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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.


Worms integrate environmental information preferring richer environments. a Integration experiments were performed by comparing worms’ chemotaxis in three different assay plates. Two of the plates contained only one of the two cues each (e.g., diacetyl (DA) and isoamyl-alcohol (IAA)). The concentrations for the two cues were predetermined via dose-response assays and equipotential concentrations (i.e., attracting the worms to the same extent) were chosen. A third assay plate contained a mixture of both cues, each cue in a concentration that is half of what was used in the single cue assay plate. b Chemotaxis dynamics for the three assays. In the single chemoattractant experiments, we used a volumetric concentration of 0.75 × 10− 5 for DA, and 0.5 × 10− 4 for IAA. Accordingly, the mixture was composed of 0.37 × 10− 5 of DA and 0.25 × 10− 4 of IAA. We fitted a linear curve to the dynamic curves and estimated the linear coefficient (β1) to describe the potential of the cues to attract animals (DA: β1 = 0.5 ± 3.3 × 10–3; IAA: β1 = 0.5 ± 2.1 × 10–3). Chemotaxis towards each of the individual cues was very similar (as pre-calibrated by dose-response assays) indicating their equipotential attraction. The mixture, however, was significantly more attractive than each of cues alone. Shown is an example of a single experiment where approximately 100 worms were loaded on each plate. c The mean projection, the component of the movement towards the target, is significantly higher in the mixture compared to each of the cues alone (P < 2.5 × 10–5 and P < 0.007, compared to DA and IAA alone, respectively). Error bars denote SEM of all tracks together taken in a specific region of the experimental field. These data are composed of 92 run bouts for DA; 96  run bouts for IAA; and 159  run bouts for the mixture
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Fig6: Worms integrate environmental information preferring richer environments. a Integration experiments were performed by comparing worms’ chemotaxis in three different assay plates. Two of the plates contained only one of the two cues each (e.g., diacetyl (DA) and isoamyl-alcohol (IAA)). The concentrations for the two cues were predetermined via dose-response assays and equipotential concentrations (i.e., attracting the worms to the same extent) were chosen. A third assay plate contained a mixture of both cues, each cue in a concentration that is half of what was used in the single cue assay plate. b Chemotaxis dynamics for the three assays. In the single chemoattractant experiments, we used a volumetric concentration of 0.75 × 10− 5 for DA, and 0.5 × 10− 4 for IAA. Accordingly, the mixture was composed of 0.37 × 10− 5 of DA and 0.25 × 10− 4 of IAA. We fitted a linear curve to the dynamic curves and estimated the linear coefficient (β1) to describe the potential of the cues to attract animals (DA: β1 = 0.5 ± 3.3 × 10–3; IAA: β1 = 0.5 ± 2.1 × 10–3). Chemotaxis towards each of the individual cues was very similar (as pre-calibrated by dose-response assays) indicating their equipotential attraction. The mixture, however, was significantly more attractive than each of cues alone. Shown is an example of a single experiment where approximately 100 worms were loaded on each plate. c The mean projection, the component of the movement towards the target, is significantly higher in the mixture compared to each of the cues alone (P < 2.5 × 10–5 and P < 0.007, compared to DA and IAA alone, respectively). Error bars denote SEM of all tracks together taken in a specific region of the experimental field. These data are composed of 92 run bouts for DA; 96  run bouts for IAA; and 159  run bouts for the mixture

Mentions: We employed our MAT system and asked whether C. elegans can integrate environmental information (i.e., attend to more than one cue at a time) presented as food odorant cues. To do this, we compared worm chemotaxis towards each of two cues, isoamyl-alcohol (IAA) and diacetyl (DA), when presented independently and when combined as a single mixture (Fig. 6a). Specifically, we used the same experimental design as detailed above for the chemotaxis assays (Figs. 3, 4, and 5). We first assayed worm chemotaxis to each cue in a dose-dependent manner and chose two equipotential concentrations (the concentration of each cue that attracts the worms to the same extent, see Methods). Both stimuli at these concentrations attract worms with the same kinetics (DA: β1 = 0.5 ± 3.3 × 10−3; IAA: β1 = 0.5 ± 2.1 × 10−3; β1 denotes the coefficient of the liner regression of chemotaxis dynamics, Fig. 6b). Interestingly, worms are significantly more attracted when these cues are combined and presented as a mixture, as many more worms reach the target area with faster kinetics (Mixture: β1 = 0.94 ± 4.1 × 10−3, Fig. 6b). Importantly, we halved the concentration of each stimulus in the mixture compared to its concentration when presented individually, and yet worms were significantly more attracted to the combination of the cues (P < 0.007 compared to IAA and P < 2.5 × 10−5 compared to DA, Fig. 6b). This suggests that worms can attend both cues concomitantly as they navigate in search of food.Fig. 6


