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3D Holographic Observatory for Long-term Monitoring of Complex Behaviors in Drosophila

View Article: PubMed Central - PubMed

ABSTRACT

Drosophila is an excellent model organism towards understanding the cognitive function, aging and neurodegeneration in humans. The effects of aging and other long-term dynamics on the behavior serve as important biomarkers in identifying such changes to the brain. In this regard, we are presenting a new imaging technique for lifetime monitoring of Drosophila in 3D at spatial and temporal resolutions capable of resolving the motion of limbs and wings using holographic principles. The developed system is capable of monitoring and extracting various behavioral parameters, such as ethograms and spatial distributions, from a group of flies simultaneously. This technique can image complicated leg and wing motions of flies at a resolution, which allows capturing specific landing responses from the same data set. Overall, this system provides a unique opportunity for high throughput screenings of behavioral changes in 3D over a long term in Drosophila.

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Algorithm flow chart illustrating the entire processing steps in order, using the hologram in Fig. 1d.(a) Hologram after enhancement (z = 0) through time average background subtraction. (b) Binary image after automatic thresholding and opening used to identify Drosophila in the FOV. Both images contain a scale bar of 2 mm. (c) 2D trajectories of detected single objects colored by speed (2D) over 2.5 s on either side of the hologram with an arrow pointing to the current time step (marked with a black circle and a ROI of size 150 × 150 pixel2). The red disks indicate locations and periods of missing data due to occlusion, which can be seen in Supplementary Video 3. (d) Illustration of the focus metric algorithm showing the recording plane with an inset of the original hologram and its reconstruction (inverted grayscale). The curve is overlaid on top of the fly arena, with a common x-axis for the z position (origin at the top left of hologram plane) and the peak corresponds to the plane of maximum focus shown by the inset of the refocused image. The arena is located at 140 mm to satisfy the paraxial approximation (Supplementary Methods). Scale bar of 2 mm on both insets.
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f2: Algorithm flow chart illustrating the entire processing steps in order, using the hologram in Fig. 1d.(a) Hologram after enhancement (z = 0) through time average background subtraction. (b) Binary image after automatic thresholding and opening used to identify Drosophila in the FOV. Both images contain a scale bar of 2 mm. (c) 2D trajectories of detected single objects colored by speed (2D) over 2.5 s on either side of the hologram with an arrow pointing to the current time step (marked with a black circle and a ROI of size 150 × 150 pixel2). The red disks indicate locations and periods of missing data due to occlusion, which can be seen in Supplementary Video 3. (d) Illustration of the focus metric algorithm showing the recording plane with an inset of the original hologram and its reconstruction (inverted grayscale). The curve is overlaid on top of the fly arena, with a common x-axis for the z position (origin at the top left of hologram plane) and the peak corresponds to the plane of maximum focus shown by the inset of the refocused image. The arena is located at 140 mm to satisfy the paraxial approximation (Supplementary Methods). Scale bar of 2 mm on both insets.

Mentions: Figure 2 illustrates our processing algorithm using a sample hologram selected from the recorded dataset (Fig. 1d). A time averaged background subtraction eliminates any noise present in the image caused due to aberrations on the imaging windows and improves the signal-to-noise ratio of the diffraction fringes (Fig. 2a). After enhancement a moving average filter is applied to smooth out variations in intensity inside the body of the fly, due to the presence of transparent wings that causes over segmentation on thresholding. An automatic threshold based on the mean value between the first two peaks of the histogram is used to segment the image in 2D as any intensity lower than this threshold belongs to a fly or random noise in the background (Supplementary Methods). A binary opening operation, with a structuring element matching the size of appendages (10-pixel disk), is performed to remove all of the limbs and wings improving the accuracy of the centroid calculation (Fig. 2b). A detection uncertainty in xy is about 3–5% of body length, which was estimated through sampling stationary objects at various locations on the image (Supplementary Methods). The 2D positions and the area of the object are then extracted through a binary labeling process.


