Single cell q-PCR derived expression profiles of identified sensory neurons

Sensory neurons are chemically and functionally heterogeneous and this heterogeneity has been examined extensively over the last several decades. These studies have employed a variety of different methodologies, including anatomical, electrophysiological and molecular approaches. Recent studies using next generation sequencing techniques have examined the transcriptome of single sensory neurons. Although, these reports have provided a wealth of exciting new information on the heterogeneity of sensory neurons, correlation with functional types is lacking. Here, we employed retrograde tracing of cutaneous and muscle afferents to examine the variety of mRNA expression profiles of individual, target-specific sensory neurons. In addition, we used an ex vivo skin/nerve/DRG/ spinal cord preparation to record and characterize the functional response properties of individual cutaneous sensory neurons that were then intracellularly labeled with fluorescent dyes, recovered from dissociated cultures and analyzed for gene expression. We found that by using single cell qPCR techniques and a limited set of genes, we can identify transcriptionally distinct groups. We have also used calcium imaging and single cell qPCR to determine the correlation between levels of mRNA expression and functional protein expression and how functional properties correlated with the different transcriptional groups. These studies show that although transcriptomics does map to functional types, within any one functional subgroup, there are highly variable patterns of gene expression. Thus, studies that rely on the expression pattern of one or a few genes as a stand in for physiological experiments, runs a high risk of data misinterpretation with respect to function. Significance statementExpression profiles of unidentified sensory neurons have been recently studied using RNASeq techniques. Here, we utilize a multifactorial approach to target identified cutaneous and muscle afferents to examine expression and functional levels of specific high priority candidate genes using ex vivo electrophysiology, Ca2+ imaging, and single cell qPCR. Using this methodology, we were able to identify specific groups of neurons with distinct functional properties that corresponded to unique transcriptional profiles. This represents the first attempt to relate neuronal phenotype with levels of gene expression in single identified afferents and highlights the importance of combining functional analysis with transcriptomics.