Automated Analysis of a Diverse Synapse Population

Abstract

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.

Probabilistic fluorescence-based synapse detection

“…For all three inhibitory synaptic queries, there is a synapse density difference of more than 50% between Layer IV and Layer V. There is also a greater than 50% synaptic density difference between Layer IV and Layer V for excitatory synapses containing VGluT2, as supported by [7] [32] [33] [34]. These results further support the validity of the proposed method by confirming known biological properties of a large dataset.…”

Multifaceted Changes in Synaptic Composition and Astrocytic Involvement in a Mouse Model of Fragile X Syndrome

“…Thus, the total density of synapses was estimated by the sum of the densities of the detected glutamatergic (synapsin + PSD95 markers) and GABAergic synapses (synapsin + GAD + gephyrin markers) resulting in approximately 1.94 synapses per µm 3 of embedded tissue. Because tissue dehydration and embedding with our protocol results in approximately 23% linear shrinkage, or 54% volumetric shrinkage [46], this equals to 0.9 synapses per µm 3 of unprocessed tissue, very similar to the reported synapse density in mouse cortex [58].…”

“…In order to quantitatively analyze large array tomography volumes, it is vital to find an appropriate synapse detection technique. The majority of published synapse detection methods use traditional machine learning approaches [46,47,48,49]. These approaches all consist of a few common steps to detect synapses.…”

Mapping the connectivity of serotonin transporter immunoreactive axons to excitatory and inhibitory neurochemical synapses in the mouse limbic brain

“…Recently, improved automated or semi-automated techniques have been developed for in vitro and in vivo quantification of synapses using fluorescence microscopy (Ippolito and Eroglu 2010; Schätzle et al 2012; Dumitriu et al 2012; Busse and Smith 2013; Fogarty et al 2013; Danielson and Lee 2014; Sanders et al 2015; Klenowski et al 2015; Sigal et al 2015). The combination of fluorescence microscopy, immunolabeling of key pre- and postsynaptic markers of excitatory/inhibitory synapses and automated software analysis, has afforded new high-throughput methodology allowing for rapid quantification of putative neurochemical synapses.…”