Automated Analysis of a Diverse Synapse Population

Synapses of the mammalian central nervous system are highly diverse in function and molecular composition. Synapse diversity per se may be critical to brain function, since memory and homeostatic mechanisms are thought to be rooted primarily in activity-dependent plastic changes in specific subsets of individual synapses. Unfortunately, the measurement of synapse diversity has been restricted by the limitations of methods capable of measuring synapse properties at the level of individual synapses. Array tomography is a new high-resolution, high-throughput proteomic imaging method that has the potential to advance the measurement of unit-level synapse diversity across large and diverse synapse populations. Here we present an automated feature extraction and classification algorithm designed to quantify synapses from high-dimensional array tomographic data too voluminous for manual analysis. We demonstrate the use of this method to quantify laminar distributions of synapses in mouse somatosensory cortex and validate the classification process by detecting the presence of known but uncommon proteomic profiles. Such classification and quantification will be highly useful in identifying specific subpopulations of synapses exhibiting plasticity in response to perturbations from the environment or the sensory periphery. Author Summary: Synaptic connections are fundamental to every aspect of brain function. There is growing recognition that individual synapses are the key sites of the functional plasticity that allows brain circuits to store and retrieve memories and to adapt to changing demands and environments. There is also a growing consensus that many neurological, psychiatric, neurodevelopmental and neurodegenerative disorders may be best understood at the level of specific, proteomically-defined synapse subsets. Here, we introduce and validate computational analysis tools designed to complement array tomography, a new high-resolution proteomic imaging method, to enable the analysis of diverse synapse populations of unprecedentedly large size at the single-synapse level. We expect these new single-synapse classification and analysis tools to substantially advance the search for the specific physical traces, or engrams, of specific memories in the brains synaptic circuits. We also expect these same tools to be useful for identifying the specific subsets of synapses that are impacted by the various synaptically-rooted afflictions of the brain.