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Thane M, Paisios E, Stöter T, Krüger AR, Gläß S, Dahse AK, Scholz N, Gerber B, Lehmann DJ, Schleyer M. High-resolution analysis of individual Drosophila melanogaster larvae uncovers individual variability in locomotion and its neurogenetic modulation. Open Biol 2023; 13:220308. [PMID: 37072034 PMCID: PMC10113034 DOI: 10.1098/rsob.220308] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/05/2023] [Indexed: 04/20/2023] Open
Abstract
Neuronally orchestrated muscular movement and locomotion are defining faculties of multicellular animals. Due to its simple brain and genetic accessibility, the larva of the fruit fly Drosophila melanogaster allows one to study these processes at tractable levels of complexity. However, although the faculty of locomotion clearly pertains to the individual, most studies of locomotion in larvae use measurements aggregated across animals, or animals tested one by one, an extravagance for larger-scale analyses. This prevents grasping the inter- and intra-individual variability in locomotion and its neurogenetic determinants. Here, we present the IMBA (individual maggot behaviour analyser) for analysing the behaviour of individual larvae within groups, reliably resolving individual identity across collisions. We use the IMBA to systematically describe the inter- and intra-individual variability in locomotion of wild-type animals, and how the variability is reduced by associative learning. We then report a novel locomotion phenotype of an adhesion GPCR mutant. We further investigated the modulation of locomotion across repeated activations of dopamine neurons in individual animals, and the transient backward locomotion induced by brief optogenetic activation of the brain-descending 'mooncrawler' neurons. In summary, the IMBA is an easy-to-use toolbox allowing an unprecedentedly rich view of the behaviour and its variability of individual larvae, with utility in multiple biomedical research contexts.
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Affiliation(s)
- Michael Thane
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Simulation and Graphics, Otto von Guerike University, Magdeburg, Germany
| | - Emmanouil Paisios
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Torsten Stöter
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anna-Rosa Krüger
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Institute of Biology, Free University of Berlin, Berlin, Germany
| | - Sebastian Gläß
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anne-Kristin Dahse
- Division of General Biochemistry, Rudolf Schönheimer Institute of Biochemistry, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Nicole Scholz
- Division of General Biochemistry, Rudolf Schönheimer Institute of Biochemistry, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Bertram Gerber
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Institute of Biology, Otto von Guericke University Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Dirk J. Lehmann
- Department of Simulation and Graphics, Otto von Guerike University, Magdeburg, Germany
- Department for Information Engineering, Faculty of Computer Science, Ostfalia University of Applied Science, Brunswick-Wolfenbuettel, Germany
| | - Michael Schleyer
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
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Giraldo D, Adden A, Kuhlemann I, Gras H, Geurten BRH. Correcting locomotion dependent observation biases in thermal preference of Drosophila. Sci Rep 2019; 9:3974. [PMID: 30850647 PMCID: PMC6408449 DOI: 10.1038/s41598-019-40459-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 02/18/2019] [Indexed: 11/21/2022] Open
Abstract
Sensing environmental temperatures is essential for the survival of ectothermic organisms. In Drosophila, two of the most used methodologies to study temperature preferences (TP) and the genes involved in thermosensation are two-choice assays and temperature gradients. Whereas two-choice assays reveal a relative TP, temperature gradients can identify the absolute Tp. One drawback of gradients is that small ectothermic animals are susceptible to cold-trapping: a physiological inability to move at the cold area of the gradient. Often cold-trapping cannot be avoided, biasing the resulting TP to lower temperatures. Two mathematical models were previously developed to correct for cold-trapping. These models, however, focus on group behaviour which can lead to overestimation of cold-trapping due to group aggregation. Here we present a mathematical model that simulates the behaviour of individual Drosophila in temperature gradients. The model takes the spatial dimension and temperature difference of the gradient into account, as well as the rearing temperature of the flies. Furthermore, it allows the quantification of cold-trapping and reveals unbiased TP. Additionally, our model reveals that flies have a range of tolerable temperatures, and this measure is more informative about the behaviour than commonly used TP. Online simulation is hosted at http://igloo.uni-goettingen.de. The code can be accessed at https://github.com/zerotonin/igloo.
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Affiliation(s)
- Diego Giraldo
- Department for Cellular Neurobiology, Institute for Zoology and Anthropology, Georg-August University Göttingen, Göttingen, Germany
| | - Andrea Adden
- Department for Cellular Neurobiology, Institute for Zoology and Anthropology, Georg-August University Göttingen, Göttingen, Germany.,Vision Group, Department of Biology, Lund University, Lund, Sweden
| | - Ilyas Kuhlemann
- Department for Biophysical Chemistry, Institute for Physical Chemistry, Georg-August University Göttingen, Göttingen, Germany
| | - Heribert Gras
- Department for Cellular Neurobiology, Institute for Zoology and Anthropology, Georg-August University Göttingen, Göttingen, Germany
| | - Bart R H Geurten
- Department for Cellular Neurobiology, Institute for Zoology and Anthropology, Georg-August University Göttingen, Göttingen, Germany.
