Analysis, control, and engineering of spatiotemporal pattern formation

Informations projet

ACE-OF-SPACE

N° de convention de subvention: 863952

DOI

10.3030/863952

Projet clôturé

Date de signature de la CE 18 Février 2020

Date de début 1 Juillet 2020

Date de fin 30 Juin 2026

Financé au titre de

• EXCELLENT SCIENCE - European Research Council (ERC)

Coût total

€ 1 997 750,00

Contribution de l’UE

€ 1 997 750,00

Coordonné par

UNIVERSITAT KONSTANZ
Germany

Periodic Reporting for period 3 - ACE-OF-SPACE (Analysis, control, and engineering of spatiotemporal pattern formation)

Période du rapport: 2023-07-01 au 2024-12-31

Résumé du contexte et des objectifs généraux du projet

A central challenge in developmental biology is to decipher how identical cells differentiate to form the mature body structure of an organism. This process is often a result of prior asymmetries in developing embryos; however, there is also evidence for self-organizing mechanisms capable of breaking the symmetry of an initially identical group of cells. Signaling molecules such as the TGF-β superfamily members BMP and Nodal play a pivotal role in these patterning processes. The ACE-OF-SPACE project addresses three critical questions: How does the cross-talk between signaling molecules orchestrate robust patterning in developing tissues? How do signaling systems autonomously organize themselves to pattern tissues when there are no prior asymmetries? What are the minimal requirements to create synthetic self-organizing systems? Understanding cellular differentiation and tissue patterning processes is essential as it provides insights into embryogenesis and potential developmental abnormalities. This knowledge can help to inform new strategies for tissue engineering or remedying developmental disorders.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

The ACE-OF-SPACE project aims to dissect and understand how the interplay between signaling pathways governs vertebrate embryogenesis and pattern formation. To analyze how signaling pathways influence vertebrate development, we have recently developed EmbryoNet, a deep-learning algorithm that can link embryonic phenotypes to defects in the major developmental signaling pathways. To determine how combinatorial signaling influences developmental gene expression, we have investigated the temporal kinetics, spatial signaling and cooperative action of the TGF-β superfamily signals Nodal and BMP using optogenetic tools, membrane-bound nanobodies and light-sheet microscopy. To understand how the interplay between signaling molecules leads to self-organized patterning, we have developed new vertebrate stem cell-based systems that are experimentally accessible and display robust patterns. Finally, we have designed and constructed synthetic biological patterning systems based on high-throughput mathematical analysis. We have assembled the synthetic system from DNA fragments and closely monitored its activity through fluorescence microscopy in bacterial colonies. Interestingly, the engineered synthetic system replicates behaviors also found in vertebrate development, paving the way to understand the minimal requirements for the emergence of patterns in multicellular systems.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

The ability of our artificial intelligence algorithm EmbryoNet (Čapek et al. Nature Methods 2023) to distinguish and classify developmental defects caused by perturbations in signaling pathways is a major advancement. Given the complexity and overlap between signaling pathways, such phenotypes are even difficult for seasoned developmental biologists to discern. EmbryoNet's ability to identify signaling-mediated phenotypes at an earlier developmental stage compared to human evaluators will enable more refined analyses of complex and overlapping signaling pathways in the next phase of our ACE-OF-SPACE project.

Opposing signal sources in a zebrafish embryo generate spatial gradients