An imaging system for standardized quantitative analysis of C. elegans behavior

Fast detection of slender bodies in high density microscopy data

Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as swimming nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping slender bodies. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of swimming Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.

Acute Toxicity Evaluation of the Disinfectant Containing Percarbonate and Tetraacetylethylenediamine by Measuring Behavioral Responses of Small Fish Using Image Analysis

Disinfectants containing percarbonate and tetraacetylethylenediamine (TAED) has been developed as an effective and relatively safe disinfectant to destroy viruses and bacteria in animals and humans, however it is known that most disinfectants can cause danger to living organisms including humans. In the current study, acute toxicity of the disinfectant composed of percarbonate and TAED was assessed by measuring behavioral responses as well as lethal concentrations of aquatic organisms such as medaka and zebrafish when they were exposed to it. First, the breeding water properties were determined by measuring dissolved oxygen (DO) and pH changes over time up to 96 h in acute toxicity tests using the medaka, and the lethal concentration 50% (LC50, 88.39 ppm) was calculated using the lethality rate of the fish. This experiment was conducted in compliance with traditional OECD guidelines. Second, the assessment of behavioral responses (locomotive activity and swimming speed) with the zebrafish were assessed by the image analysis to capture the images per second for three hours, and the collected data were processed using image analysis to calculate the locomotive activity and swimming speed. Finally, the LC50 (135.76 ppm) of the disinfectant to the fish was also measured after three hours. Overall, the data revealed that LC50 of the disinfectant may be affected by the pH of the water exposed to the disinfectant, not by the DO in the water. In addition, the results from the image analysis indicated that the behavioral responses of the fish can further assess the acute toxicity of the disinfectant at concentrations below the LC50 and there was a relationship (R² = 0.85) between the behavioral responses and the survival rate of the fish.

Megapixel camera arrays enable high-resolution animal tracking in multiwell plates

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.

High temporal resolution measurements of movement reveal novel early-life physiological decline in C. elegans

Age-related physiological changes are most notable and best-studied late in life, while the nature of aging in early- or middle-aged individuals has not been explored as thoroughly. In C. elegans, many studies of movement vs. age generally focus on three distinct phases: sustained, youthful movement; onset of rapidly progressing impairment; and gross immobility. We investigated whether this first period of early-life adult movement is a sustained “healthy” level of high function followed by a discrete “movement catastrophe”—or whether there are early-life changes in movement that precede future physiological declines. To determine how movement varies during early adult life, we followed isolated individuals throughout life with a previously unachieved combination of duration and temporal resolution. By tracking individuals across the first six days of adulthood, we observed declines in movement starting as early as the first two days of adult life, as well as high interindividual variability in total daily movement. These findings suggest that movement is a highly dynamic behavior early in life, and that factors driving movement decline may begin acting as early as the first day of adulthood. Using simulation studies based on acquired data, we suggest that too-infrequent sampling in common movement assays limits observation of early-adult changes in motility, and we propose feasible strategies and a framework for designing assays with increased sensitivity for early movement declines.

Using Convolutional Neural Networks to Measure the Physiological Age of Caenorhabditis elegans

Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening and genetic screening studies. The effects of various antiaging interventions on the rate of aging in the early stage of the aging process can be determined based on the quantification of physiological age. However, in general, the age of C. elegans is evaluated via human visual inspection of morphological changes based on personal experience and subjective judgment. For example, the rate of motor activity decay has been used to predict lifespan in early- to mid-stage aging. Using image processing, the physiological age of C. elegans can be measured and then classified into periods or classes from childhood to elderhood (e.g., 3 periods comprising days 0-2, 4-6 and 10-12) by using texture entropy (Shamir, L. et al., 2009). Our dataset consists of 913 microscopic images of C. elegans, with approximately 60 images per day from day 1 to day 14 of adulthood. We present quantitative methods to measure the physiological age of C. elegans with convolution neural networks (CNNs), which can measure age with a granularity of days rather than periods. The methods achieved a mean absolute error (MAE) of less than 1 day for the measured age of C. elegans. In our experiments, we found that after training and testing our dataset, 5 popular CNN models, 50-layer residual network (ResNet50), InceptionV3, InceptionResNetV2, 16-layer Visual Geometry Group network (VGG16) and MobileNet, measured the physiological age of C. elegans with an average testing MAE of 1.58 days. Furthermore, based on the results, we propose two models, one model for linear regression analysis and the other model for logistic regression, that combine a CNN model and a new attribute: curved_or_straight. The linear regression analysis model achieved a test MAE of 0.94 days; the logistic regression model achieved an accuracy of 84.78 percent with an error tolerance of 1 day.

Mask R-CNN Based C. Elegans Detection with a DIY Microscope

Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an AP@0.5 IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.

Exogenous misfolded protein oligomers can cross the intestinal barrier and cause a disease phenotype in C. elegans

Misfolded protein oligomers are increasingly recognized as highly cytotoxic agents in a wide range of human disorders associated with protein aggregation. In this study, we assessed the possible uptake and resulting toxic effects of model protein oligomers administered to C. elegans through the culture medium. We used an automated machine-vision, high-throughput screening procedure to monitor the phenotypic changes in the worms, in combination with confocal microscopy to monitor the diffusion of the oligomers, and oxidative stress assays to detect their toxic effects. Our results suggest that the oligomers can diffuse from the intestinal lumen to other tissues, resulting in a disease phenotype. We also observed that pre-incubation of the oligomers with a molecular chaperone (αB-crystallin) or a small molecule inhibitor of protein aggregation (squalamine), reduced the oligomer absorption. These results indicate that exogenous misfolded protein oligomers can be taken up by the worms from their environment and spread across tissues, giving rise to pathological effects in regions distant from their place of absorbance.

