Apr 29, 2026
Confocal imaging and image processing with ImageJ
- Alex Tchung1,2,3,4,
- Louis-Éric Trudeau1,5,2,3,4,6
- 1Department of pharmacology and physiology, Faculty of Medicine, Université de Montréal;
- 2Neural Signaling and Circuitry research group (SNC);
- 3Center for Interdisciplinary Research on the Brain and Learning (CIRCA);
- 4Institut Courtois d’innovation biomédicale;
- 5Department of neuroscience and physiology, Faculty of Medicine, Université de Montréal;
- 6Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
Protocol Citation: Alex Tchung, Louis-Éric Trudeau 2026. Confocal imaging and image processing with ImageJ. protocols.io https://dx.doi.org/10.17504/protocols.io.5qpvoe629l4o/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: April 20, 2026
Last Modified: April 29, 2026
Protocol Integer ID: 315416
Keywords: confocal imaging, confocal microscope, image processing with imagej, subsequent image processing with imagej, imagej, quantitative analysis of microglial cell count, iba1 fluorescence intensity, assessment of astrocyte area, image processing, microglial cell count, subsequent image processing, astrocyte area, tyrosine hydroxylase, gfap intensity, analysis of tyrosine hydroxylase, dopamine transporter
Funders Acknowledgements:
Aligning Science Across Parkinson's
Grant ID: ASAP-000525
Abstract
This protocol describes confocal imaging using a confocal microscope and subsequent image processing with ImageJ, as well as data compilation. The workflow includes quantitative analysis of microglial cell count and morphology, Iba1 fluorescence intensity, and assessment of astrocyte area and GFAP intensity. It also includes analysis of tyrosine hydroxylase (TH) and dopamine transporter (DAT) expression.
Materials
Olympus Fluoview FV1000 point-scanning confocal microscope
ImageJ software
ImageJ Macro Language
Python programming language
pandas library
numpy library
Statistical analysis and graphing software Prism.
Imaging
The imaging is performed using an Olympus Fluoview FV1000 point-scanning confocal microscope.
To minimize nonspecific bleed-through signal, images were acquired sequentially with laser excitation at specific wavelengths: 488 nm, 543 nm, and 633 nm.
Image acquisition utilized a 60x (NA 1.42) objective to ensure optimal resolution.
Three specific coronal brain slices were selected for analysis.
- The first slice was chosen when the corpus callosum from both hemispheres was nearly touching.
- The second slice was selected when the anterior commissure formed a straight line.
- The third slice included the hippocampus.
Regions of interest within the dorsal and ventral striatum were defined for further analysis.
For the reconstruction and analysis of microglia and astrocytes, z-stack images were captured at intervals of 2 microns, spanning a total thickness of 20 microns to account for slice shrinkage after mounting.
For tyrosine hydroxylase and dopamine transporter analysis, to capture the desired cellular depth, images were acquired at 15 micrometers below the surface of the slice.
Image processing
To facilitate image analysis, custom scripts written in ImageJ Macro Language were developed and cited in the below sections.
These custom scripts enabled precise and efficient analysis of the acquired images.
Microglia and astrocyte analysis:
The first step involved segmenting the images to identify cell bodies.
This segmentation process was done using a homemade ImageJ macro, which was further modified to trace the arborization of microglia and astrocytes.
Parameters such as microglia density, soma area, soma fluorescence intensity, soma perimeter, soma circularity and the area and intensity of the arborization were quantified.
For astrocytes, complex arborization only permitted surface and fluorescence intensity analysis.
Tyrosine hydroxylase and dopamine transporter analysis:
Prior to quantification, image preprocessing techniques were applied to enhance the accuracy and reliability of the analysis.
Two commonly utilized preprocessing methods were employed:
- convoluted background subtraction
- rolling ball background subtraction
The analysis of TH and DAT involved quantifying fluorescence intensity and surface occupied to provide insights into the expression levels and spatial distribution.
Data compilation
A substantial amount of data was generated through the analysis of images using ImageJ macros.
The resulting data was saved in individual CSV files, which contained the measurements extracted from the images.
To facilitate further analysis and interpretation, these CSV files were processed and compiled using the Python programming language, specifically leveraging the capabilities of the pandas and numpy libraries. Thus, the individual CSV files containing the extracted image data were compiled into a unified dataset, enabling comprehensive analysis.
Statistical analysis
The data was then imported in the statistical analysis and graphing software Prism for further statistical analysis.
Protocol references
Schneider et al., 2023.