COMPUTER-ASSISTED IMAGE-AVERAGING STRATEGIES FOR THE TOPOGRAPHIC ANALYSIS OF IN-SITU HYBRIDIZATION AUTORADIOGRAPHS

We report the application of a computer-based image-averaging strategy to the quantitative topographic analysis of in situ hybridization aut oradiographs, based upon a disparity-analysis algorithm. We illustrate this approach for a representative antisense riboprobe-the astrocytic marker, glial fibrillary acid protein (GFAP)-in the setting of fluid- percussion brain injury in rats. Sequential coronal autoradiographs in individual animals are first digitized and aligned by disparity analy sis. Next, coronal sections of all brains of a given experimental grou p are placed in register with one another, using a common anatomic ref erence level. One brain of the series serves as a template, and corres ponding sections of other brains are mapped into its contour at each l evel. in this manner, average and standard deviation image data sets m ay be generated. With thresholding techniques, individual data sets ca n be dichotomized with respect to a chosen threshold, and frequency ma ps can be generated at each coronal level, displaying numbers of brain s showing supra-threshold levels of mRNA at each pixel location. Pixel -by-pixel statistical comparison of data sets obtained under two diffe rent conditions (e.g., 30 min vs. 24 h following brain trauma) is then feasible. A digitized functional-anatomic brain atlas may be fitted t o the images to assist analysis. Computer-based image analysis of in s itu hybridization autoradiographs greatly extends the utility and appl icability of this technique.