Evaluating transportability of in-vitro cellular models to in-vivo human phenotypes using gene perturbation data

Abstract

Gene perturbation screens (e.g. CRISPR-Cas9) assess the impact of gene disruption on in-vitro cellular phenotypes (e.g., proliferation, anti-viral response, transcriptomics). In-vitro experiments can be useful models for understanding the aetiology of in-vivo (organismal) phenotypes. For example, anti-viral response in immune cells and infectious disease outcomes. However, assessing whether an in-vitro cellular model is effectively capturing in-vivo biology is challenging. An in-vitro model is transportable to an in-vivo phenotype of interest if perturbations impacting the in-vitro phenotype also impact the in-vivo phenotype with mechanism-consistent directionality and effect sizes. We propose a framework; Gene Perturbation Analysis for Transportability (GPAT), to assess model transportability using gene perturbation effect estimates from perturbation screens (in-vitro cellular phenotypes) and loss-of-function burden tests (in-vivo phenotypes). Using UK Biobank whole-genome sequence data and data from published genome-wide CRISPR-Cas9 screens, we evaluated transportability of in-vitro cellular models to in-vivo human phenotypes. In hypothesis-driven analyses, we found evidence that higher lysosomal cholesterol accumulation in-vitro is a transportable model for lower LDL-cholesterol measured in human blood plasma (P = 0.0006), consistent with the known role of lysosomes in lipid biosynthesis. In contrast, we found limited evidence for other putative in-vitro models. In hypothesis-free analyses, we found strong evidence for transportability of proliferation in cancer cell lines for in-vivo human plasma cellular phenotypes. For example, higher proliferation in an erythroleukemia cell line and lower plasma lymphocyte percentage. GPAT enables systematic evaluation of the transportability of in-vitro cellular models to in-vivo phenotypes, informing assay prioritization and supporting novel hypothesis generation.