1
|
Lebovich L, Alisch T, Redhead ES, Parker MO, Loewenstein Y, Couzin ID, de Bivort BL. Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.04.611038. [PMID: 39282352 PMCID: PMC11398310 DOI: 10.1101/2024.09.04.611038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/11/2024]
Abstract
Decision-making in animals often involves choosing actions while navigating the environment, a process markedly different from static decision paradigms commonly studied in laboratory settings. Even in decision-making assays in which animals can freely locomote, decision outcomes are often interpreted as happening at single points in space and single moments in time, a simplification that potentially glosses over important spatiotemporal dynamics. We investigated locomotor decision-making in Drosophila melanogaster in Y-shaped mazes, measuring the extent to which their future choices could be predicted through space and time. We demonstrate that turn-decisions can be reliably predicted from flies' locomotor dynamics, with distinct predictability phases emerging as flies progress through maze regions. We show that these predictability dynamics are not merely the result of maze geometry or wall-following tendencies, but instead reflect the capacity of flies to move in ways that depend on sustained locomotor signatures, suggesting an active, working memory-like process. Additionally, we demonstrate that fly mutants known to have sensory and information-processing deficits exhibit altered spatial predictability patterns, highlighting the role of visual, mechanosensory, and dopaminergic signaling in locomotor decision-making. Finally, highlighting the broad applicability of our analyses, we generalize our findings to other species and tasks. We show that human participants in a virtual Y-maze exhibited similar decision predictability dynamics as flies. This study advances our understanding of decision-making processes, emphasizing the importance of spatial and temporal dynamics of locomotor behavior in the lead-up to discrete choice outcomes.
Collapse
Affiliation(s)
- Lior Lebovich
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Tom Alisch
- Department of Organismic & Evolutionary Biology & Center for Brain Science, Harvard University, Cambridge, Massachusetts, U.S.A
| | | | | | - Yonatan Loewenstein
- The Edmond and Lily Safra Center for Brain Sciences, The Alexander Silberman Institute of Life Sciences, Dept. of Cognitive and Brain Sciences and The Federmann Center for the Study of Rationality, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Iain D Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Benjamin L de Bivort
- Department of Organismic & Evolutionary Biology & Center for Brain Science, Harvard University, Cambridge, Massachusetts, U.S.A
| |
Collapse
|
2
|
Jones H, Willis JA, Firth LC, Giachello CNG, Gilestro GF. A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster. eLife 2023; 12:RP86695. [PMID: 37938101 PMCID: PMC10631757 DOI: 10.7554/elife.86695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023] Open
Abstract
Understanding how the brain encodes behaviour is the ultimate goal of neuroscience and the ability to objectively and reproducibly describe and quantify behaviour is a necessary milestone on this path. Recent technological progresses in machine learning and computational power have boosted the development and adoption of systems leveraging on high-resolution video recording to track an animal pose and describe behaviour in all four dimensions. However, the high temporal and spatial resolution that these systems offer must come as a compromise with their throughput and accessibility. Here, we describe coccinella, an open-source reductionist framework combining high-throughput analysis of behaviour using real-time tracking on a distributed mesh of microcomputers (ethoscopes) with resource-lean statistical learning (HCTSA/Catch22). Coccinella is a reductionist system, yet outperforms state-of-the-art alternatives when exploring the pharmacobehaviour in Drosophila melanogaster.
