Smart, Event-Based Microscopy for Cell Biology

Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology.

However, microscopes are passive systems and are still very limited in their operating capacity which limits in several ways our ability to identify and image complex biological events in real time and at the proper 4D scales.

They could become much more powerful investigation systems for the life science if they were endowed with user-friendly, unsupervised decision-making algorithms, transforming microscopes into fully responsive and automated measurement devices.

Indeed, we are at a moment when smart systems and artificial intelligence are being used everywhere, including in laboratories to improve the functioning of many (scientific), however outdated preprogrammed microscopy workflows are still being routinely implemented.

The ability to employ real-time image analysis to inform, optimize and adjust the settings of ongoing image acquisitions would be a game changer for studying complex, dynamic cellular processes.

To address this issue, we have developed a pilot software, CyberSco.Py, which enables the possibility to conduct image analysis in real time (using deep learning) to trigger modifications in the acquisition settings thus alleviating the need for manual input and supervision. This allows for the implementation of novel classes of experiments that cannot be achieved with current solutions.

Within the context of this PoC, CyberSco.Py will be developed into a user-friendly software capable of smart automation of microscopy systems an their add-ons (e.g. microfluidics, temperature controls, etc...).

As such, CyberSco.Py has the potential to revolutionize the power and scope of microscopy experiments for quantitative cell biology with broad implications for the microscopy sector.