Thus, OXT enhanced the size of the endowment effect across all types of ownerships

sets: isolectin B4 –JW-55 biological activity positive subset, peptidergic subset expressing neuropeptide substance P and calcitonin gene-related peptide, and tyrosine hydroxylase -expressing subset, while large neurons express neurofilament 200 . Cutaneous mechanoreceptors, nociceptors, thremoreceptors and itch receptors have been physiologically characterized. Many nociceptors respond to multiple stimulus modalities, whereas others have more specialized response properties. Defined nociceptor subtypes have been termed C-fiber or A-fiber mechanoheat nociceptors, mechanical nociceptors, mechanically insensitive or sensitive npg Types of primary sensory neurons 84 afferents, and mechanoheat-cold nociceptors. The molecular properties of DRG neurons have been extensively studied, including their expression of various receptors and ion channels such as neuropeptide Y receptors, MAS-related G-protein-coupled receptors , voltage-gated Na+ channels, transient receptor potential channels, ATP receptors, acid-sensing ion channels and tyrosine kinase receptors . Gene expression profiles of DRG tissue have been also analyzed by microarray and RNA-sequencing techniques. Although these methods have helped to identify the expression of neuromodulators such as natriuretic peptide B and regulators of Na+, K+-ATPase, including the -subunit of NKA , they cannot provide a global view of the transcriptional profiles of individual neurons. Recently, multiple efforts have been made to analyze the transcriptional profiles of DRG neurons. The transcriptional profiles of TRPV1 lineage and Nav1.8 channel-expressing DRG neurons have been examined by RNA-seq. By single-cell PCR, Chiu et al. identified six subgroups of DRG neurons. Single-cell RNA-seq enables a better understanding of a cell’s transcriptome. Usoskin et al. performed low-coverage single-cell RNA-seq and classified the mouse DRG neurons into two PEP types, three NP types, TH type and five NF200-positive types within the traditional classification framework. However, this method resulted in transcriptional variation among DRG neurons due to the limited number of genes detected in each neuron. These studies are useful resources for analyzing the molecular determinants of sensory neuron types, but their interpretation is complicated by the large variation present within a neuron type defined by a given molecular marker. Moreover, the related functional phenotypes of the neurons have not been experimentally analyzed. Here, by integrating single-cell techniques such as high-coverage RNA-seq, in vivo patch clamp recording and single-cell PCR, we classify mouse DRG neurons into PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19822663 10 types and 14 subordinate subtypes with distinct transcriptional patterns, molecular markers and functional annotations, revealing a new catalog of somatosensory receptors. Our single-cell RNA-seq-based clustering reveal more neuron types and subtypes than previous classifications of DRG neurons, and demonstrate that traditional neuron subset-specific markers in fact label multiple neuron types. Moreover, our study suggests that based on the current understanding of molecular function and signal- ing networks, transcriptome data can partially predict the functions of neuron types. Further, neuron type-specific functional analyses are needed to confirm and elaborate the precise functions of these neuron types. Thus, neuron types can be defined by integrating their transcriptomic, morphological and functional characteristics. Results Neuron sampling and quali