Project Summary Reactive astrogliosis represents the most common neuropathological finding in brain diseases. Unfortunately, we lack fundamental molecular insight into the consequences of reactive astrogliosis on cell function. Although we presume that astrocytes’ vital physiological roles are dysfunctional in reactive astrogliosis, our community lacks key, fundamental tools that can incisively test hypotheses as to how these cells show dysfunction. This capability gap between transcriptomic analytical workflows and physiological analytical workflows represents a significant barrier for the glial biology community’s capability to understand the consequences of gliosis on astrocyte cell function. Addressing this capability gap through machine learning/artificial intelligence (ML/AI) approaches represents a specific goal delineated by a consensus editorial published in Nature Neuroscience recently by prominent glial biologists. The scientific premise of the proposed research is based on the utilization of live cell imaging of astrocytic intracellular Ca++ transients ([Ca++]i) to capture astrocyte physiological responses to external stimuli. The underlying hypothesis to be tested is that efficient segmentation of video images can occur using convolutional neural networks, and that video image feature extraction that includes pixel intensity, object texture, object shape, and directionality of astrocyte [Ca++]i transients will permit enriched clustering analysis of [Ca++]i transient wave-form types. In our preliminary data, we have already captured over 312 GB of live cell astrocyte Ca++ imaging data upon which to perform the proposed analyses. These videos capture brainstem astrocyte responses to hypoxia in vitro as well as following treatment with the endotoxin lipopolysaccharide (LPS). Thus, we will assess, for the first time, ([Ca++]i from brainstem astrocytes cultured without serum at baseline, hypoxia, and recovery, with and without LPS treatment. The objectives of the proposed research are to perform a secondary analysis of this dataset so as to develop objective analytical workflows that capture a more complete picture of astrocytic phenotypes during physiological challenges. To achieve this we will achieve three aims. We will first develop an efficient, unbiased image segmentation workflow to capture active astrocytes during physiological challenges using the UNET-based CNN algorithm. We will then identify clusters of astrocyte [Ca++]i transients wave-form types under distinct physiological challenges. Lastly, as a future direction that will lay the groundwork for our subsequent R01 application, we will modify our astrocyte imaging workflows to promote compatibility with spatial transcriptomic analysis by integrating photoconvertible reporters and image registration processes using a spatial transcriptomic platform. At the conclusion of the proposed research we will, for the first time, have a rapid, objective image analysis workflow to inte...