OrNet: Spatiotemporal Analysis of Organelle Morphology

Walter Avila, a student from Emory University, worked in the lab of Dr. Shannon Quinn.

Abstract Modeling changes in organelle morphology in response to infections is pivotal in studying pathogenic behaviors. Mitochondria are the most meaningful organelles to study because their structure changes dramatically in the presence of potentially fatal infections. Accurately modeling mitochondria could provide crucial information about severe infections. The project addresses this modeling concern by continuing development on OrNet (Organellar Networks). This Python framework constructs dynamic social network graphs of fluorescently tagged cells in microscopy videos to analyze the spatiotemporal relationships of mitochondria. These graphs are leveraged into eigenvalue arrays to describe organelle morphology over time quantitatively. We sought to improve the Temporal Anomaly Detection (TAD) technique in OrNet, which utilizes the arrays and detects when abnormal events occur in a video, indicative of organellar changes. The number of arrays rows and columns corresponds to the number of frames and eigenvalues, respectively. Currently, TAD takes the average eigenvalue at each frame and computes the distance between the average and the mean of a few preceding averages. If that distance exceeds a threshold, the frame is an outlier. This parameter-sensitive technique gives disproportionate weight to trailing eigenvalues. We assess how differently this model behaves when introducing a weighted average eigenvalue per frame. Using live videos of HeLa cells in different mitochondrial conditions, on average, the weighted average TAD declares a higher proportion of anomalous frames in cell videos from each mitochondrial condition than the simple average TAD code. This information about our model will guide how we implement OrNet in the future. Comparing how videos are analyzed differently by both TAD versions and other comparisons are necessary. We ultimately aim to use OrNet in large-scale genomic screenings of Mycobacterium tuberculosis mutants to understand better how these pathogens invade cells and induce cell death at the genetic level.