Analysis for area-specific distribution patterns of RORbeta, ER81 and Nurr1 mRNAs by cortical box method Following the determination of the cytoarchitectonic lamina and area borders by Nissl staining, we analyzed the distribution patterns of RORbeta, ER81 and Nurr1 mRNAs by the cortical box method. Figure 6A shows the standardized maps of RORbeta, ER81 and Nurr1 mRNAs for layers 2/3, 4, 5 and 6. The ISH signals of these mRNAs were mostly observed in layers 4, 5 and 6, respectively, as expected. In addition, we clearly observed area- specific distribution patterns for all the three mRNAs. These patterns coincided well with the borders determined by Nissl staining (Fig. 6A; solid and dashed lines). Actually, the spatial distribution of RORbeta mRNA was very similar to that of Nissl- GLI in layer 4 (r = 0.63, P,0.01, linear regression model analysis), and the spatial distribution of ER81 was similar to that of Nissl- GLI in layer 5 (r = 0.70, P,0.01, linear regression model analysis). However, the area difference between RORbeta and ER81 mRNAs was much larger than that expected from the Nissl-GLI. Besides, the area distribution of Nurr1 mRNA was different from that of the Nissl-GLI in layer 6, although they seemed to show the same borders. The layer maps also showed area-specific differences in the lamina specificity of these genes. For example, the expression level of RORbeta mRNA was higher in layers 2/3 of the Par1 subfield of the somatosensory cortex and the auditory cortex, which is consistent with the findings described in a previous report [8]. Furthermore, the expression levels of Nurr1 in the temporal cortex ventral area (TeV) and ectorhinal cortex (Ect) (about 90% of the mediodorsal distance at the bregma distance of 26 mm) were higher in layer 5 rather than layer 6, which is consistent with results of the double ISH (Fig. 2, panel f’). Although ER81 mRNA exhibited a shift of expression from layer 5 to layer 4 in the medial- most retrosplenial area (RSA), this may be due to the difference in the overall lamina position in this area. An advantage of our method is that the variability of gene expression across different sets of samples can be quantitatively estimated. In Fig. 6B, we mapped the coefficient of variance (CV) of each gene for each layer map. CV is the percentage of the standard deviation (SD) per average and a measure of the relative variability. By definition, CV becomes unreliable when the average is small. Thus, we excluded the areas with low gene expression values from the analyses in Fig. 6B (white areas). The map shows that the CVs were generally low (,50%) in the central regions of a cluster with high average values and were high (.80%) in the borders of those clusters. This result suggests that there is little sample-to-sample variance in the area distribution of these genes, and that the variability is concentrated at the borders. The CV showed a constantly low level in the transition regions from area Par1 to the temporal cortex, area1 (Te1), in the layer 4 map of RORbeta and from area Oc2MM to Oc2L in the layer 5 map of ER81 (Fig. 6B; white dotted lines), despite large changes in the averages. In these regions, even the area borders are reproduced across different animals. This demonstrates the high reliability of the gene expression mapping in our method. Common and different characteristics of various cortical areas captured by multivariate analyses The distribution pattern shown in Fig. 6A suggests that the RORbeta mRNA expression level is high in the sensory areas, whereas the ER81 and Nurr1 mRNA expression levels are generally high in the areas with low RORbeta mRNA expression level. This observation suggests that the distribution patterns of these mRNAs may be governed by some common rules. In an ISH Profiling of Rat Cortex attempt to discover such rules, we performed principal component analysis (PCA) using five layer maps of RORbeta (layer 4), ER81 (layers 4 and 5) and Nurr1 (layers 5 and 6). Other layer maps were excluded from the analysis, because there are very little signals, if any, in other maps, and not reliable. In this analysis, we first divided each map into 10006340 blocks, so that the spatial maps as shown in Fig. 6A could be represented by rows of data having 34,000 data points. When five of such datasets are combined, it is considered as 34,000 points plotted in a five-dimensional space. The purpose of the PCA is to find new ‘‘axes’’ to explain the variability of the 34,000 data points with the least variables. In other words, we expected PCA to decompose the five maps of spatial distribution data into fewer maps that represent the common features of spatial variations. Figure 7A shows the first two principal components (PCs) obtained by PCA. The eigenvec- tor of each PC is graphed at the bottom. These bar graphs demonstrate the contribution of each layer map in determining the PC scores that are illustrated as the colored maps on top. At first glance, PC1 is similar to the layer 4 map of RORbeta, while PC2 is similar to the layer 6 map of Nurr1. However, as the bar graphs indicate, PC1 and PC2 have contributions from all the five layer maps in various degrees and directions. For example, in addition to RORbeta patterns, the ER81 patterns in layers 4 and 5 considerably contributed to PC1, but they were in the reverse direction. This means that PC1 represents a feature shared by RORbeta and ER81 (and Nurr1 to a lesser extent), which is shown as their complementary distribution patterns. Similarly, PC2 represents a feature shared by layer 5 of ER81 and Nurr1, but in a complementary manner. There are almost no contributions of RORbeta in this component. In an attempt to decipher the meaning of the two axes represented by PC1 and PC2, we calculated the average scores of these PCs in cytoarchitectonically determined cortical areas and plotted them in the PC1–PC2 space (Fig. 7B). In this figure, the cortical areas were roughly classified into four categories. Somato- sensory, auditory, and visual domains were colored in yellow, red and blue, respectively. The medial and lateral ends of the cortex, which are colored in grey, are motor, limbic and paralimbic areas and considered to be higher areas in the cortical hierarchy in terms of sensory inputs. In the plot, the areas with the same modalities are grouped by lines to aid in the visualization. Here, we observed two features. First, the primary sensory areas (Par1, Te1, Oc1) were clustered at the lowest value of PC1 with little contribution from PC2. The higher order multisensory areas tended to be located in the higher values of PC1. Second, the higher order association areas of different modalities were dispersed in the plot, because of the contribution of different PC2 values. Together, these observations suggest the similarity of the primary sensory areas and the diversion of association areas, as far as the expression of the three ‘‘layer- specific’’ genes are concerned. Discussion We studied the area-specific expression patterns of three layer- specific genes, RORbeta, ER81 and Nurr1, using double ISH and the cortical box method [31]. Double ISH showed that the coexpression profiles of these genes are the same across areas, whereas their relative abundance and the extent of intermingling differ considerably. The cortical box method allowed us to quantitatively and objectively analyze the three-dimensional pattern of gene expression using integrated ISH data sets. We first discuss the methodological aspects of our study and then the implications of our findings in terms of the area architecture of the rat cortex. PLoS ONE | www.plosone.org 6 September 2008 | Volume 3 | Issue 9 | e3266