Real-Time high-content Super-Resolution Imaging of ES Cell States

Introduction

The development of super-resolution (SR) microscopy in recent years has revolutionized cell biology, breaking the diffraction limit of light microscopy by an order of magnitude. However, SR is currently incompatible with high-content imaging.

Project Overview

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).

Real-Time Decision-Making Module

We will deploy a real-time decision-making module, which will:

• Continuously monitor our SNAP-tagged cells using fast fluorescence imaging.
• Fix the desired cells once a change is detected.
• Switch to SR mode.

Collaborative Effort

By bringing together seven world-leading experts from four different countries, combining basic and applied research and industry, we propose several firsts:

1. The first endogenously-labelled clone library of SNAP-tag fusion proteins.
2. Utilize machine learning (ML) for real-time automated decision making, autonomously switching from fast conventional to SR imaging.
3. Combine high content with SR imaging.
4. Integrate novel, cutting-edge technologies, namely:
   • SR Radial Fluctuations (SRRF)
   • NanoJ-Fluidics
   • Single Molecule Localization Microscopy (SMLM)
   • Structured Illumination Microscopy (SIM)
5. Collect large scale imaging datasets of cell states in ESCs.
6. Provide cell-cycle stage-dependent nanoscale localization of selected nuclear and chromatin proteins (e.g. H3.3) during early ESC differentiation.

Conclusion

RT-SuperES will provide the scientific community with the first-of-its-kind commercial real-time SR-high-content imaging system and the first library of endogenously SNAP-tagged ESC clones, which are ideal, among many other things, for SR imaging.