were used for the hybridization. The fluorescent detection was performed as described using TSA-plus reagent (Perkin Elmer, Wellesley MA, USA) and HNPP fluorescent detection set (Roche diagnostics). Image acquisition The images for the single- and double-color ISH were obtained using a digital color camera DP 70 (Olympus, Tokyo, Japan) attached to a BX-51 microscope (Olympus). For the analysis by cortical box method, digital images (136061024 pixels) were captured using the 1.256 objective in the gray scale with 8 bits (10.3 mm/pixel). The background image was subtracted using Adobe Photoshop (Adobe Systems, San Jose, CA) to eliminate the shadowing effect. Standardization of regions of cortex The standardization of the rat cortex was conducted as described previously with a slight modification [31]. To achieve objective and automatic procedures, we restricted the quantifica- tion of gene expression to the posterior half of the cortex, which has clear structural landmarks. We used 17 coronal sections in each animal, which were presumed to correspond to the Bregma of 22.1 to 26.3 mm, as determined from the order of serial sections and the shape of the hippocampus [49]. Sections that contain artifacts such as tearing or bubbles in the cortex were excluded from the analysis. The averages of 16.560.83 (mean6SD) (ISH for RORbeta), 16.560.54 (ISH for ER81) and 15.861.17 (ISH for RORbeta) sections processed by ISH for each animal were used for this analysis. For the standardization of the cortex, the section images were processed as follows. The medial end of the white matter and the valley of the rhinal fissure were chosen as structural landmarks of the mediodorsal (MD) and lateroventral (LV) ends of the cortical sections, respectively. The pial surface and the border between the cortex and the white matter were chosen as the outer (OC) and inner contours (IC), respectively. Then, the image of a large part of the cortex was manually cut out on the basis of these landmarks using Adobe Photoshop (Fig. 4A). The following steps were automatically carried out using a customized software program designed by LabVIEW 7.0 (National Instruments, Austin, TX, USA). The lengths of OC and IC were measured and equally divided into 100 points. Sectors that were defined by every two adjacent points on each contour were extracted and converted to standardized rectangles by linear interpolation. These rectangles from MD to LV were aligned from left to right to form a ‘‘standardized cortical section’’ (10061000 pixels, depth and width, respectively, Fig. 4A). The standardized cortical sections are distorted toward deeper layers, because the outer contour is always longer than inner contour. We evaluated the transformation rates of standardized sections from original sections. The average rates of transformation across areas were, 9065% in layer 2/3, 10065% in layer 4, 11065% in layer 5 and 12065% in layer 6, respectively. The transformation is performed so that the local density of staining is preserved. Therefore, distortion by the transformation does not affect the overall patterns of gene expression. Although our method introduces the transformation especially in deeper layers, the boundaries of the primary sensory areas were the same for both layers 4 (RORbeta) and 6 (Nurr1) (Fig. 5), suggesting that deviation in the deeper layer was only limited. To normalize the staining intensity, the means and standard deviations (SD) of all the pixel values for the ISH images of one dataset were calculated. These pixel values were converted to 0– 100 (%), by linearly adjusting them to mean + 1 SD as 0% and ISH Profiling of Rat Cortex mean + 3 SD as 100%. The data for the sections lost or discarded were generated by linearly interpolating the adjacent serial sections. No attempts were made to count the number of positive cells, different from our previous study [31], because we considered that the staining intensity, which reflects the relative mRNA abundance, was most important in evaluating the gene expression patterns in the current study. Seventeen standardized cortical sections were aligned from the posterior to the anterior cortex to construct a ‘‘standardized cortical box’’ (Fig. 4B). To generate the standardized map of a particular layer (10006340 pixels) (width and Bregma distances, respectively), the specific layer fraction (10–30% for layer 2/3; 30–50% for layer 4; 50–75% for layer 5; 75–100% for layer 6) was extracted from the standardized cortical box (Fig. 4D) and compressed into a two- dimensional map by averaging. Post hoc smoothing (spatial averaging) was achieved using a moving window operator (41641 pixels). To create average layer maps, the maps from both hemispheres of all the animals were averaged. To create the CV map, the CV (CV = SD/average6100 (%)) was calculated for each pixel. When the average of a pixel was lower than 10%, the corresponding pixel was covered with white in the CV map, because the average near zero diverges the CV value into infinite. Visualizations were carried out using Matlab 7.0 (Mathworks, Natick, MA, USA). We plan to open the source code of cortical box method on our website. Researchers who are interested in using this method are welcome to ask details of our method before the website opening. Cortical layer and area identification To determine the location of cortical layers and areas in the standardized sections, we also applied essentially the same procedure described above to a series of adjacent Nissl-stained sections (17 sections for each animal). The local density of neurons was expressed as a GLI, which indicates the pixel intensity of each image [36–38]. To correct intersection differences in staining intensity, the intensity was normalized for each section by linearly adjusting the mean as 0% and mean +0.5 SD as 100 (%). To reduce the artifact due to staining variance, the normalized pixel values of every three adjacent standardized sections were averaged to make one averaged standardized section. Each standardized map was averaged to determine the average GLI (cytoarchitec- tonic) distribution for all the animals. Cortical layers were identified on the basis of the local peaks of the GLI layer profile (Fig. 4C). The Nissl-standardized layer 4 map was constructed by extracting layer 4 fraction (30–50%) from the standardized cortical box (Fig. 4D). Because the primary sensory areas have higher cell densities in layer 4 [8], the borders of these areas were delineated by tracing the local highest rate of GLI changes on the layer 4 map. As a result, primary somatosensory (Parietal cortex, area1 (Par1)), auditory (Temporal cortex, area1 (Te1)), and visual (Occipital cortex, area1 (Oc1)) areas were cytoarchitectonically identified as the regions that had the highest cell densities. Data analysis of standardized cortical map Analyses of standardized cortical maps were performed using Matlab and LabVIEW. For linear regression analysis, the correlation coefficient and a p-value for testing the hypothesis of no correlation between two images were calculated. For PCA, we used 5 representative data sets from the standardized layer maps (layer 4 for RORbeta, layers 4–5 for ER81, layers 5–6 for Nurr1) shown in Fig. 6B. The other maps were excluded from this analysis because they had no or very low signals. The standardized layer maps were analyzed as a P x N matrix (row x column), where P is the location number (P = 340,000 pixels (10006340 pixels)), and N PLoS ONE | www.plosone.org 11 September 2008 | Volume 3 | Issue 9 | e3266