SynapTrack: An automated tool for synapse quantification

Area of Science:

• Neuroscience
• Cell Biology
• Biophysics

Background:

• Accurate synapse quantification is crucial for understanding neural function and disease.
• High-throughput fluorescence imaging presents challenges for reliable and automated synapse detection.

Purpose of the Study:

• To develop and validate SynapTrack, an automated FIJI/ImageJ-based tool for synapse quantification.
• To enable accurate and scalable measurement of synapses in both cultured neurons and brain tissue sections.

Main Methods:

• SynapTrack utilizes channel-specific preprocessing, background subtraction, and colocalized puncta detection (SynQuant).
• For cultured neurons, it includes cell counting and dendritic length measurement for normalization.
• An adapted workflow, SynapTrack_Tissue, was developed for brain tissue sections using colocalization alone.

Main Results:

• SynapTrack reliably quantified excitatory and inhibitory synapses in hippocampal cultures, showing expected predominance of excitatory contacts.
• The tool reduced inter-sample variability through structural normalization and detected more synapses than SynBot.
• SynapTrack_Tissue provided stable measurements in heterogeneous neuropil regions without cellular normalization.

Conclusions:

• SynapTrack offers a standardized, scalable, and automated framework for synapse quantification in diverse biological samples.
• The tool's portability across imaging systems enhances its utility in neuroscience research.
• SynapTrack improves upon existing methods by offering higher detection rates and better preservation of biological differences.