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

In the method of in situ hybridization autoradiography, quantitative compar isons among multiple mRNA signals have proven difficult for many reasons, a ttributable both to technical factors (e.g. different probe specific activi ties) as well as to large differences in the patterns and levels of express ion of different genes in pathologic states. Here we report a standardized normalization procedure for in situ hybridization autoradiography, employin g a Bayes classifier, which permits the comparison of multiple mRNA probes. Autoradiograms of different probes in individual animals are first digitiz ed and converted to units of 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 transformat ion to convert each pixel from absolute activity to percentage of maximal m RNA signal for that particular probe. The normalized autoradiographic image s can then be averaged to represent group trends and can be compared by sta ndard statistical methods. We illustrate this normalization procedure using in situ hybridization autoradiography for three genes (GADD45, HSP70 and M AP2) expressed in the brains of rats studied at various recirculation times following transient (2 h) middle cerebral artery occlusion. The Bayes clas sifier is reviewed and its analytical application is presented. Step-by-ste p examples of intermediate steps are presented, construction of averaged da ta sets, and pixel-based statistical comparisons among expressed genes. (C) 1999 Elsevier Science B.V. All rights reserved.