The field of cell biology has seen major advances in both cellular imaging modalities and the development of automated image analysis platforms that increase rigor, reproducibility, and throughput for large imaging data sets. However, there remains a need for tools that provide accurate morphometric analysis of single cells with complex, dynamic cytoarchitecture in a high-throughput and unbiased manner. We developed a fully automated image-analysis algorithm to rapidly detect and quantify changes in cellular morphology using microglia cells, an innate immune cell within the central nervous system, as representative of cells that exhibit dynamic and complex cytoarchitectural changes. We used two preclinical animal models that exhibit robust changes in microglia morphology: (1) a rat model of acute organophosphate intoxication, which was used to generate fluorescently labeled images for algorithm development; and (2) a rat model of traumatic brain injury, which was used to validate the algorithm using cells labeled using chromogenic detection methods. All ex vivo brain sections were immunolabeled for IBA-1 using fluorescence or diaminobenzidine (DAB) labeling, images were acquired using a high content imaging system and analyzed using a custom-built algorithm. The exploratory data set revealed eight statistically significant and quantitative morphometric parameters that distinguished between phenotypically distinct groups of microglia. Manual validation of single-cell morphology was strongly correlated with the automated analysis and was further supported by a comparison with traditional stereology methods. Existing image analysis pipelines rely on high-resolution images of individual cells, which limits sample size and is subject to selection bias. However, our fully automated method integrates quantification of morphology and fluorescent/chromogenic signals in images from multiple brain regions acquired using high-content imaging. In summary, our free, customizable image analysis tool provides a high-throughput, unbiased method for accurately detecting and quantifying morphological changes in cells with complex morphologies.

Traumatic brain injury (TBI) often results in persistent attention and memory deficits that are associated with hippocampal dysfunction. Although deep brain stimulation (DBS) is used to treat neurological disorders related to motor dysfunction, the effectiveness of stimulation to treat cognition remains largely unknown. In this study, adult male Harlan Sprague-Dawley rats underwent a lateral fluid percussion or sham injury followed by implantation of bipolar electrodes in the medial septal nucleus (MSN) and ipsilateral hippocampus. In the first week after injury, there was a significant decrease in hippocampal theta oscillations that correlated with decreased object exploration and impaired performance in the Barnes maze spatial learning task. Continuous 7.7 Hz theta stimulation of the medial septum significantly increased hippocampal theta oscillations, restored normal object exploration, and improved spatial learning in injured animals. There were no benefits with 100 Hz gamma stimulation, and stimulation of sham animals at either frequency did not enhance performance. We conclude, therefore, that there was a theta frequency-specific benefit of DBS that restored cognitive function in brain-injured rats. These data suggest that septal theta stimulation may be an effective and novel neuromodulatory therapy for treatment of persistent cognitive deficits following TBI.