ISH Profiling of Rat Cortex Figure 7. Principal component analysis (PCA) of standardized cortical map. Cortical areas were divided into 3406100 points so that the ISH signal values could be represented by a matrix of 34000 data points. The data of five layer maps (layer 4 of RORbeta, layers 4 and 5 of ER81, and layers 5 and 6 of Nurr1) were analyzed for PCA to extract two primary components, PC1 and PC2. (A) Pseudocolored layer maps (top) and eigenvectors (bottom) of PC1 and PC2. The layer maps indicate the PC scores plotted in the two dimensional space. The eigenvectors below the layer maps show the contributions of the five layer maps in constructing each PC. (B) The averaged PC1 and PC2 scores of each cortical area (shown in the left panel) were plotted in the PC1–PC2 space. The cortical areas represented by the dots in the right panel are grouped by modality and connected by colored lines (yellow, red and blue for somatosensory, auditory and visual, respectively) in the order of PC1 scores. Gray represents other areas, including motor, limbic and paralimbic areas. The nomenclature of each area is the same as that in Fig. 5. doi:10.1371/journal.pone.0003266.g007 Cortical box method is a useful tool for gene expression analyses For histological studies, it is critically important to accurately identify various anatomical structures. This is particularly true for the study of the neocortex that consists of many areas and subareas. Traditionally, the Nissl staining patterns have been used as criteria for discrimination of cytoarchitectonic areas (e.g., [1],[3]). Nevertheless, the area differences determined by Nissl staining are often subtle and susceptible to various artifacts, such as inhomogeneous staining and sampling variances. Although quantitative methods of characterizing cortical areas [36–38], [40] enable observer-independent area demarcation, the area identification is still subtle and requires exact spatial information of the section of interest. In cats and monkeys, sulcal landmarks help in area identification. However, the rodent cortex offers no such landmarks. To circumvent this problem, we previously developed the cortical box method to analyze c-fos immunoreactivity [31], which we now applied to the analysis of ISH data. This method uses a set of coronal sections that cover the entire posterior cortex of the rat. With sufficient numbers of sections, it is possible to accurately estimate the continuity of the areas that span several sections (see Fig. 3A). Because this method transforms expression data into a standardized form, many different sets of data can be compared quantitatively. Furthermore, although the selection of ROI (region of interest) for standardization is still determined manually, this process only requires the determination of the medial and lateral ends of the cortex, which have clear landmarks and are unambiguous. The effectiveness of these features is demonstrated by the reproducibility of cytoarchitectonic areas (Fig. 5A) and low CV of the gene expression data (Fig. 6B) across different sets of samples. As we have shown in this study, this method is applicable to the sections stained by various methods including Nissl staining, immunohistochemistry, ISH and possibly other methods, such as neural tracer dyes. This method will, thus, enable us to integrate different types of histological data in the same coordinate for quantitative analyses. In the current study, we pooled the data from both right and left hemispheres of three different rats, to reduce experimental variability. Although there is a possibility that the two hemispheres show differences in gene expression, we were not able to find sign of lateralization, under the current number of dataset (n = 3). However, a study using larger number of datasets for cortical box analysis may clarify if any lateralization exists in rodent neocortex. PLoS ONE | www.plosone.org 8 September 2008 | Volume 3 | Issue 9 | e3266