oFlowSeq: a quantitative approach to identify protein coding mutations affecting cell type enrichment using mosaic CRISPR-Cas9 edited cerebral organoids

• Published in: Human genetics Vol. 142; no. 8; pp. 1281 - 1291
• Main Authors: Dawes, Pepper, Murray, Liam F., Olson, Meagan N., Barton, Nathaniel J., Smullen, Molly, Suresh, Madhusoodhanan, Yan, Guang, Zhang, Yucheng, Fernandez-Fontaine, Aria, English, Jay, Uddin, Mohammed, Pak, ChangHui, Church, George M., Chan, Yingleong, Lim, Elaine T.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023; Springer; Springer Nature B.V
• Subjects: Alzheimer's disease; Autism; Biology; Biomedical and Life Sciences; Biomedicine; Brain research; Chromosome 16; CRISPR; CRISPR-Cas Systems; Editing; Flow cytometry; Gene Editing - methods; Gene Function; Genes; Genetic aspects; Genetic diversity; Genetics; Genotype; Genotypes; Human Genetics; Humans; Medical research; Medical schools; Mental disorders; Metabolic Diseases; Molecular Medicine; Mutation; Nervous system diseases; Nestin; Neural coding; Next-generation sequencing; Organoids; Original Investigation; Proteins; Stem cells
• ISSN: 0340-6717; 1432-1203; 1432-1203
• DOI: 10.1007/s00439-023-02534-4

Cerebral organoids are comprised of diverse cell types found in the developing human brain, and can be leveraged in the identification of critical cell types perturbed by genetic risk variants in common, neuropsychiatric disorders. There is great interest in developing high-throughput technologies to associate genetic variants with cell types. Here, we describe a high-throughput, quantitative approach (oFlowSeq) by utilizing CRISPR-Cas9, FACS sorting, and next-generation sequencing. Using oFlowSeq, we found that deleterious mutations in autism-associated gene KCTD13 resulted in increased proportions of Nestin + cells and decreased proportions of TRA-1–60 + cells within mosaic cerebral organoids. We further identified that a locus-wide CRISPR-Cas9 survey of another 18 genes in the 16p11.2 locus resulted in most genes with > 2% maximum editing efficiencies for short and long indels, suggesting a high feasibility for an unbiased, locus-wide experiment using oFlowSeq. Our approach presents a novel method to identify genotype-to-cell type imbalances in an unbiased, high-throughput, quantitative manner.