Analyzing the Response Behavior of Lumbriculus variegatus (Oligochaeta: Lumbriculidae) to Different Concentrations of Copper Sulfate Based on Line Body Shape Detection and a Recurrent Self-Organizing Map

Point detection (e.g., the centroid of the body) of species has been conducted in numerous studies. However, line detection (i.e., the line body shape) of elongated species has rarely been investigated under stressful conditions. We analyzed the line movements of an Oligochaeta Lumbriculus variegatus in response to treatments with a toxic chemical, copper sulfate, at low concentrations (0.01 mg/L and 0.1 mg/L). The automatic line-tracking system was devised to identify the movement of body segments (body length) and the movements of segments (i.e., the speed and angles between segments) were recorded before and after treatment. Total body length was shortened from 31.22 (±5.18) mm to 20.91 (±4.65) mm after the 0.1 mg/L treatment. The Shannon entropy index decreased from 0.44 (±0.1) to 0.28 (±0.08) after treatment. On the other hand, the body and movement segments did not significantly change after the 0.01 mg/L treatment. Sequential movements of test organisms were further analyzed with a recurrent self-organizing map (RSOM) to determine the pattern of time-series line movements. The RSOM made it feasible to classify sequential behaviors of indicator organisms and identify various continuous body movements under stressful conditions.

Digging deeper: methodologies for high-content phenotyping in Caenorhabditis elegans

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.

Mass Surveilance of C. elegans-Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection

The nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach.

Powerful and interpretable behavioural features for quantitative phenotyping of Caenorhabditis elegans

Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provide a rich but high-dimensional representation of behaviour, and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine-learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here, we report a new set of handcrafted features to compactly quantify Caenorhabditis elegans behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild-type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets.

This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

Massively parallel C. elegans tracking provides multi-dimensional fingerprints for phenotypic discovery

BACKGROUND

The nematode worm C. elegans is a model organism widely used for studies of genetics and of human disease. The health and fitness of the worms can be quantified in different ways, such as by measuring their bending frequency, speed or lifespan. Manual assays, however, are time consuming and limited in their scope providing a strong motivation for automation.

NEW METHOD

We describe the development and application of an advanced machine vision system for characterising the behaviour of C. elegans, the Wide Field-of-View Nematode Tracking Platform (WF-NTP), which enables massively parallel data acquisition and automated multi-parameter behavioural profiling of thousands of worms simultaneously.

RESULTS

We screened more than a million worms from several established models of neurodegenerative disorders and characterised the effects of potential therapeutic molecules for Alzheimer's and Parkinson's diseases. By using very large numbers of animals we show that the sensitivity and reproducibility of behavioural assays is very greatly increased. The results reveal the ability of this platform to detect even subtle phenotypes.

COMPARISON WITH EXISTING METHODS

The WF-NTP method has substantially greater capacity compared to current automated platforms that typically either focus on characterising single worms at high resolution or tracking the properties of populations of less than 50 animals.

CONCLUSIONS

The WF-NTP extends significantly the power of existing automated platforms by combining enhanced optical imaging techniques with an advanced software platform. We anticipate that this approach will further extend the scope and utility of C. elegans as a model organism.

A network approach to discerning the identities of C. elegans in a free moving population

The study of C. elegans has led to ground-breaking discoveries in gene-function, neuronal circuits, and physiological responses. Subtle behavioral phenotypes, however, are often difficult to measure reproducibly. We have developed an experimental and computational infrastructure to simultaneously record and analyze the physical characteristics, movement, and social behaviors of dozens of interacting free-moving nematodes. Our algorithm implements a directed acyclic network that reconstructs the complex behavioral trajectories generated by individual C. elegans in a free moving population by chaining hundreds to thousands of short tracks into long contiguous trails. This technique allows for the high-throughput quantification of behavioral characteristics that require long-term observation of individual animals. The graphical interface we developed will enable researchers to uncover, in a reproducible manner, subtle time-dependent behavioral phenotypes that will allow dissection of the molecular mechanisms that give rise to organism-level behavior.

Computational Methods for Tracking, Quantitative Assessment, and Visualization of C. elegans Locomotory Behavior

The nematode Caenorhabditis elegans provides a unique opportunity to interrogate the neural basis of behavior at single neuron resolution. In C. elegans, neural circuits that control behaviors can be formulated based on its complete neural connection map, and easily assessed by applying advanced genetic tools that allow for modulation in the activity of specific neurons. Importantly, C. elegans exhibits several elaborate behaviors that can be empirically quantified and analyzed, thus providing a means to assess the contribution of specific neural circuits to behavioral output. Particularly, locomotory behavior can be recorded and analyzed with computational and mathematical tools. Here, we describe a robust single worm-tracking system, which is based on the open-source Python programming language, and an analysis system, which implements path-related algorithms. Our tracking system was designed to accommodate worms that explore a large area with frequent turns and reversals at high speeds. As a proof of principle, we used our tracker to record the movements of wild-type animals that were freshly removed from abundant bacterial food, and determined how wild-type animals change locomotory behavior over a long period of time. Consistent with previous findings, we observed that wild-type animals show a transition from area-restricted local search to global search over time. Intriguingly, we found that wild-type animals initially exhibit short, random movements interrupted by infrequent long trajectories. This movement pattern often coincides with local/global search behavior, and visually resembles Lévy flight search, a search behavior conserved across species. Our mathematical analysis showed that while most of the animals exhibited Brownian walks, approximately 20% of the animals exhibited Lévy flights, indicating that C. elegans can use Lévy flights for efficient food search. In summary, our tracker and analysis software will help analyze the neural basis of the alteration and transition of C. elegans locomotory behavior in a food-deprived condition.

A Generative Statistical Algorithm for Automatic Detection of Complex Postures

This paper presents a method for automated detection of complex (non-self-avoiding) postures of the nematode Caenorhabditis elegans and its application to analyses of locomotion defects. Our approach is based on progressively detailed statistical models that enable detection of the head and the body even in cases of severe coilers, where data from traditional trackers is limited. We restrict the input available to the algorithm to a single digitized frame, such that manual initialization is not required and the detection problem becomes embarrassingly parallel. Consequently, the proposed algorithm does not propagate detection errors and naturally integrates in a "big data" workflow used for large-scale analyses. Using this framework, we analyzed the dynamics of postures and locomotion of wild-type animals and mutants that exhibit severe coiling phenotypes. Our approach can readily be extended to additional automated tracking tasks such as tracking pairs of animals (e.g., for mating assays) or different species.