Collapse
Affiliation(s)
- Hannah Jones
- Department of Life Sciences, Imperial College LondonLondonUnited Kingdom
| | - Jenny A Willis
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Lucy C Firth
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Carlo NG Giachello
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Giorgio F Gilestro
- Department of Life Sciences, Imperial College LondonLondonUnited Kingdom
| |
Collapse
|
3
|
Yu J, Dancausse S, Paz M, Faderin T, Gaviria M, Shomar JW, Zucker D, Venkatachalam V, Klein M. Continuous, long-term crawling behavior characterized by a robotic transport system. eLife 2023; 12:e86585. [PMID: 37535068 PMCID: PMC10400072 DOI: 10.7554/elife.86585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/19/2023] [Indexed: 08/04/2023] Open
Abstract
Detailed descriptions of behavior provide critical insight into the structure and function of nervous systems. In Drosophila larvae and many other systems, short behavioral experiments have been successful in characterizing rapid responses to a range of stimuli at the population level. However, the lack of long-term continuous observation makes it difficult to dissect comprehensive behavioral dynamics of individual animals and how behavior (and therefore the nervous system) develops over time. To allow for long-term continuous observations in individual fly larvae, we have engineered a robotic instrument that automatically tracks and transports larvae throughout an arena. The flexibility and reliability of its design enables controlled stimulus delivery and continuous measurement over developmental time scales, yielding an unprecedented level of detailed locomotion data. We utilize the new system's capabilities to perform continuous observation of exploratory search behavior over a duration of 6 hr with and without a thermal gradient present, and in a single larva for over 30 hr. Long-term free-roaming behavior and analogous short-term experiments show similar dynamics that take place at the beginning of each experiment. Finally, characterization of larval thermotaxis in individuals reveals a bimodal distribution in navigation efficiency, identifying distinct phenotypes that are obfuscated when only analyzing population averages.
Collapse
Affiliation(s)
- James Yu
- Department of Physics, Northeastern UniversityBostonUnited States
| | - Stephanie Dancausse
- Department of Physics and Department of Biology, University of MiamiCoral GablesUnited States
| | - Maria Paz
- Department of Physics, Northeastern UniversityBostonUnited States
| | - Tolu Faderin
- Department of Physics, Northeastern UniversityBostonUnited States
| | - Melissa Gaviria
- Department of Physics and Department of Biology, University of MiamiCoral GablesUnited States
| | - Joseph W Shomar
- Department of Physics and Department of Biology, University of MiamiCoral GablesUnited States
| | | | | | - Mason Klein
- Department of Physics and Department of Biology, University of MiamiCoral GablesUnited States
| |
Collapse
|
4
|
Easton-Calabria AC, Thuma JA, Cronin K, Melone G, Laskowski M, Smith MAY, Pasadyn CL, de Bivort BL, Crall JD. Colony size buffers interactions between neonicotinoid exposure and cold stress in bumblebees. Proc Biol Sci 2023; 290:20230555. [PMID: 37464757 PMCID: PMC10354472 DOI: 10.1098/rspb.2023.0555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 06/21/2023] [Indexed: 07/20/2023] Open
Abstract
Social bees are critical for supporting biodiversity, ecosystem function and crop yields globally. Colony size is a key ecological trait predicted to drive sensitivity to environmental stressors and may be especially important for species with annual cycles of sociality, such as bumblebees. However, there is limited empirical evidence assessing the effect of colony size on sensitivity to environmental stressors or the mechanisms underlying these effects. Here, we examine the relationship between colony size and sensitivity to environmental stressors in bumblebees. We exposed colonies at different developmental stages briefly (2 days) to a common neonicotinoid (imidacloprid) and cold stress, while quantifying behaviour of individuals. Combined imidacloprid and cold exposure had stronger effects on both thermoregulatory behaviour and long-term colony growth in small colonies. We find that imidacloprid's effects on behaviour are mediated by body temperature and spatial location within the nest, suggesting that social thermoregulation provides a buffering effect in large colonies. Finally, we demonstrate qualitatively similar effects in size-manipulated microcolonies, suggesting that group size per se, rather than colony age, drives these patterns. Our results provide evidence that colony size is critical in driving sensitivity to stressors and may help elucidate mechanisms underlying the complex and context-specific impacts of pesticide exposure.
Collapse
Affiliation(s)
- August C. Easton-Calabria
- Department of Entomology, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Jessie A. Thuma
- Department of Biology, Tufts University, Medford, MA 02155-5801, USA
| | - Kayleigh Cronin
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Gigi Melone
- Department of Entomology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Madalyn Laskowski
- Department of Entomology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Matthew A. Y. Smith
- Department of Entomology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Cassandra L. Pasadyn
- Department of Entomology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Benjamin L. de Bivort
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - James D. Crall
- Department of Entomology, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| |
Collapse
|
5
|
Li Z, Fouad AD, Bowlin PD, Fan Y, He S, Chang MC, Du A, Teng C, Kassouni A, Ji H, Raizen DM, Fang-Yen C. A robotic system for automated genetic manipulation and analysis of Caenorhabditis elegans. PNAS NEXUS 2023; 2:pgad197. [PMID: 37416871 PMCID: PMC10321491 DOI: 10.1093/pnasnexus/pgad197] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/04/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023]
Abstract
The nematode Caenorhabditis elegans is one of the most widely studied organisms in biology due to its small size, rapid life cycle, and manipulable genetics. Research with C. elegans depends on labor-intensive and time-consuming manual procedures, imposing a major bottleneck for many studies, especially for those involving large numbers of animals. Here, we describe a general-purpose tool, WormPicker, a robotic system capable of performing complex genetic manipulations and other tasks by imaging, phenotyping, and transferring C. elegans on standard agar media. Our system uses a motorized stage to move an imaging system and a robotic arm over an array of agar plates. Machine vision tools identify animals and assay developmental stage, morphology, sex, expression of fluorescent reporters, and other phenotypes. Based on the results of these assays, the robotic arm selectively transfers individual animals using an electrically self-sterilized wire loop, with the aid of machine vision and electrical capacitance sensing. Automated C. elegans manipulation shows reliability and throughput comparable with standard manual methods. We developed software to enable the system to autonomously carry out complex protocols. To validate the effectiveness and versatility of our methods, we used the system to perform a collection of common C. elegans procedures, including genetic crossing, genetic mapping, and genomic integration of a transgene. Our robotic system will accelerate C. elegans research and open possibilities for performing genetic and pharmacological screens that would be impractical using manual methods.
Collapse
Affiliation(s)
- Zihao Li
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anthony D Fouad
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Peter D Bowlin
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yuying Fan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Siming He
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Meng-Chuan Chang
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Angelica Du
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christopher Teng
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexander Kassouni
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hongfei Ji
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - David M Raizen
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christopher Fang-Yen
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| |
Collapse
|
6
|
Petrović M, Meštrović A, Andretić Waldowski R, Filošević Vujnović A. A network-based analysis detects cocaine-induced changes in social interactions in Drosophila melanogaster. PLoS One 2023; 18:e0275795. [PMID: 36952449 PMCID: PMC10035901 DOI: 10.1371/journal.pone.0275795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 09/19/2022] [Indexed: 03/25/2023] Open
Abstract
Addiction is a multifactorial biological and behavioral disorder that is studied using animal models, based on simple behavioral responses in isolated individuals. A couple of decades ago it was shown that Drosophila melanogaster can serve as a model organism for behaviors related to alcohol, nicotine and cocaine (COC) addiction. Scoring of COC-induced behaviors in a large group of flies has been technologically challenging, so we have applied a local, middle and global level of network-based analyses to study social interaction networks (SINs) among a group of 30 untreated males compared to those that have been orally administered with 0.50 mg/mL of COC for 24 hours. In this study, we have confirmed the previously described increase in locomotion upon COC feeding. We have isolated new network-based measures associated with COC, and influenced by group on the individual behavior. COC fed flies showed a longer duration of interactions on the local level, and formed larger, more densely populated and compact, communities at the middle level. Untreated flies have a higher number of interactions with other flies in a group at the local level, and at the middle level, these interactions led to the formation of separated communities. Although the network density at the global level is higher in COC fed flies, at the middle level the modularity is higher in untreated flies. One COC specific behavior that we have isolated was an increase in the proportion of individuals that do not interact with the rest of the group, considered as the individual difference in COC induced behavior and/or consequence of group influence on individual behavior. Our approach can be expanded on different classes of drugs with the same acute response as COC to determine drug specific network-based measures and could serve as a tool to determinate genetic and environmental factors that influence both drug addiction and social interaction.
Collapse
Affiliation(s)
- Milan Petrović
- Department of Informatics, University of Rijeka, Rijeka, Croatia
- Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Rijeka, Croatia
| | - Ana Meštrović
- Department of Informatics, University of Rijeka, Rijeka, Croatia
- Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Rijeka, Croatia
| | - Rozi Andretić Waldowski
- Department of Biotechnology, Laboratory for behavioral genetics, University of Rijeka, Rijeka, Croatia
| | - Ana Filošević Vujnović
- Department of Biotechnology, Laboratory for behavioral genetics, University of Rijeka, Rijeka, Croatia
| |
Collapse
|
7
|
Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF. Towards the fully automated monitoring of ecological communities. Ecol Lett 2022; 25:2753-2775. [PMID: 36264848 PMCID: PMC9828790 DOI: 10.1111/ele.14123] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.
Collapse
Affiliation(s)
- Marc Besson
- School of Biological SciencesUniversity of BristolBristolUK,Sorbonne Université CNRS UMR Biologie des Organismes Marins, BIOMBanyuls‐sur‐MerFrance
| | - Jamie Alison
- Department of EcoscienceAarhus UniversityAarhusDenmark,UK Centre for Ecology & HydrologyBangorUK
| | - Kim Bjerge
- Department of Electrical and Computer EngineeringAarhus UniversityAarhusDenmark
| | - Thomas E. Gorochowski
- School of Biological SciencesUniversity of BristolBristolUK,BrisEngBio, School of ChemistryUniversity of BristolCantock's CloseBristolBS8 1TSUK
| | - Toke T. Høye
- Department of EcoscienceAarhus UniversityAarhusDenmark,Arctic Research CentreAarhus UniversityAarhusDenmark
| | - Tommaso Jucker
- School of Biological SciencesUniversity of BristolBristolUK
| | - Hjalte M. R. Mann
- Department of EcoscienceAarhus UniversityAarhusDenmark,Arctic Research CentreAarhus UniversityAarhusDenmark
| | | |
Collapse
|
8
|
de Bivort B, Buchanan S, Skutt-Kakaria K, Gajda E, Ayroles J, O’Leary C, Reimers P, Akhund-Zade J, Senft R, Maloney R, Ho S, Werkhoven Z, Smith MAY. Precise Quantification of Behavioral Individuality From 80 Million Decisions Across 183,000 Flies. Front Behav Neurosci 2022; 16:836626. [PMID: 35692381 PMCID: PMC9178272 DOI: 10.3389/fnbeh.2022.836626] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/22/2022] [Indexed: 01/18/2023] Open
Abstract
Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a "computational ethology natural history" study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.
Collapse
|
9
|
Long-term tracking and quantification of individual behavior in bumble bee colonies. ARTIFICIAL LIFE AND ROBOTICS 2022. [DOI: 10.1007/s10015-022-00762-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractSocial insects are ecologically dominant and provide vital ecosystem services. It is critical to understand collective responses of social insects such as bees to ecological perturbations. However, studying behavior of individual insects across entire colonies and across timescales relevant for colony performance (i.e., days or weeks) remains a central challenge. Here, we describe an approach for long-term monitoring of individuals within multiple bumble bee (Bombus spp.) colonies that combines the complementary strengths of multiple existing methods. Specifically, we combine (a) automated monitoring, (b) fiducial tag tracking, and (c) pose estimation to quantify behavior across multiple colonies over a 48 h period. Finally, we demonstrate the benefits of this approach by quantifying an important but subtle behavior (antennal activity) in bumble bee colonies, and how this behavior is impacted by a common environmental stressor (a neonicotinoid pesticide).
