Neural Representations of Airflow in Drosophila Mushroom Body

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Neural Representations of Airflow in Drosophila Mushroom Body Akira Mamiya1, Jennifer Beshel1, Chunsu Xu1,2, Yi Zhong1* 1 Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, 2 SUNY Stony Brook, Stony Brook, New York, United States of America Take home points: Goal: characterize the responses of MB neurons to changes in airflow Method: In vivo calcium imaging from multiple MB regions using genetically altered fruit fly lines and 2-photon microscopy Results: •Responses to an airflow stimulus from several sub regions of the MB •Different MB sub regions responded differently to different aspects (i.e. on/off responses) •Possibly sub sub regions respond differently

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Neural Representations of Airflow in Drosophila Mushroom Body Akira Mamiya1, Jennifer Beshel1, Chunsu Xu1,2, Yi Zhong1* 1 Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, 2 SUNY Stony Brook, Stony Brook, New York, United States of America. - PowerPoint PPT Presentation

Transcript of Neural Representations of Airflow in Drosophila Mushroom Body

Page 1: Neural Representations of Airflow in Drosophila Mushroom Body

Neural Representations of Airflow in Drosophila Mushroom BodyAkira Mamiya1, Jennifer Beshel1, Chunsu Xu1,2, Yi Zhong1*1 Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, 2 SUNY Stony Brook, Stony Brook, New York, United States of America

Take home points:

Goal: characterize the responses of MB neurons to changes in airflowMethod: In vivo calcium imaging from multiple MB regions using genetically altered fruit fly lines and 2-photon microscopyResults:

•Responses to an airflow stimulus from several sub regions of the MB•Different MB sub regions responded differently to different aspects (i.e. on/off responses)•Possibly sub sub regions respond differently•Dependent on the movement of the 3rd antennal segment suggesting JO involvement

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Method1) Get TG fruitflies that have a calcium sensor

• UAS-G-CaMP1.3 • UAS-G-CaMP1.6

2) Cross with GAL4 fruit fly lines that will allow targeted expression of the calcium sensor in specific cells• OK107-Gal4: non-selective MB general• c739-Gal4: A/B lobe neurons• g0050-Gal4 and c305a-Gal4: A’/B’ lobe neurons

3) Fix fruit fly to an imaging stage4) Puff air and measure fluorescence:

• 3s stims • 100 ml/min (1.2m/s )• 3 min ITI

5) Analyses based on ΔF/Fo which is a measure of changes in Ca++ induced florescence on a pixel by pixel basis

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Experimental recording sites and raw ΔF/Fo Figure 1

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Temporal dynamics of responses 1 s data integration window (3 frames)Averaged results

Figure 2

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Figure 2Mean On, off responses as a function of lobes

The total area involved in response

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Figure 3

•OK107-Gal4: non-selective MB general•c739-Gal4: A/B lobe neurons•g0050-Gal4 and c305a-Gal4: A’/B’ lobe neurons

Different Gal4 lines show that A,B and A’B’ have different response profiles

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Figure 4Responses generally depend on movement of the 3rd antennal segment•When glued Ca++ responses are greatly diminished

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Figure 4

By comparison with Figure 2 most areas have dropped

Responses generally depend on movement of the 3rd antennal segment•When glued Ca++ responses are greatly diminished

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Figure 5Sub region specific responses to “on” and “off” phases of the stimulus

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Figure 5Watershed Segmentation highlights sub region specific responses

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Figure 6“On Off Selectivity Index” (OSI) highlights watershed “patches” within lobes as “on” or “off” response selective (or neither)

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Figure 7Off responding patches are spatially organized and stereotypic across individuals

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Figure 8Using two Gal4 lines they show that the off and on responses are different subsets of cells

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Figure 8

g0050-Gal4 cells are significantly more off responsive than c305a-Gal4