The versatility of somatosensation arises from heterogeneous dorsal root ganglion (DRG) neurons. However, soma transcriptomes of individual human (h)DRG neurons—critical information to decipher their functions—are lacking due to technical difficulties. In this study, we isolated somata from individual hDRG neurons and conducted deep RNA sequencing (RNA-seq) to detect, on average, over 9,000 unique genes per neuron, and we identified 16 neuronal types. These results were corroborated and validated by spatial transcriptomics and RNAscope in situ hybridization. Cross-species analyses revealed divergence among potential pain-sensing neurons and the likely existence of human-specific neuronal types. Molecular-profile-informed microneurography recordings revealed temperature-sensing properties across human sensory afferent types. In summary, by employing single-soma deep RNA-seq and spatial transcriptomics, we generated an hDRG neuron atlas, which provides insights into human somatosensory physiology and serves as a foundation for translational work.

The roles of Aβ low-threshold mechanoreceptors (LTMRs) in transmitting mechanical hyperalgesia and in alleviating chronic pain have been of great interest but remain contentious. Here we utilized intersectional genetic tools, optogenetics, and high-speed imaging to specifically examine functions of Split^Cre labeled mouse Aβ-LTMRs in this regard. Genetic ablation of Split^Cre-Aβ-LTMRs increased mechanical nociception but not thermosensation in both acute and chronic inflammatory pain conditions, indicating a modality-specific role in gating mechanical nociception. Local optogenetic activation of Split^Cre-Aβ-LTMRs triggered nociception after tissue inflammation, whereas their broad activation at the dorsal column still alleviated mechanical hypersensitivity of chronic inflammation. Taking all data into consideration, we propose a model, in which Aβ-LTMRs play distinctive local and global roles in transmitting or alleviating mechanical hyperalgesia of chronic pain, respectively. Our model suggests a strategy of global activation plus local inhibition of Aβ-LTMRs for treating mechanical hyperalgesia.

Noxious stimuli activate small to medium-sized dorsal root ganglion (DRG) neurons. Intense noxious stimuli result in the release of substance P (SP) from the central terminals of these neurons. It binds to the neurokinin type 1 receptor (NK1r) and sensitises the dorsal horn neurons. SP is also released from the peripheral terminals leading to neurogenic inflammation. However, their individual contribution at spinal and peripheral levels to postincisional nociception has not been delineated as yet. Sprague–Dawley rats were administered different doses (3–100 μg) of an NK1r antagonist (L760735) by intrathecal (i.t.) route before hind paw incision. On the basis of its antinociceptive effect on guarding behaviour, the 30 μg dose was selected for further study. In different sets of animals, this was administered i.t. (postemptive) and intrawound (i.w.). Finally, in another group, drug (30 μg) was administered through both i.t and i.w. routes. The antinociceptive effect was assessed and compared. Expression of SP was examined in the spinal cord. Intrawound concentration of SP and inflammatory mediators was also evaluated. Postemptive i.t. administration significantly attenuated guarding and allodynia. Guarding was alone decreased after i.w. drug treatment. Combined drug administration further attenuated all nociceptive parameters, more so after postemptive treatment. Expression of SP in the spinal cord decreased post incision but increased in the paw tissue. Inflammatory mediators like the nerve growth factor also increased after incision. In conclusion, SP acting through the NK1r appears to be an important mediator of nociception, more so at the spinal level. These findings could have clinical relevance.

The versatility of somatosensation arises from heterogeneous dorsal root ganglion (DRG) neurons. However, soma transcriptomes of individual human DRG (hDRG) neurons – critical information to decipher their functions – are lacking due to technical difficulties. Here, we developed a novel approach to isolate individual hDRG neuron somas for deep RNA sequencing (RNA-seq). On average, >9,000 unique genes per neuron were detected, and 16 neuronal types were identified. Cross-species analyses revealed remarkable divergence among pain-sensing neurons and the existence of human-specific nociceptor types. Our deep RNA-seq dataset was especially powerful for providing insight into the molecular mechanisms underlying human somatosensation and identifying high potential novel drug targets. Our dataset also guided the selection of molecular markers to visualize different types of human afferents and the discovery of novel functional properties using single-cell in vivo electrophysiological recordings. In summary, by employing a novel soma sequencing method, we generated an unprecedented hDRG neuron atlas, providing new insights into human somatosensation, establishing a critical foundation for translational work, and clarifying human species-specific properties.

Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and a chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening.