Open AccessPopulation-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation
Author(s) -
Julie Jerber,
Daniel D Seaton,
Anna Cuomo,
Natsuhiko Kumasaka,
James Haldane,
Juliette Steer,
Minal Patel,
Daniel J. Pearce,
Malin H.L. Andersson,
Marc Jan Bonder,
Edward Mountjoy,
Maya Ghoussaini,
Madeline A. Lancaster,
John C. Marioni,
Florian T. Merkle,
Daniel Gaffney,
Oliver Stegle
Publication year - 2021
Publication title -
nature genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.861
H-Index - 573
eISSN - 1546-1718
pISSN - 1061-4036
DOI - 10.1038/s41588-021-00801-6
Subject(s) - biology , induced pluripotent stem cell , expression quantitative trait loci , cellular differentiation , population , gene expression profiling , quantitative trait locus , genetics , computational biology , gene expression , gene , genotype , single nucleotide polymorphism , embryonic stem cell , demography , sociology
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.