Dataset Information

Functional integration of "undead" neurons in the olfactory system.

ABSTRACT: Programmed cell death (PCD) is widespread during neurodevelopment, eliminating the surpluses of neuronal production. Using the Drosophila olfactory system, we examined the potential of cells fated to die to contribute to circuit evolution. Inhibition of PCD is sufficient to generate new cells that express neural markers and exhibit odor-evoked activity. These "undead" neurons express a subset of olfactory receptors that is enriched for relatively recent receptor duplicates and includes some normally found in different chemosensory organs and life stages. Moreover, undead neuron axons integrate into the olfactory circuitry in the brain, forming novel receptor/glomerular couplings. Comparison of homologous olfactory lineages across drosophilids reveals natural examples of fate change from death to a functional neuron. Last, we provide evidence that PCD contributes to evolutionary differences in carbon dioxide-sensing circuit formation in Drosophila and mosquitoes. These results reveal the remarkable potential of alterations in PCD patterning to evolve new neural pathways.

SUBMITTER: Prieto-Godino LL

PROVIDER: S-EPMC7065876 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Functional integration of "undead" neurons in the olfactory system.

Prieto-Godino Lucia L LL Silbering Ana F AF Khallaf Mohammed A MA Cruchet Steeve S Bojkowska Karolina K Pradervand Sylvain S Hansson Bill S BS Knaden Markus M Benton Richard R

Science advances 20200311 11

Programmed cell death (PCD) is widespread during neurodevelopment, eliminating the surpluses of neuronal production. Using the <i>Drosophila</i> olfactory system, we examined the potential of cells fated to die to contribute to circuit evolution. Inhibition of PCD is sufficient to generate new cells that express neural markers and exhibit odor-evoked activity. These "undead" neurons express a subset of olfactory receptors that is enriched for relatively recent receptor duplicates and includes so ...[more]

PMID: 32195354

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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

Functional integration of "undead" neurons in the olfactory system.

Publications

Functional integration of "undead" neurons in the olfactory system.

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