Real Time high content Super Resolution Imaging of ES Cell States
The development of super-resolution (SR) microscopy in recent years has revolutionized cell biology, breaking the diffraction limit of light microscopy by order of magnitude. However, SR is currently incompatible with high-content...
ver más
30/06/2027
Líder desconocido
3M€
Presupuesto del proyecto: 3M€
Líder del proyecto
Líder desconocido
Fecha límite participación
Sin fecha límite de participación.
Financiación
concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto
el día 2022-12-22
Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.
Información adicional privada
No hay información privada compartida para este proyecto. Habla con el coordinador.
Participantes
Conecta tu I+D
¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video
Información proyecto RT-SuperES
Duración del proyecto: 54 meses
Fecha Inicio: 2022-12-22
Fecha Fin: 2027-06-30
Líder del proyecto
Líder desconocido
Presupuesto del proyecto
3M€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
The development of super-resolution (SR) microscopy in recent years has revolutionized cell biology, breaking the diffraction limit of light microscopy by order of magnitude. However, SR is currently incompatible with high-content imaging. RT-SuperES will provide a groundbreaking and affordable technology with automated SR capabilities beyond the state-of-the-art. To this end, we will generate a library of endogenously-labelled SNAP-tag fusion proteins in mouse embryonic stem cells (ESCs), and deploy a real-time decision-making module, which will continuously monitor our SNAP-tagged cells using fast fluorescence imaging, and, once a change is detected, will fix the desired cells, and switch to SR mode. By bringing together seven world-leading experts from four different countries, combining basic and applied research and industry, we propose several firsts: a) The first endogenously-labelled clone library of SNAP-tag fusion proteins; b) Utilize machine learning (ML) for real-time automated decision making, autonomously switching from fast conventional to SR imaging; c) Combine high content with SR imaging; d) Integrate novel, cutting-edge technologies, namely SR Radial Fluctuations (SRRF), NanoJ-Fluidics, Single Molecule Localization Microscopy (SMLM) and Structured Illumination Microscopy (SIM); e) Collect large scale imaging datasets of cell states in ESCs, and f) Provide cell-cycle stage-dependent nanoscale localization of selected nuclear and chromatin proteins (e.g. H3.3), during early ESC differentiation. RT-SuperES will provide the scientific community with the first-of-its-kind commercial real-time SR-highcontent imaging system, and the first library of endogenously SNAP-tagged ESC clones, which are ideal, among many other things, for SR imaging.