Collapse
|
10
|
Barlow IL, Feriani L, Minga E, McDermott-Rouse A, O'Brien TJ, Liu Z, Hofbauer M, Stowers JR, Andersen EC, Ding SS, Brown AEX. Megapixel camera arrays enable high-resolution animal tracking in multiwell plates. Commun Biol 2022; 5:253. [PMID: 35322206 PMCID: PMC8943053 DOI: 10.1038/s42003-022-03206-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 03/01/2022] [Indexed: 01/13/2023] Open
Abstract
Tracking small laboratory animals such as flies, fish, and worms is used for phenotyping in neuroscience, genetics, disease modelling, and drug discovery. An imaging system with sufficient throughput and spatiotemporal resolution would be capable of imaging a large number of animals, estimating their pose, and quantifying detailed behavioural differences at a scale where hundreds of treatments could be tested simultaneously. Here we report an array of six 12-megapixel cameras that record all the wells of a 96-well plate with sufficient resolution to estimate the pose of C. elegans worms and to extract high-dimensional phenotypic fingerprints. We use the system to study behavioural variability across wild isolates, the sensitisation of worms to repeated blue light stimulation, the phenotypes of worm disease models, and worms' behavioural responses to drug treatment. Because the system is compatible with standard multiwell plates, it makes computational ethological approaches accessible in existing high-throughput pipelines.
Collapse
Affiliation(s)
- Ida L Barlow
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Luigi Feriani
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Eleni Minga
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Adam McDermott-Rouse
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Thomas James O'Brien
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - Ziwei Liu
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | | | | | - Erik C Andersen
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA
| | - Siyu Serena Ding
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
- Max Planck Institute of Animal Behavior, Konstanz, Germany
| | - André E X Brown
- Institute of Clinical Sciences, Imperial College London, London, UK.
- MRC London Institute of Medical Sciences, London, UK.
| |
Collapse
|
11
|
Smith MAY, Honegger KS, Turner G, de Bivort B. Idiosyncratic learning performance in flies. Biol Lett 2022; 18:20210424. [PMID: 35104427 PMCID: PMC8807056 DOI: 10.1098/rsbl.2021.0424] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/21/2021] [Indexed: 11/18/2022] Open
Abstract
Individuals vary in their innate behaviours, even when they have the same genome and have been reared in the same environment. The extent of individuality in plastic behaviours, like learning, is less well characterized. Also unknown is the extent to which intragenotypic differences in learning generalize: if an individual performs well in one assay, will it perform well in other assays? We investigated this using the fruit fly Drosophila melanogaster, an organism long-used to study the mechanistic basis of learning and memory. We found that isogenic flies, reared in identical laboratory conditions, and subject to classical conditioning that associated odorants with electric shock, exhibit clear individuality in their learning responses. Flies that performed well when an odour was paired with shock tended to perform well when the odour was paired with bitter taste or when other odours were paired with shock. Thus, individuality in learning performance appears to be prominent in isogenic animals reared identically, and individual differences in learning performance generalize across some aversive sensory modalities. Establishing these results in flies opens up the possibility of studying the genetic and neural circuit basis of individual differences in learning in a highly suitable model organism.
Collapse
Affiliation(s)
- Matthew A.-Y. Smith
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Kyle S. Honegger
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA
| | - Glenn Turner
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Benjamin de Bivort
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| |
Collapse
|
12
|
Klibaite U, Shaevitz JW. Paired fruit flies synchronize behavior: Uncovering social interactions in Drosophila melanogaster. PLoS Comput Biol 2020; 16:e1008230. [PMID: 33021989 PMCID: PMC7567355 DOI: 10.1371/journal.pcbi.1008230] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 10/16/2020] [Accepted: 08/09/2020] [Indexed: 11/19/2022] Open
Abstract
Social behaviors are ubiquitous and crucial to an animal's survival and success. The behaviors an animal performs in a social setting are affected by internal factors, inputs from the environment, and interactions with others. To quantify social behaviors, we need to measure both the stochastic nature of the behavior of isolated individuals and how this behavioral repertoire changes as a function of the environment and interactions between individuals. We probed the behavior of male and female fruit flies in a circular arena as individuals and within all possible pairings. By combining measurements of the animals' position in the arena with an unsupervised analysis of their behaviors, we define the effects of position in the environment and the presence of a partner on locomotion, grooming, singing, and other behaviors that make up an animal's repertoire. We find that geometric context tunes behavioral preference, pairs of animals synchronize their behavioral preferences across shared trials, and paired individuals display signatures of behavioral mimicry.
