Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling

Abstract

The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profi ling will resolve neuronal cell types into groups that refl ect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defi ned by a common property. Here we extend this approach to ask whether single neuron gene expression profi ling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups refl ect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by fi ring characteristics and electrical properties, enabling the defi nition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifi cally enriched in regular spiking neurons were identifi ed from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profi ling may be used to group and classify neurons in a manner refl ecting their known biological properties and may be used to identify cell-specifi c transcripts. © 2010 Subkhankulova, Yano, Robinson and Livesey.