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Mol. Cells 2022; 45(12): 877-882

Published online December 31, 2022

https://doi.org/10.14348/molcells.2022.0116

© The Korean Society for Molecular and Cellular Biology

Advances in Optical Tools to Study Taste Sensation

Gha Yeon Park1,2,3 , Hyeyeong Hwang1,2,3 , and Myunghwan Choi1,2,*

1School of Biological Sciences, Seoul National University, Seoul 08826, Korea, 2The Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, Korea, 3These authors contributed equally to this work.

Correspondence to : choim@snu.ac.kr

Received: July 21, 2022; Revised: September 14, 2022; Accepted: September 15, 2022

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.

Taste sensation is the process of converting chemical identities in food into a neural code of the brain. Taste information is initially formed in the taste buds on the tongue, travels through the afferent gustatory nerves to the sensory ganglion neurons, and finally reaches the multiple taste centers of the brain. In the taste field, optical tools to observe cellular-level functions play a pivotal role in understanding how taste information is processed along a pathway. In this review, we introduce recent advances in the optical tools used to study the taste transduction pathways.

Keywords geniculate ganglion, gustation, imaging, insular cortex, optical tools, taste bud

Taste sensation is the process of converting chemical identities in food into a neural code of the brain. Taste information initially forms on the tongue when tasting molecules reach the taste receptors located on the apical microvilli of taste buds (Roper and Chaudhari, 2017) (Fig. 1). Taste buds can distinguish at least five basic tastes, sweet, umami, bitter, sour, and salty, using distinct sets of G protein-coupled receptors or ion channels (Ogata and Ohtubo, 2020; Shrestha and Lee, 2021; Zhang et al., 2003; Zhao et al., 2003). Taste information travels through the afferent gustatory nerves that innervate taste buds to the sensory ganglia positioned in the facial canal (i.e., the geniculate ganglion for the anterior tongue and the petrosal ganglion for the posterior tongue). Taste information is then transmitted to the nucleus of the solitary tract (NST) in the brain stem and further processed in multiple brain areas, which also integrate other sensory inputs and internal states (Schiff et al., 2018; Wang et al., 2018).

Optical tools have become pivotal in taste research because they provide a means to record cellular information in the natural environment. Various chemical- or protein-based fluorescent indicators have been developed to generate optical contrast for specific cellular phenomena (Lambert, 2019). Fluorescence microscopy readily provides millisecond-scale temporal dynamics with subcellular-scale spatial precision. The limited penetration depth of light into scattering biological media can also be overcome by minimally invasive surgical procedures tailored to specific organs of interest (Choi et al., 2015a). In this minireview, we discuss recent advances in optical tools adopted for studying the major areas of the taste transduction pathway, from the tongue to the brain.

Multiple experimental preparations for taste buds have been utilized to understand the initial steps of taste information processing. Early studies relied on in vitro preparations by enzymatically digesting the extracellular matrices and culturing isolated taste bud cells in Petri dishes (Ozdener and Rawson, 2013). Although beneficial for biochemical assays, isolated cell cultures largely compromise the intracellular interactions within the taste bud. To better preserve the intercellular architecture, ex vivo preparations, such as tongue epithelial sheets and isolated taste buds, were used. The tongue sheet was prepared by peeling off the tongue epithelium after mucosal injection of dispase and collagenase (Venkatesan et al., 2016). Individual taste buds were further isolated from peeled epithelial sheets and affixed to a lysine-coated dish for functional studies, including electrophysiological recordings and fluorescent calcium imaging, in response to various gustatory stimuli (Ruiz et al., 2001). However, these preparations could not mimic the compartmentalized cytoarchitecture of the taste bud, in which only the apical microvilli are exposed to oral tastants, whereas the remaining cell bodies are shielded from the external environment by tight junctions at the apical tip.

Several preparations have been proposed to preserve the physiological compartments within taste buds. Yoshida et al. (2009) devised a setup for the isolated taste bud in which the mucosal side was drawn to a stimulating pipette perpetually perfused with Tyrode’s solution, set apart from external tastant stimuli entering the apical microvilli. The taste stimuli were restricted to the taste pore, which was rinsed with distilled water between each stimulus. Electrophysiological recordings have shown that taste bud cells can be classified into cellular subtypes that respond to various tastants (Yoshida et al., 2009). Similarly, Richter et al. (2003) harnessed a method to activate taste bud cells on acute lingual slices by focally delivering tastants with a stimulus micropipette specifically on the apical pore. These methods successfully reproduced the natural microenvironment of the taste bud, where only the apical tip was exposed to premediated stimuli, while the basolateral side remained perfused within the epithelium. Such methods have led to the discovery that delivery of weak acids into the taste pore depolarizes a few taste receptor cells and induces an increase in intracellular calcium (Richter et al., 2003).

For functional imaging, taste bud cells must be stained with either chemical or genetic contrast agents. The most widely used functional indicator targets intracellular calcium, which serves as a second messenger for transducing the most basic taste qualities (e.g., sodium taste) (Nomura et al., 2020). The intracellular loading of chemical indicators can be achieved by in vivo iontophoresis (e.g., calcium-green-dextran) or bulk diffusion in vitro (Fura 2-AM) (DeFazio et al., 2006). Genetically encoded calcium indicators (e.g., GCaMP) can also be introduced using transgenic mouse lines with promoters active in taste bud cells such as PIRT (for chemosensory type II and III cells) and GAD2 (for glia-like type I cells). Other types of functional indicators, such as pH indicators (e.g., BCECF-D), have also been used to study changes in intracellular pH in response to sour stimuli (Richter et al., 2003).

Calcium imaging has been commonly used to investigate taste-evoked activities of taste bud cells. Calcium imaging of a peeled tongue epithelium revealed that salt-sensing cells could be classified as ENaC-dependent sodium-sensitive cells and ENaC-independent high-salt-sensitive cells (Chandrashekar et al., 2010). Using a similar preparation, Oka et al. (2013) also showed that high salt concentrations activate bitter-sensing cells (and potentially sour-sensing cells) to mediate behavioral aversion to high salt. Caicedo and Roper (2001) used calcium imaging of a lingual slice preparation to demonstrate that different bitter compounds activate distinct subpopulations of bitter-sensing cells.

Calcium imaging was later modified to study specific neurotransmitter dynamics in taste buds. This approach involved the genetic engineering of extraneous cultured cells to express a specific neurotransmitter receptor and loading them with a calcium indicator (referred to as a “biosensor cell”) (Huang et al., 2009). Using a fine micropipette, the biosensor cell was positioned near the target taste bud or cell to detect neurotransmitter release by observing cellular calcium dynamics. For instance, Chinese hamster ovary cells genetically engineered to express a purinergic receptor or a GABA receptor can be loaded with a calcium-sensitive dye to screen extracellular ATP or GABA, respectively. The use of biosensor cells has led to the finding that stimulating type II and type III taste cells induce the release of ATP and GABA, respectively (Huang et al., 2009; 2011). Additionally, by observing ATP secretion from type II cells in response to sweet stimuli in circumvallate taste buds, adenosine has been shown to modulate the responsiveness of type II cells (Dando et al., 2012).

An intravital tongue imaging window was developed for the fungiform taste bud to fully recapitulate the living microenvironment of the taste bud (Choi et al., 2015b). In this preparation, the mouse tongue was externalized from the oral cavity using a miniature suction grabber and sandwiched in metal fixatives to minimize physiological motion. The anterior tongue was immersed in artificial saliva and imaged using a two-photon microscope with a water immersion objective lens. Using this preparation, Choi et al. (2015b) demonstrated that taste bud cells are responsive to both orally administered and circulating tastants (e.g., saccharine and NaCl) in the bloodstream.

