PIEZO2 and perineal mechanosensation are essential for sexual function.

Despite the potential importance of genital mechanosensation for sexual reproduction, little is known about how perineal touch influences mating. We explored how mechanosensation affords exquisite awareness of the genitals and controls reproduction in mice and humans. Using genetic strategies and in vivo functional imaging, we demonstrated that the mechanosensitive ion channel PIEZO2 (piezo-type mechanosensitive ion channel component 2) is necessary for behavioral sensitivity to perineal touch. PIEZO2 function is needed for triggering a touch-evoked erection reflex and successful mating in both male and female mice. Humans with complete loss of PIEZO2 function have genital hyposensitivity and experience no direct pleasure from gentle touch or vibration. Together, our results help explain how perineal mechanoreceptors detect the gentlest of stimuli and trigger physiologically important sexual responses, thus providing a platform for exploring the sensory basis of sexual pleasure and its relationship to affective touch.

Single-nucleus transcriptomic analysis of human dorsal root ganglion neurons.

Somatosensory neurons with cell bodies in the dorsal root ganglia (DRG) project to the skin, muscles, bones, and viscera to detect touch and temperature as well as to mediate proprioception and many types of interoception. In addition, the somatosensory system conveys the clinically relevant noxious sensations of pain and itch. Here, we used single nuclear transcriptomics to characterize transcriptomic classes of human DRG neurons that detect these diverse types of stimuli. Notably, multiple types of human DRG neurons have transcriptomic features that resemble their mouse counterparts although expression of genes considered important for sensory function often differed between species. More unexpectedly, we identified several transcriptomic classes with no clear equivalent in the other species. This dataset should serve as a valuable resource for the community, for example as means of focusing translational efforts on molecules with conserved expression across species.

Decoding Cellular Mechanisms for Mechanosensory Discrimination.

Single-cell RNA-sequencing and in vivo functional imaging provide expansive but disconnected views of neuronal diversity. Here, we developed a strategy for linking these modes of classification to explore molecular and cellular mechanisms responsible for detecting and encoding touch. By broadly mapping function to neuronal class, we uncovered a clear transcriptomic logic responsible for the sensitivity and selectivity of mammalian mechanosensory neurons. Notably, cell types with divergent gene-expression profiles often shared very similar properties, but we also discovered transcriptomically related neurons with specialized and divergent functions. Applying our approach to knockout mice revealed that Piezo2 differentially tunes all types of mechanosensory neurons with marked cell-class dependence. Together, our data demonstrate how mechanical stimuli recruit characteristic ensembles of transcriptomically defined neurons, providing rules to help explain the discriminatory power of touch. We anticipate a similar approach could expose fundamental principles governing representation of information throughout the nervous system.

Assigning transcriptomic class in the trigeminal ganglion using multiplex in situ hybridization and machine learning.

ABSTRACT: Single cell sequencing has provided unprecedented information about the transcriptomic diversity of somatosensory systems. Here, we describe a simple and versatile in situ hybridization (ISH)-based approach for mapping this information back to the tissue. We illustrate the power of this approach by demonstrating that ISH localization with just 8 probes is sufficient to distinguish all major classes of neurons in sections of the trigeminal ganglion. To further simplify the approach and make transcriptomic class assignment and cell segmentation automatic, we developed a machine learning approach for analyzing images from multiprobe ISH experiments. We demonstrate the power of in situ class assignment by examining the expression patterns of voltage-gated sodium channels that play roles in distinct somatosensory processes and pain. Specifically, this analysis resolves intrinsic problems with single cell sequencing related to the sparseness of data leading to ambiguity about gene expression patterns. We also used the multiplex in situ approach to study the projection fields of the different neuronal classes. Our results demonstrate that the surface of the eye and meninges are targeted by broad arrays of neural classes despite their very different sensory properties but exhibit idiotypic patterns of innervation at a quantitative level. Very surprisingly, itch-related neurons extensively innervated the meninges, indicating that these transcriptomic cell classes are not simply labeled lines for triggering itch. Together, these results substantiate the importance of a sensory neuron's peripheral and central connections as well as its transcriptomic class in determining its role in sensation.

Diversity amongst trigeminal neurons revealed by high throughput single cell sequencing.

The trigeminal ganglion contains somatosensory neurons that detect a range of thermal, mechanical and chemical cues and innervate unique sensory compartments in the head and neck including the eyes, nose, mouth, meninges and vibrissae. We used single-cell sequencing and in situ hybridization to examine the cellular diversity of the trigeminal ganglion in mice, defining thirteen clusters of neurons. We show that clusters are well conserved in dorsal root ganglia suggesting they represent distinct functional classes of somatosensory neurons and not specialization associated with their sensory targets. Notably, functionally important genes (e.g. the mechanosensory channel Piezo2 and the capsaicin gated ion channel Trpv1) segregate into multiple clusters and often are expressed in subsets of cells within a cluster. Therefore, the 13 genetically-defined classes are likely to be physiologically heterogeneous rather than highly parallel (i.e., redundant) lines of sensory input. Our analysis harnesses the power of single-cell sequencing to provide a unique platform for in silico expression profiling that complements other approaches linking gene-expression with function and exposes unexpected diversity in the somatosensory system.