PROJECT SUMMARY The afferent sensory neurons that innervate taste buds have eluded definition at the molecular level until very recently. Specifically, it has been unclear if separate classes of neurons innervate the well-established receptor cell types in the taste bud. It has also been unclear whether there are dedicated neuronal circuits that connect peripheral receptor cells, afferent sensory neurons and the first relay neurons in the Nucleus of the Solitary Tract in the brainstem. Progress has been impeded by the lack of molecular markers to distinguish gustatory neuronal cell types. We have recently completed a study on single-cell RNAseq of geniculate ganglion neurons. Our detailed bioinformatics analyses, which are publicly accessible, revealed neuronal subtypes based on deep separations in their transcriptome profiles. Our study also yielded markers which permit us to distinguish the classes of gustatory neurons. We propose to examine the three main classes, T1, T2 and T3, in several ways. We will assess whether each class of neurons associates with taste buds in a given receptive field, or with particular taste bud cells types. We will also examine the central projections of neurons of each class for insights into their potential roles in gustation. Second, we will evaluate whether the gustatory neuron types preferentially convey information on particular taste qualities. And finally, we will examine the significance of the novel hybrid gustatory-mechanosensory neuron type that we recently reported, using in vivo confocal Ca2+ imaging in transgenic mice expressing the Ca2+ indicator, GCaMP. Together, these analyses will be the first to assess the extent of heterogeneity in the peripheral and central connectivity and functional properties of defined subtypes of gustatory neurons.
|Effective start/end date||3/1/19 → 2/29/24|
- National Institutes of Health: $461,199.00
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.