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Mapping genetic effects on cellular phenotypes with “cell villages”
Abstract
Summary: Tens of thousands of genetic variants shape human phenotypes, mostly by unknown cellular mechanisms. Here we describe Census-seq, a way to measure cellular phenotypes in cells from many people simultaneously. Analogous to pooled CRISPR screens but for natural variation, Census-seq associates cellular phenotypes to donors’ genotypes by quantifying the presence of each donor’s DNA in cell “villages” before and after sorting or selection for cellular traits of interest. Census-seq enables population-scale cell-biological phenotyping with low cost and high internal control. We demonstrate Census-seq through investigation of genetic effects on the SMN protein whose deficiency underlies spinal muscular atrophy (SMA). Census-seq quantified and mapped effects of many common alleles on SMN protein levels and response to SMN-targeted therapeutics, including a common, cryptic non-responder allele. We provide tools enabling population-scale cell experiments and explain how Census-seq can be used to map genetic effects on diverse cell phenotypes. Abstract Figure: Highlights: Census-seq reveals how inherited genetic variation affects cell phenotypes Genetic analysis of cellular traits in cell villages of >100 donors Characterizing human alleles that shape SMN protein expression and drug responses Development of protocols and software to enable cellular population genetics
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“Recent hiPSC genetic modeling efforts have utilized either a pooled patient iPSC strategy to uncover disease phenotypes in the context of patient genetic heterogeneity or a comprehensive isogenic platform to assess the effect of disease mutations by limiting genetic background variation (Lim et al., 2020; Mitchell et al., 2020; Ramos et al., 2021).”
From Cholesterol and matrisome pathways dysregulated in astrocytes and microglia (2022) · cites this paper as Mitchell et al., 2020
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Metadata
- Authors
- Mitchell, J. M., Nemesh, J., Ghosh, S., Handsaker, R. E., Mello, C. J., Meyer, D., Raghunathan, K., de Rivera, H., Tegtmeyer, M., Hawes, D., Neumann, A., Nehme, R., Eggan, K., & McCarroll, S. A.
- ORCID
- Nehme, Ralda
- Publisher
- openRxiv
- Published
- 2020-06-29
- Cited by
- 63
- References
- 10
- Type
- Preprint
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