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Layana Castro PE, Garví AG, Sánchez-Salmerón AJ. Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences. Heliyon 2023; 9:e14715. [PMID: 37025880 PMCID: PMC10070602 DOI: 10.1016/j.heliyon.2023.e14715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
Pose estimation of C. elegans in image sequences is challenging and even more difficult in low-resolution images. Problems range from occlusions, loss of worm identity, and overlaps to aggregations that are too complex or difficult to resolve, even for the human eye. Neural networks, on the other hand, have shown good results in both low-resolution and high-resolution images. However, training in a neural network model requires a very large and balanced dataset, which is sometimes impossible or too expensive to obtain. In this article, a novel method for predicting C. elegans poses in cases of multi-worm aggregation and aggregation with noise is proposed. To solve this problem we use an improved U-Net model capable of obtaining images of the next aggregated worm posture. This neural network model was trained/validated using a custom-generated dataset with a synthetic image simulator. Subsequently, tested with a dataset of real images. The results obtained were greater than 75% in precision and 0.65 with Intersection over Union (IoU) values.
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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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Walker AC, Bhargava R, Brust AS, Owji AA, Czyż DM. Time-off-pick Assay to Measure Caenorhabditis elegans Motility. Bio Protoc 2022; 12:e4436. [PMID: 35864904 PMCID: PMC9257836 DOI: 10.21769/bioprotoc.4436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/05/2022] [Accepted: 05/10/2022] [Indexed: 12/29/2022] Open
Abstract
Caenorhabditis elegans is a simple metazoan that is often used as a model organism to study various human ailments with impaired motility phenotypes, including protein conformational diseases. Numerous motility assays that measure neuro-muscular function have been employed using C. elegans . Here, we describe "time-off-pick" (TOP), a novel assay for assessing motility in C. elegans . TOP is conducted by sliding an eyebrow hair under the mid-section of the worm and counting the number of seconds it takes for the worm to crawl completely off. The time it takes for the worm to crawl off the eyebrow hair is proportional to the severity of its motility defect. Other readouts of motility include crawling or swimming phenotypes, and although widely established, have some limitations. For example, worms that are roller mutants are less suitable for crawling or swimming assays. We demonstrated that our novel TOP assay is sensitive to age-dependent changes in motility, thus, providing another more inclusive method to assess motor function in C. elegans . Graphical abstract: Conceptual overview of the "time-off-pick" (TOP) assay. Various C. elegans models exhibit age-dependent defects in motility. The time it takes for a worm to crawl off of an eyebrow pick that is slid under its mid-section is measured in TOP seconds. A greater TOP is indicative of a greater motility defect. Eventually, worms with phenotypes that lead to paralysis will not be able to leave the pick.
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Affiliation(s)
- Alyssa C. Walker
- Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA
| | - Rohan Bhargava
- Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA
| | - Amanda S. Brust
- Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA
| | - Ali A. Owji
- Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA
| | - Daniel M. Czyż
- Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA
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*For correspondence:
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Layana Castro PE, Puchalt JC, García Garví A, Sánchez-Salmerón AJ. Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm. SENSORS (BASEL, SWITZERLAND) 2021; 21:5622. [PMID: 34451062 PMCID: PMC8402443 DOI: 10.3390/s21165622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/13/2021] [Accepted: 08/18/2021] [Indexed: 11/29/2022]
Abstract
Automatic tracking of Caenorhabditis elegans (C. egans) in standard Petri dishes is challenging due to high-resolution image requirements when fully monitoring a Petri dish, but mainly due to potential losses of individual worm identity caused by aggregation of worms, overlaps and body contact. To date, trackers only automate tests for individual worm behaviors, canceling data when body contact occurs. However, essays automating contact behaviors still require solutions to this problem. In this work, we propose a solution to this difficulty using computer vision techniques. On the one hand, a skeletonization method is applied to extract skeletons in overlap and contact situations. On the other hand, new optimization methods are proposed to solve the identity problem during these situations. Experiments were performed with 70 tracks and 3779 poses (skeletons) of C. elegans. Several cost functions with different criteria have been evaluated, and the best results gave an accuracy of 99.42% in overlapping with other worms and noise on the plate using the modified skeleton algorithm and 98.73% precision using the classical skeleton algorithm.
