Probabilistic fluorescence-based synapse detection

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.

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

“…Overall synapse densities A much more accurate detection of synapses is achieved by using combinations of synaptic makers, ideally at least one presynaptic and one postsynaptic marker, as specified by our synapse detection algorithm [43,44]. Indeed, using such combinations of synaptic markers, the detected synapse densities and distributions in WT mice are consistent with previous estimates as shown in Figure 4.…”

“…Figure 2 shows an example pipeline going from the raw input data to the result probability map. The details of the synapse detection method are described in [43] and its applications to antibody characterization are studied in [44]. Both report extensive validation, indicating that the tool is ready to address the novel biological questions in this work.…”

“…Since PSD-95 is a postsynaptic protein and synapsin is presynaptic protein, this simple glutamatergic synapse query follows the known biological model for a glutamatergic synapses. In [43], the query comprises only of presynaptic and postsynaptic makers. In this work, we have expanded the query to comprise of presynaptic, postsynaptic, and astrocytic markers.…”

A Computational Synaptic Antibody Characterization and Screening Framework for Array Tomography

“…The approach is outlined in Figure 2. The proposed SACT combines automatic synapse detection from (Simhal et al, 2017) with a novel puncta detection and characterization computational tool. We should note that SACT is a framework; additional measurements can be added and adapted as needed depending on the desired antibody characterization features.…”

“…The data used for validating SACT was derived from serial sections of plastic-embedded tissue that was immunofluorescently labeled with the tested antibody alongside one or more reference antibodies, chosen depending on the antigen. A selected area was then imaged on at least 3 consecutive sections, the (Simhal et al, 2017) to study the desired properties of the antibody.…”