1 / 14

Neural Representations of Airflow in Drosophila Mushroom Body

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.

Download Presentation

Neural Representations of Airflow in Drosophila Mushroom Body

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 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 • Dependent on the movement of the 3rd antennal segment suggesting JO involvement

  2. Method • Get TG fruitflies that have a calcium sensor • UAS-G-CaMP1.3 • UAS-G-CaMP1.6 • 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 • Fix fruit fly to an imaging stage • Puff air and measure fluorescence: • 3s stims • 100 ml/min (1.2m/s ) • 3 min ITI • Analyses based on ΔF/Fo which is a measure of changes in Ca++ induced florescence on a pixel by pixel basis

  3. Figure 1 Experimental recording sites and raw ΔF/Fo

  4. Temporal dynamics of responses • 1 s data integration window (3 frames) • Averaged results Figure 2

  5. Figure 2 Mean On, off responses as a function of lobes The total area involved in response

  6. Figure 3 Different Gal4 lines show that A,B and A’B’ have different response profiles • OK107-Gal4: non-selective MB general • c739-Gal4: A/B lobe neurons • g0050-Gal4 and c305a-Gal4: A’/B’ lobe neurons

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

  8. Figure 4 • Responses generally depend on movement of the 3rd antennal segment • When glued Ca++ responses are greatly diminished By comparison with Figure 2 most areas have dropped

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

  10. Figure 5 Watershed Segmentation highlights sub region specific responses

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

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

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

  14. Figure 8 g0050-Gal4 cells are significantly more off responsive than c305a-Gal4

More Related