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Integration of single-cell multi-omics data across space and time to unlock cellular trajectories

Project description

Evolution of cell phenotype with multiomics

Genomics, transcriptomics, proteomics and metabolomics data provide a comprehensive view of biological processes and cellular functions. Multiomics capture the dynamic interplay between different molecular levels and their impact on cellular phenotypes. This holistic approach is crucial for understanding complex biological systems, uncovering disease mechanisms and developing targeted therapies. The ERC-funded MULTIview-CELL project aims to integrate high-throughput single-cell multiomics data to understand cell phenotypic evolution across space and time. The study will focus on muscle stem cells and molecular regulators that determine cell trajectories. The goal is to advance fundamental biology and generate novel insight into cell differentiation.

Objective

The introduction of high-throughput single-cell sequencing has produced a flood of data at the resolution of the single cell, including spatiotemporal information and different molecular facets of a cell, a.k.a. multi-omics. Their integration through MultiModal Learning (MML), aimed at combining multiple complementary views, offers great promise to understand the spatiotemporal phenotypic evolution of a cell and its molecular regulators. However, integrating multi-omics data across space and time is a huge computational challenge requiring radically new MML approaches.

MULTIview-CELL will infer multimodal spatiotemporal phenotypic cell trajectories by combining back-translation, to allow the unsupervised dimensionality reduction of multimodal data, with a new Optimal Transport distance, allowing the spatiotemporal pairing of cells (Aim1). MULTIview-CELL will then pinpoint the molecular regulators of such trajectories by combining new Graph Convolutional Networks with topological evolutions and Heterogeneous Multilayer Graphs, allowing the integration of graphs inferred from multimodal data (Aim2). Finally, all developed methods will be implemented in open-source software, with an emphasis on GPU-friendly scalable computations, a unique feature among existing single-cell tools (Aim3).

These core contributions will impact Machine Learning, but more importantly, will have profound biological implications. The application of the tools developed to cutting-edge single-cell data from muscle stem cells will lead to new biological hypotheses on their heterogeneity and crosstalk, to be validated through wet-lab experiments (Transversal Tasks). In addition, by allowing to answer longstanding questions on the spatiotemporal phenotypic evolution of a cell, MULTIview-CELL will catalyze the generation of crucial knowledge in fundamental biology and it will be key to preventing disease onset or therapy resistance, thus impacting health, society and economy.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2023-STG

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Host institution

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 285 938,00
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 285 938,00

Beneficiaries (1)

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