Amer-Denis Akkad

BACKGROUND: The human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low-input RNA sequencing have allowed definitions of cellular transcriptomes at single-cell resolution at scale, we have applied these approaches to assess the cellular and transcriptional diversity of the nonfailing human heart. METHODS: Microfluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from 7 human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based on transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic data. RESULTS: We sequenced the transcriptomes of 287 269 single cardiac nuclei, revealing 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include 2 distinct groups of resident macrophages, 4 endothelial subtypes, and 2 fibroblast subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Intersection of our data set with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity. CONCLUSIONS: Using large-scale single nuclei RNA sequencing, we defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

Abstract Droplet-based single-cell assays, including scRNA-seq, snRNA-seq, and CITE-seq, produce a significant amount of background noise counts, the hallmark of which is non-zero counts in cell-free droplets and off-target gene expression in unexpected cell types. The presence of such systematic background noise is a potential source of batch effect and spurious differential gene expression. Here we develop a deep generative model for noise-contaminated data that is structured to reflect the phenomenology of background noise generation in droplet-based single-cell assays. The proposed model successfully distinguishes cell-containing from cell-free droplets without supervision, learns the profile of background noise, and retrieves a noise-free quantification in an end-to-end fashion. We present a scalable and robust implementation of our method as a module in the open-source software package CellBender . We show that CellBender operates close to the theoretically optimal denoising limit in simulated datasets, and present extensive evaluations using real datasets and experimental benchmarks drawn from different tissues, protocols, and modalities to show that CellBender significantly improves the agreement of droplet-based single-cell data with established gene expression patterns, and that the learned background noise profile provides evidence for degraded or uncaptured cell types.