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SynapseJ: An Automated, Synapse Identification Macro for ImageJ.

Juan Felipe Moreno Manrique1, Parker R Voit1, Kathryn E Windsor1

  • 1Department of Biology, Trinity University, San Antonio, TX, United States.

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|October 22, 2021
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Summary
This summary is machine-generated.

This study introduces an open-source ImageJ macro for analyzing synaptic protein colocalization in confocal microscopy images. The tool accurately identifies synapses, overcoming limitations of traditional methods and enabling new research avenues.

Keywords:
appositionexcitatoryinhibitorymaximaoverlappunctathreshold

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Area of Science:

  • Neuroscience
  • Cell Biology
  • Microscopy and Imaging

Background:

  • Electron microscopy is the gold standard for synapse detection but has limitations.
  • Immunostaining for pre- and post-synaptic proteins infers synapses by marker apposition.
  • Current analysis tools for identifying these appositions are lacking.

Purpose of the Study:

  • To develop and validate an open-source ImageJ/FIJI macro for accurate synapse identification using multichannel confocal microscopy.
  • To provide a tool for analyzing pre- and post-synaptic protein colocalization in situ.
  • To overcome limitations of existing methods for synapse analysis.

Main Methods:

  • Utilized ImageJ/FIJI macro for analyzing multichannel, z-stack confocal images.
  • Developed two methods to identify puncta in sparsely or densely labeled images.
  • Applied methods to analyze bassoon (pre-synaptic) with gephyrin and N-methyl-d-aspartate (NMDA) receptor subunit 1 (NR1) (post-synaptic) markers.

Main Results:

  • Successfully identified inhibitory synapses using bassoon and gephyrin in various brain regions.
  • Enabled correlation of morphometry data by identifying specifically overlapping puncta.
  • Extended analysis to examine puncta overlapping with cell-type specific cytoplasmic markers, including PPN-SNc connections.

Conclusions:

  • The developed macro provides a robust, freely available tool for analyzing synaptic protein colocalization in situ.
  • This innovation expands the capabilities for studying synapse structure and function.
  • The open-source nature of the code encourages further development and application in neuroscience research.