The intravital imaging window was elaborated in due course by the integration of a microfluidic channel, named “µTongue (microfluidics-on-a-tongue)” (Han and Choi, 2018) (Fig. 2A). The µTongue permits programmable delivery of various tastants of interest topically on the anterior tongue to observe taste-induced functional activities of taste cells in real time (Han et al., 2021a). The imaging system was later improved by introducing an axially elongated beam (i.e., Bessel beam) into two-photon microscopy, which largely resolved the issue of the focal shift introduced by high-index tastant solutions, such as high concentrations of NaCl and sucrose (Han et al., 2021b).

Sensory information from the tongue travels to the sensory ganglia located in the facial canal of the head (Fig. 1). As illustrated in Fig. 1, there are two relay stations: the geniculate ganglion innervating the fungiform taste buds in the anterior tongue and the petrosal ganglion innervating circumvallate taste buds in the posterior tongue. The most widely studied are the geniculate ganglion housing cell bodies of pseudounipolar sensory neurons receiving taste information through the chorda tympani nerve (Yokota and Bradley, 2016; 2017) and transmitting signals to NST in the brainstem and thalamus. Observing the neuronal population activity in the geniculate ganglion provides input gustatory information to the brain.

As neuronal firing is accompanied by an increase in intracellular calcium, the spatiotemporal activities of the geniculate neurons can be imaged using genetically encoded calcium indicators (e.g., GCaMP) (Lee et al., 2019; Wu and Dvoryanchikov, 2015). Previous studies used transgenic reporter mice with pan-neuronal promoters such as Thy1-GcaMP6 and SNAP25-GcaMP6. An alternative approach is the viral delivery of genetically encoded calcium indicators by injecting retrograde viral vectors into the NST, where geniculate neurons project and form synapses (Yarmolinsky et al., 2009).

Once the animal model is prepared, the geniculate ganglion must be surgically exposed to ensure optical access. The geniculate ganglion can be reached via either the ventral or dorsal routes, which reveal intact geniculate neurons without perturbing the connected nerve fibers (Fowler and Macpherson, 2021; Lundy and Contreras, 1999; Wu and Dvoryanchikov, 2015; Yokota and Bradley, 2016). Tracheotomy is often performed to ensure adequate physiological respiration. As geniculate neurons lie deep within the facial canal, the imaging system must have an adequate working distance of >1 cm. Typically, widefield or confocal fluorescence microscopes with long working distance objective lenses (e.g., 10X, 0.3 NA, 16 mm working distance) are used. A narrow microendoscopic probe consisting of a gradient index (GRIN) lens was also developed as an alternative, allowing better spatial resolution with a higher numerical aperture (Fig. 2B). Time-series images of the geniculate neurons can be recorded in response to various tastants delivered to the anterior tongue. Physiological fluid from the cochlear promontory may interfere with data acquisition and a tapered suction tip can be introduced to continuously remove the fluid. The acquired temporal traces for each neuron are extracted by manual regions-of-interest or advanced algorithms such as CNMF (Pnevmatikakis et al., 2016) or CaImAn (Giovannucci et al., 2019). To resolve data that are accompanied by excessive noise, deep-learning-based denoising algorithms, such as DeepCAD (Li et al., 2021) or DeepInterpolation (Lecoq et al., 2021), can be used to robustly extract signals of interest.

Calcium imaging of geniculate neurons was used to study the coding logic of peripheral taste information. Barretto et al. (2015) used two-photon microendoscopy in a Thy1-GCaMP mouse to show that geniculate neurons were primarily responsive to a single taste quality. In this study, only a minor portion of the neurons were reported to be responsive to multiple taste qualities, supporting the labeled line coding model. Later, Wu et al. (2015) also performed calcium imaging of geniculate neurons after surgical exposure of the geniculate ganglion. By administering different concentrations of tastants to a mouse, this study revealed that the proportion of neurons responding to multiple taste qualities exhibited a sharp dependence on the concentration of tastants (Wu et al., 2015). Even single-tuned geniculate neurons at low tastant concentrations showed heterogeneous responses to multiple taste qualities at high concentrations. This study suggests that the strict labeled-line coding model may not fully account for peripheral taste encoding (Roper and Chaudhari, 2017).

To measure the activity of neurons in the rostral NST (rNST), where the primary central neuronal station of gustatory information is occupied, electrophysiological recording using a microelectrode is the gold standard approach (Peng et al., 2015). Alternatively, fiber photometry can be used to monitor the calcium activity of genetically targeted neuronal subpopulations in the rNST (Gunaydin et al., 2014; Jin et al., 2021; Zhang et al., 2019) (Fig. 2C). Typically, the time-series acquisition of a fluorescent signal through an implanted multimode fiber is stable even under awake behaving conditions. Fiber photometry was used to reveal that Pdyn neurons (sour-sensing neurons) in the rNST respond specifically to sour tastants (Zhang et al., 2019) and that licking behavior activates Sst- and Calb2-neurons in the rNST (Jin et al., 2021). Furthermore, fiber implants can also be used to deliver optogenetic photostimulation on specific neurons expressing light-sensitive opsins in the rNST (e.g., channelrhodopsin-2) (Jin et al., 2021; Zhang et al., 2019). For example, Jin et al. (2021) showed that optogenetic activation of sour neurons in the rNST induced prototypical acid-aversion behavior.

Although fiber photometry readily provides information on population-level neural dynamics, it lacks the spatial information to discern individual neuronal activities. To overcome this limitation, miniature integrated microscopes (miniscopes) were developed by miniaturizing conventional widefield fluorescence microscopy into a head-mountable size (Ghosh et al., 2011). In the miniscope, a thin GRIN lens (diameter = 0.3-2 mm) replaces the conventional objective lens so that it can be inserted into the targeted brain area with minimal invasiveness. Other parts, including a tube lens, fluorescence filters, a light source, and a camera, are compacted to a small form factor, so they can be mounted on the head of an awake behaving mouse. Although not widely adopted for taste research, miniscopes will be useful in investigating individual neuronal activities in deep brain areas such as the rNST, PbN (parabrachial nucleus), and VPM (ventral posteromedial nucleus) (Fig. 1).

Two-photon fluorescence microscopy is advantageous for observing individual neuronal activities in the insular cortex, which is the primary sensory cortex for gustation. Chen et al. (2011) prepared an open cranial window model at the insular cortex located ~1 mm above the intersection of the middle cerebral artery and rhinal vein. Observing the neuronal response to five basic tastes in an anesthetized mouse, this study revealed the topographic segregation of different taste qualities in the primary gustatory cortex. Another group later introduced a miniature prism mirror and GRIN probe to a two-photon microscope to perform a comparable study in a head-fixed awake mouse (Fig. 2D) (Chen et al., 2021). Notably, the present study reported a scattered distribution of neurons responding to distinct taste qualities rather than topographic segregation.

