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A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

Anwer, Danish M. LU ; Gubinelli, Francesco LU ; Kurt, Yunus A. LU ; Sarauskyte, Livija LU ; Jacobs, Febe LU ; Venuti, Chiara LU ; Sandoval, Ivette M. ; Yang, Yiyi LU orcid ; Stancati, Jennifer and Mazzocchi, Martina , et al. (2023) In PLoS ONE 18(5 MAY).
Abstract

Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast... (More)

Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast and automatized quantification of microglial cells; this tool was compared with manual quantification (ground truth), and with alternative free-ware such as the threshold-based ImageJ and the machine learning-based Ilastik. We first trained the algorithms on brain tissue obtained from rats and non-human primate immunohistochemically labelled for microglia. Subsequently we validated the accuracy of the trained algorithms in a preclinical rodent model of Parkinson's disease and demonstrated the robustness of the algorithms on tissue obtained from mice, as well as from images provided by three collaborating laboratories. Our results indicate that machine learning algorithms can detect and quantify microglial cells in all the three mammalian species in a precise manner, equipotent to the one observed following manual counting. Using this tool, we were able to detect and quantify small changes between the hemispheres, suggesting the power and reliability of the algorithm. Such a tool will be very useful for investigation of microglial response in disease development, as well as in the investigation of compatible novel therapeutics targeting the brain. As all network weights and labelled training data are made available, together with our step-by-step user guide, we anticipate that many laboratories will implement machine learning-based quantification of microglial cells in their research.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
18
issue
5 MAY
article number
e0284480
publisher
Public Library of Science (PLoS)
external identifiers
  • pmid:37126506
  • scopus:85159250063
ISSN
1932-6203
DOI
10.1371/journal.pone.0284480
language
English
LU publication?
yes
id
b9ee1109-ff27-4e02-a45e-6153cff5a14e
date added to LUP
2023-08-15 09:17:44
date last changed
2024-04-20 00:33:33
@article{b9ee1109-ff27-4e02-a45e-6153cff5a14e,
  abstract     = {{<p>Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast and automatized quantification of microglial cells; this tool was compared with manual quantification (ground truth), and with alternative free-ware such as the threshold-based ImageJ and the machine learning-based Ilastik. We first trained the algorithms on brain tissue obtained from rats and non-human primate immunohistochemically labelled for microglia. Subsequently we validated the accuracy of the trained algorithms in a preclinical rodent model of Parkinson's disease and demonstrated the robustness of the algorithms on tissue obtained from mice, as well as from images provided by three collaborating laboratories. Our results indicate that machine learning algorithms can detect and quantify microglial cells in all the three mammalian species in a precise manner, equipotent to the one observed following manual counting. Using this tool, we were able to detect and quantify small changes between the hemispheres, suggesting the power and reliability of the algorithm. Such a tool will be very useful for investigation of microglial response in disease development, as well as in the investigation of compatible novel therapeutics targeting the brain. As all network weights and labelled training data are made available, together with our step-by-step user guide, we anticipate that many laboratories will implement machine learning-based quantification of microglial cells in their research.</p>}},
  author       = {{Anwer, Danish M. and Gubinelli, Francesco and Kurt, Yunus A. and Sarauskyte, Livija and Jacobs, Febe and Venuti, Chiara and Sandoval, Ivette M. and Yang, Yiyi and Stancati, Jennifer and Mazzocchi, Martina and Brandi, Edoardo and O'Keeffe, Gerard and Steece-Collier, Kathy and Li, Jia Yi and Deierborg, Tomas and Manfredsson, Fredric P. and Davidsson, Marcus and Heuer, Andreas}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  number       = {{5 MAY}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0284480}},
  doi          = {{10.1371/journal.pone.0284480}},
  volume       = {{18}},
  year         = {{2023}},
}