3D Holographic Observatory for Long-term Monitoring of Complex Behaviors in Drosophila
Algorithm flow chart illustrating the entire processing steps in order, using the hologram in Fig. 1d.(a) Hologram after enhancement (z = 0) through time average background subtraction. (b) Binary image after automatic thresholding and opening used to identify Drosophila in the FOV. Both images contain a scale bar of 2 mm. (c) 2D trajectories of detected single objects colored by speed (2D) over 2.5 s on either side of the hologram with an arrow pointing to the current time step (marked with a black circle and a ROI of size 150 × 150 pixel2). The red disks indicate locations and periods of missing data due to occlusion, which can be seen in Supplementary Video 3. (d) Illustration of the focus metric algorithm showing the recording plane with an inset of the original hologram and its reconstruction (inverted grayscale). The curve is overlaid on top of the fly arena, with a common x-axis for the z position (origin at the top left of hologram plane) and the peak corresponds to the plane of maximum focus shown by the inset of the refocused image. The arena is located at 140 mm to satisfy the paraxial approximation (Supplementary Methods). Scale bar of 2 mm on both insets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5015086&req=5

f2: Algorithm flow chart illustrating the entire processing steps in order, using the hologram in Fig. 1d.(a) Hologram after enhancement (z = 0) through time average background subtraction. (b) Binary image after automatic thresholding and opening used to identify Drosophila in the FOV. Both images contain a scale bar of 2 mm. (c) 2D trajectories of detected single objects colored by speed (2D) over 2.5 s on either side of the hologram with an arrow pointing to the current time step (marked with a black circle and a ROI of size 150 × 150 pixel2). The red disks indicate locations and periods of missing data due to occlusion, which can be seen in Supplementary Video 3. (d) Illustration of the focus metric algorithm showing the recording plane with an inset of the original hologram and its reconstruction (inverted grayscale). The curve is overlaid on top of the fly arena, with a common x-axis for the z position (origin at the top left of hologram plane) and the peak corresponds to the plane of maximum focus shown by the inset of the refocused image. The arena is located at 140 mm to satisfy the paraxial approximation (Supplementary Methods). Scale bar of 2 mm on both insets.
Mentions: Figure 2 illustrates our processing algorithm using a sample hologram selected from the recorded dataset (Fig. 1d). A time averaged background subtraction eliminates any noise present in the image caused due to aberrations on the imaging windows and improves the signal-to-noise ratio of the diffraction fringes (Fig. 2a). After enhancement a moving average filter is applied to smooth out variations in intensity inside the body of the fly, due to the presence of transparent wings that causes over segmentation on thresholding. An automatic threshold based on the mean value between the first two peaks of the histogram is used to segment the image in 2D as any intensity lower than this threshold belongs to a fly or random noise in the background (Supplementary Methods). A binary opening operation, with a structuring element matching the size of appendages (10-pixel disk), is performed to remove all of the limbs and wings improving the accuracy of the centroid calculation (Fig. 2b). A detection uncertainty in xy is about 3–5% of body length, which was estimated through sampling stationary objects at various locations on the image (Supplementary Methods). The 2D positions and the area of the object are then extracted through a binary labeling process.

View Article: PubMed Central - PubMed

ABSTRACT

Drosophila is an excellent model organism towards understanding the cognitive function, aging and neurodegeneration in humans. The effects of aging and other long-term dynamics on the behavior serve as important biomarkers in identifying such changes to the brain. In this regard, we are presenting a new imaging technique for lifetime monitoring of Drosophila in 3D at spatial and temporal resolutions capable of resolving the motion of limbs and wings using holographic principles. The developed system is capable of monitoring and extracting various behavioral parameters, such as ethograms and spatial distributions, from a group of flies simultaneously. This technique can image complicated leg and wing motions of flies at a resolution, which allows capturing specific landing responses from the same data set. Overall, this system provides a unique opportunity for high throughput screenings of behavioral changes in 3D over a long term in Drosophila.

No MeSH data available.


Related in: MedlinePlus