Teng-Jui Lin presented his research at the 25th UW Annual Undergraduate Research Symposium.
Title: Incorporating Visually Aided Morpho-Phenotyping Image Recognition into Robust Microglial Shape Analysis
Abstract: Microglia—the brain’s immune cells—change shape upon external stimuli from either environmental cues or direct injury. Quantifying changes in microglial shape is essential for understanding disease, injury, and their potential as therapeutic targets. Although we can use confocal imaging of immunofluorescent stains to visualize microglia, we lack software to robustly quantify their shapes in a high throughput manner. Therefore, we developed a microglial shape analysis pipeline using Python. In this larger collaborative project, I optimize and incorporate Visually Aided Morpho-Phenotyping Image Recognition (VAMPIRE), a machine-learning-based method that classifies shapes of microglia, into the pipeline. VAMPIRE visualizes the shapes of classified microglia and characterizes their shape heterogeneity using shape metrics. This project aims to demonstrate the robustness and validity of VAMPIRE in the pipeline and when applied to different microglia imaging datasets. To demonstrate VAMPIRE is robust in tissue, I apply VAMPIRE on two datasets: images from an ex vivo organotypic rat brain slice model of oxygen-glucose deprivation and images from an in vivo rat model of hypoxic ischemia. The ex vivo and in vivo datasets provide a representation of different tissue samples—live 300µm thick brain slices and fixed 30µm brain sections—to show how VAMPIRE is robust for classifying microglia generated from different experimental methods. The application of VAMPIRE on tissue models captures changes in microglial shape in response to injury and treatment, allowing comparisons between injury and treatment response. The overall pipeline and VAMPIRE analysis are cell and disease agnostic; therefore, the same methodology can be applied to other models, cell types, and species.
Link: https://expo.uw.edu/expo/apply/635/proceedings/offering_session?id=1372
