Transcriptome Profiling Reveals Degree of Variability in Induced Pluripotent Stem Cell Lines: Impact for Human Disease Modeling
Abstract
Induced pluripotent stem cell (iPSC) technology has become an important tool for disease modeling. Insufficient data on the variability among iPSC lines derived from a single somatic parental cell line have in practice led to generation and analysis of several, usually three, iPSC sister lines from each parental cell line. We established iPSC lines from a human fibroblast line (HDF-K1) and used transcriptome sequencing to investigate the variation among three sister lines (iPSC-K1A, B, and C). For comparison, we analyzed the transcriptome of an iPSC line (iPSC-K5B) derived from a different fibroblast line (HDF-K5), a human embryonic stem cell (ESC) line (ESC-HS181), as well as the two parental fibroblast lines. All iPSC lines fulfilled stringent criteria for pluripotency. In an unbiased cluster analysis, all stem cell lines (four iPSCs and one ESC) clustered together as opposed to the parental fibroblasts. The transcriptome profiles of the three iPSC sister lines were indistinguishable from each other, and functional pathway analysis did not reveal any significant hits. In contrast, the expression profiles of the ESC line and the iPSC-K5B line were distinct from that of the sister lines iPSC-K1A, B, and C. Differentiation to em...Continue Reading
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Modeling ALS with iPSCs reveals that mutant SOD1 misregulates neurofilament balance in motor neurons
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