Ischemic heart disease is the single most common cause of death worldwide with an annual death rate of over 9 million people. Genome-wide association studies have uncovered over 200 genetic loci underlying the disease, providing a deeper understanding of the causal mechanisms leading to it. However, in order to understand ischemic heart disease at the cellular and molecular level, it is necessary to identify the cell-type-specific circuits enabling dissection of driver variants, genes, and signaling pathways in normal and diseased tissues. Here, we provide the first detailed single-cell dissection of the cell types and disease-associated gene expression changes in the living human heart, using cardiac biopsies collected during open-heart surgery from control, ischemic heart disease, and ischemic and non-ischemic heart failure patients. We identify 84 cell types/states, grouped in 12 major cell types. We define markers for each cell type, providing the first extensive reference set for the live human heart. These major cell types include cardiovascular cells (cardiomyocytes, endothelial cells, fibroblasts), rarer cell types (B lymphocytes, neurons, Schwann cells), and rich populations of previously understudied layer-specific epicardial and endocardial cells. In addition, we reveal substantial differences in disease-associated gene expression at the cell subtype level, revealing arterial pericytes as having a central role in the pathogenesis of ischemic heart disease and heart failure. Our results demonstrate the importance of high-resolution cellular subtype mapping in gaining mechanistic insight into human cardiovascular disease.
Single nuclei RNA sequencing (snRNA-seq) is widely used to study tissues and diseases. However, the technique remains challenging, as cytoplasmic RNA often contaminates nuclei-containing droplets and may even complicate the removal of empty droplets. Incomplete removal of contaminated background signal masks cell type-specific signal and interferes with differential gene expression analysis. Thus, removing empty droplets and highly contaminated nuclei is of paramount importance in snRNA-seq analysis. This is especially the case in solid tissues such as the heart. Here, we present QClus, a novel nuclei filtering method targeted to human heart samples. In the human heart, most of the contamination originates from cytoplasmic RNA, stemming from cardiomyocytes. Therefore, we use specific metrics such as splicing, mitochondrial gene expression, nuclear gene expression, and non-cardiomyocyte and cardiomyocyte marker gene expression to cluster nuclei and filter empty and highly contaminated droplets. This approach combined with other filtering steps enables for flexible, automated, and reliable cleaning of samples with varying number of nuclei, quality, and contamination levels. To confirm the robustness of our method, we ran QClus on our dataset of 32 human heart samples and compared results to six alternative methods. None of the methods resulted in the same filtering quality as QClus, each of them failing significantly in some samples. As snRNA-seq is predominantly used on unique human tissue samples, sample failure due to inadequate data processing seems unacceptable. Our study shows that, instead of using universal preprocessing methods, challenging tissue types, such as heart tissue, may benefit from methods that are more specific to the tissue under study, as QClus outperforms the other methods primarily due to improved integration of sample type-specific features.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.