Teng-Jui Lin gave a poster presentation at the 2022 AIChE Annual Student Conference and won the 2nd place award in Food, Pharmaceutical and Biotechnology.
Title: Quantifying microglia morphology across neuroinflammatory rat models with unsupervised machine learning
Abstract: Microglia, the brain’s resident immune cells, transition between various morphological states in response to neuroinflammation and therapeutics while exhibiting regional heterogeneity. Immunofluorescent images acquired by confocal microscopy reveal microglia morphology, but we lack robust and high-throughput software for quantitative morphological analysis that is essential for understanding microglia’s reactivity to neuroinflammation in different rat models. We developed an image-based morphological analysis pipeline in Python to characterize microglia at individual and population levels. Individual analysis calculates morphological parameters of microglia perimeter, area, and circularity, whereas population analysis clusters microglia into shape modes with an unsupervised machine learning method. We applied the machine learning method to images obtained from two ex vivo organotypic rat brain slice models that induce neuroinflammation: oxygen-glucose deprivation and lipopolysaccharide. The shape modes determined by machine learning capture heterogeneity, regional variation, and injury and treatment response of microglia morphology in both models. The distribution of circular, crescent, and rod-like shape modes is heterogeneous and positively skewed toward the circular shape mode that has the least perimeter, representative of an unbranched ameboid morphology. When comparing injury groups with the non-treated control group, morphological features of area, perimeter, and circularity show time-, dose-, and region-dependent changes. The therapeutic treatment group reverses most changes to the level of control group. In future studies, we will investigate the role of mitochondria-induced energy failure in activating microglia’s morphological response to neuroinflammation and define the relationship between microglia and mitochondria morphology and functional states using our machine learning method. By quantifying and linking microglia’s morphological response to neuroinflammation and functional states across conditions, our method enables non-destructive assessment of microglial reactivity to neuroinflammation and therapeutic performance across disease models.
Link: https://plan.core-apps.com/aiche2022/abstract/fff17dc0b6a8ca31838d53b5ac406918
