One Million Heart Cells: Integrating Public Data to Explore the Changing Cellular Landscape in Atrial Fibrillation

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia, and it has been implicated in one in five strokes. While previous studies have suggested that genes expressed in cardiomyocyte subpopulations play a role in AF, our understanding has been limited by the difficulty of isolating these large, fragile cells for single cell analysis. By bringing together large reference datasets from healthy hearts with data from several studies that each isolated small numbers of cardiomyocytes from donors with AF, we can gain new insights into the role of these cells in arrhythmia. Over a million single cells and nuclei were imported into Galaxy from sc/snRNA-seq datasets in public repositories. Together, these studies covered ten regions of the adult human heart as well as the foetal heart, and included samples from donors with AF or Heart Failure. The latest version of the Seurat pipeline was wrapped for Galaxy and used to integrate and cluster this combined dataset, alongside the existing Scanpy tools for comparison. Over 300,000 cardiomyocytes were identified, mainly as single nuclei from the healthy reference datasets. Subclustering of these cardiomyocytes revealed an atrial subpopulation associated with AF, which was then characterised using Galaxy’s Single Cell Omics Lab. Public data has the potential to answer many new questions, particularly in single cell omics, where the original studies often focus on just a subset of the genes or cell types included in the datasets. Galaxy’s public servers enable anyone to run their own analyses on this data with just a laptop and an internet connection, even at the scale of a million cells. Integrating the heart datasets has enabled new comparisons and analyses to be run that wouldn’t have been possible when analysing them separately. The results will contribute to our understanding of AF and help identify new avenues for future research, including potential therapeutic targets.