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

• Published in: PloS one Vol. 18; no. 5; p. e0284480
• Main Authors: Anwer, Danish M., Gubinelli, Francesco, Kurt, Yunus A., Sarauskyte, Livija, Jacobs, Febe, Venuti, Chiara, Sandoval, Ivette M., Yang, Yiyi, Stancati, Jennifer, Mazzocchi, Martina, Brandi, Edoardo, O’Keeffe, Gerard, Steece-Collier, Kathy, Li, Jia-Yi, Deierborg, Tomas, Manfredsson, Fredric P., Davidsson, Marcus, Heuer, Andreas
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2023; Public Library of Science (PLoS)
• Subjects: Algorithms; Alzheimer's disease; Alzheimers disease; Analysis; Animal tissues; Animals; Basic Medicine; Biocompatibility; Biology and Life Sciences; Biomedical research; Brain; Brain research; Care and treatment; Central nervous system diseases; Comparative analysis; Computer and Information Sciences; Data mining; Diagnosis; Engineering and Technology; EU directives; Gene expression; Health aspects; Hemispheres; Humidity; Inflammation; Laboratories; Laboratory animals; Learning algorithms; Leukocyte migration; Machine Learning; Macrophages; Mammals; Medical and Health Sciences; Medicin och hälsovetenskap; Medicine and Health Sciences; Medicinska och farmaceutiska grundvetenskaper; Mice; Microglia; Microglia - metabolism; Microglial cells; Monkeys & apes; Morphology; Movement disorders; Mutation; Nervous system diseases; Neural networks; Neurodegenerative diseases; Neurosciences; Neurovetenskaper; Open source software; Parkinson Disease - metabolism; Parkinson's disease; Parkinsons disease; Physical Sciences; Primates; Rats; Reproducibility of Results; Research and Analysis Methods; Rodents; Sweden; Fiji; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0284480

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.