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Joint regression-classification deep learning framework for analyzing fluorescence lifetime images using NADH and

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This study introduces a deep learning method to identify microglia, crucial brain cells, using fluorescence lifetime imaging microscopy. The novel approach enhances microglia detection by analyzing metabolic co-enzymes FAD and NADH together.

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Area of Science:

  • Neuroscience
  • Biophysics
  • Computational Biology

Background:

  • Microglia are key resident macrophages in the central nervous system (CNS), playing vital roles in brain development and neurodegenerative diseases.
  • Accurate identification of microglia is essential for understanding their functions in the CNS, a topic of significant recent research.
  • Existing methods for microglia detection face challenges in accurately interpreting complex cellular data.

Purpose of the Study:

  • To develop a deep neural network (DNN) based joint classification-regression model for identifying microglia using fluorescence lifetime imaging microscopy (FLIM) data.
  • To integrate fluorescence lifetime data from two metabolic co-enzymes, flavin adenine dinucleotide (FAD) and nicotinamide adenine dinucleotide (NADH), within a single model.
  • To improve the accuracy and efficiency of microglia classification compared to methods analyzing co-enzymes independently.

Main Methods:

  • Development of a deep neural network architecture employing a joint classification-regression strategy.
  • Incorporation of fluorescence lifetime data from both FAD and NADH co-enzymes into the model.
  • Joint training of the classification and regression components to leverage combined strengths.
  • Validation using experimental datasets derived from mouse brain tissue.

Main Results:

  • The proposed joint learning model demonstrated superior performance in classifying microglia compared to models using FAD or NADH data independently.
  • The integrated approach provided a more accurate representation of lifetime data and a richer data source for the classification engine.
  • The method proved efficient in distinguishing microglia from other cell types in the analyzed datasets.

Conclusions:

  • The developed deep learning approach offers an effective and efficient method for microglia identification using FLIM data.
  • Jointly analyzing FAD and NADH fluorescence lifetime data enhances classification accuracy.
  • This technique holds promise for advancing research into the roles of microglia in CNS health and disease.