Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine. Here we reduced this complexity by focusing on cellular organization-a key readout and driver of cell behaviour-at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration.
View Article and Find Full Text PDFAlthough some cell types may be defined anatomically or by physiological function, a rigorous definition of cell state remains elusive. Here, we develop a quantitative, imaging-based platform for the systematic and automated classification of subcellular organization in single cells. We use this platform to quantify subcellular organization and gene expression in >30,000 individual human induced pluripotent stem cell-derived cardiomyocytes, producing a publicly available dataset that describes the population distributions of local and global sarcomere organization, mRNA abundance, and correlations between these traits.
View Article and Find Full Text PDFBackground: The giant sarcomere protein titin is important in both heart health and disease. Mutations in the gene encoding for titin () are the leading known cause of familial dilated cardiomyopathy. The uneven distribution of these mutations within motivated us to seek a more complete understanding of this gene and the isoforms it encodes in cardiomyocyte (CM) sarcomere formation and function.
View Article and Find Full Text PDF