The organization of the transcriptional network in specific neuronal classes

Genome‐wide expression profiling has aided the understanding of the molecular basis of neuronal diversity, but achieving broad functional insight remains a considerable challenge. Here, we perform the first systems‐level analysis of microarray data from single neuronal populations using weighted gene co‐expression network analysis to examine how neuronal transcriptome organization relates to neuronal function and diversity. We systematically validate network predictions using published proteomic and genomic data. Several network modules of co‐expressed genes correspond to interneuron development programs, in which the hub genes are known to be critical for interneuron specification. Other co‐expression modules relate to fundamental cellular functions, such as energy production, firing rate, trafficking, and synapses, suggesting that fundamental aspects of neuronal diversity are produced by quantitative variation in basic metabolic processes. We identify two transcriptionally distinct mitochondrial modules and demonstrate that one corresponds to mitochondria enriched in neuronal processes and synapses, whereas the other represents a population restricted to the soma. Finally, we show that galectin‐1 is a new interneuron marker, and we validate network predictions in vivo using Rgs4 and Dlx1/2 knockout mice. These analyses provide a basis for understanding how specific aspects of neuronal phenotypic diversity are organized at the transcriptional level. Synopsis Understanding the molecular basis of neuronal diversity has been aided by the ability to perform genome‐wide expression profiling, but achieving broad functional insight remains a considerable challenge. Systems‐level analyses that consider relationships between genes permit association of gene expression variation with specific cell phenotypes. Weighted gene co‐expression network analysis (WGCNA) groups functionally related genes into modules in an unsupervised manner (Zhang and Horvath, 2005 ; Horvath et al , 2006 ; Oldham et al , 2006 , 2008 ), based on the self‐organizing properties of complex systems (Barabasi and Albert, 1999 ; Ravasz et al , 2002 ). The modularity of the system allows independent analysis of the components, and the identification of relationships between genes facilitates gene annotation based on network position without assumptions about gene function. Here, we analyzed published microarray data from single neuronal populations (Sugino et al , 2006 ) using WGCNA to organize th