WormGender - Open-Source Software for Automatic Caenorhabditis elegans Sex Ratio Measurement

Fast and quantitative analysis of animal phenotypes is one of the major challenges of current biology. Here we report the WormGender open-source software, which is designed for accurate quantification of sex ratio in Caenorhabditis elegans. The software functions include, i) automatic recognition and counting of adult hermaphrodites and males, ii) a manual inspection feature that enables manual correction of errors, and iii) flexibility to use new training images to optimize the software for different imaging conditions. We evaluated the performance of our software by comparing manual and automated assessment of sex ratio. Our data showed that the WormGender software provided overall accurate sex ratio measurements. We further demonstrated the usage of WormGender by quantifying the high incidence of male (him) phenotype in 27 mutant strains. Mutants of nine genes (brc-1, C30G12.6, cep-1, coh-3, him-3, him-5, him-8, skr-1, unc-86) showed significant him phenotype. The WormGender is written in Java and can be installed and run on both Windows and Mac platforms. The source code is freely available together with a user manual and sample data at http://www.QuantWorm.org/. The source code and sample data are also available at http://dx.doi.org/10.6084/m9.figshare.1541248.

Model-independent phenotyping of C. elegans locomotion using scale-invariant feature transform

To uncover the genetic basis of behavioral traits in the model organism C. elegans, a common strategy is to study locomotion defects in mutants. Despite efforts to introduce (semi-)automated phenotyping strategies, current methods overwhelmingly depend on worm-specific features that must be hand-crafted and as such are not generalizable for phenotyping motility in other animal models. Hence, there is an ongoing need for robust algorithms that can automatically analyze and classify motility phenotypes quantitatively. To this end, we have developed a fully-automated approach to characterize C. elegans’ phenotypes that does not require the definition of nematode-specific features. Rather, we make use of the popular computer vision Scale-Invariant Feature Transform (SIFT) from which we construct histograms of commonly-observed SIFT features to represent nematode motility. We first evaluated our method on a synthetic dataset simulating a range of nematode crawling gaits. Next, we evaluated our algorithm on two distinct datasets of crawling C. elegans with mutants affecting neuromuscular structure and function. Not only is our algorithm able to detect differences between strains, results capture similarities in locomotory phenotypes that lead to clustering that is consistent with expectations based on genetic relationships. Our proposed approach generalizes directly and should be applicable to other animal models. Such applicability holds promise for computational ethology as more groups collect high-resolution image data of animal behavior.

An automated system for quantitative analysis of Drosophila larval locomotion

BACKGROUND

Drosophila larvae have been used as a model to study to genetic and cellular circuitries modulating behaviors. One of the challenges in behavioral study is the quantification of complex phenotypes such as locomotive behaviors. Experimental capability can be greatly enhanced by an automatic single-animal tracker that records an animal at a high resolution for an extended period, and analyzes multiple behavioral parameters.

RESULTS

Here we present MaggotTracker, a single-animal tracking system for Drosophila larval locomotion analysis. This system controls the motorized microscope stage while taking a video, so that the animal remains in the viewing center. It then reduces the animal to 13 evenly distributed points along the midline, and computes over 20 parameters evaluating the shape, peristalsis movement, stamina, and track of the animal. To demonstrate its utility, we applied MaggotTracker to analyze both wild-type and mutant animals to identify factors affecting locomotive behaviors. Each animal was tracked for four minutes. Our analysis on Canton-S third-instar larvae revealed that the distance an animal travelled was correlated to its striding speed rather than the percentage of time the animal spent striding, and that the striding speed was correlated to both the distance and the duration of one stride. Sexual dimorphism was observed in body length but not in locomotive parameters such as speed. Locomotive parameters were affected by animal developmental stage and the crawling surface. No significant changes in movement speed were detected in mutants of circadian genes such as period (per), timeout, and timeless (tim). The MaggotTracker analysis showed that ether a go-go (eag), Shaker (Sh), slowpoke (slo), and dunce (dnc) mutant larvae had severe phenotypes in multiple locomotive parameters such as stride distance and speed, consistent with their function in neuromuscular junctions. Further, the phenotypic patterns of the K(+) channel genes eag, Sh and slo are highly similar.

CONCLUSIONS

These results showed that MaggotTracker is an efficient tool for automatic phenotyping. The MaggotTracker software as well as the data presented here can be downloaded from our open-access site www.WormLoco.org/Mag.

Toward a science of computational ethology

The new field of “Computational Ethology” is made possible by advances in technology, mathematics, and engineering that allow scientists to automate the measurement and the analysis of animal behavior. We explore the opportunities and long-term directions of research in this area.

Robust tracking and quantification of C. elegans body shape and locomotion through coiling, entanglement, and omega bends

The behavior of the well-characterized nematode, Caenorhabditis elegans (C. elegans), is often used to study the neurologic control of sensory and motor systems in models of health and neurodegenerative disease. To advance the quantification of behaviors to match the progress made in the breakthroughs of genetics, RNA, proteins, and neuronal circuitry, analysis must be able to extract subtle changes in worm locomotion across a population. The analysis of worm crawling motion is complex due to self-overlap, coiling, and entanglement. Using current techniques, the scope of the analysis is typically restricted to worms to their non-occluded, uncoiled state which is incomplete and fundamentally biased. Using a model describing the worm shape and crawling motion, we designed a deformable shape estimation algorithm that is robust to coiling and entanglement. This model-based shape estimation algorithm has been incorporated into a framework where multiple worms can be automatically detected and tracked simultaneously throughout the entire video sequence, thereby increasing throughput as well as data validity. The newly developed algorithms were validated against 10 manually labeled datasets obtained from video sequences comprised of various image resolutions and video frame rates. The data presented demonstrate that tracking methods incorporated in WormLab enable stable and accurate detection of these worms through coiling and entanglement. Such challenging tracking scenarios are common occurrences during normal worm locomotion. The ability for the described approach to provide stable and accurate detection of C. elegans is critical to achieve unbiased locomotory analysis of worm motion.

Caenorhabditis elegans segmentation using texture-based models for motility phenotyping

With widening interests in using model organisms for reverse genetic approaches and biomimmetic microrobotics, motility phenotyping of the nematode Caenorhabditis elegans is expanding across a growing array of locomotive environments. One ongoing bottleneck lies in providing users with automatic nematode segmentations of C. elegans in image sequences featuring complex and dynamic visual cues, a first and necessary step prior to extracting motility phenotypes. Here, we propose to tackle such automatic segmentation challenges by introducing a novel texture factor model (TFM). Our approach revolves around the use of combined intensity- and texture-based features integrated within a probabilistic framework. This strategy first provides a coarse nematode segmentation from which a Markov random field model is used to refine the segmentation by inferring pixels belonging to the nematode using an approximate inference technique. Finally, informative priors can then be estimated and integrated in our framework to provide coherent segmentations across image sequences. We validate our TFM method across a wide range of motility environments. Not only does TFM assure comparative performances to existing segmentation methods on traditional environments featuring static backgrounds, it importantly provides state-of-the-art C. elegans segmentations for dynamic environments such as the recently introduced wet granular media. We show how such segmentations may be used to compute nematode "skeletons" from which motility phenotypes can then be extracted. Overall, our TFM method provides users with a tangible solution to tackle the growing needs of C. elegans segmentation in challenging motility environments.

Automated, high-throughput, motility analysis in Caenorhabditis elegans and parasitic nematodes: Applications in the search for new anthelmintics

The scale of the damage worldwide to human health, animal health and agricultural crops resulting from parasitic nematodes, together with the paucity of treatments and the threat of developing resistance to the limited set of widely-deployed chemical tools, underlines the urgent need to develop novel drugs and chemicals to control nematode parasites. Robust chemical screens which can be automated are a key part of that discovery process. Hitherto, the successful automation of nematode behaviours has been a bottleneck in the chemical discovery process. As the measurement of nematode motility can provide a direct scalar readout of the activity of the neuromuscular system and an indirect measure of the health of the animal, this omission is acute. Motility offers a useful assay for high-throughput, phenotypic drug/chemical screening and several recent developments have helped realise, at least in part, the potential of nematode-based drug screening. Here we review the challenges encountered in automating nematode motility and some important developments in the application of machine vision, statistical imaging and tracking approaches which enable the automated characterisation of nematode movement. Such developments facilitate automated screening for new drugs and chemicals aimed at controlling human and animal nematode parasites (anthelmintics) and plant nematode parasites (nematicides).

An integrated in vitro imaging platform for characterizing filarial parasite behavior within a multicellular microenvironment

Lymphatic Filariasis, a Neglected Tropical Disease, is caused by thread-like parasitic worms, including B. malayi, which migrate to the human lymphatic system following transmission. The parasites reside in collecting lymphatic vessels and lymph nodes for years, often resulting in lymphedema, elephantiasis or hydrocele. The mechanisms driving worm migration and retention within the lymphatics are currently unknown. We have developed an integrated in vitro imaging platform capable of quantifying B. malayi migration and behavior in a multicellular microenvironment relevant to the initial site of worm injection by incorporating the worm in a Polydimethylsiloxane (PDMS) microchannel in the presence of human dermal lymphatic endothelial cells (LECs) and human dermal fibroblasts (HDFs). The platform utilizes a motorized controllable microscope with CO2 and temperature regulation to allow for worm tracking experiments with high resolution over large length and time scales. Using post-acquisition algorithms, we quantified four parameters: 1) speed, 2) thrashing intensity, 3) percentage of time spent in a given cell region and 4) persistence ratio. We demonstrated the utility of our system by quantifying these parameters for L3 B. malayi in the presence of LECs and HDFs. Speed and thrashing increased in the presence of both cell types and were altered within minutes upon exposure to the anthelmintic drug, tetramisole. The worms displayed no targeted migration towards either cell type for the time course of this study (3 hours). When cells were not present in the chamber, worm thrashing correlated directly with worm speed. However, this correlation was lost in the presence of cells. The described platform provides the ability to further study B. malayi migration and behavior.

CeleST: computer vision software for quantitative analysis of C. elegans swim behavior reveals novel features of locomotion

In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim “gaits” in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes “graceful” from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible.

QuantWorm: a comprehensive software package for Caenorhabditis elegans phenotypic assays

Phenotypic assays are crucial in genetics; however, traditional methods that rely on human observation are unsuitable for quantitative, large-scale experiments. Furthermore, there is an increasing need for comprehensive analyses of multiple phenotypes to provide multidimensional information. Here we developed an automated, high-throughput computer imaging system for quantifying multiple Caenorhabditis elegans phenotypes. Our imaging system is composed of a microscope equipped with a digital camera and a motorized stage connected to a computer running the QuantWorm software package. Currently, the software package contains one data acquisition module and four image analysis programs: WormLifespan, WormLocomotion, WormLength, and WormEgg. The data acquisition module collects images and videos. The WormLifespan software counts the number of moving worms by using two time-lapse images; the WormLocomotion software computes the velocity of moving worms; the WormLength software measures worm body size; and the WormEgg software counts the number of eggs. To evaluate the performance of our software, we compared the results of our software with manual measurements. We then demonstrated the application of the QuantWorm software in a drug assay and a genetic assay. Overall, the QuantWorm software provided accurate measurements at a high speed. Software source code, executable programs, and sample images are available at www.quantworm.org. Our software package has several advantages over current imaging systems for C. elegans. It is an all-in-one package for quantifying multiple phenotypes. The QuantWorm software is written in Java and its source code is freely available, so it does not require use of commercial software or libraries. It can be run on multiple platforms and easily customized to cope with new methods and requirements.