Optical tools are essential in understanding how taste information is processed from the tongue to the brain. Considering the recent phenomenal advances in optical tools for neuroscience, we anticipate that optical approaches will be increasingly used in taste research. In terms of optical hardware, volumetric imaging techniques, such as light-sheet microscopy (Stelzer et al., 2021), will provide a more comprehensive dataset on each taste organ, for example, on how a taste bud processes taste information from the apical microvilli to the afferent nerves. Adopting advanced genetically encoded indicators will provide access to veiled information, such as membrane potentials (e.g., QuasArs, Archon) or neurotransmitter dynamics (e.g., GRAB-ATP, iGABASnFR), within the taste bud in vivo (Bando et al., 2019; Wang et al., 2018). Moreover, computational analysis and modeling advances are necessary to translate the acquired large-scale datasets into scientific knowledge. Inevitably, the taste field is expected to involve interdisciplinary collaborations with optics, genetics, and computational neuroscience.

This work was supported by the Samsung Science and Technology Foundation under Project Number SSTF-BA2002-14 and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07042834, 2019M3E5D2A01058329, 2019M3A9E2061789, 2020M3C1B8016137, 2020R1A5A 1018081).

G.Y.P. and H.H. wrote the manuscript. M.C. supervised the paper and provided feedback.

Fig. 1. The taste transduction pathway from the taste buds on the tongue to the insular cortex in the brain. The left image is a two-photon microscopic image of the mouse tongue with an inverted look-up table (magenta, autofluorescence; purple, second harmonic generation signal). In the schematic image on the right, the major areas responsible for taste information processing are illustrated. IC, insular cortex; VPM, ventral posteromedial nucleus; PbN, parabrachial nucleus; rNST, rostral nucleus of the solitary tract.
Fig. 2. Representative optical techniques used for studying the taste transduction pathway. (A) Microfluidics-integrated imaging window for the anterior tongue (µTongue [microfluidics-on-a-tongue]). (B) In vivo imaging preparation for the geniculate ganglion using a microendoscopic gradient refractive index (GRIN) probe. Reproduced from the article of Barretto et al. (2015) (Nature 517, 373-376) with original copyright holder’s permission. (C) Fiber photometry on rostral nucleus of the solitary tract (rNST). Reproduced from the article of Jin et al. (2021) (Cell 184, 257-271.e16) with original copyright holder’s permission. (D) Two-photon imaging of the insular cortex in an awake head-fixed mouse using a prism mirror.
  1. Bando Y., Sakamoto M., Kim S., Ayzenshtat I., and Yuste R. (2019). Comparative evaluation of genetically encoded voltage indicators. Cell Rep. 26, 802-813.e4.
    Pubmed KoreaMed CrossRef
  2. Barretto R.P.J., Gillis-Smith S., Chandrashekar J., Yarmolinsky D.A., Schnitzer M.J., Ryba N.J.P., and Zuker C.S. (2015). The neural representation of taste quality at the periphery. Nature 517, 373-376.
    Pubmed KoreaMed CrossRef
  3. Caicedo A. and Roper S.D. (2001). Taste receptor cells that discriminate between bitter stimuli. Science 291, 1557-1560.
    Pubmed KoreaMed CrossRef
  4. Chandrashekar J., Kuhn C., Oka Y., Yarmolinsky D.A., Hummler E., Ryba N.J.P., and Zuker C.S. (2010). The cells and peripheral representation of sodium taste in mice. Nature 464, 297-301.
    Pubmed KoreaMed CrossRef
  5. Chen K., Kogan J.F., and Fontanini A. (2021). Spatially distributed representation of taste quality in the gustatory insular cortex of behaving mice. Curr. Biol. 31, 247-256.e4.
    Pubmed KoreaMed CrossRef
  6. Chen X., Gabitto M., Peng Y., Ryba N.J.P., and Zuker C.S. (2011). A gustotopic map of taste qualities in the mammalian brain. Science 333, 1262-1266.
    Pubmed KoreaMed CrossRef
  7. Choi M., Kwok S.J.J., and Yun S.H. (2015a). In vivo fluorescence microscopy: lessons from observing cell behavior in their native environment. Physiology (Bethesda) 30, 40-49.
    Pubmed KoreaMed CrossRef
  8. Choi M., Lee W.M., and Yun S.H. (2015b). Intravital microscopic interrogation of peripheral taste sensation. Sci. Rep. 5, 8661.
    Pubmed KoreaMed CrossRef
  9. Dando R., Dvoryanchikov G., Pereira E., Chaudhari N., and Roper S.D. (2012). Adenosine enhances sweet taste through A2B receptors inthe taste bud. J. Neurosci. 32, 322-330.
    Pubmed KoreaMed CrossRef
  10. DeFazio R.A., Dvoryanchikov G., Maruyama Y., Kim J.W., Pereira E., Roper S.D., and Chaudhari N. (2006). Separate populations of receptor cells and presynaptic cells in mouse taste buds. J. Neurosci. 26, 3971-3980.
    Pubmed KoreaMed CrossRef
  11. Fowler B.E. and Macpherson L.J. (2021). In vivo calcium imaging of mouse geniculate ganglion neuron responses to taste stimuli. J. Vis. Exp. , 10.3791/62172.
    Pubmed KoreaMed CrossRef
  12. Ghosh K.K., Burns L.D., Cocker E.D., Nimmerjahn A., Ziv Y., Gamal A.E., and Schnitzer M.J. (2011). Miniaturized integration of a fluorescence microscope. Nat. Methods 8, 871-878.
    Pubmed KoreaMed CrossRef
  13. Giovannucci A., Friedrich J., Gunn P., Kalfon J., Brown B.L., Koay S.A., Taxidis J., Najafi F., Gauthier J.L., and Zhou P., et al. (2019). CaImAn an open source tool for scalable calcium imaging data analysis. Elife 8, e38173.
    Pubmed KoreaMed CrossRef
  14. Gunaydin L.A., Grosenick L., Finkelstein J.C., Kauvar I.V., Fenno L.E., Adhikari A., Lammel S., Mirzabekov J.J., Airan R.D., and Zalocusky K.A., et al. (2014). Natural neural projection dynamics underlying social behavior. Cell 157, 1535-1551.
    Pubmed KoreaMed CrossRef
  15. Han J. and Choi M. (2018). Comprehensive functional screening of taste sensation in vivo. BioRxiv . https://doi.org/10.1101/371682.
    CrossRef
  16. Han J., Choi P., and Choi M. (2021a). µTongue: a microfluidics-based functional imaging platform for the tongue in vivo. J. Vis. Exp. , 10.3791/62361.
    Pubmed CrossRef
  17. Han J., Kim S., Choi P., Lee S., Jo Y., Kim E., and Choi M. (2021b). Robust functional imaging of taste sensation with a Bessel beam. Biomed. Opt. Express 12, 5855-5864.
    Pubmed KoreaMed CrossRef
  18. Huang Y.A., Dando R., and Roper S.D. (2009). Autocrine and paracrine roles for ATP and serotonin in mouse taste buds. J. Neurosci. 29, 13909-13918.
    Pubmed KoreaMed CrossRef
