Abstract
The organization of proteins in the apposed nanodomains of pre- and postsynaptic compartments is thought to play a pivotal role in synaptic strength and plasticity. As such, the alignment between pre- and postsynaptic proteins may regulate, for example, the rate of presynaptic release or the strength of postsynaptic signaling. However, the analysis of these structures has mainly been restricted to subsets of synapses, providing a limited view of the diversity of synaptic protein cluster remodeling during synaptic plasticity. To characterize changes in the organization of synaptic nanodomains during synaptic plasticity over a large population of synapses, we combined STimulated Emission Depletion (STED) nanoscopy with a Python-based statistical object distance analysis (pySODA), in dissociated cultured hippocampal circuits exposed to treatments driving different forms of synaptic plasticity. The nanoscale organization, characterized in terms of coupling properties, of presynaptic (Bassoon, RIM1/2) and postsynaptic (PSD95, Homer1c) scaffold proteins was differently altered in response to plasticity-inducing stimuli. For the Bassoon - PSD95 pair, treatments driving synaptic potentiation caused an increase in their coupling probability, whereas a stimulus driving synaptic depression had an opposite effect. To enrich the characterization of the synaptic cluster remodeling at the population level, we applied unsupervised machine learning approaches to include selected morphological features into a multidimensional analysis. This combined analysis revealed a large diversity of synaptic protein cluster subtypes exhibiting differential activity-dependent remodeling, yet with common features depending on the expected direction of plasticity. The expanded palette of synaptic features revealed by our unbiased approach should provide a basis to further explore the widely diverse molecular mechanisms of synaptic plasticity.
Highlights
Learning and memory at the molecular level is characterized by a remodeling of protein organization at synapses
PySODA identified neighboring clusters found within concentric rings spaced 15 nm apart, providing distance-dependent coupling probabilities (Supplementary Figure 1 and section Materials and Methods)
We implemented a high-throughput analysis framework based on statistical object distance analysis, Python-based statistical object distance analysis (pySODA), to investigate the diversity of synaptic remodeling at the population level and discriminate distinct characteristics of synaptic protein clusters
Summary
Learning and memory at the molecular level is characterized by a remodeling of protein organization at synapses. The wealth of the synaptic proteome gives rise to supercomplexes of structural and functional proteins that encode synaptic function through multiple signaling cascades (Frank and Grant, 2017). This allows synapses to respond to a rich variety of stimuli and shape circuit activity (Branco and Staras, 2009). To understand the molecular mechanisms underlying learning and memory at the circuit level, the heterogeneous synaptic population, beyond the individual synapses, needs to be considered.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
