotation largely depends on the prior knowledge on the expression profiles of cell type-specific features. A comprehensive list of genes differen-tially expressed among cell types can improve the accuracy for cell type discovery. However, although there are a large amount of scRNA-seq data available, different datasets are generally generated from different laboratories, using different techniques, and/or under different conditions, and the DEGs identified in different datasets are usually different. With the help of ExpressHeart, we can obtain a confident list of DEGs for a certain cell type across multiple datasets.

For the cross-species distribution comparison between mouse and zebrafish, the uninjured zebrafish cells from Zebrafish-Ma-2021 were first extracted, and then compared with the wildtype mouse cells from Mouse-Wang-2021. Only genes having mouse-zebrafish homologs are visualized in ExpressHeart.