bioRxivBioinformaticsPreprint
Transcriptional evaluation of the developmental accuracy, reproducibility and robustness of kidney organoids derived from human pluripotent stem cells
Belinda PhipsoniD, Pei Xuan EriD, Lorna J. HaleiD, David Yen, Kynan T. LawloriD, Minoru TakasatoiD, Jane Q. J. Sun, Ernst Jurgen WolvetangiD, Alicia OshlackiD, Melissa Helen LittleiD
Abstract
We have previously reported a protocol for the directed differentiation of human induced pluripotent stem cells to kidney organoids comprised of nephrons, proximal and distal epithelium, vasculature and surrounding interstitial elements. The utility of this protocol for applications such as disease modelling will rely implicitly on the developmental accuracy of the model, technical robustness of the protocol and transferability between iPSC lines. Here we report extensive transcriptional analyses of the sources of variation across the timecourse of differentiation from pluripotency to complete kidney organoid, focussing on repeated differentiations to day 18 organoid. Individual organoids generated within the same differentiation experiment show Spearmans correlation coefficients of >0.99. The greatest source of variation was seen between experimental batch, with the enrichment for genes that also varied temporally between day 10 and day 25 organoids implicating nephron maturation as contributing to transcriptional variance between individual differentiation experiments. A morphological analysis revealed a transition from renal vesicle to capillary loop stage nephrons across the same time period. Distinct iPSC clones were also shown to display congruent transcriptional programs with inter-experimental and inter-clonal variation most strongly associated with nephron patterning. Even epithelial cells isolated from organoids showed transcriptional alignment with total organoids of the same day of differentiation. This data provides a framework for managing experimental variation, thereby increasing the utility of this approach for personalised medicine and functional genomics.
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