Quantitation of multiple gene expression by in situ hybridization autoradiography: accurate normalization using Bayes...

In the method of in situ hybridization autoradiography, quantitative comparisons among multiple mRNA signals have proven difficult for many reasons, attributable both to technical factors (e.g. different probe specific activities) as well as to large differences in the patterns and levels of expression of different genes in pathologic states. Here we report a standardized normalization procedure for in situ hybridization autoradiography, employing a Bayes classifier, which permits the comparison of multiple mRNA probes. Autoradiograms of different probes in individual animals are first digitized and converted to units of radioactivity. Next, pixel-distribution histograms are generated for each mRNA signal. The Bayes classifier is then used to establish an optimal threshold to distinguish activated and non-activated pixels. This threshold also defines the minimal level of mRNA expression. The maximal mRNA signal is defined as the mean + 3 SD of the activated pixel distribution. We then use a linear transformation to convert each pixel from absolute activity to percentage of maximal mRNA signal for that particular probe. The normalized autoradiographic images can then be averaged to represent group trends and can be compared by standard statistical methods. We illustrate this normalization procedure using in situ hybridization autoradiography for three genes (GADD45, HSP70 and MAP2) expressed in the brains of rats studied at various recirculation times following transient (2 h) middle cerebral artery occlusion. The Bayes classifier is reviewed and its analytical application is presented. Step-by-step examples of intermediate steps are presented, construction of averaged data sets, and pixel-based statistical comparisons among expressed genes.