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iSCORE-PD: an isogenic stem cell collection to research Parkinson's disease.

iSCORE-PD: an isogenic stem cell collection to research Parkinson's disease.

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US · Author affiliation · country only

Dominick P. Purpura Department of Neuroscience, Rose F. Kennedy Center, Albert Einstein College of Medicine, Bronx, NY, USA.
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Berkeley, US · Author affiliation

Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
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Bethesda, US · Author affiliation

Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
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London, GB · Author affiliation

Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK.
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Santa Cruz, US · Author affiliation

LiftOff Biosolution, Santa Cruz, CA, USA.
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San Francisco, US · Author affiliation

Chan Zuckerberg Biohub, San Francisco, CA, USA.
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Original abstract

Genome-edited human pluripotent stem cells (hPSCs) provide a powerful platform to study complex diseases such as Parkinson's disease (PD). Here, we describe iSCORE-PD, an isogenic collection of 65 genome-edited hPSC lines carrying disease-causing or high-risk variants in 11 PD-linked genes (SNCA, PRKN, PINK1, DJ1/PARK7, LRRK2, ATP13A2, FBXO7, DNAJC6, SYNJ1, VPS13C, and GBA1). All lines are derived from a well-characterized female hESC line and subjected to extensive quality control. Whole-genome sequencing reveals that genetic variation between lines, largely confined to non-coding regions, is minimal relative to inter-individual differences in patient-derived hiPSCs, with most variation arising from random mutations acquired during cell culture rather than genome-editing-induced off-target effects. Including multiple independently derived clones per mutation can control for this random genetic drift. Our systematic approach ensures high quality of this publicly available iSCORE-PD resource, highlights the advantages of prime editing over conventional CRISPR/Cas9 methods, and establishes best practices for generating disease-modeling hPSC collections.

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