Probabilistic Fluorescence-Based Synapse Detection

Brain function results from communication between neurons connected by complex synaptic networks. Synapses are themselves highly complex and diverse signaling machines, containing protein products of hundreds of different genes, some in hundreds of copies, arranged in precise lattice at each individual synapse. Synapses are fundamental not only to synaptic network function but also to network development, adaptation, and memory. In addition, abnormalities of synapse numbers or molecular components are implicated in most mental and neurological disorders. Despite their obvious importance, mammalian synapse populations have so far resisted detailed quantitative study. In human brains and most animal nervous systems, synapses are very small and very densely packed: there are approximately 1 billion synapses per cubic millimeter of human cortex. This volumetric density poses very substantial challenges to proteometric analysis at the critical level of the individual synapse. The present work describes new probabilistic image analysis methods for single-synapse analysis of synapse populations in both animal and human brains.Comment: Current awaiting peer revie

Probabilistic fluorescence-based synapse detection

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.

Fragile X Syndrome (FXS), a common inheritable form of intellectual disability, is known to alter neocortical circuits. However, its impact on the diverse synapse types comprising these circuits, or on the involvement of astrocytes, is not well known. We used immunofluorescent array tomography to quantify different synaptic populations and their association with astrocytes in layers 1 through 4 of the adult somatosensory cortex of a FXS mouse model, the FMR1 knockout mouse. The collected multi-channel data contained approximately 1.6 million synapses which were analyzed using a probabilistic synapse detector. Our study reveals complex, synapse-type and layer specific changes in the neocortical circuitry of FMR1 knockout mice. We report an increase of small glutamatergic VGluT1 synapses in layer 4 accompanied by a decrease in large VGluT1 synapses in layers 1 and 4. VGluT2 synapses show a rather consistent decrease in density in layers 1 and 2/3. In all layers, we observe the loss of large inhibitory synapses. Lastly, astrocytic association of excitatory synapses decreases. The ability to dissect the circuit deficits by synapse type and astrocytic involvement will be crucial for understanding how these changes affect circuit function, and ultimately defining targets for therapeutic intervention

Developing an object-based colocalisation analysis method to measure synaptic diversity

Protein colocalisation is of particular importance in the study of protein function. To address the inadequacies of previous colocalisation analysis methods, the novel Vicinity-based Localisation Adjacency Determination (VLAD) object-based colocalisation analysis method was developed. VLAD provides three main colocalisation measurements: the proportion of colocalising objects in a dataset, the probability of true colocalisation for individual objects, and the spatial relationship (distance) between colocalising objects. VLAD, validated by extensive testing in simulated data in a wide range of conditions (localisation densities, levels of colocalisation and colocalisation distances), was shown to outperform the state-of-the-art colocalisation analysis method SODA (Statistical Object Distance Analysis). VLAD was used to study the distribution and colocalisation of three key synaptic proteins: GluN1 (obligatory subunit of NMDA receptors), PSD95 and SAP102 (scaffolding proteins at excitatory synapses). In total, over 62.5 million puncta or puncta assemblies of these proteins were analysed in the mouse hippocampus during early development, making this the largest triple colocalisation brain mapping study of this sort. GluN1, PSD95 and SAP102 associate in a combinatorial fashion, giving rise to 7 synaptic protein punctum subtypes. The subtype compositions of the hippocampal subregions diverge in development and the differences in subtype compositions in the adult hippocampus may underlie the distinct functions performed by each component of the hippocampal circuit. It was found that a high proportion of the puncta of each protein were non-colocalising in the adult mouse – 67% of GluN1, 48% of PSD95 and 27% of SAP102. Interestingly, NMDA receptors (GluN1) appear to colocalise with PSD95 only in the presence of SAP102, hinting at a possible codependence between these proteins. This study demonstrated the potential of VLAD in the field of brain mapping