Probabilistic Fluorescence-Based Synapse Detection
This work addresses the problem of quantifying synapses for researchers in neuroscience and medicine, where abnormalities are linked to mental and neurological disorders, representing a novel method for a known bottleneck.
The authors tackled the challenge of quantitatively studying densely packed mammalian synapses by developing new probabilistic image analysis methods for single-synapse analysis in animal and human brains, enabling detailed proteometric analysis at the individual synapse level.
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.