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

Laurence J. Howe*, Yurii S. Aulchenko, George Davey Smith, Neil M. Davies, Jorge Esparza-Gordillo, Toby Johnson, Jimmy Z. Liu, Tom G. Richardson, Philippe Sanseau, Robert A. Scott, Daniel D. Seaton, Ashwini Sharma, Adrian Cortes

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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). In-vitro experiments can be useful models for in-vivo (organismal) phenotypes (e.g., immune cell anti-viral response and infectious diseases). However, assessing whether an in-vitro cellular model effectively captures in-vivo biology is challenging. An in-vitro model is ‘transportable’ to an in-vivo phenotype 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) and loss-of-function burden tests (in-vivo). In hypothesis-driven analyses, GPAT provides evidence for model transportability of higher lysosomal cholesterol accumulation in-vitro to lower human plasma LDL-cholesterol (P = 0.0006), consistent with the known role of lysosomes in lipid biosynthesis. In contrast, there was limited evidence for other putative in-vitro models. In hypothesis-free analyses, we find evidence for transportability of cancer cell line proliferation to in-vivo human plasma cellular phenotypes (e.g. erythroleukemia proliferation and plasma lymphocyte percentage). Here we show that perturbation data can be used to evaluate transportability of in-vitro cellular models, informing assay prioritisation and supporting novel hypothesis generation.
Original languageEnglish
Article number513
Number of pages13
JournalNature Communications
Volume17
Issue number1
Early online date13 Dec 2025
DOIs
Publication statusPublished - 14 Jan 2026

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Research Groups and Themes

  • Bristol Population Health Science Institute

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  • Integrative Epidemiology Unit

    Davey Smith, G. (Principal Investigator)

    1/04/2331/03/28

    Project: Research

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