Abstract
The electrical diversity of neurons arises from the expression of different combinations of ion channels. The gene expression rules governing these combinations are not known. We examined the expression of twenty-six ion channel genes in a broad range of single neocortical neuron cell types. Using expression data from a subset of twenty-six ion channel genes in ten different neocortical neuronal types, classified according to their electrophysiological properties, morphologies and anatomical positions, we first developed an incremental Support Vector Machine (iSVM) model that prioritizes the predictive value of single and combinations of genes for the rest of the expression pattern. With this approach we could predict the expression patterns for the ten neuronal types with an average 10-fold cross validation accuracy of 87% and for a further fourteen neuronal types not used in building the model, with an average accuracy of 75%. The expression of the genes for HCN4, Kv2.2, Kv3.2 and Caβ3 were found to be particularly strong predictors of ion channel gene combinations, while expression of the Kv1.4 and Kv3.3 genes has no predictive value. Using a logic gate analysis, we then extracted a spectrum of observed combinatorial gene expression rules of twenty ion channels in different neocortical neurons. We also show that when applied to a completely random and independent data, the model could not extract any rules and that it is only possible to extract them if the data has consistent expression patterns. This novel strategy can be used for predictive reverse engineering combinatorial expression rules from single-cell data and could help identify candidate transcription regulatory processes.
Highlights
Experimental and computational informatics studies have revealed more than 270 genes associated with voltage-gated ion channels in the Rattus norvegicus (Gene Ontology: GO:0005244 as of January 2011)
It is expected that neurons only express a small fraction of the ion channels [13,15], but the low expression frequency of their genes might be due to the drawback of single-cell gene expression profiling and multiplex RT-PCR where a significant amount of false negatives may be present because of mRNA harvesting or amplification failures [12,16]
We present a computational multi-parametric approach for extracting combinatorial expression rules of ion channel genes in ten different neuronal types of the neocortex
Summary
Experimental and computational informatics studies have revealed more than 270 genes associated with voltage-gated ion channels in the Rattus norvegicus (Gene Ontology: GO:0005244 as of January 2011) It is the combinations in which these genes are expressed as well as the precise spatial distribution and biophysical properties of the channels they code for that underlies the diversity of neuronal electrical properties [1]. Previous studies have localized and identified the distribution of ion channels in specific neurons [2,3,4,5,6] and attempted to match gene expression profiles with different neuronal cell types based on their electrical or morphological characteristics [6,7,8,9,10,11] These studies provided important insight into the correlation between single ion channels and the electrical behavior of neurons, they do not address combinatorial rules of gene expression in different classes of neurons. The major advance of the study by Toledo Rodriguez et al was that it enabled the identification of clusters of ion channel gene expression, correlations between genes expressed and the electrical phenotypes, and the further prediction of electrical properties of neurons from expression profiles
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