Combining genetic code expansion and bioorthogonal click labeling with deep learning and particle averaging for improved super-resolution imaging

• Applicants: Professor Dr. Philip Kollmannsberger; Professor Dr. Markus Sauer
• Subject Area: Biophysics ; Analytical Chemistry ; Bioinformatics and Theoretical Biology ; Organic Molecular Chemistry - Synthesis and Characterisation ; Structural Biology
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 440773101

In recent years, super-resolution microscopy has been established as a powerful method for subdiffraction-resolution fluorescence imaging of cells and tissue. With further improvements in spatial resolution, e.g. in combination with expansion microscopy, the size and density of fluorescence markers will become the two main limiting factors for super-resolution microscopy in the next years. We address both challenges together using a combination of experimental and computational approaches. On the one hand, we will reduce fluorescent probe size with novel labeling strategies based on the use of unnatural amino acids, and combine them with optimized approaches of expansion microscopy. On the other hand, we will improve the achievable resolution at low labeling density in expanded volumes by training novel 3D neural network architectures on semi-synthetic ground truth to reconstruct full-resolution localization maps, and use improved particle averaging methods to determine the 3D architecture of macromolecular structures. We will then use these new methods to map membrane-bound receptors and intracellular structures with unprecedented resolution.

• DFG Programme: Research Grants