Studying the molecular mechanisms underlying synaptic transmission in the brain is challenging in part because synapses are tiny (less than a micron), exhibit a wide range of shapes and internal structures, undergo activity-dependent plasticity, and their molecular components are dynamic.

To better understand the mechanisms responsible of the onset of neurodegenerative disorders we must be able to observe the molecular dynamics and interactions of synaptic proteins at their scale: the nanoscale. We use quantitative super-resolution microscopy techniques to characterize molecular interactions inside neuronal cells with unprecedented spatiotemporal resolution.

These techniques come with several layers of complex­ity in their implementation. This has limited their adoption as well as their adaptability to multi-color, multi-modal, and long-term imaging in living neurons. Developing machine learning (ML) assisted frameworks for optical nanoscopy allows real-time optimization of multi-modal live-cell imaging at the nanoscale as well as for quantitative high throughput image analysis. We develop ML-assisted microscopy to characterize activity-dependent remodelling of neuronal proteins.

Our ML approaches not only automate image analysis but also enable multi-dimensional analysis of synaptic nanostructures and of the heterogeneity of the synapse population.