GSE131230
Deep sequencing of somatosensory neurons reveals molecular determinants of intrinsic physiological properties
To explore the molecular basis of the distinct intrinsic membrane properties and other dstinguishing features of functionally defined DRG neuron subtypes, we bulk-sequenced RNA at high depth of genetically-labeled DRG neurons to generate transcriptome profiles of eight major DRG neuron subtypes. The trancriptome profiles revealed differentially expressed and functionally relevant genes, including voltage-gated ion channels. Guided by the transcriptome pofiles, electrophysiological analyses using pharmacological and genetic manipulations as well as computational modeling of DRG neuron subtypes were undertaken to assess the functions of select voltage-gated potassium channels (Kv1, Kv2, Kv3, and Kv4) in shaping action potential (AP) waveforms and firing patterns of the DRG neuron subtypes. Our findings show that the transcriptome profiles have predictive value for defining ion channel contributions to sensory neuron subtype-specific intrinsic physiological properties. Overall design: We made use of genetic tools developed that selectively label each of eight DRG neuron subtypes with fluorescent reporters, first purifying labeled neurons to homogeneity using flow cytometry (FACS) and then extracting RNA from these purified neuronal populations. DRGs from all axial levels were used for FACS, except for proprioceptors which were purified from thoracic ganglia, because the PVIRES-Cre; Ai14 proprioceptor labeling strategy was found to label a subset of limb level cutaneous LTMRs as well. DRGs from multiple mice were combined for FACS, and neurons from multiple rounds of sorting were combined to obtain sufficient amounts of RNA for each sequencing reaction. At least three biological replicates with a total of 26 samples were sequenced for each neuronal subtype. RNA libraries were prepared and subsequently sequenced using an Illumina HiSeq2000 platform at an average depth of ~70 million mapped reads per sample. This depth translates to an average detection level of 12,226 genes per sample. By using well characterized mouse lines for specific labeling of neuronal subtypes, this analysis links gene expression patterns to sensory neuron subtypes defined by their distinct in vivo properties as well as their corresponding intrinsic properties.