Collapse
Affiliation(s)
- Ugne Klibaite
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA
| | - Joshua W Shaevitz
- Department of Physics, Princeton University, Princeton, New Jersey, USA
| |
Collapse
|
13
|
Individual, but not population asymmetries, are modulated by social environment and genotype in Drosophila melanogaster. Sci Rep 2020; 10:4480. [PMID: 32161330 PMCID: PMC7066193 DOI: 10.1038/s41598-020-61410-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 02/18/2020] [Indexed: 11/09/2022] Open
Abstract
Theory predicts that social interactions can induce an alignment of behavioral asymmetries between individuals (i.e., population-level lateralization), but evidence for this effect is mixed. To understand how interaction with other individuals affects behavioral asymmetries, we systematically manipulated the social environment of Drosophila melanogaster, testing individual flies and dyads (female-male, female-female and male-male pairs). In these social contexts we measured individual and population asymmetries in individual behaviors (circling asymmetry, wing use) and dyadic behaviors (relative position and orientation between two flies) in five different genotypes. We reasoned that if coordination between individuals drives alignment of behavioral asymmetries, greater alignment at the population-level should be observed in social contexts compared to solitary individuals. We observed that the presence of other individuals influenced the behavior and position of flies but had unexpected effects on individual and population asymmetries: individual-level asymmetries were strong and modulated by the social context but population-level asymmetries were mild or absent. Moreover, the strength of individual-level asymmetries differed between strains, but this was not the case for population-level asymmetries. These findings suggest that the degree of social interaction found in Drosophila is insufficient to drive population-level behavioral asymmetries.
Collapse
|
14
|
Williamson WR, Peek MY, Breads P, Coop B, Card GM. Tools for Rapid High-Resolution Behavioral Phenotyping of Automatically Isolated Drosophila. Cell Rep 2019; 25:1636-1649.e5. [PMID: 30404015 DOI: 10.1016/j.celrep.2018.10.048] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 08/06/2018] [Accepted: 10/11/2018] [Indexed: 02/07/2023] Open
Abstract
Sparse manipulation of neuron excitability during free behavior is critical for identifying neural substrates of behavior. Genetic tools for precise neuronal manipulation exist in the fruit fly, Drosophila melanogaster, but behavioral tools are still lacking to identify potentially subtle phenotypes only detectible using high-throughput and high spatiotemporal resolution. We developed three assay components that can be used modularly to study natural and optogenetically induced behaviors. FlyGate automatically releases flies one at a time into an assay. FlyDetect tracks flies in real time, is robust to severe occlusions, and can be used to track appendages, such as the head. GlobeDisplay is a spherical projection system covering the fly's visual receptive field with a single projector. We demonstrate the utility of these components in an integrated system, FlyPEZ, by comprehensively modeling the input-output function for directional looming-evoked escape takeoffs and describing a millisecond-timescale phenotype from genetic silencing of a single visual projection neuron type.
Collapse
Affiliation(s)
| | - Martin Y Peek
- Janelia Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Patrick Breads
- Janelia Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Brian Coop
- Janelia Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Gwyneth M Card
- Janelia Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA.
