which can be easily manipulated for various analyses. One application of the cortical box method is to show gene expression at a given lamina position as a two-dimensional ‘‘layer map’’, which represents a map of a virtual tangential section (Fig. 4D). To make maps for layers 2/3, 4, 5 and 6, we first determined the borders of these layers based on Nissl staining, as well as the ISH patterns of the three layer-specific genes, RORbeta, ER81 and Nurr1. Figure 4C shows the lamina distribution of the Nissl-grey level index (GLI) [36–38] and that of the three mRNAs, averaged over the central portion of the standardized cortical box (see Materials and Methods). We observed four distinct peaks in the Nissl-GLI, which are considered to correspond to the cytoarchi- tectonic layers 2/3, 4, 5 and 6. As expected, the latter three peaks coincided very well with the peaks of RORbeta (around cortical depth of 30–50%), ER81 (around cortical depth of 50–75%) and Nurr1 (around cortical depth of 75–100%) (Fig. 4C). Within the regions defined in our method, we observed little variance in the positions of the lamina borders. On the basis of this data, we determined the lamina borders to make layer maps that are described in the following sections. To relate the expression patterns of the three layer-specific genes to the cytoarchitectonic areas determined by the standard method, we applied the cortical box method to Nissl staining using the GLI, which has been previously used to define area borders [36–38]. Figure 5A shows the layer 4 maps of Nissl-GLI obtained from three different rats. In these maps, three clusters of high Nissl-GLI were observed, which were considered to correspond to somatosensory, auditory, and visual areas (Fig. 5A; bordered by thick lines). The GLI distributions were not homogeneous within ISH Profiling of Rat Cortex the three clusters and we could draw potential borders for the subareas (solid and dashed lines). These borders were semi- automatically determined on the basis of the differential map (Fig. 5B, see Materials and Methods). The locations of the area borders determined in this way generally well matched those of the standard atlases [8], [39] (Fig. 5A) (see Discussion for detailed comparisons with standard atlases). The borders in Fig. 5A are determined from the average of six different sets of Nissl-stained samples. The layer maps of the right and left hemispheres (n = 3 each) are shown separately in Fig. 5C. Although there are some variances between these two maps (e.g., compare the mediodorsal regions of the middle and right panels of Fig. 5C), we were able to determine the borders that match well for both sets of samples by averaging the data. The maps of other layers also suggest the consistency of the area borders determined from the layer 4 map (Fig. 5D). For example, the expression changes in the layer 2/3 map generally occurred at the same border as that in the layer 4 map. However, there were several important differences in the patterns. For example, the GLIs of the mediodorsal areas (agranular retrosplenial cortex (RSA) and frontal cortex, area 1 (Fr1)) were high in layer 2/3 but not in layer 4. Also, the GLI in area HL (hind limb), a subregion of the somatosensory areas, was low in layers 2/3 but high in layer 4. Despite such differences in the area distribution patterns between layers, the borders for the changes in the GLI were the same for layers 2/3 and 4. Similarly, the GLIs of layers 4 and 5 were mostly complementary and the same borders were observed. Thus, the Nissl-GLI is considered to faithfully reflect the cytoarchitectonic area map. Figure 5. Spatial distribution of Nissl-GLI in standardized cortical maps. (A) Averaged standardized layer 4 map of the Nissl-gray level index (GLI) was constructed from both hemispheres of three animals. (B) The areal borders were determined on the basis of the differential map, which shows the local differences of the GLI values. (C) Standardized layer 4 maps of GLI constructed using the data from the left and right hemispheres of three animals. The two independent maps exhibit very similar patterns despite no overlaps in the samples used for image processing. (D) Nissl GLI maps for different layers. AIP, agranular insular cortex, posterior part; DZ, parietal cortex, area 1, dysgranular zone; Ect, ectorhinal cortex; Fr1, frontal cortex, area 1; HL, parietal cortex, hindlimb area; Oc1B, occipital cortex, area1, binocular part; Oc1M, occipital cortex, area 1, monocular part; Oc2L, occipital cortex area 2, lateral part; Oc2ML, occipital cortex, area 2 mediolateral part; Oc2MM, occipital cortex, area 2, mediomedial part; Par1, parietal cortex, area 1; Par1L, parietal cortex, area1, lateral part; Par2, parietal cortex, area2; ParP, parietal cortex, posterior area; ParVC, parietal cortex, ventral area, caudal part; PRh, perirhinal area; RSA, agranular retrosplenial cortex; Te1, temporal cortex, area 1; Te3R, temporal cortex, area 3, rostral part; Te3V, temporal cortex, area 3, ventral part; TeV, temporal cortex ventral area. doi:10.1371/journal.pone.0003266.g005 PLoS ONE | www.plosone.org 5 September 2008 | Volume 3 | Issue 9 | e3266