Analysis, control, and engineering of spatiotemporal pattern formation

Informacje na temat projektu

ACE-OF-SPACE

Identyfikator umowy o grant: 863952

DOI

10.3030/863952

Projekt został zamknięty

Data podpisania przez KE 18 Lutego 2020

Data rozpoczęcia 1 Lipca 2020

Data zakończenia 30 Czerwca 2026

Finansowanie w ramach

• EXCELLENT SCIENCE - European Research Council (ERC)

Koszt całkowity

€ 1 997 750,00

Wkład UE

€ 1 997 750,00

Koordynowany przez

UNIVERSITAT KONSTANZ
Germany

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

Okres sprawozdawczy: 2023-07-01 do 2024-12-31

Podsumowanie kontekstu i ogólnych celów projektu

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.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

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.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

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