Dec 07, 2023

Public workspaceNeuromelanin-positive Neuron Density in Substantia Nigra Image Analysis

DOI
dx.doi.org/10.17504/protocols.io.14egn2kq6g5d/v1
  • 1Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK;
  • 2Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20 815, USA;
  • 3QSBB, 1 Wakefield Street London Greater London WC1N 1PJ, UK
Hemanth Ramesh Nelvagal
Open access
DOI: dx.doi.org/10.17504/protocols.io.14egn2kq6g5d/v1
External link: https://www.ucl.ac.uk/ion/research/our-departments/clinical-and-movement-neurosciences/centres-and-projects/queen-square-1
Protocol Citation: Hemanth Ramesh Nelvagal, Toby J Curless, Zane Jaunmuktane 2023. Neuromelanin-positive Neuron Density in Substantia Nigra Image Analysis. protocols.io https://dx.doi.org/10.17504/protocols.io.14egn2kq6g5d/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 27, 2023
Last Modified: May 31, 2024
Protocol Integer ID: 81103
Keywords: ASAPCRN, Neuromelanin, Neuron Density, Substantia Nigra, H&E, Annotation, Script, QuPath, Image Analysis
Funders Acknowledgement:
The Michael J. Fox Foundation for Parkinson’s Research (MJFF) and the Aligning Science Across Parkinson’s (ASAP) Initiative
Grant ID: ASAP-000478
Abstract
The protocol covers the steps to measure neuromelanin-positive neuron density in substantia nigra using image analysis tools including NZConnect (Hamamatsu), a web-based whole-slide image (WSI) viewer, Cellpose and QuPath.
Annotation and Deconvolution
Annotation and Deconvolution
Manually annotate the substantia nigra on NZConnect (Hamamatsu), a web-based whole-slide image (WSI) viewer.

Computational step
Download the annotations using a Python script, and then import into QuPath [1] using a Groovy script
Segmentation and Calculating Neuromelanin-positive Cell Density
Segmentation and Calculating Neuromelanin-positive Cell Density
Segment neuromelanin cells using Cellpose [2,3] via the QuPath Cellpose extension [4], followed by an object classifier to filter out non-specific detections.
Computational step
Calculate neuromelanin-positive cell density by the number of neuromelanin-positive cells divided by the area of the region of interest (neuromelanin-positive cells per mm^2).




Note
References
[1] Bankhead, P., Loughrey, M.B., Fernández, J.A. et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017). https://doi.org/10.1038/s41598-017-17204-5

[2] Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). https://doi.org/10.1038/s41592-020-01018-x

[3] Pachitariu, M., Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022). https://doi.org/10.1038/s41592-022-01663-4

[4] BIO/Pqupath-extension-cellpose
https://github.com/BIOP/qupath-extension-cellpose
Protocol references
References
[1] Bankhead, P., Loughrey, M.B., Fernández, J.A. et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017). https://doi.org/10.1038/s41598-017-17204-5

[2] Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). https://doi.org/10.1038/s41592-020-01018-x

[3] Pachitariu, M., Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022). https://doi.org/10.1038/s41592-022-01663-4

[4] BIO/Pqupath-extension-cellpose
https://github.com/BIOP/qupath-extension-cellpose