Taming Human Genetic Variability: Transcriptomic Meta-Analysis Guides the Experimental Design and Interpretation of iPSC-Based Disease Modeling
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Both the promises and pitfalls of the cell reprogramming research platform rest on human genetic variation, making the measurement of its impact one of the most urgent issues in the field. Harnessing large transcriptomics datasets of induced pluripotent stem cells (iPSC), we investigate the implications of this variability for iPSC-based disease modeling. In particular, we show that the widespread use of more than one clone per individual in combination with current analytical practices is detrimental to the robustness of the findings. We then proceed to identify methods to address this challenge and leverage multiple clones per individual. Finally, we evaluate the specificity and sensitivity of different sample sizes and experimental designs, presenting computational tools for power analysis. These findings and tools reframe the nature of replicates used in disease modeling and provide important resources for the design, analysis, and interpretation of iPSC-based studies.
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Principal component analysis corroborated a significant impact of individual genetic backgrounds on transcriptional variability, with most knockdown lines clustering with their respective control scr line.
“Confirming our previous observations in the two largest cohorts of iPSC lines (29), a principal component analysis (PCA) corroborated the significant impact of individual genetic backgrounds on transcriptional variability, with most “KD lines” clustering with their respective control “scr line.””
The quality of the numerical differential expression analysis was validated using HipSci data and iPSCpoweR tools, and all analyses identified very low number of spurious DEGs.
“To validate the quality of our numerical differential expression analysis, we took advantage of HipSci data (55, 56) and iPSCpoweR tools (29).”
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From Primary Cells to Organoids: Expanding the Biological Relevance of In Vitro Cellular Models
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- Authors
- Germain, P.-L., & Testa, G.
- Affiliations
- European Institute of Oncology (IT)
- University of Milan (IT)
- Corresponding author
- Germain, Pierre-Luc
- Testa, Giuseppe
- Venue
- Stem Cell Reports
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 8
- Issue
- 6
- Pages
- 1784-1796
- ISSN
- 2213-6711 (ISSN-L)
- PMC
- PMC5470233
- OpenAlex
- W2623260545
- Cited by
- 94
- References
- 34
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 8 · 2023: 6 · 2022: 13 · 2021: 14 · 2020: 17 · 2019: 12 · 2018: 6 · 2017: 3
- FWCI
- 4.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Stem Cell Reports (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Research Council (DISEASEAVATARS 616441)
- Telethon Foundation (GGP14265)
- Italian Association for Cancer Research
- Umberto Veronesi Foundation
- Italian Ministry of Health
- Regione Lombardia
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Keywords
- induced pluripotent stem cells, experimental design, sample size, statistical power analysis, replicates, cellular reprogramming
- MeSH terms
- Humans, Models, Biological, Research Design, Genetic Variation, Gene Expression Profiling, Induced Pluripotent Stem Cells
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