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A multi-animal tracker for studying complex behaviors

View Article: PubMed Central - PubMed

ABSTRACT

Background: Animals exhibit astonishingly complex behaviors. Studying the subtle features of these behaviors requires quantitative, high-throughput, and accurate systems that can cope with the often rich perplexing data.

Results: Here, we present a Multi-Animal Tracker (MAT) that provides a user-friendly, end-to-end solution for imaging, tracking, and analyzing complex behaviors of multiple animals simultaneously. At the core of the tracker is a machine learning algorithm that provides immense flexibility to image various animals (e.g., worms, flies, zebrafish, etc.) under different experimental setups and conditions. Focusing on C. elegans worms, we demonstrate the vast advantages of using this MAT in studying complex behaviors. Beginning with chemotaxis, we show that approximately 100 animals can be tracked simultaneously, providing rich behavioral data. Interestingly, we reveal that worms’ directional changes are biased, rather than random – a strategy that significantly enhances chemotaxis performance. Next, we show that worms can integrate environmental information and that directional changes mediate the enhanced chemotaxis towards richer environments. Finally, offering high-throughput and accurate tracking, we show that the system is highly suitable for longitudinal studies of aging- and proteotoxicity-associated locomotion deficits, enabling large-scale drug and genetic screens.

Conclusions: Together, our tracker provides a powerful and simple system to study complex behaviors in a quantitative, high-throughput, and accurate manner.

Electronic supplementary material: The online version of this article (doi:10.1186/s12915-017-0363-9) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus

Worms improve their orientation towards the target following the exit from a pirouette. a (I) Angular bearing histogram of worms oriented off-course (90° < B < 270°) immediately before initiating the pirouettes (BBefore); (II) Angular bearing histogram of the worms from (I) immediately after the pirouettes (BAfter); (III) A histogram of the cosine of the difference in the bearings before and after a pirouette cos(BBefore – BAfter) for worms that were initially off-course. The histogram shows two peaks (around 1 and –1) indicating that worms tend to perform either extreme (e.g., 180°) or minute (e.g., 0°) angle changes. However, the tendency to perform a pirouette with a larger angular difference in bearing is significantly more probable (P < 10–6, permutation tests, see Methods). The data is composed of 13,297 disoriented pirouette events. b (I) Angular histogram of bearings for worms oriented on-course (0° < B < 90° or −90° < B < 0°) immediately before the initiation of a pirouette (BBefore). (II) Angular histogram of worms bearing that are on-course immediately after the pirouette (BAfter). (III) A histogram of the difference in the bearings before and after a pirouette cos(BBefore – BAfter) for on-course worms. As in off-course worms, these initially on-course oriented worms tend to perform either extreme or minute angle changes, but the frequency of minute changes (cos(Δ angle) = 1) is significantly higher (P < 10–6, see Methods). The data is composed of 15,368 oriented pirouette events. c Simulations of chemotaxis trajectories. We used three different strategies for choosing the exit angle from a pirouette (see Methods for details). The experimentally observed principle, where the exit angle is sampled according to the entry angle, provides an efficient chemotaxis strategy reflected by the significantly shorter time to reach the target point (P < 0.007 and P < 2.5 × 10–3, Wilcoxon rank-sum test for random and uniform sampling, respectively). Error bars denote SEM of the number of simulated worms in each simulation. Overall, we simulated 250 worms per strategy
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Fig5: Worms improve their orientation towards the target following the exit from a pirouette. a (I) Angular bearing histogram of worms oriented off-course (90° < B < 270°) immediately before initiating the pirouettes (BBefore); (II) Angular bearing histogram of the worms from (I) immediately after the pirouettes (BAfter); (III) A histogram of the cosine of the difference in the bearings before and after a pirouette cos(BBefore – BAfter) for worms that were initially off-course. The histogram shows two peaks (around 1 and –1) indicating that worms tend to perform either extreme (e.g., 180°) or minute (e.g., 0°) angle changes. However, the tendency to perform a pirouette with a larger angular difference in bearing is significantly more probable (P < 10–6, permutation tests, see Methods). The data is composed of 13,297 disoriented pirouette events. b (I) Angular histogram of bearings for worms oriented on-course (0° < B < 90° or −90° < B < 0°) immediately before the initiation of a pirouette (BBefore). (II) Angular histogram of worms bearing that are on-course immediately after the pirouette (BAfter). (III) A histogram of the difference in the bearings before and after a pirouette cos(BBefore – BAfter) for on-course worms. As in off-course worms, these initially on-course oriented worms tend to perform either extreme or minute angle changes, but the frequency of minute changes (cos(Δ angle) = 1) is significantly higher (P < 10–6, see Methods). The data is composed of 15,368 oriented pirouette events. c Simulations of chemotaxis trajectories. We used three different strategies for choosing the exit angle from a pirouette (see Methods for details). The experimentally observed principle, where the exit angle is sampled according to the entry angle, provides an efficient chemotaxis strategy reflected by the significantly shorter time to reach the target point (P < 0.007 and P < 2.5 × 10–3, Wilcoxon rank-sum test for random and uniform sampling, respectively). Error bars denote SEM of the number of simulated worms in each simulation. Overall, we simulated 250 worms per strategy

