January 1, 2018
ed in this study were provided by Eli Lilly as 10 mM stocks in DMSO, in heat-sealed 96-well plates or purchased from Tocris and dissolved in DMSO (Sigma Catalog Number D5879, Lot Number SHBF7682V). HP D300 T8 cassettes for pilot experiment were from Tecan. All other automated liquid handling consumables, experiments in stages 1 and 2, were from Perkin Elmer. Concentration of each drug was select;ed in this study were provided by Eli Lilly as 10 mM stocks in DMSO, in heat-sealed 96-well plates or purchased from Tocris and dissolved in DMSO (Sigma Catalog Number D5879, Lot Number SHBF7682V). HP D300 T8 cassettes for pilot experiment were from Tecan. All other automated liquid handling consumables, experiments in stages 1 and 2, were from Perkin Elmer. Concentration of each drug was select
Here we provide a resource of 4320 transcriptional signatures generated from hiPSC NPCs (derived from 12 SZ cases and 12 controls) and 8 CCLs treated with 135 SZ-relevant drugs. By combining two emerging technologies, hiPSC-based models with in silico drug-screening methodologies, we established the feasibility of transcriptomic-based drug screens of patient-derived neural cells. Drug-induced perturbations were overall very similar between hiPSC NPCs and CCLs; however, when specifically considering differential drug-induced perturbations in SZ hiPSC NPCs, relative to control hiPSC NPCs and particularly CCLs, select drugs induced differential responses in subsets of genes, and those differentially impacted gene sets were enriched for SZ biology. Although many drugâgene perturbations were shared, there were important differences between cell types. While more drugs showed larger differences in drug-induced perturbations between hiPSC NPCs and CCLs than between hiPSC NPC cell line groups, surprisingly, CCLs were overall less drug responsive than either SZ or control hiPSC NPCs. Our data suggests that inclusion of patient-derived neural cell lines will enrich the results for transcriptomic responses relevant to disease processes. Importantly, we identified drugs capable of ameliorating a SZ-related transcriptional signature in hiPSC NPCs; the genes differentially impacted by many of these drugs (i.e., trimethobenzamide, loxapine) specifically enriched for SZ biology in our subsequent analyses. Our ability to independently identify a common set of 18 drugs that both reversed SZ signatures in hiPSC NPCs (Fig. 3c [/articles/s41467-018-06515-4#Fig3]) and differentially regulated SZ-set genes in SZ hiPSC NPCs (Fig. 5b [/articles/s41467-018-06515-4#Fig5]) supports the validity of our transcriptomic drug-screening approach. Although our SZ-signature and SZ-set analyses identified groups of genes differentially regulated by select drugs on a cell type (hiPSC NPC vs. CCL) or diagnosis (SZ vs. control)-specific basis, it was by chemogenomic analysis that we identified hypotheses as to why these differential effects occurred.
Readhead, B; Hartley, BJ; Eastwood, BJ; Collier, DA; Evans, D; Farias, R; He, C; Hoffman, G; Sklar, P; Dudley, JT; Schadt, EE; SaviÄ, R; Brennand, KJ;
Journal: Nat Commun Pages: 4412