Track-a-worm, an open-source system for quantitative assessment of C. elegans locomotory and bending behavior

A major challenge of neuroscience is to understand the circuit and gene bases of behavior. C. elegans is commonly used as a model system to investigate how various gene products function at specific tissue, cellular, and synaptic foci to produce complicated locomotory and bending behavior. The investigation generally requires quantitative behavioral analyses using an automated single-worm tracker, which constantly records and analyzes the position and body shape of a freely moving worm at a high magnification. Many single-worm trackers have been developed to meet lab-specific needs, but none has been widely implemented for various reasons, such as hardware difficult to assemble, and software lacking sufficient functionality, having closed source code, or using a programming language that is not broadly accessible. The lack of a versatile system convenient for wide implementation makes data comparisons difficult and compels other labs to develop new worm trackers. Here we describe Track-A-Worm, a system rich in functionality, open in source code, and easy to use. The system includes plug-and-play hardware (a stereomicroscope, a digital camera and a motorized stage), custom software written to run with Matlab in Windows 7, and a detailed user manual. Grayscale images are automatically converted to binary images followed by head identification and placement of 13 markers along a deduced spline. The software can extract and quantify a variety of parameters, including distance traveled, average speed, distance/time/speed of forward and backward locomotion, frequency and amplitude of dominant bends, overall bending activities measured as root mean square, and sum of all bends. It also plots worm travel path, bend trace, and bend frequency spectrum. All functionality is performed through graphical user interfaces and data is exported to clearly-annotated and documented Excel files. These features make Track-A-Worm a good candidate for implementation in other labs.

Systematic profiling of Caenorhabditis elegans locomotive behaviors reveals additional components in G-protein Gαq signaling

Genetic screens have been widely applied to uncover genetic mechanisms of movement disorders. However, most screens rely on human observations of qualitative differences. Here we demonstrate the application of an automatic imaging system to conduct a quantitative screen for genes regulating the locomotive behavior in Caenorhabditis elegans. Two hundred twenty-seven neuronal signaling genes with viable homozygous mutants were selected for this study. We tracked and recorded each animal for 4 min and analyzed over 4,400 animals of 239 genotypes to obtain a quantitative, 10-parameter behavioral profile for each genotype. We discovered 87 genes whose inactivation causes movement defects, including 50 genes that had never been associated with locomotive defects. Computational analysis of the high-content behavioral profiles predicted 370 genetic interactions among these genes. Network partition revealed several functional modules regulating locomotive behaviors, including sensory genes that detect environmental conditions, genes that function in multiple types of excitable cells, and genes in the signaling pathway of the G protein Gαq, a protein that is essential for animal life and behavior. We developed quantitative epistasis analysis methods to analyze the locomotive profiles and validated the prediction of the γ isoform of phospholipase C as a component in the Gαq pathway. These results provided a system-level understanding of how neuronal signaling genes coordinate locomotive behaviors. This study also demonstrated the power of quantitative approaches in genetic studies.

Light microscopy applications in systems biology: opportunities and challenges

Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology.

C. elegans tracking and behavioral measurement

We have developed instrumentation, image processing, and data analysis techniques to quantify the locomotory behavior of C. elegans as it crawls on the surface of an agar plate. For the study of the genetic, biochemical, and neuronal basis of behavior, C. elegans is an ideal organism because it is genetically tractable, amenable to microscopy, and shows a number of complex behaviors, including taxis, learning, and social interaction. Behavioral analysis based on tracking the movements of worms as they crawl on agar plates have been particularly useful in the study of sensory behavior, locomotion, and general mutational phenotyping. Our system works by moving the camera and illumination system as the worms crawls on a stationary agar plate, which ensures no mechanical stimulus is transmitted to the worm. Our tracking system is easy to use and includes a semi-automatic calibration feature. A challenge of all video tracking systems is that it generates an enormous amount of data that is intrinsically high dimensional. Our image processing and data analysis programs deal with this challenge by reducing the worms shape into a set of independent components, which comprehensively reconstruct the worms behavior as a function of only 3-4 dimensions. As an example of the process we show that the worm enters and exits its reversal state in a phase specific manner.

The proboscis extension reflex to evaluate learning and memory in honeybees (Apis mellifera): some caveats

The proboscis extension reflex (PER) is widely used in a classical conditioning (Pavlovian) context to evaluate learning and memory of a variety of insect species. The literature is particularly prodigious for honeybees (Apis mellifera) with more than a thousand publications. Imagination appears to be the only limit to the types of challenges to which researchers subject honeybees, including all the sensory modalities and a broad diversity of environmental treatments. Accordingly, some remarkable insights have been achieved using PER. However, there are several challenges to evaluating the PER literature that warrant a careful and thorough review. We assess here variation in methods that makes interpretation of studies, even those researching the same question, tenuous. We suggest that the numerous variables that might influence experimental outcomes from PER be thoroughly detailed by researchers. Moreover, the influence of individual variables on results needs to carefully evaluated, as well as among two or more variables. Our intent is to encourage investigation of the influence of numerous variables on PER results.

A liquid phase based C. elegans behavioral analysis system identifies motor activity loss in a nematode Parkinson's disease model

Motor activity of Caenorhabditis elegans is widely used to study the mechanisms ranging from basic neuronal functions to human neurodegenerative diseases. It may also serve as a paradigm to screen for potential therapeutic reagents treating these diseases. Here, we developed an automated, 96-well plate and liquid phase based system that quantifies nematode motor activity in real time. Using this system, we identified an adult-onset, ageing-associated motor activity loss in a transgenic nematode line expressing human pathogenic G2019S mutant LRRK2 (leucine-rich repeat kinase 2), the leading genetic cause of Parkinson's disease characterized by dopaminergic neurodegeneration associated motor deficient mainly in elder citizens. Thus, our system may be used as a platform to screen for potential therapeutic drugs treating Parkinson's disease. It can also be used to monitor motor activity of nematodes in liquid phase at similar scenario.

