Objectives

Light microscopy is routinely used in research and the diagnostic field around the world, enabling the visualisation of objects down to 250 nm in size.

Super-resolution (SR) microscopy is gaining ground over optical microscopy as it bypasses the diffraction limit and significantly improves the resolution capacity.

Funded by the European Innovation Council, the RT-SuperES project aims to develop an automated SR technology that offers the possibility of real-time imaging that can switch from conventional to SR fluorescence microscopy.

In the scope of RT-SuperES, we will use this SR system to study the differentiation of embryonic stem cells.

Generation of an endogenously-tagged Halo-tag-fusion proteins in mouse ESCs:

We designed three plasmids (small DNA packages) that can enter stem cells and integrate into their DNA.
They add a small tag so we can track the cell’s own proteins over time.
The plasmids include selection markers and are designed to minimise disruption to normal gene function.

Development of AI-driven adaptive microscopy and fluidics:

We are developing AI tools that automatically analyse microscope images and recognise interesting cell states as they happen.
Based on these decisions, the system can adjust how cells are imaged or trigger further processing, such as advanced imaging or sample preparation.
This allows the microscope to focus on the most relevant cells while reducing unnecessary imaging and stress on the samples.

Development of a unique SR microscope that will smoothly offer multiple levels of resolution, compatible with both live and fixed cells:

We are building a new microscope that can switch smoothly between standard and super-resolution imaging.
The system works with both living cells and fixed samples, allowing detailed imaging at different stages of an experiment.
This flexibility makes it possible to capture fast cellular changes and then zoom in to see fine structural details.

Integration of the SR system into the high-content

imaging platform:

We are integrating the super-resolution microscope into a high-content imaging platform capable of automatically handling many samples.
This allows large numbers of cells to be imaged consistently and efficiently.
The result is detailed, high-resolution data collected at scale.

Using the Halo-tag library to demonstrate automated high content SR imaging using RT-SuperES, to characterise at unprecedented detail, cellular states in ESCs, and to select individual clones for detailed examination:

We use the Halo-Tag stem cell library to run automated, high-content super-resolution imaging with RT-SuperES.
This lets us map stem cell states in far greater detail than standard imaging.
The system can then pick specific cell lines (clones) for deeper follow-up analysis.