Alexei M. Bygrave

SynAPSeg provides a framework and dataset for deep learning-based synapse detection and quantification, achieving expert-level performance and revealing age-related synaptic changes.

The paper identifies sex and experience-dependent regulation of synaptic protein turnover using SILAM in the hippocampus, finding sex-specific changes in ASD risk genes and experience-induced stabilization of PSD proteins.

This study identifies Btbd11 as a regulator of glutamatergic synapse organization in GABAergic inhibitory interneurons, showing its role in phase separation and synaptic nanoarchitecture.

The paper identifies 70 extracellular proteins interacting with AMPARs during synaptic plasticity, showing OBCAM and NTM regulate AMPAR mobility.

This study identifies experience-induced changes in the hippocampal PSD proteome and phosphoproteome in mice following inhibitory avoidance training or immediate shock.

This study identifies Btbd11 as an inhibitory interneuron-specific protein that supports glutamatergic synapse structure and function, with implications for neural circuitry and behavior.

This paper introduces SLED, a splicing-based method to control gene expression in specific cell types, demonstrating its utility in targeting neurons, photoreceptors, and cancer cells.

The paper develops tools to track synaptic plasticity in vivo by imaging endogenous AMPA receptors and generates a detailed spatiotemporal map of synaptic changes during behavior.

The paper decodes working memory-related neural connectivity in mice and humans across tasks, finding task-specific and distributed patterns with high prediction accuracy.

This paper introduces TKIT, a CRISPR/Cas9 method for precise genomic knock-in in neurons, achieving up to 42% efficiency in mouse neurons and demonstrating its use in rat neurons for visualizing endogenous proteins.

The paper assesses multiple electrophysiological metrics of long-range neuronal communication in a mouse model and finds significant lack of redundancy among them.