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Affiliation(s)
| | | | | | - Antonio-José Sánchez-Salmerón
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain; (P.E.L.C.); (J.C.P.); (A.G.G.)
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Layana Castro PE, Puchalt JC, Sánchez-Salmerón AJ. Improving skeleton algorithm for helping Caenorhabditis elegans trackers. Sci Rep 2020; 10:22247. [PMID: 33335258 PMCID: PMC7746747 DOI: 10.1038/s41598-020-79430-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 12/08/2020] [Indexed: 11/09/2022] Open
Abstract
One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.
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Affiliation(s)
- Pablo E Layana Castro
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
| | - Joan Carles Puchalt
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
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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.
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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.
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Automated behavioural analysis reveals the basic behavioural repertoire of the urochordate Ciona intestinalis. Sci Rep 2019; 9:2416. [PMID: 30787329 PMCID: PMC6382837 DOI: 10.1038/s41598-019-38791-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 01/09/2019] [Indexed: 12/17/2022] Open
Abstract
Quantitative analysis of animal behaviour in model organisms is becoming an increasingly essential approach for tackling the great challenge of understanding how activity in the brain gives rise to behaviour. Here we used automated image-based tracking to extract behavioural features from an organism of great importance in understanding the evolution of chordates, the free-swimming larval form of the tunicate Ciona intestinalis, which has a compact and fully mapped nervous system composed of only 231 neurons. We analysed hundreds of videos of larvae and we extracted basic geometric and physical descriptors of larval behaviour. Importantly, we used machine learning methods to create an objective ontology of behaviours for C. intestinalis larvae. We identified eleven behavioural modes using agglomerative clustering. Using our pipeline for quantitative behavioural analysis, we demonstrate that C. intestinalis larvae exhibit sensory arousal and thigmotaxis. Notably, the anxiotropic drug modafinil modulates thigmotactic behaviour. Furthermore, we tested the robustness of the larval behavioural repertoire by comparing different rearing conditions, ages and group sizes. This study shows that C. intestinalis larval behaviour can be broken down to a set of stereotyped behaviours that are used to different extents in a context-dependent manner.
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Alves LGA, Winter PB, Ferreira LN, Brielmann RM, Morimoto RI, Amaral LAN. Long-range correlations and fractal dynamics in C. elegans: Changes with aging and stress. Phys Rev E 2017; 96:022417. [PMID: 28950588 PMCID: PMC6011659 DOI: 10.1103/physreve.96.022417] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Indexed: 12/21/2022]
Abstract
Reduced motor control is one of the most frequent features associated with aging and disease. Nonlinear and fractal analyses have proved to be useful in investigating human physiological alterations with age and disease. Similar findings have not been established for any of the model organisms typically studied by biologists, though. If the physiology of a simpler model organism displays the same characteristics, this fact would open a new research window on the control mechanisms that organisms use to regulate physiological processes during aging and stress. Here, we use a recently introduced animal-tracking technology to simultaneously follow tens of Caenorhabdits elegans for several hours and use tools from fractal physiology to quantitatively evaluate the effects of aging and temperature stress on nematode motility. Similar to human physiological signals, scaling analysis reveals long-range correlations in numerous motility variables, fractal properties in behavioral shifts, and fluctuation dynamics over a wide range of timescales. These properties change as a result of a superposition of age and stress-related adaptive mechanisms that regulate motility.
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Affiliation(s)
- Luiz G A Alves
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
- Department of Physics, State University of Maringá, Maringá, PR 87020-900, Brazil
- National Institute of Science and Technology for Complex Systems, CNPq, Rio de Janeiro, RJ 22290-180, Brazil
| | - Peter B Winter
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
| | - Leonardo N Ferreira
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP 13566-590, Brazil
| | - Renée M Brielmann
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois 60208, USA
| | - Richard I Morimoto
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois 60208, USA
| | - Luís A N Amaral
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208, USA
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