Mentions: Our experimental results not only recapitulated these observations (Fig. 5a), but also provided novel understanding of this complex behavior; worms, originally directed towards the chemoattractant, tend to maintain their general direction following a pirouette (Fig. 5b, see also Methods). This, in addition to the observations made by Shimomura et al. [12], explains why both directed (–90° < BBefore < +90°) and undirected (+90° < BBefore < 270°) trajectories improve their general direction following a pirouette (Fig. 5aII, bII; Rayleigh Z-test, P < 10–5, for both directed and undirected). Directional changes that follow pirouette events show a bimodal distribution where small (ΔB ≈ 0° rad, cos(ΔB) ≈ 1) and large (ΔB ≈ ± 180° rad, cos(ΔB) ≈ –1) changes make nearly half of the total directional changes (Fig. 5aIII, bIII).Fig. 5


A multi-animal tracker for studying complex behaviors
Worms improve their orientation towards the target following the exit from a pirouette. a (I) Angular bearing histogram of worms oriented off-course (90° < B < 270°) immediately before initiating the pirouettes (BBefore); (II) Angular bearing histogram of the worms from (I) immediately after the pirouettes (BAfter); (III) A histogram of the cosine of the difference in the bearings before and after a pirouette cos(BBefore – BAfter) for worms that were initially off-course. The histogram shows two peaks (around 1 and –1) indicating that worms tend to perform either extreme (e.g., 180°) or minute (e.g., 0°) angle changes. However, the tendency to perform a pirouette with a larger angular difference in bearing is significantly more probable (P < 10–6, permutation tests, see Methods). The data is composed of 13,297 disoriented pirouette events. b (I) Angular histogram of bearings for worms oriented on-course (0° < B < 90° or −90° < B < 0°) immediately before the initiation of a pirouette (BBefore). (II) Angular histogram of worms bearing that are on-course immediately after the pirouette (BAfter). (III) A histogram of the difference in the bearings before and after a pirouette cos(BBefore – BAfter) for on-course worms. As in off-course worms, these initially on-course oriented worms tend to perform either extreme or minute angle changes, but the frequency of minute changes (cos(Δ angle) = 1) is significantly higher (P < 10–6, see Methods). The data is composed of 15,368 oriented pirouette events. c Simulations of chemotaxis trajectories. We used three different strategies for choosing the exit angle from a pirouette (see Methods for details). The experimentally observed principle, where the exit angle is sampled according to the entry angle, provides an efficient chemotaxis strategy reflected by the significantly shorter time to reach the target point (P < 0.007 and P < 2.5 × 10–3, Wilcoxon rank-sum test for random and uniform sampling, respectively). Error bars denote SEM of the number of simulated worms in each simulation. Overall, we simulated 250 worms per strategy
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Fig5: Worms improve their orientation towards the target following the exit from a pirouette. a (I) Angular bearing histogram of worms oriented off-course (90° < B < 270°) immediately before initiating the pirouettes (BBefore); (II) Angular bearing histogram of the worms from (I) immediately after the pirouettes (BAfter); (III) A histogram of the cosine of the difference in the bearings before and after a pirouette cos(BBefore – BAfter) for worms that were initially off-course. The histogram shows two peaks (around 1 and –1) indicating that worms tend to perform either extreme (e.g., 180°) or minute (e.g., 0°) angle changes. However, the tendency to perform a pirouette with a larger angular difference in bearing is significantly more probable (P < 10–6, permutation tests, see Methods). The data is composed of 13,297 disoriented