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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.


Analysis of chemotaxis parameters. The software extracts several parameters that describe the chemotaxis performance: (a) mean probability for a pirouette, (b) mean probability for a reversal, and (c) mean run lengths (time between reversals). All these parameters are dose dependent – mean probabilities for pirouettes and reversals increase with decreases is the concentration of the attractant. Conversely, mean run length time decreases. Error bars denote SEM of individual tracks. For the different concentrations of isoamyl-alcohol: 10–2, we averaged 447 tracks in total; 10–3, 1195 tracks in total; 10–4, 1470 tracks in total. d Probability for a pirouette increases the further the animal is located from the attractant, while the speed remains constant throughout the course of chemotaxis. Points are averages taken from 64 experiments comprising 7505 pirouette events and > 105 speed points
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Fig4: Analysis of chemotaxis parameters. The software extracts several parameters that describe the chemotaxis performance: (a) mean probability for a pirouette, (b) mean probability for a reversal, and (c) mean run lengths (time between reversals). All these parameters are dose dependent – mean probabilities for pirouettes and reversals increase with decreases is the concentration of the attractant. Conversely, mean run length time decreases. Error bars denote SEM of individual tracks. For the different concentrations of isoamyl-alcohol: 10–2, we averaged 447 tracks in total; 10–3, 1195 tracks in total; 10–4, 1470 tracks in total. d Probability for a pirouette increases the further the animal is located from the attractant, while the speed remains constant throughout the course of chemotaxis. Points are averages taken from 64 experiments comprising 7505 pirouette events and > 105 speed points

Mentions: Three key movement features define chemotaxis behavior in worms and presumably in other animals (e.g., fly larvae [25]). These include reversals/sharp turns, runs, and pirouettes that are defined as bouts of multiple sharp turns and reversals [12, 26]. As expected, these three parameters are dose dependent – the higher the chemoattractant concentration, the fewer the pirouettes and reversals and the longer the run times (Fig. 4a–c, Additional file 11: Figure S3). Interestingly, we find that an animal’s speed remains constant throughout the chemotactic behavior and is independent of the distance from the target. Conversely, the probability for a pirouette grows linearly with the distance from the target (Fig. 4d).Fig. 4


A multi-animal tracker for studying complex behaviors
Analysis of chemotaxis parameters. The software extracts several parameters that describe the chemotaxis performance: (a) mean probability for a pirouette, (b) mean probability for a reversal, and (c) mean run lengths (time between reversals). All these parameters are dose dependent – mean probabilities for pirouettes and reversals increase with decreases is the concentration of the attractant. Conversely, mean run length time decreases. Error bars denote SEM of individual tracks. For the different concentrations of isoamyl-alcohol: 10–2, we averaged 447 tracks in total; 10–3, 1195 tracks in total; 10–4, 1470 tracks in total. d Probability for a pirouette increases the further the animal is located from the attractant, while the speed remains constant throughout the course of chemotaxis. Points are averages taken from 64 experiments comprising 7505 pirouette events and > 105 speed points
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5383998&req=5

Fig4: Analysis of chemotaxis parameters. The software extracts several parameters that describe the chemotaxis performance: (a) mean probability for a pirouette, (b) mean probability for a reversal, and (c) mean run lengths (time between reversals). All these parameters are dose dependent – mean probabilities for pirouettes and reversals increase with decreases is the concentration of the attractant. Conversely, mean run length time decreases. Error bars denote SEM of individual tracks. For the different concentrations of isoamyl-alcohol: 10–2, we averaged 447 tracks in total; 10–3, 1195 tracks in total; 10–4, 1470 tracks in total. d Probability for a pirouette increases the further the animal is located from the attractant, while the speed remains constant throughout the course of chemotaxis. Points are averages taken from 64 experiments comprising 7505 pirouette events and > 105 speed points
Mentions: Three key movement features define chemotaxis behavior in worms and presumably in other animals (e.g., fly larvae [25]). These include reversals/sharp turns, runs, and pirouettes that are defined as bouts of multiple sharp turns and reversals [12, 26]. As expected, these three parameters are dose dependent – the higher the chemoattractant concentration, the fewer the pirouettes and reversals and the longer the run times (Fig. 4a–c, Additional file 11: Figure S3). Interestingly, we find that an animal’s speed remains constant throughout the chemotactic behavior and is independent of the distance from the target. Conversely, the probability for a pirouette grows linearly with the distance from the target (Fig. 4d).Fig. 4

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.