Integrating multimodal, multiscale imaging and artificial intelligence for early amyloid detection in their native environment

Current understanding of the molecular mechanisms triggering Alzheimer’s disease (AD) is very limited.

We propose a multimodal imaging approach powered by artificial intelligence (AI) to uncover spatiotemporal patterns to detect early (subtle) structural changes and characterize molecular structures directly in their native environments.

Amyloid structures resolved directly in cells and tissues will provide important knowledge for the development of new anti-amyloid interventions. The work will involve transgenic mice, brain tissue from AD patients, and neuronal  2D and 3D cultures.

The consortium´s goals are: - Combine multimodal structural imaging with AI to develop a comprehensive framework for deep molecular profiling of heterogenic samples like brain tissue. - Investigate the relationship between amyloid structure and toxicity using a modeling approach. - Explore the formation of amyloid in living systems.The strong complementarity within the consortium (biospectroscopy, multiplex imaging, and AI) allows for the design of streamlined multidimensional work packages, ensuring swift progression with mutual contributions from consortium members.

Joint supervision of postdoctoral researchers and research exchange will ensure effective communication and project implementation, with significant mobility of young researchers expected to be a key feature.

Our consortium is highly inclusive, gender-equal, and transnational, promoting strong integration, data, and skill transfer.