Bending amplitude - a new quantitative assay of C. elegans locomotion: identification of phenotypes for mutants in genes encoding muscle focal adhesion components

The nematode Caenorhabditis elegans uses striated muscle in its body wall for locomotion. The myofilament lattice is organized such that all the thin filament attachment structures (dense bodies, analogous to Z-disks) and thick filament organizing centers (M-lines) are attached to the muscle cell membrane. Thus, the force of muscle contraction is transmitted through these structures and allows locomotion of the worm. Dense bodies and M-lines are compositionally similar to focal adhesions and costameres, and are based on integrin and associated proteins. Null mutants for many of the newly discovered dense body and M-line proteins do not have obvious locomotion defects when observed casually, or when assayed by counting the number of times a worm moves back and forth in liquid. We hypothesized that many of these proteins, located as they are in muscle focal adhesions, function in force transmission, but we had not used an appropriate or sufficiently sensitive assay to reveal this function. Recently, we have developed a new quantitative assay of C. elegans locomotion that measures the maximum bending amplitude of an adult worm as it moves backwards. The assay had been used to reveal locomotion defects for null mutants of genes encoding ATN-1 (α-actinin) and PKN-1 (protein kinase N). Here, we describe the details of this method, and apply it to 21 loss of function mutants in 17 additional genes, most of which encode components of muscle attachment structures. As compared to wild type, mutants in 11 genes were found to have less ability to bend, and mutants in one gene were found to have greater ability to bend. Loss of function mutants for eight proteins had been reported to have normal locomotion (ZYX-1 (zyxin), ALP-1 (Enigma), DIM-1, SCPL-1), or locomotion that was not previously investigated (FRG-1 (FRG1), KIN-32 (focal adhesion kinase), LIM-8), or had only slightly decreased locomotion (PFN-3 (profilin)).

Preparation of samples for single-worm tracking

Neurobiological research in genetically tractable organisms relies heavily on robust assays for behavioral phenotypes. The simple body plan of the nematode Caenorhabditis elegans makes it particularly amenable to the use of automated microscopy and image analysis to describe behavioral patterns quantitatively. This protocol first describes the preparation and use of media for growing and maintaining worms for tracking. The second part of the protocol describes how to prepare a single young adult worm for recording during video analysis. Although the protocol was developed for use in a single-worm tracker, it addresses factors important for the generation of reproducible, standardized images in all systems.

A microfluidic platform for high-sensitivity, real-time drug screening on C. elegans and parasitic nematodes

This paper describes a new microfluidic platform for screening drugs and their dose response on the locomotion behavior of free living nematodes and parasitic nematodes. The system offers a higher sensitivity drug screening chip which employs a combination of existing and newly developed methods. Real-time observation of the entire drug application process (i.e. the innate pre-exposure locomotion, the transient response during drug exposure and the time-resolved, post-exposure behavior) at a single worm resolution is made possible. The chip enables the monitoring of four nematode parameters (number of worms responsive, number of worms leaving the drug well, average worm velocity and time until unresponsiveness). Each parameter generates an inherently different dose response; allowing for a higher resolution when screening for resistance. We expect this worm chip could be used as a robust cross species, cross drug platform. Existing nematode motility and migration assays do not offer this level of sophistication. The device comprises two principal components: behavioral microchannels to study nematode motility and a drug well for administering the dose and observing drug effects as a function of exposure time. The drug screening experiment can be described by three main steps: (i) 'pre-exposure study'- worms are inserted into the behavioral channels and their locomotion is characterized, (ii) 'dose exposure'- worms are guided from the behavioral microchannels into the drug well and held for a predefined time, during which time their transient response to the dose is characterized and (iii) 'post-exposure study'- worms are guided back into the behavioral microchannels where their locomotion (i.e. their time-resolved response to the dose) is characterized and compared to pre-exposure motility. The direction of nematodes' movement is reliably controlled by the application of an electric field within a defined range. Control experiments (e.g. in the absence of any drug) confirm that the applied electric fields do not affect the worms' motility or viability. We demonstrate the workability of the microfluidic platform on free living Caenorhabditis elegans (wild-type N2 and levamisole resistant ZZ15 lev-8) and parasitic Oesophagotomum dentatum (levamisole-sensitive, SENS and levamisole-resistant, LEVR) using levamisole (a well-studied anthelmintic) as the test drug. The proposed scheme of drug screening on a microfluidic device is expected to significantly improve the resolution, sensitivity and data throughput of in vivo testing, while offering new details on the transient and time-resolved exposure effects of new and existing anthelmintics.

High-throughput behavioral analysis in C. elegans

We have designed a real-time computer vision system, the Multi-Worm Tracker (MWT), that can simultaneously quantify the behavior of dozens of Caenorhabditis elegans on a traditional petri plate at video rates. Three traditional behavioral paradigms are examined using this system: spontaneous movement on food, where the behavior changes over tens of minutes; chemotaxis, where turning events must be detected accurately to determine strategy; and habituation of response to tap, where the response is stochastic and changes over time. In each case, manual analysis or automated single-worm tracking would be tedious and time-consuming, but the MWT system allows rapid quantification of behavior with minimal human effort. Thus, this system will enablelarge scale forward and reverse genetic screens for complex behaviors.

Profiling a Caenorhabditis elegans behavioral parametric dataset with a supervised K-means clustering algorithm identifies genetic networks regulating locomotion

Defining genetic networks underlying animal behavior in a high throughput manner is an important but challenging task that has not yet been achieved for any organism. Using Caenorhabditis elegans, we collected quantitative parametric data related to various aspects of locomotion from wild type and 31 mutant worm strains with single mutations in genes functioning in sensory reception, neurotransmission, G-protein signaling, neuromuscular control or other facets of motor regulation. We applied unsupervised and constrained K-means clustering algorithms to the data and found that the genes that clustered together due to the behavioral similarity of their mutants encoded proteins in the same signaling networks. This approach provides a framework to identify genes and genetic networks underlying worm neuromotor function in a high-throughput manner. A publicly accessible database harboring the visual and quantitative behavioral data collected in this study adds valuable information to the rapidly growing C. elegans databanks that can be employed in a similar context.

