A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model
Lucas Stetzik1, Gabriela Mercado1, Lindsey Smith2
1Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, United States.
Abstract:
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.
Insights
Automated deep learning accurately quantifies microglial morphology and cell counts from brain tissue slides. This method overcomes limitations of current techniques for studying neuroinflammation and neurological diseases.
Area of Science:
- Neuroscience
- Immunology
- Computational Biology
Background:
- Microglial functional state is crucial in brain pathophysiology.
- Current automated methods for analyzing microglial morphology have limitations including bias and hardware dependency.
- Efficient and reliable methods are needed to quantify microglial changes in research.
Purpose of the Study:
- To develop and validate an automated deep learning method for quantifying microglial morphology and cell counts.
- To overcome limitations of existing automated analysis techniques.
- To provide a reproducible workflow for microglial analysis in neuropathology research.
Main Methods:
- Utilized commercially available deep learning software (Aiforia® Cloud) for image analysis.
- Quantified microglial morphology and cell counts from Iba1-stained histopathological slides.
- Developed a comprehensive workflow including training details and annotation strategies.
Main Results:
- Demonstrated effective application of the deep learning method across independent datasets.
- Validated the approach in mouse models of viral infection and Parkinson's disease.
- Showcased the ability to accurately quantify microglial morphology and cell counts.
Conclusions:
- Deep learning offers an efficient and robust solution for analyzing microglial morphology in neuropathology.
- The developed workflow and available models facilitate reproducible microglial analysis across research settings.
- This method supports the study of neuroinflammation and neurological disease pathogenesis.
