Apr 29, 2026
Version 1
Automated Quantification of Dopaminergic Neurons in Floating Sections V.1
- Nicolas Giguère1,2,3,4,
- 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: Nicolas Giguère, Alex Tchung, Louis-Éric Trudeau 2026. Automated Quantification of Dopaminergic Neurons in Floating Sections. protocols.io https://dx.doi.org/10.17504/protocols.io.n2bvjkwengk5/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 21, 2026
Last Modified: April 29, 2026
Protocol Integer ID: 315463
Keywords: automated quantification of dopaminergic neuron, quantification of dopaminergic neuron, floating brain section, dopaminergic neuron, ventral tegmental area, brain section, floating section, sections standardized procedure for automated detection, image acquisition, based segmentation, using image acquisition
Funders Acknowledgements:
Aligning Science Across Parkinson's
Grant ID: ASAP-000525
Abstract
Standardized procedure for automated detection and quantification of dopaminergic neurons in the substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) in floating brain sections using image acquisition, AI-based segmentation, and downstream data analysis.
Materials
- Slide scanner (Zeiss AxioScan 7)
- Analysis Workstation
- Zen software (Zeiss)
- NIS-Elements Advance Research software (Nikon) with GA3 pipelines Jobs (https://github.com/Louis-EricTrudeau/Trudeau-lab/tree/main/Mukherjee-2026/GA3), and Batch Deconvolution
- GraphPad Prism
- Brain atlas and TH atlas (see step 11)
Before start
An GA3 macro in NIS-Elements (Nikon imaging software) is an analysis script that automates image processing and quantitative measurements.
All GA3 macros necessary for this protocol are available here: https://github.com/Louis-EricTrudeau/Trudeau-lab/tree/main/Mukherjee-2026/GA3
Sample preparation
Cut the mouse brain coronally at 40µm thickness and collect one 40 µm coronal section out of every three for immunohistochemistry.
Perform an immunohistochemistry labeling for DAPI, TH, NeuN, and RFP (for a genetic mouse model expressing tdTomato in dopamine neurons).
Image Acquisition on Zeiss AxioScan 7 slide scanner
Scan regions of interest with a slide scanner.
Define one region of interest (ROI) per hemisphere (Figure 1) from pre-scan at 5x.
Include all sections containing RFP-positive neurons.
Capture reference screenshots of selected regions.
Figure 1
Acquire images of defined ROIs at 20x in z-stacks at 1µm interval using the following channels: DAPI, TH, NeuN, and RFP.
Use parameters shown in Figure 2 and 3.
Figure 2
Figure 3
TIFF Conversion and Channel Extraction
Convert images to TIFF format in batch using Zen.
Processing - Batch
Batch Method: OME TIFF-Export
Method Parameters: see Figure 4
Figure 4
Apply GA3 “2-Export with 1 channel” on NIS-Elements software to extract the RFP channel.
AI-Based Segmentation
AI-1 Training:
Perform FAST deconvolution on 4 representative images using NIS-Elements software.
Generate segmentation masks using the GA3 "3-Pre-AI-1 macro on decon":
- Adjust BrightSpots (grow) and FilterObjects boxes
- Save images with the segmentation masks.
Train AI-1 using "Jobs Segment Object AI" in NIS-Elements software.
AI-1 Application:
Apply AI-1 to 7 additional images using GA3 “4-Run AI-1 on samples TIFF”.
Manually refine segmentation masks using the NIS binary editor.
Save corrected images and masks.
AI-2 Training:
Train AI-2 with the corrected datasets using "Jobs Segment Object AI" in NIS-Elements software.
AI-2 Batch Processing:
Apply GA3 “6-Run AI-2 on TIFF” to all images in batch mode.
Anatomical Delineation
Delineate the SNc and VTA regions (containing DA neurons) using GA3 "7-Draw SNc and VTA".
Use standard atlas and TH Atlas as references:
atlas TH with SNc and VTA traced.pdf1,011.1KB Automated Cell Counting
Perform automated quantification using GA3 "8-Count SNc and VTA DA neurons".
Export results as CSV files.
Adjust output directory if needed.
Bregma Coordinate Annotation
Open each image manually.
Assign rostro-caudal coordinates relative to bregma in the Excel output file.
Use standard atlas and TH Atlas as references (see document step 11).
Data Analysis
Import data into GraphPad Prism.
Generate curve plots of the number off cell counted for SNc or VTA relative to bregma coordinates.
AUC Calculation
Transfer data to AUC calculation sheet.
Compute area under the curve (AUC) per animal.
Data Reporting
Record AUC values in dataset: "VTA or SNc TD-Tomato+ cells"
Normalize values relative to WT and KO controls.
Record normalized values in: "VTA or SNc TD-Tomato+ cells (%)"
6. Quality Control
Verify segmentation accuracy visually after AI processing.
Confirm correct anatomical delineation using atlas references.