Dataset Information

Drosophila sensory neurons require Dscam for dendritic self-avoidance and proper dendritic field organization.

ABSTRACT: A neuron's dendrites typically do not cross one another. This intrinsic self-avoidance mechanism ensures unambiguous processing of sensory or synaptic inputs. Moreover, some neurons respect the territory of others of the same type, a phenomenon known as tiling. Different types of neurons, however, often have overlapping dendritic fields. We found that Down's syndrome Cell Adhesion Molecule (Dscam) is required for dendritic self-avoidance of all four classes of Drosophila dendritic arborization (da) neurons. However, neighboring mutant class IV da neurons still exhibited tiling, suggesting that self-avoidance and tiling differ in their recognition and repulsion mechanisms. Introducing 1 of the 38,016 Dscam isoforms to da neurons in Dscam mutants was sufficient to significantly restore self-avoidance. Remarkably, expression of a common Dscam isoform in da neurons of different classes prevented their dendrites from sharing the same territory, suggesting that coexistence of dendritic fields of different neuronal classes requires divergent expression of Dscam isoforms.

SUBMITTER: Soba P

PROVIDER: S-EPMC1963441 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature

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Drosophila sensory neurons require Dscam for dendritic self-avoidance and proper dendritic field organization.

Soba Peter P Zhu Sijun S Emoto Kazuo K Younger Susan S Yang Shun-Jen SJ Yu Hung-Hsiang HH Lee Tzumin T Jan Lily Yeh LY Jan Yuh-Nung YN

Neuron 20070501 3

A neuron's dendrites typically do not cross one another. This intrinsic self-avoidance mechanism ensures unambiguous processing of sensory or synaptic inputs. Moreover, some neurons respect the territory of others of the same type, a phenomenon known as tiling. Different types of neurons, however, often have overlapping dendritic fields. We found that Down's syndrome Cell Adhesion Molecule (Dscam) is required for dendritic self-avoidance of all four classes of Drosophila dendritic arborization ( ...[more]

PMID: 17481394

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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

Drosophila sensory neurons require Dscam for dendritic self-avoidance and proper dendritic field organization.

Publications

Drosophila sensory neurons require Dscam for dendritic self-avoidance and proper dendritic field organization.

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