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Determining cell fate specification and genetic contribution to cardiac disease risk in hiPSC-derived cardiomyocytes at single cell resolution

By Quan Nguyen, Samuel W. Lukowski, Han Sheng Chiu, Clayton E Friedman, Anne Senabouth, Liam Crowhurst, Timothy J.C Bruxmer, Angelika N. Christ, Nathan J. Palpant, Joseph E Powell

Posted 05 Dec 2017
bioRxiv DOI: 10.1101/229336

The majority of genetic loci underlying common disease risk act through changing genome regulation, and are routinely linked to expression quantitative trait loci, where gene expression is measured using bulk populations of mature cells. A crucial step that is missing is evidence of variation in the expression of these genes as cells progress from a pluripotent to mature state. This is especially important for cardiovascular disease, as the majority of cardiac cells have limited properties for renewal postneonatal. To investigate the dynamic changes in gene expression across the cardiac lineage, we generated RNA-sequencing data captured from 43,168 single cells progressing through in vitro cardiac-directed differentiation from pluripotency. We developed a novel and generalized unsupervised cell clustering approach and a machine learning method for prediction of cell transition. Using these methods, we were able to reconstruct the cell fate choices as cells transition from a pluripotent state to mature cardiomyocytes, uncovering intermediate cell populations that do not progress to maturity, and distinct cell trajectories that terminate in cardiomyocytes that differ in their contractile forces. Second, we identify new gene markers that denote lineage specification and demonstrate a substantial increase in their utility for cell identification over current pluripotent and cardiogenic markers. By integrating results from analysis of the single cell lineage RNA-sequence data with population-based GWAS of cardiovascular disease and cardiac tissue eQTLs, we show that the pathogenicity of disease-associated genes is highly dynamic as cells transition across their developmental lineage, and exhibit variation between cell fate trajectories. Through the integration of single cell RNA-sequence data with population-scale genetic data we have identified genes significantly altered at cell specification events providing insights into a context-dependent role in cardiovascular disease risk. This study provides a valuable data resource focused on in vitro cardiomyocyte differentiation to understand cardiac disease coupled with new analytical methods with broad applications to single-cell data.

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