  19. Huang Y.A., Pereira E., and Roper S.D. (2011). Acid stimulation (sour taste) elicits GABA and serotonin release from mouse taste cells. PLoS One 6, e25471.
    Pubmed KoreaMed CrossRef
  20. Jin H., Fishman Z.H., Ye M., Wang L., and Zuker C.S. (2021). Top-down control of sweet and bitter taste in the mammalian brain. Cell 184, 257-271.e16.
    Pubmed CrossRef
  21. Lambert T.J. (2019). FPbase: a community-editable fluorescent protein database. Nat. Methods 16, 277-278.
    Pubmed CrossRef
  22. Lecoq J., Oliver M., Siegle J.H., Orlova N., Ledochowitsch P., and Koch C. (2021). Removing independent noise in systems neuroscience data using DeepInterpolation. Nat. Methods 18, 1401-1408.
    Pubmed KoreaMed CrossRef
  23. Lee S., Augustine V., Zhao Y., Ebisu H., Ho B., Kong D., and Oka Y. (2019). Chemosensory modulation of neural circuits for sodium appetite. Nature 568, 93-97.
    Pubmed KoreaMed CrossRef
  24. Li X., Zhang G., Wu J., Zhang Y., Zhao Z., Lin X., Qiao H., Xie H., Wang H., and Fang L., et al. (2021). Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising. Nat. Methods 18, 1395-1400.
    Pubmed CrossRef
  25. Lundy R.F. Jr. and Contreras R.J. Jr. (1999). Gustatory neuron types in rat geniculate ganglion. J. Neurophysiol. 82, 2970-2988.
    Pubmed CrossRef
  26. Nomura K., Nakanishi M., Ishidate F., Iwata K., and Taruno A. (2020). All-electrical Ca2+-independent signal transduction mediates attractive sodium taste in taste buds. Neuron 106, 816-829.e6.
    Pubmed CrossRef
  27. Ogata T. and Ohtubo Y. (2020). Quantitative analysis of taste bud cell numbers in the circumvallate and foliate taste buds of mice. Chem. Senses 45, 261-273.
    Pubmed CrossRef
  28. Oka Y., Butnaru M., Von Buchholtz L., Ryba N.J.P., and Zuker C.S. (2013). High salt recruits aversive taste pathways. Nature 494, 472-475.
    Pubmed KoreaMed CrossRef
  29. Ozdener M.H. and Rawson N.E. (2013). Primary culture of mammalian taste epithelium. Methods Mol. Biol. 945, 95-107.
    Pubmed KoreaMed CrossRef
  30. Peng Y., Gillis-Smith S., Jin H., Tränkner D., Ryba N.J.P., and Zuker C.S. (2015). Sweet and bitter taste in the brain of awake behaving animals. Nature 527, 512-515.
    Pubmed KoreaMed CrossRef
  31. Pnevmatikakis E.A., Soudry D., Gao Y., Machado T.A., Merel J., Pfau D., Reardon T., Mu Y., Lacefield C., and Yang W., et al. (2016). Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89, 285-299.
    Pubmed KoreaMed CrossRef
  32. Richter T.A., Caicedo A., and Roper S.D. (2003). Sour taste stimuli evoke Ca2+ and pH responses in mouse taste cells. J. Physiol. 547, 475-483.
    Pubmed KoreaMed CrossRef
  33. Roper S.D. and Chaudhari N. (2017). Taste buds: cells, signals and synapses. Nat. Rev. Neurosci. 18, 485-497.
    Pubmed KoreaMed CrossRef
  34. Ruiz C.J., Stone L.M., Mcpheeters M., Ogura T., Böttger B., Lasher R.S., Finger T.E., and Kinnamon S.C. (2001). Maintenance of rat taste buds in primary culture. Chem. Senses 26, 861-873.
    Pubmed CrossRef
  35. Schiff H.C., Bouhuis A.L., Yu K., Penzo M.A., Li H., He M., and Li B. (2018). An insula-central amygdala circuit for guiding tastant-reinforced choice behavior. J. Neurosci. 38, 1418-1429.
    Pubmed KoreaMed CrossRef
  36. Shrestha B. and Lee Y. (2021). Mechanisms of carboxylic acid attraction in Drosophila melanogaster. Mol. Cells 44, 900-910.
    Pubmed KoreaMed CrossRef
  37. Stelzer E.H.K., Strobl F., Chang B.J., Preusser F., Preibisch S., McDole K., and Fiolka R. (2021). Light sheet fluorescence microscopy. Nat. Rev. Methods Primers 1, 73.
    Pubmed CrossRef
  38. Venkatesan N., Boggs K., and Liu H.X. (2016). Taste bud labeling in whole tongue epithelial sheet in adult mice. Tissue Eng. Part C Methods 22, 332-337.
    Pubmed KoreaMed CrossRef
  39. Wang H., Jing M., and Li Y. (2018). Lighting up the brain: genetically encoded fluorescent sensors for imaging neurotransmitters and neuromodulators. Curr. Opin. Neurobiol. 50, 171-178.
    Pubmed KoreaMed CrossRef
  40. Wu A. and Dvoryanchikov G. (2015). Live animal calcium imaging of the geniculate ganglion. Research Square . https://doi.org/10.1038/protex.2015.106.
  41. Wu A., Dvoryanchikov G., Pereira E., Chaudhari N., and Roper S.D. (2015). Breadth of tuning in taste afferent neurons varies with stimulus strength. Nat. Commun. 6, 8171.
    Pubmed KoreaMed CrossRef
  42. Yarmolinsky D.A., Zuker C.S., and Ryba N.J.P. (2009). Common sense about taste: from mammals to insects. Cell 139, 234-244.
    Pubmed KoreaMed CrossRef
  43. Yokota Y. and Bradley R.M. (2016). Receptive field size, chemical and thermal responses, and fiber conduction velocity of rat chorda tympani geniculate ganglion neurons. J. Neurophysiol. 115, 3062-3072.
    Pubmed KoreaMed CrossRef
  44. Yokota Y. and Bradley R.M. (2017). Geniculate ganglion neurons are multimodal and variable in receptive field characteristics. Neuroscience 367, 147-158.
    Pubmed CrossRef
  45. Yoshida R., Miyauchi A., Yasuo T., Jyotaki M., Murata Y., Yasumatsu K., Shigemura N., Yanagawa Y., Obata K., and Ueno H., et al. (2009). Discrimination of taste qualities among mouse fungiform taste bud cells. J. Physiol. 587, 4425-4439.
    Pubmed KoreaMed CrossRef
  46. Zhang Y., Hoon M.A., Chandrashekar J., Mueller K.L., Cook B., Wu D., Zuker C.S., and Ryba N.J. (2003). Coding of sweet, bitter, and umami tastes: different receptor cells sharing similar signaling pathways. Cell 112, 293-301.
    Pubmed CrossRef
  47. Zhang J., Jin H., Zhang W., Ding C., O'Keeffe S., Ye M., and Zuker C.S. (2019). Sour sensing from the tongue to the brain. Cell 179, 392-402.e15.
    Pubmed CrossRef
  48. Zhao G.Q., Zhang Y., Hoon M.A., Chandrashekar J., Erlenbach I., Ryba N.J.P., and Zuker C.S. (2003). The receptors for mammalian sweet and umami taste. Cell 115, 255-266.
    Pubmed CrossRef

Article

Minireview

Mol. Cells 2022; 45(12): 877-882

Published online December 31, 2022 https://doi.org/10.14348/molcells.2022.0116

Copyright © The Korean Society for Molecular and Cellular Biology.

Advances in Optical Tools to Study Taste Sensation

Gha Yeon Park1,2,3 , Hyeyeong Hwang1,2,3 , and Myunghwan Choi1,2,*

1School of Biological Sciences, Seoul National University, Seoul 08826, Korea, 2The Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, Korea, 3These authors contributed equally to this work.

Correspondence to:choim@snu.ac.kr

Received: July 21, 2022; Revised: September 14, 2022; Accepted: September 15, 2022

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.