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Berh D, Scherzinger A, Otto N, Jiang X, Klämbt C, Risse B. Automatic non-invasive heartbeat quantification of Drosophila pupae. Comput Biol Med 2018; 93:189-199. [DOI: 10.1016/j.compbiomed.2017.12.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/18/2017] [Accepted: 12/20/2017] [Indexed: 10/18/2022]
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Risse B, Berh D, Otto N, Klämbt C, Jiang X. FIMTrack: An open source tracking and locomotion analysis software for small animals. PLoS Comput Biol 2017; 13:e1005530. [PMID: 28493862 PMCID: PMC5444858 DOI: 10.1371/journal.pcbi.1005530] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 05/25/2017] [Accepted: 04/20/2017] [Indexed: 11/18/2022] Open
Abstract
Imaging and analyzing the locomotion behavior of small animals such as Drosophila larvae or C. elegans worms has become an integral subject of biological research. In the past we have introduced FIM, a novel imaging system feasible to extract high contrast images. This system in combination with the associated tracking software FIMTrack is already used by many groups all over the world. However, so far there has not been an in-depth discussion of the technical aspects. Here we elaborate on the implementation details of FIMTrack and give an in-depth explanation of the used algorithms. Among others, the software offers several tracking strategies to cover a wide range of different model organisms, locomotion types, and camera properties. Furthermore, the software facilitates stimuli-based analysis in combination with built-in manual tracking and correction functionalities. All features are integrated in an easy-to-use graphical user interface. To demonstrate the potential of FIMTrack we provide an evaluation of its accuracy using manually labeled data. The source code is available under the GNU GPLv3 at https://github.com/i-git/FIMTrack and pre-compiled binaries for Windows and Mac are available at http://fim.uni-muenster.de.
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Affiliation(s)
- Benjamin Risse
- School of Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Dimitri Berh
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Nils Otto
- Department of Behaviour and Neuronal Biology, University of Münster, Münster, Germany
| | - Christian Klämbt
- Department of Behaviour and Neuronal Biology, University of Münster, Münster, Germany
| | - Xiaoyi Jiang
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
- * E-mail:
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Kunc M, Arefin B, Hyrsl P, Theopold U. Monitoring the effect of pathogenic nematodes on locomotion of Drosophila larvae. Fly (Austin) 2017. [PMID: 28631995 PMCID: PMC5552269 DOI: 10.1080/19336934.2017.1297350] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
One of the key factors that determine the interaction between hosts and their parasites is the frequency of their interactions, which depends on the locomotory behavior of both parts. To address host behavior we used natural infections involving insect pathogenic nematodes and Drosophila melanogaster larvae as hosts. Using a modified version of a recently described method (FIMTrack) to assess several parameters in larger sets of animals, we initially detected specific differences in larval food searching when comparing Drosophila strains. These differences were further influenced by the presence of nematodes. Given a choice, Drosophila larvae clearly avoided nematodes irrespective of their genetic background. Our newly developed methods will be useful to test candidate genes and pathways involved in host/pathogen interactions in general and to assess specific parameters of their interaction.
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Affiliation(s)
- Martin Kunc
- a Department of Molecular Biosciences, The Wenner-Gren Institute (MBW) , Stockholm University , Stockholm , Sweden.,b Department of Animal Physiology and Immunology, Institute of Experimental Biology, Faculty of Science , Masaryk University , Brno , Czech Republic
| | - Badrul Arefin
- a Department of Molecular Biosciences, The Wenner-Gren Institute (MBW) , Stockholm University , Stockholm , Sweden
| | - Pavel Hyrsl
- b Department of Animal Physiology and Immunology, Institute of Experimental Biology, Faculty of Science , Masaryk University , Brno , Czech Republic
| | - Ulrich Theopold
- a Department of Molecular Biosciences, The Wenner-Gren Institute (MBW) , Stockholm University , Stockholm , Sweden
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Berh D, Risse B, Michels T, Otto N, Klambt C. An FIM-Based Long-Term In-Vial Monitoring System for Drosophila Larvae. IEEE Trans Biomed Eng 2016; 64:1862-1874. [PMID: 28113288 DOI: 10.1109/tbme.2016.2628203] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Drosophila larvae are an insightful model and the automated analysis of their behavior is an integral readout in behavioral biology. Current tracking systems, however, entail a disturbance of the animals, are labor-intensive, and cannot be easily used for long-term monitoring purposes. Here, we present a novel monitoring system for Drosophila larvae, which allows us to analyze the animals in cylindrical culture vials. By utilizing the frustrated total internal reflection in combination with a multi-camera/microcomputer setup, we image the complete housing vial surface and, thus, the larvae for days. We introduce a calibration scheme to stitch the images from the multi-camera system and unfold arbitrary cylindrical surfaces to support different vials. As a result, imaging and analysis of a whole population can be done implicitly. For the first time, this allows us to extract long-term activity quantities of larvae without disturbing the animals. We demonstrate the capabilities of this new setup by automatically quantifying the activity of multiple larvae moving in a vial. The accuracy of the system and the spatio-temporal resolution are sufficient to obtain motion trajectories and higher level features, such as body bending. This new setup can be used for in-vial activity monitoring and behavioral analysis and is capable of gathering millions of data points without both disturbing the animals and increasing labor time. In total, we have analyzed 107 671 frames resulting in 8650 trajectories, which are longer than 30 s, and obtained more than 4.2 × 106 measurements.
