Dataset Information

Renewal and Differentiation of GCD Necklace Olfactory Sensory Neurons.

ABSTRACT: Both canonical olfactory sensory neurons (OSNs) and sensory neurons belonging to the guanylate cyclase D (GCD) "necklace" subsystem are housed in the main olfactory epithelium, which is continuously bombarded by toxins, pathogens, and debris from the outside world. Canonical OSNs address this challenge, in part, by undergoing renewal through neurogenesis; however, it is not clear whether GCD OSNs also continuously regenerate and, if so, whether newborn GCD precursors follow a similar developmental trajectory to that taken by canonical OSNs. Here, we demonstrate that GCD OSNs are born throughout adulthood and can persist in the epithelium for several months. Phosphodiesterase 2A is upregulated early in the differentiation process, followed by the sequential downregulation of β-tubulin and the upregulation of CART protein. The GCD and MS4A receptors that confer sensory responses upon GCD neurons are initially expressed midway through this process but become most highly expressed once CART levels are maximal late in GCD OSN development. GCD OSN maturation is accompanied by a horizontal migration of neurons toward the central, curved portions of the cul-de-sac regions where necklace cells are concentrated. These findings demonstrate that-like their canonical counterparts-GCD OSNs undergo continuous renewal and define a GCD-specific developmental trajectory linking neurogenesis, maturation, and migration.

SUBMITTER: Bloom ML

PROVIDER: S-EPMC7320222 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in the spike domain by Drosophila OSNs. In order to apply system identification techniques, we built a novel low-turbulence odor delivery system that allowed us to deliver airborne stimuli in a precise and reproducible fashion. The system provides a 1% tolerance in stimulus reproducibility and an exact control of odor concentration and concentration gradient on a millisecond time scale. Using this novel setup, we recorded and analyzed the in-vivo response of OSNs to a wide range of time-varying odor waveforms. We report for the first time that across trials the response of OR59b OSNs is very precise and reproducible. Further, we empirically show that the response of an OSN depends not only on the concentration, but also on the rate of change of the odor concentration. Moreover, we demonstrate that a two-dimensional (2D) Encoding Manifold in a concentration-concentration gradient space provides a quantitative description of the neuron's response. We then use the white noise system identification methodology to construct one-dimensional (1D) and two-dimensional (2D) Linear-Nonlinear-Poisson (LNP) cascade models of the sensory neuron for a fixed mean odor concentration and fixed contrast. We show that in terms of predicting the intensity rate of the spike train, the 2D LNP model performs on par with the 1D LNP model, with a root mean-square error (RMSE) increase of about 5 to 10%. Surprisingly, we find that for a fixed contrast of the white noise odor waveforms, the nonlinear block of each of the two models changes with the mean input concentration. The shape of the nonlinearities of both the 1D and the 2D LNP model appears to be, for a fixed mean of the odor waveform, independent of the stimulus contrast. This suggests that white noise system identification of Or59b OSNs only depends on the first moment of the odor concentration. Finally, by comparing the 2D Encoding Manifold and the 2D LNP model, we demonstrate that the OSN identification results depend on the particular type of the employed test odor waveforms. This suggests an adaptive neural encoding model for Or59b OSNs that changes its nonlinearity in response to the odor concentration waveforms.

Dataset Information

Renewal and Differentiation of GCD Necklace Olfactory Sensory Neurons.

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