There is growing evidence for the key role of microglial functional state in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools Aiforia® Cloud (Aifoira Inc., Cambridge, MA, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinson's disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are available within the Aiforia® platform for study-specific adaptation and validation.
Keywords: Parkinson’s disease; deep learning; microglia; olfactory dysfunction; synuclein; viral infection.
Copyright © 2022 Stetzik, Mercado, Smith, George, Quansah, Luda, Schulz, Meyerdirk, Lindquist, Bergsma, Jones, Brundin, Henderson, Pospisilik and Brundin.