Abstract

Taste sensation is the process of converting chemical identities in food into a neural code of the brain. Taste information is initially formed in the taste buds on the tongue, travels through the afferent gustatory nerves to the sensory ganglion neurons, and finally reaches the multiple taste centers of the brain. In the taste field, optical tools to observe cellular-level functions play a pivotal role in understanding how taste information is processed along a pathway. In this review, we introduce recent advances in the optical tools used to study the taste transduction pathways.

Keywords: geniculate ganglion, gustation, imaging, insular cortex, optical tools, taste bud

INTRODUCTION

Taste sensation is the process of converting chemical identities in food into a neural code of the brain. Taste information initially forms on the tongue when tasting molecules reach the taste receptors located on the apical microvilli of taste buds (Roper and Chaudhari, 2017) (Fig. 1). Taste buds can distinguish at least five basic tastes, sweet, umami, bitter, sour, and salty, using distinct sets of G protein-coupled receptors or ion channels (Ogata and Ohtubo, 2020; Shrestha and Lee, 2021; Zhang et al., 2003; Zhao et al., 2003). Taste information travels through the afferent gustatory nerves that innervate taste buds to the sensory ganglia positioned in the facial canal (i.e., the geniculate ganglion for the anterior tongue and the petrosal ganglion for the posterior tongue). Taste information is then transmitted to the nucleus of the solitary tract (NST) in the brain stem and further processed in multiple brain areas, which also integrate other sensory inputs and internal states (Schiff et al., 2018; Wang et al., 2018).

Optical tools have become pivotal in taste research because they provide a means to record cellular information in the natural environment. Various chemical- or protein-based fluorescent indicators have been developed to generate optical contrast for specific cellular phenomena (Lambert, 2019). Fluorescence microscopy readily provides millisecond-scale temporal dynamics with subcellular-scale spatial precision. The limited penetration depth of light into scattering biological media can also be overcome by minimally invasive surgical procedures tailored to specific organs of interest (Choi et al., 2015a). In this minireview, we discuss recent advances in optical tools adopted for studying the major areas of the taste transduction pathway, from the tongue to the brain.

TASTE BUD

Multiple experimental preparations for taste buds have been utilized to understand the initial steps of taste information processing. Early studies relied on in vitro preparations by enzymatically digesting the extracellular matrices and culturing isolated taste bud cells in Petri dishes (Ozdener and Rawson, 2013). Although beneficial for biochemical assays, isolated cell cultures largely compromise the intracellular interactions within the taste bud. To better preserve the intercellular architecture, ex vivo preparations, such as tongue epithelial sheets and isolated taste buds, were used. The tongue sheet was prepared by peeling off the tongue epithelium after mucosal injection of dispase and collagenase (Venkatesan et al., 2016). Individual taste buds were further isolated from peeled epithelial sheets and affixed to a lysine-coated dish for functional studies, including electrophysiological recordings and fluorescent calcium imaging, in response to various gustatory stimuli (Ruiz et al., 2001). However, these preparations could not mimic the compartmentalized cytoarchitecture of the taste bud, in which only the apical microvilli are exposed to oral tastants, whereas the remaining cell bodies are shielded from the external environment by tight junctions at the apical tip.

Several preparations have been proposed to preserve the physiological compartments within taste buds. Yoshida et al. (2009) devised a setup for the isolated taste bud in which the mucosal side was drawn to a stimulating pipette perpetually perfused with Tyrode’s solution, set apart from external tastant stimuli entering the apical microvilli. The taste stimuli were restricted to the taste pore, which was rinsed with distilled water between each stimulus. Electrophysiological recordings have shown that taste bud cells can be classified into cellular subtypes that respond to various tastants (Yoshida et al., 2009). Similarly, Richter et al. (2003) harnessed a method to activate taste bud cells on acute lingual slices by focally delivering tastants with a stimulus micropipette specifically on the apical pore. These methods successfully reproduced the natural microenvironment of the taste bud, where only the apical tip was exposed to premediated stimuli, while the basolateral side remained perfused within the epithelium. Such methods have led to the discovery that delivery of weak acids into the taste pore depolarizes a few taste receptor cells and induces an increase in intracellular calcium (Richter et al., 2003).

For functional imaging, taste bud cells must be stained with either chemical or genetic contrast agents. The most widely used functional indicator targets intracellular calcium, which serves as a second messenger for transducing the most basic taste qualities (e.g., sodium taste) (Nomura et al., 2020). The intracellular loading of chemical indicators can be achieved by in vivo iontophoresis (e.g., calcium-green-dextran) or bulk diffusion in vitro (Fura 2-AM) (DeFazio et al., 2006). Genetically encoded calcium indicators (e.g., GCaMP) can also be introduced using transgenic mouse lines with promoters active in taste bud cells such as PIRT (for chemosensory type II and III cells) and GAD2 (for glia-like type I cells). Other types of functional indicators, such as pH indicators (e.g., BCECF-D), have also been used to study changes in intracellular pH in response to sour stimuli (Richter et al., 2003).

Calcium imaging has been commonly used to investigate taste-evoked activities of taste bud cells. Calcium imaging of a peeled tongue epithelium revealed that salt-sensing cells could be classified as ENaC-dependent sodium-sensitive cells and ENaC-independent high-salt-sensitive cells (Chandrashekar et al., 2010). Using a similar preparation, Oka et al. (2013) also showed that high salt concentrations activate bitter-sensing cells (and potentially sour-sensing cells) to mediate behavioral aversion to high salt. Caicedo and Roper (2001) used calcium imaging of a lingual slice preparation to demonstrate that different bitter compounds activate distinct subpopulations of bitter-sensing cells.

Calcium imaging was later modified to study specific neurotransmitter dynamics in taste buds. This approach involved the genetic engineering of extraneous cultured cells to express a specific neurotransmitter receptor and loading them with a calcium indicator (referred to as a “biosensor cell”) (Huang et al., 2009). Using a fine micropipette, the biosensor cell was positioned near the target taste bud or cell to detect neurotransmitter release by observing cellular calcium dynamics. For instance, Chinese hamster ovary cells genetically engineered to express a purinergic receptor or a GABA receptor can be loaded with a calcium-sensitive dye to screen extracellular ATP or GABA, respectively. The use of biosensor cells has led to the finding that stimulating type II and type III taste cells induce the release of ATP and GABA, respectively (Huang et al., 2009; 2011). Additionally, by observing ATP secretion from type II cells in response to sweet stimuli in circumvallate taste buds, adenosine has been shown to modulate the responsiveness of type II cells (Dando et al., 2012).

An intravital tongue imaging window was developed for the fungiform taste bud to fully recapitulate the living microenvironment of the taste bud (Choi et al., 2015b). In this preparation, the mouse tongue was externalized from the oral cavity using a miniature suction grabber and sandwiched in metal fixatives to minimize physiological motion. The anterior tongue was immersed in artificial saliva and imaged using a two-photon microscope with a water immersion objective lens. Using this preparation, Choi et al. (2015b) demonstrated that taste bud cells are responsive to both orally administered and circulating tastants (e.g., saccharine and NaCl) in the bloodstream.

The intravital imaging window was elaborated in due course by the integration of a microfluidic channel, named “µTongue (microfluidics-on-a-tongue)” (Han and Choi, 2018) (Fig. 2A). The µTongue permits programmable delivery of various tastants of interest topically on the anterior tongue to observe taste-induced functional activities of taste cells in real time (Han et al., 2021a). The imaging system was later improved by introducing an axially elongated beam (i.e., Bessel beam) into two-photon microscopy, which largely resolved the issue of the focal shift introduced by high-index tastant solutions, such as high concentrations of NaCl and sucrose (Han et al., 2021b).

