Abstract 111: Conjoining Gene Expression And Cell Organization With Imaging To Explore Cell States In Hipsc-derived Cardiomyocytes

Abstract

The Allen Institute for Cell Science is developing a state space of stem cell structural signatures to study changes in cellular organization of human induced pluripotent stem cells (hiPSCs) and other cell states through differentiation. Towards this goal, we have used CRISPR/Cas9 to generate a collection of ~50 endogenous fluorescently tagged hiPSC lines (www.allencell.org), each expressing a monoallelic EGFP-tagged protein that localizes to a particular cellular structure or organelle. In this study, we focuson hiPSC-derived cardiomyocytes and compare the relationship between sarcomeric structural organization and gene expression signatures at large scale. We developed several tools and novel quantitative approaches to achieve this: 1) scarless GFP-tagging of cardiac genes such as ACTN2 to study the organization and morphogenesis of the contractile apparatus; 2) a robust protocol for differentiation of hiPSCs into cardiomyocytes and methods for preparing cells for imaging; and 3) a quantitative, image-based platform for the systematic and automated classification of subcellular organization in single cells. We use these approaches 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. While the mRNA abundance of some phenotypically important genes correlates with subcellular organization (e.g., MYH7), these two cellular metrics are heterogeneous and often uncorrelated, which suggests that geneexpression alone is not sufficient to classify cell states. Instead, we posit thatcell state should be defined by observing full distributions of quantitative, multidimensional traits in single cells that also account for space, time, and function. This platform provides a multidimensional approach to classify hiPSC-derived cardiomyocytes based on structural organization and gene expression in single cells.