Continuous, long-term crawling behavior characterized by a robotic transport system
- Department of Physics, Northeastern University, United States
- Department of Physics and Department of Biology, University of Miami, United States
- FlySorter, LLC, United States
Figures
Figure 1 with 2 supplements
The automated larva transport robot enables continuous, long-term observation of fly larva crawling behavior.
(A) Schematic illustrating the fly larva’s simple search behavior. They search their environment in a modified 2D random walk, with 20 example paths (black) shown. Trajectories are characterized by an alternating series of forward crawling runs (red) and turns (blue). (B) Isometric CAD schematic of the transport robot. The robot is built from a modified 3D printer with a custom nozzle. Feedback from a mounted overhead camera allows for tight coordination with the moving arm to safely and robustly interact with the experimental arena. (C) The nozzle is built as a narrow tube that allows air and vacuum flow with a flat plastic disk fitted at the bottom. The disk provides ample surface area for a water droplet to form, and the droplet’s surface pressure can pick up larvae while minimizing stress on the animal during the interaction. (D) Top and side view schematics of the flat crawling arena. Larvae crawl atop an agar substrate, which is kept hydrated by a surrounding moat. The robot nozzle picks up larvae as they approach the edge of the arena and transports them back to the center to continue their freeroaming behavior. (E) Flowchart of the larva pick-up feedback process. In standby mode, the camera records a video of larval behavior. When it detects a larva nearing the perimeter, it triggers the pick-up protocol for the robot. The manipulator arm moves to a point in the moat nearest to the larva and dips the nozzle in, forming a droplet at the tip to be used for pick-up. The camera provides a more recent position for the moving larva as the robot attempts a pick-up. If feedback from the camera suggests a successful pick-up, it attempts a drop-off. Otherwise, the manipulator repeats its attempt after receiving an updated larval position. Multiple failed attempts can trigger small perturbations to robot calibration parameters to allow better flexibility through reinforcement learning before continuing pick-up attempts. Similarly, the robot performs multiple drop-off attempts at the center of the arena until it receives a positive confirmation from the camera, at which point the system returns to its original standby mode. (F) Photographs before (top) and after (bottom) the robot moves a larva from the perimeter to the center of the arena.
Figure 1—figure supplement 1
Time lapse from a movie of 22 larvae crawling on a 60 mm agar dish.
Larvae move freely until they are near the edges, where they dwell. Circles segment the arena into equal areas (1/3 in each region). Blue numbers indicate the number of larvae in the region.
Figure 1—video 1
Real-time video showing pick-up and drop-off as in panel F, three larvae transported from the edge of the arena to the center.
Figure 2
Analysis pipeline.
(A) Raw video acquired during the experiment is fed into computer vision software that tracks each larva while maintaining their identity. (B) A posture tracker analyzes the isolated crops of each larva to determine its posture and orientation. (C) A state tracker determines the behavioral state of each animal at each time point. (D) Compiling all information from the preceding algorithms allows the pipeline to identify and calculate a wide variety of behavioral features.
Figure 3
Observations of continuous free roaming for 6 hr.
(A) Larval crawl speed over time, comparing the results from continuous 6 hr observation recorded on the robot system (blue, ) to shorter 10 min observation of larger numbers of starved larvae (colored, per bar, from 10 experiments with 20 larvae each). (B) Larval crawl speed over 10 min after starvation. We observe a decline in crawl speed ( mm/s2) comparable to that observed during the first hour of the continuous observation ( mm/s2). (C) Larval turn rate over time. Similar to larval crawl speed (A), there is a noticeable drop in turn rate over the first hour, indicating an overall decrease in activity. (D) Analysis of larval crawl speed before and after an interaction with the larva picker robot. We plot larval crawl speed during the 5 min immediately before (purple) and the 5 min immediately after (red) an interaction with the robot, that is, a pick-up and drop-off event (vertical black dashed line). After a 1–2 min transient (, Student’s t-test), speed returns to the mean pre-interaction level (horizontal black line). For all panels, shaded regions indicate standard deviation from the mean.
Figure 4
Long-term observation of a single larva.
In order to maintain exploratory search behavior in a fly larva without starving it, the robot automatically delivers a drop of apple juice (≈0.1 g/mL sugar concentration). The larva is allowed to eat for 1−2 min, after which the robot uses water and air to rinse the larva, which then continues roaming freely. This protocol allows for continuous observation of larval behavior over developmental time scales. (A) The larva’s trajectory over a 30 hr duration, with its path (green) stitched together by matching each corresponding robot pick-up and drop-off positions (orange markers) by translation (no rotation, e.g. up is always toward same edge of agar) to produce a continuous trajectory. The larva begins at the top right and ends its run at the bottom (purple markers). (B) A ×20 magnification on a small section of the path, showing the scale of the path compared to the larva’s body (black bar, ≈ 1 mm). (C) We plot a number of behavioral features observed during its search trajectory, including its speed (red), body bend angle (orange), trajectory curvature (green), turn rate (blue), turn size (purple), and turn handedness (olive). Turn handedness is calculated as , such that a handedness of 1.0 indicates all left turns, and a handedness of −1.0 indicates all right turns. Dotted gray lines indicate time of robot pick-up events.
Figure 5
Comparison of navigation index in a zero gradient environment (A, ) and in a presence of a linear thermal gradient of 0.035°C/mm (B, ).
Navigation efficiency is calculated as a dimensionless index equal to , such that +1.0 is parallel to gradient, 0.0 is normal to gradient, and −1.0 is anti-parallel to gradient. We observe a clear increase in average navigation index (dashed lines) when exposed to a thermal gradient (increase from to [, Student’s t-test]), but we do not observe any significant pattern of change in that index over time. Solid line indicates a navigation index of zero, indicative of no preference in crawling direction. Shaded region indicates one standard deviation.
Figure 6
Examination of inter- and intra-animal variability via analysis of the probability distribution of observed larval thermal navigation index.
Each distribution shows the probability (vertical axis) of observing a certain navigation index (horizontal axis) at any given time in a series of observations for single individuals (purple), or a series of average observations for the population at each time point (red). When dissecting probability distributions of observed behavior, we notice that the same population (inter-animal) mean can be produced by two individual (intra-animal) distributions. (A) Simulated example of individual probability distributions with high intra-animal variability. The thin, lighter traces are probability distributions of eight individual animals. The resulting mean of intra-animal distribution (thick purple) closely resembles the population mean (thick red). (B) Simulated example of individual probability distributions with high inter-animal variability. The thin, lighter traces are probability distributions of eight individual animals. The resulting mean of intra-animal distribution (purple) forms a multimodal distribution despite a similar population mean (red) as (A, C). Empirical probability distribution of navigation index observed without a thermal gradient. There is high intra-animal variability but low inter-animal variability, such that the intra-animal mean forms a similar distribution to the population mean, as in the simulated results in panel A. individual larvae. (D) Empirical probability distribution of navigation index observed in presence of a thermal gradient (0.035°C/mm). In contrast to (C), during thermotaxis, the intra-animal mean forms a bimodal distribution (, compared to in C) despite each individual distribution remaining unimodal ( with gradient, without). individual larvae. This more closely resembles a distribution with high inter-animal variability as in the simulated results in panel B. (E) Individual empirical probability distribution of navigation index observed without a thermal gradient, displaying the same data used to generate panel C. (F) Individual empirical probability distribution of navigation index observed in presence of a thermal gradient, displaying the same data used to generate (D).
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Continuous, long-term crawling behavior characterized by a robotic transport system
eLife 12:e86585.
https://doi.org/10.7554/eLife.86585