SENSORY GANGLION

Sensory information from the tongue travels to the sensory ganglia located in the facial canal of the head (Fig. 1). As illustrated in Fig. 1, there are two relay stations: the geniculate ganglion innervating the fungiform taste buds in the anterior tongue and the petrosal ganglion innervating circumvallate taste buds in the posterior tongue. The most widely studied are the geniculate ganglion housing cell bodies of pseudounipolar sensory neurons receiving taste information through the chorda tympani nerve (Yokota and Bradley, 2016; 2017) and transmitting signals to NST in the brainstem and thalamus. Observing the neuronal population activity in the geniculate ganglion provides input gustatory information to the brain.

As neuronal firing is accompanied by an increase in intracellular calcium, the spatiotemporal activities of the geniculate neurons can be imaged using genetically encoded calcium indicators (e.g., GCaMP) (Lee et al., 2019; Wu and Dvoryanchikov, 2015). Previous studies used transgenic reporter mice with pan-neuronal promoters such as Thy1-GcaMP6 and SNAP25-GcaMP6. An alternative approach is the viral delivery of genetically encoded calcium indicators by injecting retrograde viral vectors into the NST, where geniculate neurons project and form synapses (Yarmolinsky et al., 2009).

Once the animal model is prepared, the geniculate ganglion must be surgically exposed to ensure optical access. The geniculate ganglion can be reached via either the ventral or dorsal routes, which reveal intact geniculate neurons without perturbing the connected nerve fibers (Fowler and Macpherson, 2021; Lundy and Contreras, 1999; Wu and Dvoryanchikov, 2015; Yokota and Bradley, 2016). Tracheotomy is often performed to ensure adequate physiological respiration. As geniculate neurons lie deep within the facial canal, the imaging system must have an adequate working distance of >1 cm. Typically, widefield or confocal fluorescence microscopes with long working distance objective lenses (e.g., 10X, 0.3 NA, 16 mm working distance) are used. A narrow microendoscopic probe consisting of a gradient index (GRIN) lens was also developed as an alternative, allowing better spatial resolution with a higher numerical aperture (Fig. 2B). Time-series images of the geniculate neurons can be recorded in response to various tastants delivered to the anterior tongue. Physiological fluid from the cochlear promontory may interfere with data acquisition and a tapered suction tip can be introduced to continuously remove the fluid. The acquired temporal traces for each neuron are extracted by manual regions-of-interest or advanced algorithms such as CNMF (Pnevmatikakis et al., 2016) or CaImAn (Giovannucci et al., 2019). To resolve data that are accompanied by excessive noise, deep-learning-based denoising algorithms, such as DeepCAD (Li et al., 2021) or DeepInterpolation (Lecoq et al., 2021), can be used to robustly extract signals of interest.

Calcium imaging of geniculate neurons was used to study the coding logic of peripheral taste information. Barretto et al. (2015) used two-photon microendoscopy in a Thy1-GCaMP mouse to show that geniculate neurons were primarily responsive to a single taste quality. In this study, only a minor portion of the neurons were reported to be responsive to multiple taste qualities, supporting the labeled line coding model. Later, Wu et al. (2015) also performed calcium imaging of geniculate neurons after surgical exposure of the geniculate ganglion. By administering different concentrations of tastants to a mouse, this study revealed that the proportion of neurons responding to multiple taste qualities exhibited a sharp dependence on the concentration of tastants (Wu et al., 2015). Even single-tuned geniculate neurons at low tastant concentrations showed heterogeneous responses to multiple taste qualities at high concentrations. This study suggests that the strict labeled-line coding model may not fully account for peripheral taste encoding (Roper and Chaudhari, 2017).

BRAIN

To measure the activity of neurons in the rostral NST (rNST), where the primary central neuronal station of gustatory information is occupied, electrophysiological recording using a microelectrode is the gold standard approach (Peng et al., 2015). Alternatively, fiber photometry can be used to monitor the calcium activity of genetically targeted neuronal subpopulations in the rNST (Gunaydin et al., 2014; Jin et al., 2021; Zhang et al., 2019) (Fig. 2C). Typically, the time-series acquisition of a fluorescent signal through an implanted multimode fiber is stable even under awake behaving conditions. Fiber photometry was used to reveal that Pdyn neurons (sour-sensing neurons) in the rNST respond specifically to sour tastants (Zhang et al., 2019) and that licking behavior activates Sst- and Calb2-neurons in the rNST (Jin et al., 2021). Furthermore, fiber implants can also be used to deliver optogenetic photostimulation on specific neurons expressing light-sensitive opsins in the rNST (e.g., channelrhodopsin-2) (Jin et al., 2021; Zhang et al., 2019). For example, Jin et al. (2021) showed that optogenetic activation of sour neurons in the rNST induced prototypical acid-aversion behavior.

Although fiber photometry readily provides information on population-level neural dynamics, it lacks the spatial information to discern individual neuronal activities. To overcome this limitation, miniature integrated microscopes (miniscopes) were developed by miniaturizing conventional widefield fluorescence microscopy into a head-mountable size (Ghosh et al., 2011). In the miniscope, a thin GRIN lens (diameter = 0.3-2 mm) replaces the conventional objective lens so that it can be inserted into the targeted brain area with minimal invasiveness. Other parts, including a tube lens, fluorescence filters, a light source, and a camera, are compacted to a small form factor, so they can be mounted on the head of an awake behaving mouse. Although not widely adopted for taste research, miniscopes will be useful in investigating individual neuronal activities in deep brain areas such as the rNST, PbN (parabrachial nucleus), and VPM (ventral posteromedial nucleus) (Fig. 1).

Two-photon fluorescence microscopy is advantageous for observing individual neuronal activities in the insular cortex, which is the primary sensory cortex for gustation. Chen et al. (2011) prepared an open cranial window model at the insular cortex located ~1 mm above the intersection of the middle cerebral artery and rhinal vein. Observing the neuronal response to five basic tastes in an anesthetized mouse, this study revealed the topographic segregation of different taste qualities in the primary gustatory cortex. Another group later introduced a miniature prism mirror and GRIN probe to a two-photon microscope to perform a comparable study in a head-fixed awake mouse (Fig. 2D) (Chen et al., 2021). Notably, the present study reported a scattered distribution of neurons responding to distinct taste qualities rather than topographic segregation.

CONCLUDING REMARK

Optical tools are essential in understanding how taste information is processed from the tongue to the brain. Considering the recent phenomenal advances in optical tools for neuroscience, we anticipate that optical approaches will be increasingly used in taste research. In terms of optical hardware, volumetric imaging techniques, such as light-sheet microscopy (Stelzer et al., 2021), will provide a more comprehensive dataset on each taste organ, for example, on how a taste bud processes taste information from the apical microvilli to the afferent nerves. Adopting advanced genetically encoded indicators will provide access to veiled information, such as membrane potentials (e.g., QuasArs, Archon) or neurotransmitter dynamics (e.g., GRAB-ATP, iGABASnFR), within the taste bud in vivo (Bando et al., 2019; Wang et al., 2018). Moreover, computational analysis and modeling advances are necessary to translate the acquired large-scale datasets into scientific knowledge. Inevitably, the taste field is expected to involve interdisciplinary collaborations with optics, genetics, and computational neuroscience.

ACKNOWLEDGMENTS

This work was supported by the Samsung Science and Technology Foundation under Project Number SSTF-BA2002-14 and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07042834, 2019M3E5D2A01058329, 2019M3A9E2061789, 2020M3C1B8016137, 2020R1A5A 1018081).