| |
Collapse
|
15
|
Graving JM, Chae D, Naik H, Li L, Koger B, Costelloe BR, Couzin ID. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 2019; 8:e47994. [PMID: 31570119 PMCID: PMC6897514 DOI: 10.7554/elife.47994;select dbms_pipe.receive_message(chr(79)||chr(103)||chr(106)||chr(65),5) from dual--] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023] Open
Abstract
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
Collapse
Affiliation(s)
- Jacob M Graving
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Daniel Chae
- Department of Computer SciencePrinceton UniversityPrincetonUnited States
| | - Hemal Naik
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
- Chair for Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
| | - Liang Li
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Benjamin Koger
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Blair R Costelloe
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Iain D Couzin
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| |
Collapse
|
16
|
Graving JM, Chae D, Naik H, Li L, Koger B, Costelloe BR, Couzin ID. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 2019; 8:e47994. [PMID: 31570119 PMCID: PMC6897514 DOI: 10.7554/elife.47994;select dbms_pipe.receive_message(chr(79)||chr(103)||chr(106)||chr(65),0) from dual--] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023] Open
Abstract
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
Collapse
Affiliation(s)
- Jacob M Graving
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Daniel Chae
- Department of Computer SciencePrinceton UniversityPrincetonUnited States
| | - Hemal Naik
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
- Chair for Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
| | - Liang Li
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Benjamin Koger
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Blair R Costelloe
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Iain D Couzin
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| |
Collapse
|
17
|
Graving JM, Chae D, Naik H, Li L, Koger B, Costelloe BR, Couzin ID. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 2019; 8:e47994. [PMID: 31570119 PMCID: PMC6897514 DOI: 10.7554/elife.47994] [Citation(s) in RCA: 240] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 09/18/2019] [Indexed: 12/24/2022] Open
Abstract
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
Collapse
Affiliation(s)
- Jacob M Graving
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Daniel Chae
- Department of Computer SciencePrinceton UniversityPrincetonUnited States
| | - Hemal Naik
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
- Chair for Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
| | - Liang Li
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Benjamin Koger
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Blair R Costelloe
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Iain D Couzin
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| |
Collapse
|
18
|
Patel DS, Xu N, Lu H. Digging deeper: methodologies for high-content phenotyping in Caenorhabditis elegans. Lab Anim (NY) 2019; 48:207-216. [PMID: 31217565 DOI: 10.1038/s41684-019-0326-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 05/17/2019] [Indexed: 11/09/2022]
Abstract
Deep phenotyping is an emerging conceptual paradigm and experimental approach aimed at measuring and linking many aspects of a phenotype to understand its underlying biology. To date, deep phenotyping has been applied mostly in cultured cells and used less in multicellular organisms. However, in the past decade, it has increasingly been recognized that deep phenotyping could lead to a better understanding of how genetics, environment and stochasticity affect the development, physiology and behavior of an organism. The nematode Caenorhabditis elegans is an invaluable model system for studying how genes affect a phenotypic trait, and new technologies have taken advantage of the worm's physical attributes to increase the throughput and informational content of experiments. Coupling of these technical advancements with computational and analytical tools has enabled a boom in deep-phenotyping studies of C. elegans. In this Review, we highlight how these new technologies and tools are digging into the biological origins of complex, multidimensional phenotypes.
Collapse
Affiliation(s)
- Dhaval S Patel
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Nan Xu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.,The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| |
Collapse
|
19
|
Nouvian M, Galizia CG. Aversive Training of Honey Bees in an Automated Y-Maze. Front Physiol 2019; 10:678. [PMID: 31231238 PMCID: PMC6558987 DOI: 10.3389/fphys.2019.00678] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 05/13/2019] [Indexed: 11/13/2022] Open
Abstract
Honeybees have remarkable learning abilities given their small brains, and have thus been established as a powerful model organism for the study of learning and memory. Most of our current knowledge is based on appetitive paradigms, in which a previously neutral stimulus (e.g., a visual, olfactory, or tactile stimulus) is paired with a reward. Here, we present a novel apparatus, the yAPIS, for aversive training of walking honey bees. This system consists in three arms of equal length and at 120° from each other. Within each arm, colored lights (λ = 375, 465 or 520 nm) or odors (here limonene or linalool) can be delivered to provide conditioned stimuli (CS). A metal grid placed on the floor and roof delivers the punishment in the form of mild electric shocks (unconditioned stimulus, US). Our training protocol followed a fully classical procedure, in which the bee was exposed sequentially to a CS paired with shocks (CS+) and to another CS not punished (CS-). Learning performance was measured during a second phase, which took advantage of the Y-shape of the apparatus and of real-time tracking to present the bee with a choice situation, e.g., between the CS+ and the CS-. Bees reliably chose the CS- over the CS+ after only a few training trials with either colors or odors, and retained this memory for at least a day, except for the shorter wavelength (λ = 375 nm) that produced mixed results. This behavior was largely the result of the bees avoiding the CS+, as no evidence was found for attraction to the CS-. Interestingly, trained bees initially placed in the CS+ spontaneously escaped to a CS- arm if given the opportunity, even though they could never do so during the training. Finally, honey bees trained with compound stimuli (color + odor) later avoided either components of the CS+. Thus, the yAPIS is a fast, versatile and high-throughput way to train honey bees in aversive paradigms. It also opens the door for controlled laboratory experiments investigating bimodal integration and learning, a field that remains in its infancy.
