Dataset Information

Centrioles are amplified in cycling progenitors of olfactory sensory neurons.

ABSTRACT: Olfaction in most animals is mediated by neurons bearing cilia that are accessible to the environment. Olfactory sensory neurons (OSNs) in chordates usually have multiple cilia, each with a centriole at its base. OSNs differentiate from stem cells in the olfactory epithelium, and how the epithelium generates cells with many centrioles is not yet understood. We show that centrioles are amplified via centriole rosette formation in both embryonic development and turnover of the olfactory epithelium in adult mice, and rosette-bearing cells often have free centrioles in addition. Cells with amplified centrioles can go on to divide, with centrioles clustered at each pole. Additionally, we found that centrioles are amplified in immediate neuronal precursors (INPs) concomitant with elevation of mRNA for polo-like kinase 4 (Plk4) and SCL/Tal1-interrupting locus gene (Stil), key regulators of centriole duplication. These results support a model in which centriole amplification occurs during a transient state characterized by elevated Plk4 and Stil in early INP cells. These cells then go on to divide at least once to become OSNs, demonstrating that cell division with amplified centrioles, known to be tolerated in disease states, can occur as part of a normal developmental program.

SUBMITTER: Ching K

PROVIDER: S-EPMC7518617 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Centrioles are amplified in cycling progenitors of olfactory sensory neurons.

Ching Kaitlin K Stearns Tim T

PLoS biology 20200915 9

Olfaction in most animals is mediated by neurons bearing cilia that are accessible to the environment. Olfactory sensory neurons (OSNs) in chordates usually have multiple cilia, each with a centriole at its base. OSNs differentiate from stem cells in the olfactory epithelium, and how the epithelium generates cells with many centrioles is not yet understood. We show that centrioles are amplified via centriole rosette formation in both embryonic development and turnover of the olfactory epithelium ...[more]

PMID: 32931487

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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

Centrioles are amplified in cycling progenitors of olfactory sensory neurons.

Publications

Centrioles are amplified in cycling progenitors of olfactory sensory neurons.

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