A multi-animal tracker for studying complex behaviors
Worms integrate environmental information preferring richer environments. a Integration experiments were performed by comparing worms’ chemotaxis in three different assay plates. Two of the plates contained only one of the two cues each (e.g., diacetyl (DA) and isoamyl-alcohol (IAA)). The concentrations for the two cues were predetermined via dose-response assays and equipotential concentrations (i.e., attracting the worms to the same extent) were chosen. A third assay plate contained a mixture of both cues, each cue in a concentration that is half of what was used in the single cue assay plate. b Chemotaxis dynamics for the three assays. In the single chemoattractant experiments, we used a volumetric concentration of 0.75 × 10− 5 for DA, and 0.5 × 10− 4 for IAA. Accordingly, the mixture was composed of 0.37 × 10− 5 of DA and 0.25 × 10− 4 of IAA. We fitted a linear curve to the dynamic curves and estimated the linear coefficient (β1) to describe the potential of the cues to attract animals (DA: β1 = 0.5 ± 3.3 × 10–3; IAA: β1 = 0.5 ± 2.1 × 10–3). Chemotaxis towards each of the individual cues was very similar (as pre-calibrated by dose-response assays) indicating their equipotential attraction. The mixture, however, was significantly more attractive than each of cues alone. Shown is an example of a single experiment where approximately 100 worms were loaded on each plate. c The mean projection, the component of the movement towards the target, is significantly higher in the mixture compared to each of the cues alone (P < 2.5 × 10–5 and P < 0.007, compared to DA and IAA alone, respectively). Error bars denote SEM of all tracks together taken in a specific region of the experimental field. These data are composed of 92 run bouts for DA; 96  run bouts for IAA; and 159  run bouts for the mixture
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Fig6: Worms integrate environmental information preferring richer environments. a Integration experiments were performed by comparing worms’ chemotaxis in three different assay plates. Two of the plates contained only one of the two cues each (e.g., diacetyl (DA) and isoamyl-alcohol (IAA)). The concentrations for the two cues were predetermined via dose-response assays and equipotential concentrations (i.e., attracting the worms to the same extent) were chosen. A third assay plate contained a mixture of both cues, each cue in a concentration that is half of what was used in the single cue assay plate. b Chemotaxis dynamics for the three assays. In the single chemoattractant experiments, we used a volumetric concentration of 0.75 × 10− 5 for DA, and 0.5 × 10− 4 for IAA. Accordingly, the mixture was composed of 0.37 × 10− 5 of DA and 0.25 × 10− 4 of IAA. We fitted a linear curve to the dynamic curves and estimated the linear coefficient (β1) to describe the potential of the cues to attract animals (DA: β1 = 0.5 ± 3.3 × 10–3; IAA: β1 = 0.5 ± 2.1 × 10–3). Chemotaxis towards each of the individual cues was very similar (as pre-calibrated by dose-response assays) indicating their equipotential attraction. The mixture, however, was significantly more attractive than each of cues alone. Shown is an example of a single experiment where approximately 100 worms were loaded on each plate. c The mean projection, the component of the movement towards the target, is significantly higher in the mixture compared to each of the cues alone (P < 2.5 × 10–5 and P < 0.007, compared to DA and IAA alone, respectively). Error bars denote SEM of all tracks together taken in a specific region of the experimental field. These data are composed of 92 run bouts for DA; 96  run bouts for IAA; and 159  run bouts for the mixture
Mentions: We employed our MAT system and asked whether C. elegans can integrate environmental information (i.e., attend to more than one cue at a time) presented as food odorant cues. To do this, we compared worm chemotaxis towards each of two cues, isoamyl-alcohol (IAA) and diacetyl (DA), when presented independently and when combined as a single mixture (Fig. 6a). Specifically, we used the same experimental design as detailed above for the chemotaxis assays (Figs. 3, 4, and 5). We first assayed worm chemotaxis to each cue in a dose-dependent manner and chose two equipotential concentrations (the concentration of each cue that attracts the worms to the same extent, see Methods). Both stimuli at these concentrations attract worms with the same kinetics (DA: β1 = 0.5 ± 3.3 × 10−3; IAA: β1 = 0.5 ± 2.1 × 10−3; β1 denotes the coefficient of the liner regression of chemotaxis dynamics, Fig. 6b). Interestingly, worms are significantly more attracted when these cues are combined and presented as a mixture, as many more worms reach the target area with faster kinetics (Mixture: β1 = 0.94 ± 4.1 × 10−3, Fig. 6b). Importantly, we halved the concentration of each stimulus in the mixture compared to its concentration when presented individually, and yet worms were significantly more attracted to the combination of the cues (P < 0.007 compared to IAA and P < 2.5 × 10−5 compared to DA, Fig. 6b). This suggests that worms can attend both cues concomitantly as they navigate in search of food.Fig. 6

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.