pirouette events. b (I) Angular histogram of bearings for worms oriented on-course (0° < B < 90° or −90° < B < 0°) immediately before the initiation of a pirouette (BBefore). (II) Angular histogram of worms bearing that are on-course immediately after the pirouette (BAfter). (III) A histogram of the difference in the bearings before and after a pirouette cos(BBefore – BAfter) for on-course worms. As in off-course worms, these initially on-course oriented worms tend to perform either extreme or minute angle changes, but the frequency of minute changes (cos(Δ angle) = 1) is significantly higher (P < 10–6, see Methods). The data is composed of 15,368 oriented pirouette events. c Simulations of chemotaxis trajectories. We used three different strategies for choosing the exit angle from a pirouette (see Methods for details). The experimentally observed principle, where the exit angle is sampled according to the entry angle, provides an efficient chemotaxis strategy reflected by the significantly shorter time to reach the target point (P < 0.007 and P < 2.5 × 10–3, Wilcoxon rank-sum test for random and uniform sampling, respectively). Error bars denote SEM of the number of simulated worms in each simulation. Overall, we simulated 250 worms per strategy
Mentions: Our experimental results not only recapitulated these observations (Fig. 5a), but also provided novel understanding of this complex behavior; worms, originally directed towards the chemoattractant, tend to maintain their general direction following a pirouette (Fig. 5b, see also Methods). This, in addition to the observations made by Shimomura et al. [12], explains why both directed (–90° < BBefore < +90°) and undirected (+90° < BBefore < 270°) trajectories improve their general direction following a pirouette (Fig. 5aII, bII; Rayleigh Z-test, P < 10–5, for both directed and undirected). Directional changes that follow pirouette events show a bimodal distribution where small (ΔB ≈ 0° rad, cos(ΔB) ≈ 1) and large (ΔB ≈ ± 180° rad, cos(ΔB) ≈ –1) changes make nearly half of the total directional changes (Fig. 5aIII, bIII).Fig. 5

View Article: PubMed Central - PubMed

ABSTRACT

Background: Animals exhibit astonishingly complex behaviors. Studying the subtle features of these behaviors requires quantitative, high-throughput, and accurate systems that can cope with the often rich perplexing data.

Results: Here, we present a Multi-Animal Tracker (MAT) that provides a user-friendly, end-to-end solution for imaging, tracking, and analyzing complex behaviors of multiple animals simultaneously. At the core of the tracker is a machine learning algorithm that provides immense flexibility to image various animals (e.g., worms, flies, zebrafish, etc.) under different experimental setups and conditions. Focusing on C. elegans worms, we demonstrate the vast advantages of using this MAT in studying complex behaviors. Beginning with chemotaxis, we show that approximately 100 animals can be tracked simultaneously, providing rich behavioral data. Interestingly, we reveal that worms&rsquo; directional changes are biased, rather than random &ndash; a strategy that significantly enhances chemotaxis performance. Next, we show that worms can integrate environmental information and that directional changes mediate the enhanced chemotaxis towards richer environments. Finally, offering high-throughput and accurate tracking, we show that the system is highly suitable for longitudinal studies of aging- and proteotoxicity-associated locomotion deficits, enabling large-scale drug and genetic screens.

Conclusions: Together, our tracker provides a powerful and simple system to study complex behaviors in a quantitative, high-throughput, and accurate manner.

Electronic supplementary material: The online version of this article (doi:10.1186/s12915-017-0363-9) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus