ABI: Innovation: Analyzing Neuroglial Cell Dynamics in their Natural Environment with Video Microscopy

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

Microscope videos of brain and nerve cells provide scientists unprecedented access to these cells as they develop, interact with each other, and respond to injury, disease, or other changes in their environment. However, these videos frequently contain dozens or hundreds of cells, behaving in complicated ways not easily discernable to a human viewer. This project hosted at the University of Virginia is innovating computer-based processing to pick out these cells and analyze their movements automatically. By simplifying these tasks, this software will permit studying large quantities of data for meaningful patterns and rules governing these cells' behavior. A front-end interface and back-end modules will enable scaling these capabilities from single videos to enormous databases, limited only by available computing power. These new capabilities will help scientists uncover new insights about these critical cells, leading researchers closer to understanding similarities and differences in cell behavior among animals used to study diseases and disorders that affect millions of Americans. By bringing together biologists and engineers, this project also provides exciting new experiences for students to learn about the future possibilities at the intersection of these fields. Training high-school teachers in image processing and its applications provides this well-rounded experience to even more students. To help neuroscientists observe the behavior of neurons and glia in their native setting, this software will automate the processing and analysis of complicated movements and interactions among dozens or hundreds of cells in high-resolution microscope videos. The interface will be both scalable and efficient, permitting rapid application of video enhancement, segmentation, and tracking software to large databases of microscope videos. The content-aware enhancement will suppress clutter while preserving cell features. Time-series segmentation will identify both cell bodies and ramified processes as they move between video frames and slices of the z-stack. Transport theory will enable tracking cell movements and other changes without having to construct a complicated model that could bias the results. Plugins under development for common packages like ImageJ and Vaa3D will allow scientists to integrate these tools with existing workflows. A modular design will facilitate expanding and refining the capabilities of this software over time. These software components can track how cells such as microglia and oligodendrocyte progenitor cells react to their environment and how these behaviors change during infection. Microscopy videos of mice, zebrafish, and other animal models will reveal insights not currently accessible due to the complicated behaviors of these cells. The collaborative nature of this project will provide valuable experiences for students embedded in the investigators? laboratories to learn more about image processing and biological applications. Training high school teachers on biological image processing during the summer will enable these teachers to share these experiences with students at schools across central Virginia and beyond. The software and research products will become available online at https://pages.shanti.virginia.edu/Neuroglia_Image_Toolkit/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.