Areal and laminar differentiation in the mouse neocortex using large scale gene expression data

Abstract

Although cytoarchitectonic organization of the mammalian cortex into different lamina has been well-studied, identifying the architectural differences that distinguish cortical areas from one another is more challenging. Localization of large anatomical structures is possible using magnetic resonance imaging or invasive techniques (such as anterograde or retrograde tracing), but identifying patterns in gene expression architecture is limited as gene products do not necessarily identify an immediate functional consequence of a specialized area. Expression of specific genes in the mouse and human cortex is most often identified across entire lamina, and areal patterning of expression (when it exists) is most easily differentiated on a layer-by-layer basis. Since cortical organization is defined by the expression of large sets of genes, the task of identifying individual (or groups of structures) cannot be done using individual areal markers. In this manuscript we describe a methodology for clustering gene expression correlation profiles in the C57Bl/6J mouse cortex to identify large-scale genetic relationships between layers and areas. By using the Anatomic Gene Expression Atlas ( http://mouse.brain-map.org/agea/) derived from in situ hybridization data in the Allen Brain Atlas, we show that a consistent expression based organization of areal patterning in the mouse cortex exists when clustered on a laminar basis. Surface-based mapping and visualization techniques are used as a representation to clarify these relationships.