logo

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

Location: France

Source: EU Funding & Tenders Portal

Project
Ongoing

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 evolutio

Status
Under Construction
Location
France
Updated
22 Jul 2026
Source
EU Funding & Tenders Portal

ConstructChat

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

Pick a question to edit it, or write your own.

First answer free · no account

I am a

Project Information FAQ

Project Information

3 Q
The project “Integration of single-cell multi-omics data across space and time to unlock cellular trajectories” is an infrastructure initiative, located in France. Taiyo aggregates data on it from EU Funding & Tenders Portal.

Participants

Not available for this record.

Status

Original status

ongoing

Taiyo status

Under Construction

Taiyo last update

2026-07-22

Contact

Not available for this record.

Description

Description

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.

Location

Country

France

Source

Source

EU Funding & Tenders Portal

URL

More Details

Record type

Project

Article published date

2026-02-15

More on this project

Research

Free account

View all details

You are reading the public part. A free account opens the rest:

  • Every detail on this project
  • News and media coverage
  • Technical research
View all details

Free account · no card · lands you back on this project