Evaluation of variability in human kidney organoids. (January 2019)

• Record Type: Journal Article
• Authors: Phipson, Belinda; Er, Pei; Combes, Alexander; Forbes, Thomas; Howden, Sara; Zappia, Luke; Yen, Hsan-Jan; Lawlor, Kynan; Hale, Lorna; Sun, Jane; Wolvetang, Ernst; Takasato, Minoru; Oshlack, Alicia; Little, Melissa
• Is Part Of: Nature methods. Volume 16:Number 1(2019)
• Page Start: 79
• Page End: 87
• Publication Date: 2019-01
• Subjects: Life sciences -- Methodology -- Periodicals; Life sciences -- Research -- Periodicals; Biology -- Methodology -- Periodicals; Biology -- Research -- Periodicals; 570.72
• DOI: 10.1038/s41592-018-0253-2
• Languages: English
• ISSNs: 1548-7091

Abstract

Abstract The utility of human pluripotent stem cell–derived kidney organoids relies implicitly on the robustness and transferability of the protocol. Here we analyze the sources of transcriptional variation in a specific kidney organoid protocol. Although individual organoids within a differentiation batch showed strong transcriptional correlation, we noted significant variation between experimental batches, particularly in genes associated with temporal maturation. Single-cell profiling revealed shifts in nephron patterning and proportions of component cells. Distinct induced pluripotent stem cell clones showed congruent transcriptional programs, with interexperimental and interclonal variation also strongly associated with nephron patterning. Epithelial cells isolated from organoids aligned with total organoids at the same day of differentiation, again implicating relative maturation as a confounder. This understanding of experimental variation facilitated an optimized analysis of organoid-based disease modeling, thereby increasing the utility of kidney organoids for personalized medicine and functional genomics. A transcriptional analysis of kidney organoids reveals batch effects as the key drivers of variation, mainly through differences in maturity, and provides a list of highly variable genes and a method for estimating differentiation stage for improved disease modeling.