B-T.11: Differences of ex vivo and in vivo experimental models reveal challenges for AI-based perturbation prediction tools
Keywords
ex vivo culture, transcriptomics, prediction tools
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Ex vivo cell culture systems are widely used in biological research, but their ability to accurately reflect in vivo biology remains unclear. We developed a computational framework to quantify how experimental model context shapes gene expression. Across various datasets, we identified consistent baseline shifts indicating systematic biases introduced by ex vivo culture. Next, by analyzing interaction effects between perturbations and the experimental model, we found that baseline differences propagate into perturbation responses, leading to substantial divergence in perturbation effects between ex vivo and in vivo contexts. Notably, some perturbations showed discordant or even opposing perturbation effects, posing challenges for prediction across experimental models.
Using AI-based prediction tools, we next attempted to infer in vivo perturbation effects from baseline and perturbed ex vivo data, together with baseline in vivo profiles, but performance remained limited, particularly for perturbations with opposing trends. Linear modelling showed that models trained across perturbations performed poorly in prediction task, suggesting that it is challenging to predict perturbation-specific responses.
Taken together, we show that baseline transcriptional differences encode systematic, generalizable shifts that can inform ex vivo model adaptations and cross-prediction (computational) modelling tools, but accurate prediction of perturbation effects requires incorporating perturbation-specific structure rather than relying on global patterns. Overall, these findings evidence the need for experimental model- and perturbation- aware modelling strategies to improve the translation of ex vivo perturbation data to in vivo biology.
Co-authors: Cynthia del Valle, Wolfgang Esser-Skala, Nikolaus Fortelny, David Lara-Astiaso
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