Synaptic transmission can be carefully tuned through plasticity mechanisms that regulate synaptic strength, structure, and number. In vivo measurements demonstrate remarkable spine dynamics, with subsets of synapses persisting for months. This correlates with the extreme longevity of certain memories, which can persist for an organism’s lifetime. The molecular basis supporting the long-term stability of specific synapses and the long-term durability of memories remains unknown. At the protein level, most proteins persist for a relatively short amount of time before they are degraded and replaced with new molecules. However, recent work has identified a population of proteins, including those present at the synapse, that are exceptionally long-lived. It has been speculated that long-lived proteins (LLPs) could contribute to long-term synapse stability, function, and memory. Here, we used stable isotope labeling in mammals (SILAM) to first identify LLPs in the post synaptic density (PSD) of the hippocampus and subsequently determine if protein turnover rates varied by sex or following learning. We identified novel synaptic LLPs and found that both sex and experience can regulate synaptic protein turnover rates. We identified sex-dependent changes in protein turnover rates in autism spectrum disorder (ASD) risk genes, including increased stability of Gabrg2, a GABA-A receptor subunit, in male mice. Furthermore, we observed stabilization of a subset of PSD proteins, such as Shank3, following contextual fear conditioning. We propose that sex and experience dependent changes in protein turnover rates could help explain sex-differences in psychiatric risk and aid our understanding of the molecular mechanisms that support learning and memory.

Synapses are the fundamental units of neural computation, yet quantifying their organization across circuit-level scales remains a critical bottleneck in neuroscience. While advances in fluorescent labeling and imaging can generate vast datasets, analysis is often the limiting factor. Several deep learning-based tools have been proposed to ameliorate these issues. However, existing applications primarily focus on dendritic spines and lack robust solutions for segmenting synaptic puncta in dense tissue preparations. To address this, we introduce SynAPSeg, which encompasses an open-source framework for deep learning-based analysis and, to the best of our knowledge, the first large-scale, publicly available instance segmentation dataset specifically curated for synaptic puncta. We use this dataset to train deep learning models that reach the performance of human experts across a unique benchmark dataset. SynAPSeg integrates these models into an interactive interface, with support for multi-dimensional data, enabling fully automated segmentation and quantification pipelines alongside an annotation module for refinement and validation. We demonstrate the framework’s scalability by performing the first comprehensive mapping of nearly 4 million excitatory postsynaptic PSD95 puncta within inhibitory interneurons across the dorsal hippocampus, revealing regional differences in synapse properties. Finally, we show SynAPSeg’s utility for 3D quantification by applying these models to study aging-associated synaptic changes in CA1 parvalbumin (PV)-positive inhibitory neurons. Through this approach, we uncover a reduction in PSD95 density along PV dendrites in the aged CA1, indicating reduced glutamatergic recruitment of PV neurons which could contribute to age-related cognitive decline. Collectively, these results demonstrate that SynAPSeg provides a scalable solution for comprehensively studying synaptic architecture in health and disease.

The mechanisms that underlie glutamatergic synapse organization and function in GABAergic inhibitory interneurons (INs) are not well described, despite evidence that impaired glutamatergic excitation of INs is implicated in psychiatric disorders such as schizophrenia and anxiety. Glutamatergic synapses received by INs have unique basal transmission properties and exhibit distinct synaptic plasticity compared to those received by excitatory neurons, likely due to cell-type specific differences in postsynaptic density (PSD) composition and maintenance mechanisms. In the present study, we show that the interneuron-specific protein Btbd11 regulates excitatory synapse transmission in hippocampal interneurons through promotion of phase separation and support of postsynaptic nanoarchitecture. Btbd11 forms a phase separated protein complex with Psd-95 and TARPγ2 and impacts the stability of TARPγ2 and GluA1 within glutamatergic IN synapses in an expression- and phase separation-dependent manner. Using super resolution imaging, we show that Btbd11 displays nanoscale clustering properties within IN synapses that correlate with Psd-95 nanostructure. Furthermore, genetic deletion of Btbd11 decreases PSD protein expression, reduces synapse size, and disrupts Psd-95 nanocluster organization. These effects manifest as a drastic reduction in glutamatergic synaptic transmission onto INs when Btbd11 is deleted. Together, these data provide insights into a novel cell type-specific synaptic regulatory mechanism in an understudied synapse population.

Background: The prevalence of retinopathy of prematurity (ROP) is studied in populations defined by gestational age (GA) only, GA or birthweight (BW), and GA and BW. Aggregate differences in gestational age week-specific prevalence estimates are unknown. Methods: Literature search of publications between 1971 and 2024 and data extraction using predefined search terms and inclusion criteria. Results: ROP prevalence changes notably at around 28wks GA, decreasing appreciably at older gestational ages. Including BW alongside GA (as is done in some ROP screening guidelines) affects the variability of ROP prevalence data and may obscure the true population-level association between GA and ROP severity. Conclusion: Clinical study populations for ROP research should be defined by GA thresholds only without additional BW inclusion criteria.