Molecular Signatures of Mouse TRPV1-Lineage Neurons Revealed by RNA-Seq Transcriptome Analysis

Abstract Disorders of pain neural systems are frequently chronic and, when recalcitrant to treatment, can severely degrade the quality of life. The pain pathway begins with sensory neurons in dorsal root or trigeminal ganglia, and the neuronal subpopulations that express the transient receptor potential cation channel, subfamily V, member 1 (TRPV1) ion channel transduce sensations of painful heat and inflammation and play a fundamental role in clinical pain arising from cancer and arthritis. In the present study, we elucidate the complete transcriptomes of neurons from the TRPV1 lineage and a non-TRPV1 neuroglial population in sensory ganglia through the combined application of next-gen deep RNA-Seq, genetic neuronal labeling with fluorescence-activated cell sorting, or neuron-selective chemoablation. RNA-Seq accurately quantitates gene expression, a difficult parameter to determine with most other methods, especially for very low and very high expressed genes. Differentially expressed genes are present at every level of cellular function from the nucleus to the plasma membrane. We identified many ligand receptor pairs in the TRPV1 population, suggesting that autonomous presynaptic regulation may be a major regulatory mechanism in nociceptive neurons. The data define, in a quantitative, cell population–specific fashion, the molecular signature of a distinct and clinically important group of pain-sensing neurons and provide an overall framework for understanding the transcriptome of TRPV1 nociceptive neurons. Perspective Next-gen RNA-Seq, combined with molecular genetics, provides a comprehensive and quantitative measurement of transcripts in TRPV1 lineage neurons and a contrasting transcriptome from non-TRPV1 neurons and cells. The transcriptome highlights previously unrecognized protein families, identifies multiple molecular circuits for excitatory or inhibitory autocrine and paracrine signaling, and suggests new combinatorial approaches to pain control.