Imagine a camera that can watch cells in a crowd and instantly zoom in with extreme detail the moment a cell starts to change. Instead of guessing when to look, an AI acts like a smart scout that triggers a high-definition snapshot only when something important happens. This allows scientists to see the tiny inner workings of stem cells without missing the exact moment they transform.
Current super-resolution microscopy is too slow for high-content imaging, meaning researchers miss critical, fast-moving cellular transitions. This creates a bottleneck in drug screening and stem cell research where timing is everything.
A commercial real-time super-resolution imaging system and a library of endogenously SNAP-tagged ESC clones.
If you are a drug discovery firm dealing with slow and imprecise drug screening assays — this project developed a commercial imaging system and novel assays that aid in developing new medicines by tracking cell fate transitions at a nanoscale.
If you are a stem cell therapy developer dealing with the difficulty of monitoring how cells transition into different states — this project developed an ML-based control system for high-content SR microscopes that enables real-time observations of cellular behavior.
If you are a microscopy manufacturer dealing with the incompatibility between high-content imaging and super-resolution — this project developed an affordable technology with automated SR capabilities that establishes a new premium market for advanced SR technology.
Based on available project data, the specific price is not listed, but the objective is to provide a groundbreaking and affordable technology.
Yes, the project aims to produce commercial imaging systems and novel assays specifically designed for the biotechnology and pharmaceutical industries.
The project will result in a library of endogenously Halo-tagged proteins in embryonic stem cells that will be freely available to academia and industry.
The system integrates a Machine Learning (ML)-based control module that autonomously switches from fast conventional imaging to super-resolution mode when a change is detected.
The project period runs from 2023-07-01 to 2027-06-30, suggesting the final commercial system will be ready toward the end of this window.
The consortium is composed of 10 partners across 4 countries (DE, FR, IL, PT). It shows a strong research-to-market pipeline with 5 research organizations and 3 universities, balanced by 2 industry SMEs, resulting in a 20% industry ratio. This structure suggests a heavy focus on technical development with a clear path toward commercialization through the SME partners.
Contact The Hebrew University of Jerusalem
Contact us to explore licensing opportunities for the SNAP-tag fusion protein library.
