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

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.

Keywords: LSTM; deep learning; fluorescence lifetime; microglia activation state; morphology.