Abstract
SummaryLarge cohorts of human induced pluripotent stem cells (iPSCs) from healthy donors are a potentially powerful tool for investigating the relationship between genetic variants and cellular behavior. Here, we integrate high content imaging of cell shape, proliferation, and other phenotypes with gene expression and DNA sequence datasets from over 100 human iPSC lines. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we extracted factors that captured the effects of intrinsic (genetic concordance between different cell lines from the same donor) and extrinsic (cell responses to different fibronectin concentrations) conditions. We identify genes that correlate in expression with intrinsic and extrinsic PEER factors and associate outlier cell behavior with genes containing rare deleterious non-synonymous SNVs. Our study, thus, establishes a strategy for examining the genetic basis of inter-individual variability in cell behavior.
Highlights
That the applications of human induced pluripotent stem cells for disease modeling and drug discovery are well established, attention is turning to the creation of large cohorts of hiPSCs from healthy donors
By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we extracted factors that captured the effects of intrinsic and extrinsic conditions
We identify genes that correlate in expression with intrinsic and extrinsic PEER factors and associate outlier cell behavior with genes containing rare deleterious non-synonymous Single Nucleotide Variation (SNV)
Summary
That the applications of human induced pluripotent stem cells (hiPSCs) for disease modeling and drug discovery are well established, attention is turning to the creation of large cohorts of hiPSCs from healthy donors. These offer a unique opportunity to examine common genetic variants and their effects on gene expression and cellular phenotypes (Warren et al, 2017; Pashos et al, 2017; Carcamo-Orive et al, 2017; DeBoever et al, 2017; Kilpinen et al, 2017). We set out to identify genetic drivers of cell behavior
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