Descripción del proyecto
Modelización basada en aprendizaje automático de las respuestas unicelulares ante perturbaciones asociadas a fármacos
El equipo del proyecto DeepCell, financiado con fondos europeos, tiene como objetivo desarrollar un modelo sistémico del comportamiento celular ante las perturbaciones inducidas por fármacos mediante lecturas genómicas unicelulares y el aprendizaje automático. El estudio preliminar demostró la posibilidad de predecir los cambios de expresión génica de un conjunto de células en respuesta a estímulos. Un grupo de investigadores aplicará una metodología de aprendizaje profundo multicondición y multimodal para la resolución genómica normal y espacial, a fin de crear un modelo para la respuesta de la expresión celular ante diversas perturbaciones. La flexibilidad del modelo actual permitirá analizar los efectos de los estímulos farmacológicos combinados y caracterizar el panorama regulador génico. Como prueba de concepto, el modelo se utilizará para identificar dianas que regulen la selección del linaje enteroendocrino en el intestino.
Objetivo
Advances in single-cell genomics (SCG) allow us to read out a cell’s molecular state with unprecedented detail, increasingly so across perturbations. To fully understand a cellular system, one must be able to predict its internal state in response to all perturbations. Yet such modeling in SCG is currently limited to descriptive statistics. Building upon my expertise in machine learning, I propose to systematically model a cell’s behavior under perturbation, focusing on the largely untouched area of drug-induced perturbations with multiomics SCG readouts. A sufficiently generic model will predict perturbed cellular states, enabling the design of optimal treatments in new cell-types.
In a pilot study, we predicted gene expression changes of a cell ensemble in response to stimuli. DeepCell builds upon this approach: Based on a multi-condition, multi-modal deep-learning approach for both normal and spatially-resolved genomics, we will set up a constrained, interpretable model for the cellular expression response to diverse perturbations. The added flexibility of the DeepCell model versus classical small-scale systems biology models will allow us to interrogate the effects of combined drug stimuli and characterize the gene regulatory landscape by interpretation of the learned deep network.
DeepCell provides unique possibilities to capitalize on cell-based drug screens to address fundamental questions in gene regulation and predicting treatment outcomes. As a proof of concept, I will identify targets that regulate enteroendocrine lineage selection in the intestine. I will set up a 500-compound single-cell organoid RNA-seq screen based on compounds from a spatial imaging screen across 200,000 intestinal organoids, both of which we will model with DeepCell. We will leverage those models to predict optimal treatment for obese mice.
DeepCell opens up the possibility of in silico drug screens, with the potential to expedite drug discovery and impact clinical settings.
Ámbito científico
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
85764 Neuherberg
Alemania