Dataset Information

Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages.

ABSTRACT: Human genome variation contributes to diversity in neurodevelopmental outcomes and vulnerabilities; recognizing the underlying molecular and cellular mechanisms will require scalable approaches. Here, we describe a "cell village" experimental platform we used to analyze genetic, molecular, and phenotypic heterogeneity across neural progenitor cells from 44 human donors cultured in a shared in vitro environment using algorithms (Dropulation and Census-seq) to assign cells and phenotypes to individual donors. Through rapid induction of human stem cell-derived neural progenitor cells, measurements of natural genetic variation, and CRISPR-Cas9 genetic perturbations, we identified a common variant that regulates antiviral IFITM3 expression and explains most inter-individual variation in susceptibility to the Zika virus. We also detected expression QTLs corresponding to GWAS loci for brain traits and discovered novel disease-relevant regulators of progenitor proliferation and differentiation such as CACHD1. This approach provides scalable ways to elucidate the effects of genes and genetic variation on cellular phenotypes.

SUBMITTER: Wells MF

PROVIDER: S-EPMC10581885 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages.

Wells Michael F MF Nemesh James J Ghosh Sulagna S Mitchell Jana M JM Salick Max R MR Mello Curtis J CJ Meyer Daniel D Pietilainen Olli O Piccioni Federica F Guss Ellen J EJ Raghunathan Kavya K Tegtmeyer Matthew M Hawes Derek D Neumann Anna A Worringer Kathleen A KA Ho Daniel D Kommineni Sravya S Chan Karrie K Peterson Brant K BK Raymond Joseph J JJ Gold John T JT Siekmann Marco T MT Zuccaro Emanuela E Nehme Ralda R Kaykas Ajamete A Eggan Kevin K McCarroll Steven A SA

Cell stem cell 20230215 3

Human genome variation contributes to diversity in neurodevelopmental outcomes and vulnerabilities; recognizing the underlying molecular and cellular mechanisms will require scalable approaches. Here, we describe a "cell village" experimental platform we used to analyze genetic, molecular, and phenotypic heterogeneity across neural progenitor cells from 44 human donors cultured in a shared in vitro environment using algorithms (Dropulation and Census-seq) to assign cells and phenotypes to indivi ...[more]

PMID: 36796362

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Project description:DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an "expression quantitative trait locus" (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how "dynamic eQTL" were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.

Dataset Information

Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages.

Publications

Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages.

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