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Updated: Aug 15, 2025
Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ
Published on: June 5, 2018
A deep learning framework for classifying microglia activation state using morphology and intrinsic fluorescence
Lopamudra Mukherjee1, Md Abdul Kader Sagar2, Jonathan N Ouellette2
1Department of Computer Science, University of Wisconsin, Whitewater, WI, United States.
Abstract:
Microglia are the immune cell in the central nervous system (CNS) and exist in a surveillant state characterized by a ramified form in the healthy brain. In response to brain injury or disease including neurodegenerative diseases, they become activated and change their morphology. Due to known correlation between this activation and neuroinflammation, there is great interest in improved approaches for studying microglial activation in the context of CNS disease mechanisms. One classic approach has utilized Microglia's morphology as one of the key indicators of its activation and correlated with its functional state. More recently microglial activation has been shown to have intrinsic NADH metabolic signatures that are detectable via fluorescence lifetime imaging (FLIM). Despite the promise of morphology and metabolism as key fingerprints of microglial function, they has not been analyzed together due to lack of an appropriate computational framework. Here we present a deep neural network to study the effect of both morphology and FLIM metabolic signatures toward identifying its activation status. Our model is tested on 1, 000+ cells (ground truth generated using LPS treatment) and provides a state-of-the-art framework to identify microglial activation and its role in neurodegenerative diseases.
Insights
Researchers developed a deep neural network to analyze microglial activation in the central nervous system (CNS). This new framework integrates cell morphology and NADH metabolism signatures for improved neuroinflammation and neurodegenerative disease research.
Area of Science:
- Neuroscience
- Immunology
- Computational Biology
Background:
- Microglia are the primary immune cells of the central nervous system (CNS).
- Microglial activation, indicated by morphological changes and neuroinflammation, is crucial in CNS diseases.
- Current methods often analyze microglial morphology or metabolism separately.
Purpose of the Study:
- To develop a computational framework integrating microglial morphology and metabolic signatures (NADH via FLIM) for activation status identification.
- To provide a novel approach for studying microglial activation in neurodegenerative diseases.
Main Methods:
- Development of a deep neural network model.
- Integration of microglial morphology and fluorescence lifetime imaging (FLIM) data of NADH metabolism.
- Validation using over 1,000 cells with ground truth generated by LPS treatment.
Main Results:
- The deep neural network effectively analyzes both morphology and metabolic signatures.
- The framework provides a state-of-the-art method for identifying microglial activation status.
- Demonstrated the combined utility of morphology and metabolism in understanding microglial function.
Conclusions:
- The novel deep learning framework enables simultaneous analysis of microglial morphology and metabolism.
- This integrated approach enhances the study of microglial activation in CNS diseases, particularly neurodegeneration.
- Offers a powerful tool for advancing research into neuroinflammation and disease mechanisms.
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