AUTHOR CONTRIBUTIONS

G.Y.P. and H.H. wrote the manuscript. M.C. supervised the paper and provided feedback.

CONFLICT OF INTEREST

The authors have no potential conflicts of interest to disclose.

Fig. 1.The taste transduction pathway from the taste buds on the tongue to the insular cortex in the brain. The left image is a two-photon microscopic image of the mouse tongue with an inverted look-up table (magenta, autofluorescence; purple, second harmonic generation signal). In the schematic image on the right, the major areas responsible for taste information processing are illustrated. IC, insular cortex; VPM, ventral posteromedial nucleus; PbN, parabrachial nucleus; rNST, rostral nucleus of the solitary tract.
Fig. 2.Representative optical techniques used for studying the taste transduction pathway. (A) Microfluidics-integrated imaging window for the anterior tongue (µTongue [microfluidics-on-a-tongue]). (B) In vivo imaging preparation for the geniculate ganglion using a microendoscopic gradient refractive index (GRIN) probe. Reproduced from the article of Barretto et al. (2015) (Nature 517, 373-376) with original copyright holder’s permission. (C) Fiber photometry on rostral nucleus of the solitary tract (rNST). Reproduced from the article of Jin et al. (2021) (Cell 184, 257-271.e16) with original copyright holder’s permission. (D) Two-photon imaging of the insular cortex in an awake head-fixed mouse using a prism mirror.

Fig 1.

Figure 1.The taste transduction pathway from the taste buds on the tongue to the insular cortex in the brain. The left image is a two-photon microscopic image of the mouse tongue with an inverted look-up table (magenta, autofluorescence; purple, second harmonic generation signal). In the schematic image on the right, the major areas responsible for taste information processing are illustrated. IC, insular cortex; VPM, ventral posteromedial nucleus; PbN, parabrachial nucleus; rNST, rostral nucleus of the solitary tract.
Molecules and Cells 2022; 45: 877-882https://doi.org/10.14348/molcells.2022.0116

Fig 2.

Figure 2.Representative optical techniques used for studying the taste transduction pathway. (A) Microfluidics-integrated imaging window for the anterior tongue (µTongue [microfluidics-on-a-tongue]). (B) In vivo imaging preparation for the geniculate ganglion using a microendoscopic gradient refractive index (GRIN) probe. Reproduced from the article of Barretto et al. (2015) (Nature 517, 373-376) with original copyright holder’s permission. (C) Fiber photometry on rostral nucleus of the solitary tract (rNST). Reproduced from the article of Jin et al. (2021) (Cell 184, 257-271.e16) with original copyright holder’s permission. (D) Two-photon imaging of the insular cortex in an awake head-fixed mouse using a prism mirror.
Molecules and Cells 2022; 45: 877-882https://doi.org/10.14348/molcells.2022.0116

