Date of Award
5-2024
Document Type
Honors Thesis
Degree Name
Bachelor of Science
Department
Biological Science
Advisor/Committee Chair
Ewan C. McNay
Committee Member
Gabriele Fuchs
Abstract
For those determined to study disease states, systems, or circuits in vivo, histology is the alpha and omega of our investigations. We often begin and end by confirming the location and extent of our perturbations. Yet, as deeply rooted as it is in our experimental practice, histological analyses have remained largely stagnant. Experimenter drawn regions-of-interest (ROIs), intensity normalizing by eye, and inconsistent output metrics are just some of the limitations of current image analysis pipelines. To address these challenges, I developed RoiA, a FIJI-enabled macro which automatically characterizes ROIs in a provided image and analyzes key metrics across three different channels. Among its use cases, I recently employed RoiA to assess tissue damage in fixed brain sections from rats that had bilateral hippocampal implantations of two different sampling cannula and corresponding probes: microdialysis (MD) and cerebral open flow microperfusion (cOFM). Given the probes’ differences in sampling method and material, we expected to see differences in tissue damage across two categories: (i) gliosis, the aggregation of glial cells and (ii) inflammation, marked by an increase in proinflammatory markers. These proxies for tissue damage were stained for using neuron-specific and glia-specific markers (NeuroTrace and GFAP), a microglia-specific marker (TMEM-119), and a marker for the proinflammatory cytokine CD68. RoiA revealed that tissue probed with cOFM probes possessed greater GFAP, TMEM-119, and CD68 integrated density surrounding the probe site as compared to MD. Moreover, in both cOFM and MD tissue, the macro identified greater TMEM-119, and CD68 area in the 100-micron region proximal to the probe site than in the 200-micron region, indicating probe-site localized gliosis. Across all three channels, cOFM-probed tissue possessed greater integrated density to area ratios, a measure of relative signaling that indicates that the aggregated glial cells were more active. Ultimately, these results demonstrate RoiA’s ability to capture the nuances within an image dataset and its applicability across an array of image analysis use cases.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Buddhavarapu, Teja Rathnam, "RoiA: An Open-Source ImageJ Macro for Region-of-Interest Creation and Analysis" (2024). ALL - Honors Theses. 24.
https://scholarsarchive.library.albany.edu/all_honors/24