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Risse B, Otto N, Berh D, Kiel M, Klambt C. FIM$^{2c\;}$: Multicolor, Multipurpose Imaging System to Manipulate and Analyze Animal Behavior. IEEE Trans Biomed Eng 2016; 64:610-620. [PMID: 28113210 DOI: 10.1109/tbme.2016.2570598] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
In vivo whole-body imaging of small animals plays an important role for biomedical studies. In particular, animals like the fruit fly Drosophila melanogaster or the nematode Caenorhabditis elegans are popular model organisms for preclinical research since they offer sophisticated genetic tool-kits. Recording these translucent animals with high contrast in a large arena is however not trivial. Furthermore, fluorescent proteins are widely used to mark cells in vivo and report their functions. This paper introduces a novel optical imaging technique called FIM2c enabling simultaneous detection of the animals posture and movement as well as fluorescent markers like green fluorescent protein (GFP). FIM2c utilizes frustrated total internal reflection of two distinct wavelengths and captures both, reflected and emitted light. The resultant two-color high-contrast images are superb compared to other imaging systems for larvae or worms. This multipurpose method enables a large variety of different experimental approaches. For example, FIM2c can be used to image GFP positive cells/tissues/animals and supports the integration of fluorescent tracers into multitarget tracking paradigms. Moreover, optogenetic tools can be applied in large-scale behavioral analysis to manipulate and study neuronal functions. To demonstrate the benefit of our system, we use FIM2c to resolve colliding larvae in a high-throughput approach, which was impossible given the existing tools. Finally, we present a comprehensive database including images and locomotion features of more than 1300 resolved collisions available for the community. In conclusion, FIM2c is a versatile tool for advanced imaging and locomotion analysis for a variety of different model organisms.
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Risse B, Otto N, Berh D, Jiang X, Klämbt C. FIM imaging and FIMtrack: two new tools allowing high-throughput and cost effective locomotion analysis. J Vis Exp 2014:52207. [PMID: 25591081 PMCID: PMC4354465 DOI: 10.3791/52207] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The analysis of neuronal network function requires a reliable measurement of behavioral traits. Since the behavior of freely moving animals is variable to a certain degree, many animals have to be analyzed, to obtain statistically significant data. This in turn requires a computer assisted automated quantification of locomotion patterns. To obtain high contrast images of almost translucent and small moving objects, a novel imaging technique based on frustrated total internal reflection called FIM was developed. In this setup, animals are only illuminated with infrared light at the very specific position of contact with the underlying crawling surface. This methodology results in very high contrast images. Subsequently, these high contrast images are processed using established contour tracking algorithms. Based on this, we developed the FIMTrack software, which serves to extract a number of features needed to quantitatively describe a large variety of locomotion characteristics. During the development of this software package, we focused our efforts on an open source architecture allowing the easy addition of further modules. The program operates platform independent and is accompanied by an intuitive GUI guiding the user through data analysis. All locomotion parameter values are given in form of csv files allowing further data analyses. In addition, a Results Viewer integrated into the tracking software provides the opportunity to interactively review and adjust the output, as might be needed during stimulus integration. The power of FIM and FIMTrack is demonstrated by studying the locomotion of Drosophila larvae.
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Affiliation(s)
- Benjamin Risse
- Institute of Neuro and Behavioral Biology, Westfälische Wilhelms-Universität Münster; Department of Mathematics and Computer Science, Westfälische Wilhelms-Universität Münster
| | - Nils Otto
- Institute of Neuro and Behavioral Biology, Westfälische Wilhelms-Universität Münster
| | - Dimitri Berh
- Institute of Neuro and Behavioral Biology, Westfälische Wilhelms-Universität Münster; Department of Mathematics and Computer Science, Westfälische Wilhelms-Universität Münster
| | - Xiaoyi Jiang
- Department of Mathematics and Computer Science, Westfälische Wilhelms-Universität Münster
| | - Christian Klämbt
- Institute of Neuro and Behavioral Biology, Westfälische Wilhelms-Universität Münster;
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