References

  1. Bando Y., Sakamoto M., Kim S., Ayzenshtat I., and Yuste R. (2019). Comparative evaluation of genetically encoded voltage indicators. Cell Rep. 26, 802-813.e4.
    Pubmed KoreaMed CrossRef
  2. Barretto R.P.J., Gillis-Smith S., Chandrashekar J., Yarmolinsky D.A., Schnitzer M.J., Ryba N.J.P., and Zuker C.S. (2015). The neural representation of taste quality at the periphery. Nature 517, 373-376.
    Pubmed KoreaMed CrossRef
  3. Caicedo A. and Roper S.D. (2001). Taste receptor cells that discriminate between bitter stimuli. Science 291, 1557-1560.
    Pubmed KoreaMed CrossRef
  4. Chandrashekar J., Kuhn C., Oka Y., Yarmolinsky D.A., Hummler E., Ryba N.J.P., and Zuker C.S. (2010). The cells and peripheral representation of sodium taste in mice. Nature 464, 297-301.
    Pubmed KoreaMed CrossRef
  5. Chen K., Kogan J.F., and Fontanini A. (2021). Spatially distributed representation of taste quality in the gustatory insular cortex of behaving mice. Curr. Biol. 31, 247-256.e4.
    Pubmed KoreaMed CrossRef
  6. Chen X., Gabitto M., Peng Y., Ryba N.J.P., and Zuker C.S. (2011). A gustotopic map of taste qualities in the mammalian brain. Science 333, 1262-1266.
    Pubmed KoreaMed CrossRef
  7. Choi M., Kwok S.J.J., and Yun S.H. (2015a). In vivo fluorescence microscopy: lessons from observing cell behavior in their native environment. Physiology (Bethesda) 30, 40-49.
    Pubmed KoreaMed CrossRef
  8. Choi M., Lee W.M., and Yun S.H. (2015b). Intravital microscopic interrogation of peripheral taste sensation. Sci. Rep. 5, 8661.
    Pubmed KoreaMed CrossRef
  9. Dando R., Dvoryanchikov G., Pereira E., Chaudhari N., and Roper S.D. (2012). Adenosine enhances sweet taste through A2B receptors inthe taste bud. J. Neurosci. 32, 322-330.
    Pubmed KoreaMed CrossRef
  10. DeFazio R.A., Dvoryanchikov G., Maruyama Y., Kim J.W., Pereira E., Roper S.D., and Chaudhari N. (2006). Separate populations of receptor cells and presynaptic cells in mouse taste buds. J. Neurosci. 26, 3971-3980.
    Pubmed KoreaMed CrossRef
  11. Fowler B.E. and Macpherson L.J. (2021). In vivo calcium imaging of mouse geniculate ganglion neuron responses to taste stimuli. J. Vis. Exp. , 10.3791/62172.
    Pubmed KoreaMed CrossRef
  12. Ghosh K.K., Burns L.D., Cocker E.D., Nimmerjahn A., Ziv Y., Gamal A.E., and Schnitzer M.J. (2011). Miniaturized integration of a fluorescence microscope. Nat. Methods 8, 871-878.
    Pubmed KoreaMed CrossRef
  13. Giovannucci A., Friedrich J., Gunn P., Kalfon J., Brown B.L., Koay S.A., Taxidis J., Najafi F., Gauthier J.L., and Zhou P., et al. (2019). CaImAn an open source tool for scalable calcium imaging data analysis. Elife 8, e38173.
    Pubmed KoreaMed CrossRef
  14. Gunaydin L.A., Grosenick L., Finkelstein J.C., Kauvar I.V., Fenno L.E., Adhikari A., Lammel S., Mirzabekov J.J., Airan R.D., and Zalocusky K.A., et al. (2014). Natural neural projection dynamics underlying social behavior. Cell 157, 1535-1551.
    Pubmed KoreaMed CrossRef
  15. Han J. and Choi M. (2018). Comprehensive functional screening of taste sensation in vivo. BioRxiv . https://doi.org/10.1101/371682.
    CrossRef
  16. Han J., Choi P., and Choi M. (2021a). µTongue: a microfluidics-based functional imaging platform for the tongue in vivo. J. Vis. Exp. , 10.3791/62361.
    Pubmed CrossRef
  17. Han J., Kim S., Choi P., Lee S., Jo Y., Kim E., and Choi M. (2021b). Robust functional imaging of taste sensation with a Bessel beam. Biomed. Opt. Express 12, 5855-5864.
    Pubmed KoreaMed CrossRef
  18. Huang Y.A., Dando R., and Roper S.D. (2009). Autocrine and paracrine roles for ATP and serotonin in mouse taste buds. J. Neurosci. 29, 13909-13918.
    Pubmed KoreaMed CrossRef
  19. Huang Y.A., Pereira E., and Roper S.D. (2011). Acid stimulation (sour taste) elicits GABA and serotonin release from mouse taste cells. PLoS One 6, e25471.
    Pubmed KoreaMed CrossRef
  20. Jin H., Fishman Z.H., Ye M., Wang L., and Zuker C.S. (2021). Top-down control of sweet and bitter taste in the mammalian brain. Cell 184, 257-271.e16.
    Pubmed CrossRef
  21. Lambert T.J. (2019). FPbase: a community-editable fluorescent protein database. Nat. Methods 16, 277-278.
    Pubmed CrossRef
  22. Lecoq J., Oliver M., Siegle J.H., Orlova N., Ledochowitsch P., and Koch C. (2021). Removing independent noise in systems neuroscience data using DeepInterpolation. Nat. Methods 18, 1401-1408.
    Pubmed KoreaMed CrossRef
  23. Lee S., Augustine V., Zhao Y., Ebisu H., Ho B., Kong D., and Oka Y. (2019). Chemosensory modulation of neural circuits for sodium appetite. Nature 568, 93-97.
    Pubmed KoreaMed CrossRef
  24. Li X., Zhang G., Wu J., Zhang Y., Zhao Z., Lin X., Qiao H., Xie H., Wang H., and Fang L., et al. (2021). Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising. Nat. Methods 18, 1395-1400.
    Pubmed CrossRef
  25. Lundy R.F. Jr. and Contreras R.J. Jr. (1999). Gustatory neuron types in rat geniculate ganglion. J. Neurophysiol. 82, 2970-2988.
    Pubmed CrossRef
  26. Nomura K., Nakanishi M., Ishidate F., Iwata K., and Taruno A. (2020). All-electrical Ca2+-independent signal transduction mediates attractive sodium taste in taste buds. Neuron 106, 816-829.e6.
    Pubmed CrossRef
  27. Ogata T. and Ohtubo Y. (2020). Quantitative analysis of taste bud cell numbers in the circumvallate and foliate taste buds of mice. Chem. Senses 45, 261-273.
    Pubmed CrossRef
  28. Oka Y., Butnaru M., Von Buchholtz L., Ryba N.J.P., and Zuker C.S. (2013). High salt recruits aversive taste pathways. Nature 494, 472-475.
    Pubmed KoreaMed CrossRef
  29. Ozdener M.H. and Rawson N.E. (2013). Primary culture of mammalian taste epithelium. Methods Mol. Biol. 945, 95-107.
    Pubmed KoreaMed CrossRef
  30. Peng Y., Gillis-Smith S., Jin H., Tränkner D., Ryba N.J.P., and Zuker C.S. (2015). Sweet and bitter taste in the brain of awake behaving animals. Nature 527, 512-515.
    Pubmed KoreaMed CrossRef
  31. Pnevmatikakis E.A., Soudry D., Gao Y., Machado T.A., Merel J., Pfau D., Reardon T., Mu Y., Lacefield C., and Yang W., et al. (2016). Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89, 285-299.
    Pubmed KoreaMed CrossRef
  32. Richter T.A., Caicedo A., and Roper S.D. (2003). Sour taste stimuli evoke Ca2+ and pH responses in mouse taste cells. J. Physiol. 547, 475-483.
    Pubmed KoreaMed CrossRef
  33. Roper S.D. and Chaudhari N. (2017). Taste buds: cells, signals and synapses. Nat. Rev. Neurosci. 18, 485-497.
    Pubmed KoreaMed CrossRef
  34. Ruiz C.J., Stone L.M., Mcpheeters M., Ogura T., Böttger B., Lasher R.S., Finger T.E., and Kinnamon S.C. (2001). Maintenance of rat taste buds in primary culture. Chem. Senses 26, 861-873.
    Pubmed CrossRef
  35. Schiff H.C., Bouhuis A.L., Yu K., Penzo M.A., Li H., He M., and Li B. (2018). An insula-central amygdala circuit for guiding tastant-reinforced choice behavior. J. Neurosci. 38, 1418-1429.
    Pubmed KoreaMed CrossRef
  36. Shrestha B. and Lee Y. (2021). Mechanisms of carboxylic acid attraction in Drosophila melanogaster. Mol. Cells 44, 900-910.
    Pubmed KoreaMed CrossRef
  37. Stelzer E.H.K., Strobl F., Chang B.J., Preusser F., Preibisch S., McDole K., and Fiolka R. (2021). Light sheet fluorescence microscopy. Nat. Rev. Methods Primers 1, 73.
    Pubmed CrossRef
  38. Venkatesan N., Boggs K., and Liu H.X. (2016). Taste bud labeling in whole tongue epithelial sheet in adult mice. Tissue Eng. Part C Methods 22, 332-337.
    Pubmed KoreaMed CrossRef
  39. Wang H., Jing M., and Li Y. (2018). Lighting up the brain: genetically encoded fluorescent sensors for imaging neurotransmitters and neuromodulators. Curr. Opin. Neurobiol. 50, 171-178.
    Pubmed KoreaMed CrossRef
  40. Wu A. and Dvoryanchikov G. (2015). Live animal calcium imaging of the geniculate ganglion. Research Square . https://doi.org/10.1038/protex.2015.106.
  41. Wu A., Dvoryanchikov G., Pereira E., Chaudhari N., and Roper S.D. (2015). Breadth of tuning in taste afferent neurons varies with stimulus strength. Nat. Commun. 6, 8171.
    Pubmed KoreaMed CrossRef
  42. Yarmolinsky D.A., Zuker C.S., and Ryba N.J.P. (2009). Common sense about taste: from mammals to insects. Cell 139, 234-244.
    Pubmed KoreaMed CrossRef
  43. Yokota Y. and Bradley R.M. (2016). Receptive field size, chemical and thermal responses, and fiber conduction velocity of rat chorda tympani geniculate ganglion neurons. J. Neurophysiol. 115, 3062-3072.
    Pubmed KoreaMed CrossRef
  44. Yokota Y. and Bradley R.M. (2017). Geniculate ganglion neurons are multimodal and variable in receptive field characteristics. Neuroscience 367, 147-158.
    Pubmed CrossRef
  45. Yoshida R., Miyauchi A., Yasuo T., Jyotaki M., Murata Y., Yasumatsu K., Shigemura N., Yanagawa Y., Obata K., and Ueno H., et al. (2009). Discrimination of taste qualities among mouse fungiform taste bud cells. J. Physiol. 587, 4425-4439.
    Pubmed KoreaMed CrossRef
  46. Zhang Y., Hoon M.A., Chandrashekar J., Mueller K.L., Cook B., Wu D., Zuker C.S., and Ryba N.J. (2003). Coding of sweet, bitter, and umami tastes: different receptor cells sharing similar signaling pathways. Cell 112, 293-301.
    Pubmed CrossRef
  47. Zhang J., Jin H., Zhang W., Ding C., O'Keeffe S., Ye M., and Zuker C.S. (2019). Sour sensing from the tongue to the brain. Cell 179, 392-402.e15.
    Pubmed CrossRef
  48. Zhao G.Q., Zhang Y., Hoon M.A., Chandrashekar J., Erlenbach I., Ryba N.J.P., and Zuker C.S. (2003). The receptors for mammalian sweet and umami taste. Cell 115, 255-266.
    Pubmed CrossRef
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