Probabilistic Fluorescence-Based Synapse Detection

TL;DR

This paper introduces new probabilistic image analysis techniques for detailed, single-synapse level study of synaptic populations in animal and human brains, addressing longstanding challenges in neuroscience research.

Contribution

The work presents novel probabilistic methods enabling quantitative analysis of individual synapses in densely packed brain tissue.

Findings

Effective analysis of synapse populations achieved

Application demonstrated in both animal and human brain tissues

Enhanced understanding of synaptic diversity and structure

Abstract

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.