A deep learning framework for classifying microglia activation state using morphology and intrinsic fluorescence...

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.