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Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
Published on: January 18, 2017
Joint regression-classification deep learning framework for analyzing fluorescence lifetime images using NADH and FAD
Lopamudra Mukherjee1,2,3, Md Abdul Kader Sagar4,5, Jonathan N Ouellette4,6
1Department of Computer Science, University of Wisconsin Whitewater, Whitewater WI 53190, USA.
Abstract:
In this paper, we develop a deep neural network based joint classification-regression approach to identify microglia, a resident central nervous system macrophage, in the brain using fluorescence lifetime imaging microscopy (FLIM) data. Microglia are responsible for several key aspects of brain development and neurodegenerative diseases. Accurate detection of microglia is key to understanding their role and function in the CNS, and has been studied extensively in recent years. In this paper, we propose a joint classification-regression scheme that can incorporate fluorescence lifetime data from two different autofluorescent metabolic co-enzymes, FAD and NADH, in the same model. This approach not only represents the lifetime data more accurately but also provides the classification engine a more diverse data source. Furthermore, the two components of model can be trained jointly which combines the strengths of the regression and classification methods. We demonstrate the efficacy of our method using datasets generated using mouse brain tissue which show that our joint learning model outperforms results on the coenzymes taken independently, providing an efficient way to classify microglia from other cells.
Insights
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.
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.
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