The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation
Open accessPublished version of a preprint
Abstract
Studying the function of common genetic variants in primary human tissues and during development is challenging. To address this, we use an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points. The proportion of neurons produced by each cell line is highly reproducible and is predictable by robust molecular markers expressed in pluripotent cells. Expression quantitative trait loci (eQTL) were characterized at different stages of neuronal development and in response to rotenone-induced oxidative stress. Of these, 1,284 eQTL colocalize with known neurological trait risk loci, and 46% are not found in the Genotype–Tissue Expression (GTEx) catalog. Our study illustrates how coupling scRNA-seq with long-term iPSC differentiation enables mechanistic studies of human trait-associated genetic variants in otherwise inaccessible cell states. Single-cell RNA-seq analysis of iPSC neural differentiation identifies markers that predict line-to-line differences in cell fate potential and eQTLs that are specific to different stages of differentiation and that overlap with GWAS risk variants for neurological traits.
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Metadata
- Authors
- Jerber, J., Seaton, D. D., Cuomo, A. S. E., Kumasaka, N., Haldane, J., Steer, J., Patel, M., Pearce, D., Andersson, M., Bonder, M. J., Mountjoy, E., Ghoussaini, M., Lancaster, M. A., HipSci Consortium, Marioni, J. C., Merkle, F. T., Gaffney, D. J., & Stegle, O.
- Venue
- Nature Genetics
- Published
- 2021-03
- Volume
- 53
- Issue
- 3
- Pages
- 304-312
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W3134873297
- Cited by
- 300
- References
- 77
- FWCI
- 13.22
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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- Jerber, J., Seaton, D. D., Cuomo, A. S. E., Kumasaka, N., Haldane, J., Steer, J., Patel, M., Pearce, D., Andersson, M., Bonder, M. J., Mountjoy, E., Ghoussaini, M., Lancaster, M. A., HipSci Consortium, Marioni, J. C., Merkle, F. T., Gaffney, D. J., & Stegle, O. (2021). Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nature Genetics. https://doi.org/10.1038/s41588-021-00801-6
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