Abstract
Deeper exploration of the brain’s vast synaptic networks will require new tools for high-throughput structural and molecular profiling of the diverse populations of synapses that compose those networks. Fluorescence microscopy (FM) and electron microscopy (EM) offer complementary advantages and disadvantages for single-synapse analysis. FM combines exquisite molecular discrimination capacities with high speed and low cost, but rigorous discrimination between synaptic and non-synaptic fluorescence signals is challenging. In contrast, EM remains the gold standard for reliable identification of a synapse, but offers only limited molecular discrimination and is slow and costly. To develop and test single-synapse image analysis methods, we have used datasets from conjugate array tomography (cAT), which provides voxel-conjugate FM and EM (annotated) images of the same individual synapses. We report a novel unsupervised probabilistic method for detection of synapses from multiplex FM (muxFM) image data, and evaluate this method both by comparison to EM gold standard annotated data and by examining its capacity to reproduce known important features of cortical synapse distributions. The proposed probabilistic model-based synapse detector accepts molecular-morphological synapse models as user queries, and delivers a volumetric map of the probability that each voxel represents part of a synapse. Taking human annotation of cAT EM data as ground truth, we show that our algorithm detects synapses from muxFM data alone as successfully as human annotators seeing only the muxFM data, and accurately reproduces known architectural features of cortical synapse distributions. This approach opens the door to data-driven discovery of new synapse types and their density. We suggest that our probabilistic synapse detector will also be useful for analysis of standard confocal and super-resolution FM images, where EM cross-validation is not practical.
Highlights
Deeper understanding of the basic mechanisms and pathologies of the brain’s synaptic networks will require advances in our quantitative understanding of structural, molecular, and functional diversity within the vast populations of individual synapses that define those networks [1] [2] [3] [4]
Brain function results from communication between neurons connected by complex synaptic networks
Abnormalities of synapse numbers or their molecular components have been implicated in a variety of mental and neurological disorders
Summary
Deeper understanding of the basic mechanisms and pathologies of the brain’s synaptic networks will require advances in our quantitative understanding of structural, molecular, and functional diversity within the vast populations of individual synapses that define those networks [1] [2] [3] [4]. We introduce and characterize a novel image analysis method for automated detection and molecular measurement of individual synapses and single-synapse molecular profiling of diverse synapse populations from multiplex fluorescence microscopy (muxFM) image data. The proposed methodology for structural identification and molecular analysis of single synapses at scale will be an enabling step toward deeper experimental analysis of the relationships between synaptic structure, molecules, and function. The relationship in particular with [8] will be further discussed later in this paper
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