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Evaluation of variability in human kidney organoids
Open accessAbstract
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.
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Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Self-citationfeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors
Takahashi, K., Tanabe, K., Ohnuki, M., Narita, M., Ichisaka, T., Tomoda, K., & Yamanaka, S. (2007) · Cell · doi:10.1016/j.cell.2007.11.019 · Cited by 20,221
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876
Self-citationGene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107
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Jansen, J., Reimer, K. C., Nagai, J. S., Varghese, F. S., Overheul, G. J., de Beer, M., Roverts, R., Daviran, D., Fermin, L. A. S., Willemsen, B., et al. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2021.12.010 · Cited by 259
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Yi, S. A., Zhang, Y., Rathnam, C., Pongkulapa, T., & Lee, K. (2021) · Advanced Materials · doi:10.1002/adma.202007949 · Cited by 218
Subramanian, A., Sidhom, E.-H., Emani, M., Vernon, K., Sahakian, N., Zhou, Y., Kost-Alimova, M., Slyper, M., Waldman, J., Dionne, D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13382-0 · Cited by 211
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Metadata
- Authors
- Phipson, B., Er, P. X., Combes, A. N., Forbes, T. A., Howden, S. E., Zappia, L., Yen, H.-J., Lawlor, K. T., Hale, L. J., Sun, J., Wolvetang, E., Takasato, M., Oshlack, A., & Little, M. H.
- Venue
- Nature Methods
- Published
- 2018-12-20
- Volume
- 16
- Issue
- 1
- Pages
- 79-87
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2904111838
- Cited by
- 253
- References
- 47
- FWCI
- 8.40
- Type
- journal article
- Language
- en
- Open access
- green
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- Phipson, B., Er, P. X., Combes, A. N., Forbes, T. A., Howden, S. E., Zappia, L., Yen, H.-J., Lawlor, K. T., Hale, L. J., Sun, J., Wolvetang, E., Takasato, M., Oshlack, A., & Little, M. H. (2019). Evaluation of variability in human kidney organoids. Nature Methods. https://doi.org/10.1038/s41592-018-0253-2
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