Collapse
Affiliation(s)
- Morgane Nouvian
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - C. Giovanni Galizia
- Department of Biology, University of Konstanz, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
| |
Collapse
|
20
|
Wu Q, Kumar N, Velagala V, Zartman JJ. Tools to reverse-engineer multicellular systems: case studies using the fruit fly. J Biol Eng 2019; 13:33. [PMID: 31049075 PMCID: PMC6480878 DOI: 10.1186/s13036-019-0161-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 04/07/2019] [Indexed: 01/08/2023] Open
Abstract
Reverse-engineering how complex multicellular systems develop and function is a grand challenge for systems bioengineers. This challenge has motivated the creation of a suite of bioengineering tools to develop increasingly quantitative descriptions of multicellular systems. Here, we survey a selection of these tools including microfluidic devices, imaging and computer vision techniques. We provide a selected overview of the emerging cross-talk between engineering methods and quantitative investigations within developmental biology. In particular, the review highlights selected recent examples from the Drosophila system, an excellent platform for understanding the interplay between genetics and biophysics. In sum, the integrative approaches that combine multiple advances in these fields are increasingly necessary to enable a deeper understanding of how to analyze both natural and synthetic multicellular systems.
Collapse
Affiliation(s)
- Qinfeng Wu
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Nilay Kumar
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Vijay Velagala
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Jeremiah J. Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556 USA
| |
Collapse
|
21
|
Crall JD, Switzer CM, Oppenheimer RL, Ford Versypt AN, Dey B, Brown A, Eyster M, Guérin C, Pierce NE, Combes SA, de Bivort BL. Neonicotinoid exposure disrupts bumblebee nest behavior, social networks, and thermoregulation. Science 2018; 362:683-686. [DOI: 10.1126/science.aat1598] [Citation(s) in RCA: 128] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 09/26/2018] [Indexed: 11/02/2022]
Abstract
Neonicotinoid pesticides can negatively affect bee colonies, but the behavioral mechanisms by which these compounds impair colony growth remain unclear. Here, we investigate imidacloprid’s effects on bumblebee worker behavior within the nest, using an automated, robotic platform for continuous, multicolony monitoring of uniquely identified workers. We find that exposure to field-realistic levels of imidacloprid impairs nursing and alters social and spatial dynamics within nests, but that these effects vary substantially with time of day. In the field, imidacloprid impairs colony thermoregulation, including the construction of an insulating wax canopy. Our results show that neonicotinoids induce widespread disruption of within-nest worker behavior that may contribute to impaired growth, highlighting the potential of automated techniques for characterizing the multifaceted, dynamic impacts of stressors on behavior in bee colonies.
Collapse
Affiliation(s)
- James D. Crall
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Planetary Health Alliance, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Callin M. Switzer
- eScience Institute, University of Washington, Seattle, WA, USA
- Department of Biology, University of Washington, Seattle, WA, USA
| | | | - Ashlee N. Ford Versypt
- School of Chemical Engineering, Oklahoma State University, Stillwater, OK, USA
- Interdisciplinary Toxicology Program, Oklahoma State University, Stillwater, OK, USA
| | - Biswadip Dey
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA
| | - Andrea Brown
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Mackay Eyster
- Biology Department, University of Massachusetts Amherst, Amherst, MA, USA
| | - Claire Guérin
- Department of Ecology and Evolution, Université de Lausanne, Lausanne, Switzerland
| | - Naomi E. Pierce
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Stacey A. Combes
- Department of Neurobiology, Physiology, and Behavior, University of California, Davis, Davis, CA, USA
| | - Benjamin L. de Bivort
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| |
Collapse
|