ABA: Atlas-Based Analysis
In 2020, Justin Bourgeois and colleagues published their open source project, Atlas-Based Analysis (ABA), a FIJI-based tool for immunohistochemical analysis. ABA is used to semi-automatically define regions of interest (ROIs) by adjusting experimental images to a published rat brain atlas. C-fos can be quantified in the ROI assigned by the atlas, which allows multiple users to find consistent results in reduced time.
Immunohistochemistry is a common and useful method to quantify neural markers in defined brain regions. While many programs have been developed to automatically quantify these neural markers within an ROI, manual assignment of ROIs can lead to variability between observers analyzing the same image. ABA has a few simple steps to automate both c-Fos quantification and ROIs. Landmarks in experimental images are manually matched to superficial (external) landmarks in an image from Paxinos and Watson’s rat brain atlas. A plugin exists to account for deep (internal) landmarks in the images. Once matched, plugins will adjust the experimental image so that it has a more accurate overlay and ROI coordinates saved from the atlas can be assigned to the experimental image. An additional plugin can then count c-Fos positive cells and will save data based on individual images and ROIs across images. Bourgeois and colleagues tested efficiency and consistency of ABA across users quantifying c-Fos positive cells in a single brain section, and found that ABA-mediated analysis reduces processing time by ~70% compared to manual. They also found that interobserver variability is greatly reduced when accounting for superficial landmarks, and even more so when including deep landmarks.
FIJI can be installed for free through ImageJ and all plugins and text files are available to download from the Kopec Lab Github page. The open source nature of ABA will allow it to be adapted for new atlases, landmarks, ROIs, and quantification measurements, making it widely applicable for reliable immunohistochemical analysis. Automated ROI assignment combined with automated quantification through ABA use will lead to reduced variability and image processing time across users.