Real-time multimodal optical control of neurons and muscles in freely behaving Caenorhabditis elegans

The ability to optically excite or silence specific cells using optogenetics has become a powerful tool to interrogate the nervous system. Optogenetic experiments in small organisms have mostly been performed using whole-field illumination and genetic targeting, but these strategies do not always provide adequate cellular specificity. Targeted illumination can be a valuable alternative but it has only been shown in motionless animals without the ability to observe behavior output. We present a real-time, multimodal illumination technology that allows both tracking and recording the behavior of freely moving C. elegans while stimulating specific cells that express channelrhodopsin-2 or MAC. We used this system to optically manipulate nodes in the C. elegans touch circuit and study the roles of sensory and command neurons and the ultimate behavioral output. This technology enhances our ability to control, alter, observe and investigate how neurons, muscles and circuits ultimately produce behavior in animals using optogenetics.

Enhancement of odor avoidance regulated by dopamine signaling in Caenorhabditis elegans

The enhancement of sensory responses after prior exposure to a stimulus is a fundamental mechanism of neural function in animals. Its molecular basis, however, has not been studied in as much depth as the reduction of sensory responses, such as adaptation or habituation. We report here that the avoidance behavior of the nematode Caenorhabditis elegans in response to repellent odors (2-nonanone or 1-octanol) is enhanced rather than reduced after preexposure to the odors. This enhancement effect of preexposure was maintained for at least 1 h after the conditioning. The enhancement of 2-nonanone avoidance was not dependent on the presence or absence of food during conditioning, which generally functions as a strong positive or negative unconditioned stimulus in the animals. These results suggest that the enhancement is acquired as a type of nonassociative learning. In addition, genetic and pharmacological analyses revealed that the enhancement of 2-nonanone avoidance requires dopamine signaling via D(2)-like dopamine receptor DOP-3, which functions in a pair of RIC interneurons to regulate the enhancement. Because dopamine signaling has been tightly linked with food-related information to modulate various behaviors of C. elegans, it may play different role in the regulation of the enhancement of 2-nonanone avoidance. Thus, our data suggest a new genetic and pharmacological paradigm for nonassociative enhancement of neural responses that is regulated by dopamine signaling.

Multi-environment model estimation for motility analysis of Caenorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Microfluidic bioassay to characterize parasitic nematode phenotype and anthelmintic resistance

With increasing resistance to anti-parasitic drugs, it has become more important to detect and recognize phenotypes of resistant isolates. Molecular methods of detecting resistant isolates are limited at present. Here, we introduce a microfluidic bioassay to measure phenotype using parameters of nematode locomotion. We illustrate the technique on larvae of an animal parasite Oesophagostomum dentatum. Parameters of sinusoidal motion such as propagation velocity, wavelength, wave amplitude, and oscillation frequency depended on the levamisole-sensitivity of the isolate of parasitic nematode. The levamisole-sensitive isolate (SENS) had a mean wave amplitude of 135 μm, which was larger than 123 μm of the levamisole-resistant isolate (LEVR). SENS had a mean wavelength of 373 μm, which was less than 393 μm of LEVR. The mean propagation velocity of SENS, 149 μm s-1, was similar to LEVR, 143 μm s-1. The propagation velocity of the isolates was inhibited by levamisole in a concentration-dependent manner above 0.5 μm. The EC50 for SENS was 3 μm and the EC50 for LEVR was 10 μm. This microfluidic technology advances present-day nematode migration assays and provides a better quantification and increased drug sensitivity. It is anticipated that the bioassay will facilitate study of resistance to other anthelmintic drugs that affect locomotion.

Material properties of Caenorhabditis elegans swimming at low Reynolds number

Undulatory locomotion, as seen in the nematode Caenorhabditis elegans, is a common swimming gait of organisms in the low Reynolds number regime, where viscous forces are dominant. Although the nematode's motility is expected to be a strong function of its material properties, measurements remain scarce. Here, the swimming behavior of C. elegans is investigated in experiments and in a simple model. Experiments reveal that nematodes swim in a periodic fashion and generate traveling waves that decay from head to tail. The model is able to capture the experiments' main features and is used to estimate the nematode's Young's modulus E and tissue viscosity eta. For wild-type C. elegans, we find E approximately 3.77 kPa and eta approximately -860 Pa.s; values of eta for live C. elegans are negative because the tissue is generating rather than dissipating energy. Results show that material properties are sensitive to changes in muscle functional properties, and are useful quantitative tools with which to more accurately describe new and existing muscle mutants.

Discovery of veterinary antiparasitic agents in the 21st century: a view from industry

Discovery of antiparasitic agents is a challenging process, requiring discovery of molecules with the ability to kill parasites but not their hosts. Customer preference is for fewer doses and ease of application, but this is not always compatible with reduced withdrawal times, human food safety and/or user safety. This article describes some of the difficulties faced by researchers in the search for new antiparasitic agents, while highlighting advances that may improve the discovery process and the chance of success in discovering novel drugs.

LRRK2-mediated neurodegeneration and dysfunction of dopaminergic neurons in a Caenorhabditis elegans model of Parkinson's disease

Mutations in LRRK2 are thus far the most frequent known cause of autosomal dominant and idiopathic Parkinson's disease (PD) with prevalent mutations being found within the GTPase (R1441C/G) and kinase (G2019S) domains. Previous in vitro studies have revealed that R1441C and G2019S mutations are associated with increased kinase activity. To better understand LRRK2-linked PD pathogenesis in vivo, we have generated transgenic C. elegans overexpressing human LRRK2 wild type, R1441C and G2019S in dopaminergic (DA) neurons. Overexpression of these LRRK2 proteins causes age-dependent DA neurodegeneration, behavioral deficits, and locomotor dysfunction that are accompanied by a reduction of dopamine levels in vivo. In comparison, R1441C and G2019S mutants cause more severe phenotypes than the wild type protein. Interestingly, treatment with exogenous dopamine rescues the LRRK2-induced behavioral and locomotor phenotypes. In contrast, expression of the GTP binding defective mutant, K1347A, or knockout of the C. elegans LRRK2 homolog, LRK-1, prevents the LRRK2-induced neurodegeneration and behavioral abnormalities. Hence, our transgenic LRRK2 C. elegans models recapitulate key features of PD including progressive neurodegeneration, impairment of dopamine-dependent behavior and locomotor function, and reduction in dopamine levels. Furthermore, our findings provide strong support for the critical role of GTPase/kinase activity in LRRK2-linked pathologies. These invertebrate models will be useful for studying pathogenesis of PD and for development of potential therapeutics for the disease.

AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of Caenorhabditis elegans

Background

The pharyngeal microcircuit of the nematode Caenorhabditis elegans serves as a model for analysing neural network activity and is amenable to electrophysiological recording techniques. One such technique is the electropharyngeogram (EPG) which has provided insight into the genetic basis of feeding behaviour, neurotransmission and muscle excitability. However, the detailed manual analysis of the digital recordings necessary to identify subtle differences in activity that reflect modulatory changes within the underlying network is time consuming and low throughput. To address this we have developed an automated system for the high-throughput and discrete analysis of EPG recordings (AutoEPG).

Methodology/Principal Findings

AutoEPG employs a tailor made signal processing algorithm that automatically detects different features of the EPG signal including those that report on the relaxation and contraction of the muscle and neuronal activity. Manual verification of the detection algorithm has demonstrated AutoEPG is capable of very high levels of accuracy. We have further validated the software by analysing existing mutant strains with known pharyngeal phenotypes detectable by the EPG. In doing so, we have more precisely defined an evolutionarily conserved role for the calcium-dependent potassium channel, SLO-1, in modulating the rhythmic activity of neural networks.

Conclusions/Significance

AutoEPG enables the consistent analysis of EPG recordings, significantly increases analysis throughput and allows the robust identification of subtle changes in the electrical activity of the pharyngeal nervous system. It is anticipated that AutoEPG will further add to the experimental tractability of the C. elegans pharynx as a model neural circuit.

An Automated Micropositioning System for Investigating C. Elegans Locomotive Behavior

This paper presents a visually servoed micropositioning system capable of automatically extracting locomotive features of Caenorhabditis elegans online at a full 30 Hz. The employment of Gaussian Pyramid Level-2 images significantly reduces the image size by 16-fold and permits real-time feature extraction, without sacrificing accuracy due to the cubic smoothing spline fitting. The automated micropositioning system is capable of revealing subtle differences in locomotive behavior across strains. A total of 128 worms of four C. elegans strains with different numbers of muscle arms were continuously tracked for 3 min per sample, and locomotive features were extracted online. Validated by experiments, the innovation in image analysis, or data reduction without sacrificing accuracy allows for rapid, online, and accurate analysis of streaming videos of C. elegans and other similar microorganisms.

Fast, automated measurement of nematode swimming (thrashing) without morphometry

Background

The "thrashing assay", in which nematodes are placed in liquid and the frequency of lateral swimming ("thrashing") movements estimated, is a well-established method for measuring motility in the genetic model organism Caenorhabditis elegans as well as in parasitic nematodes. It is used as an index of the effects of drugs, chemicals or mutations on motility and has proved useful in identifying mutants affecting behaviour. However, the method is laborious, subject to experimenter error, and therefore does not permit high-throughput applications. Existing automation methods usually involve analysis of worm shape, but this is computationally demanding and error-prone. Here we present a novel, robust and rapid method of automatically counting the thrashing frequency of worms that avoids morphometry but nonetheless gives a direct measure of thrashing frequency. Our method uses principal components analysis to remove the background, followed by computation of a covariance matrix of the remaining image frames from which the interval between statistically-similar frames is estimated.

Results

We tested the performance of our covariance method in measuring thrashing rates of worms using mutations that affect motility and found that it accurately substituted for laborious, manual measurements over a wide range of thrashing rates. The algorithm used also enabled us to determine a dose-dependent inhibition of thrashing frequency by the anthelmintic drug, levamisole, illustrating the suitability of the system for assaying the effects of drugs and chemicals on motility. Furthermore, the algorithm successfully measured the actions of levamisole on a parasitic nematode, Haemonchus contortus, which undergoes complex contorted shapes whilst swimming, without alterations in the code or of any parameters, indicating that it is applicable to different nematode species, including parasitic nematodes. Our method is capable of analyzing a 30 s movie in less than 30 s and can therefore be deployed in rapid screens.

Conclusion

We demonstrate that a covariance-based method yields a fast, reliable, automated measurement of C. elegans motility which can replace the far more time-consuming, manual method. The absence of a morphometry step means that the method can be applied to any nematode that swims in liquid and, together with its speed, this simplicity lends itself to deployment in large-scale chemical and genetic screens.

Drosophotoxicology: the growing potential for Drosophila in neurotoxicology

Understanding neurotoxic mechanisms is a challenge of deciphering which genes and gene products in the developing or mature nervous system are targeted for disruption by chemicals we encounter in our environment. Our understanding of nervous system development and physiology is highly advanced due in large part to studies conducted in simple non-mammalian models. The paucity of toxicological data for the more than 80,000 chemicals in commercial use today, and the approximately 2000 new chemicals introduced each year, makes development of sensitive and rapid assays to screen for neurotoxicity paramount. In this article I advocate the use of Drosophila in the modern regimen of toxicological testing, emphasizing its unique attributes for assaying neurodevelopment and behavior. Features of the Drosophila model are reviewed and a generalized overall scheme for its use in toxicology is presented. Examples of where the fly has proven fruitful in evaluating common toxicants in our environment are also highlighted. Attention is drawn to three areas where development and application of the fly model might benefit toxicology the most: 1) optimizing sensitive endpoints for pathway-specific screening, 2) accommodating high throughput demands for analysis of chemical toxicant libraries, 3) optimizing genetic and molecular protocols for more rapid identification toxicant-by-gene interactions. While there are shortcomings in the Drosophila model, which exclude it from effective toxicological testing in certain arenas, conservation of fundamental cellular and developmental mechanisms between flies and man is extensive enough to warrant a central role for the Drosophila model in toxicological testing of today.