AI for Super-Resolution Microscopy Enhancement – CNRS/INRAE Collaboration

In collaboration with CNRS (Eric Hosy’s team) and INRAE

BionomeeX, in collaboration with CNRS and INRAE researchers, developed an AI-powered algorithm to enhance Single-Particle Tracking Photoactivated Localization Microscopy (SptPALM).

The goal of this project is to improve the resolution and accuracy of particle tracking in live-cell imaging, while minimizing phototoxicity and photobleaching.

Project Objectives and Scientific Impact

• 🔗 Extend particle track lengths up to three times longer than achievable with traditional high-power laser approaches, enabling more complete and accurate observations of molecular dynamics.

• 💡 Maintain ultra-low laser conditions, reducing cellular stress and preserving sample integrity during imaging.

• 📊 Facilitate quantitative analysis of molecular behavior at the single-particle level, providing insights into cellular mechanisms, protein interactions, and dynamic biological processes.

BionomeeX’s Contributions

• 🧠 Development of AI algorithms specifically designed to enhance SptPALM data by reconstructing particle trajectories from noisy and low-intensity signals.

• ⚙️ Integration with microscopy workflows, enabling researchers to process large datasets efficiently and extract reliable particle tracking information.

• 🔬 Validation and optimization of the algorithm in collaboration with CNRS and INRAE teams, ensuring robust performance under diverse experimental conditions.

Toward Next-Generation Microscopy

This collaboration demonstrates how AI can push the boundaries of super-resolution microscopy, allowing researchers to obtain highly accurate molecular tracking data under safer, low-intensity imaging conditions.

It opens new possibilities for studying dynamic cellular processes with unprecedented detail, bridging the gap between artificial intelligence and cutting-edge biological imaging.