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.

Brittany Dorsey, a sophomore from Mercer University, worked with Dr. Shannon Quinn and Dr. Fred Quinn to test the use of a new method to detect changes in organelle morphology.

Abstract:   Intracellular bacterial pathogens have the capacity to greatly alter target organelles’ morphology, which can easily be visualized through fluorescence microscopy, but is more difficult to quantify succinctly. Work is being done to consider Gaussian Mixture Models as a viable solution by viewing mitochondria as social networks, but there are difficulties with this method. Therefore, the goal of the current project is to explore the feasibility of SpatialDE as an alternative way to quantify the spatiotemporal changes in organelle morphology. Using time series footage of mitochondria, three morphological phenotypes were analyzed: control, fragmented, and fused. The raw video was converted to a three-dimensional matrix of pixel values, which was then raster scanned into a two-dimensional matrix. This matrix was normalized, then input into the SpatialDE framework using the programming language Python. The data frame output gave 18 different variable values for each pixel location throughout the footage, which was converted back into “image” format in order to be analyzed. The results showed little to no discernable patterns between treatments. In comparison, the Gaussian Mixture Model output shows clear similarities among phenotypes. Therefore, it was determined that Gaussian Mixture Models continue to be the best option to model spatiotemporal changes in diffuse organelles. Once this method is fully developed, the mechanisms by which bacterial virulence factors transform mitochondrial structure in host cells will be better understood, which will have crucial implications for structural biology and biomedicine.

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