Journal of Neurogenetics

Olfactory conditioning in Drosophila melanogaster is a widely used behavioral paradigm for uncovering the neural basis of learning and memory. The traditional T-maze assay, however, relies on group-level assessments and cannot examine individual flies learning and memory abilities. To address this limitation, we developed the Individual Drosophila Olfactory Conditioner (iDOC), an apparatus for single-fly conditioning and memory assessment. Using iDOC, we demonstrated aversive olfactory learning, short-term memory (STM), anesthesia-resistant memory (ARM), and protein synthesis-dependent long-term memory (LTM) in individual flies, consistent with previous experiments conducted using the T-maze. As an individual memory assessment method, iDOC can be integrated with other behavioral assays at the single animal level, such as sleep monitoring. Notably, we found that spaced training, which induces LTM, also promotes post-learning sleep, while massed training, which induces ARM, did not. iDOC thus provides a powerful platform for investigating learning and memory and its interplay with other behavioral processes at the individual animal level. HighlightsO_LIDeveloped iDOC for high-throughput, single-fly aversive olfactory training and memory assessment C_LIO_LIDemonstrated aversive learning, STM, ARM, and LTM in individual flies using iDOC C_LIO_LISpaced conditioning, and not massed conditioning, promotes sleep C_LI

The neural basis of behaviour is identified by systematically disrupting the activity of specific neurons and screening for loss in phenotype. Robust, high-scoring behavioural assays are thus necessary for identifying the neural circuits of novel behaviours. Here, we report the design and use of a Y-maze based classical olfactory learning and memory assay in Drosophila. Appetitive memory scores in our Y-mazes are considerably better and longer-lasting than that from a commonly used T-maze design. We found that the mechanism that traps flies in their choice of an odour is mainly responsible for the improving scores in the Y-mazes. Using Y-mazes, we could assay significant 24 h gustatory aversive memories in flies. These aversive memories are susceptible to protein synthesis inhibitor cycloheximide (CXM) and therefore embodies long-term memory (LTM). When anaesthesia resistant memory (ARM) deficient radish mutant flies are trained with dry sucrose, 24 h memory is severely disrupted. However, when we trained with 2 M sucrose-agar and tested in Y-mazes, radish mutants exhibited a residual 24 appetitive memory. This memory is not ARM, and we show that it is not CXM sensitive LTM either. It could be a third form of appetitive consolidated memory in flies. The Y-maze assembly described here is particularly sensitive and will thus enable the study of new memory phenotypes in Drosophila.

Measurements of Drosophila fecundity are used in a wide variety of studies, such as investigations of stem cell biology, nutrition, behavior, and toxicology. In addition, because fecundity assays are performed on live flies, they are suitable for longitudinal studies such as investigations of aging or prolonged chemical exposure. However, standard Drosophila fecundity assays have been difficult to perform in a high-throughput manner because experimental factors such as the physiological state of the flies and environmental cues must be carefully controlled to achieve consistent results. In addition, exposing flies to a large number of different experimental conditions (such as chemical additives in the diet) and manually counting the number of eggs laid to determine the impact on fecundity is time-consuming. We have overcome these challenges by combining a new multiwell fly culture strategy with a novel 3D-printed fly transfer device to rapidly and accurately transfer flies from one plate to another; the RoboCam, a low-cost, custom built robotic camera to capture images of the wells automatically; and an image segmentation pipeline to automatically identify and quantify eggs. We show that this method is compatible with robust and consistent egg laying throughout the assay period; and demonstrate that the automated pipeline for quantifying fecundity is very accurate (r2 = 0.98 for the correlation between the automated egg counts and the ground truth) In addition, we show that this method can be used to efficiently detect the effects on fecundity induced by dietary exposure to chemicals. Taken together, this strategy substantially increases the efficiency and reproducibility of high throughput egg laying assays that require exposing flies to multiple different media conditions.

There are thousands of Mendelian diseases with more being discovered weekly and the majority have no approved treatments. To address this need, we require scalable approaches that are relatively inexpensive compared to traditional drug development. In the absence of a validated drug target, phenotypic screening in model organisms provides a route for identifying candidate treatments. Success requires a screenable phenotype, however the right phenotype and assay may not be obvious for pleiotropic neuromuscular disorders. Here we show that high-throughput imaging and quantitative phenotyping can be conducted systematically on a panel of C. elegans disease model strains. We used CRISPR genome-editing to create 25 worm models of human Mendelian diseases and phenotyped them using a single standardised assay. All but two strains were significantly different from wild-type controls in at least one feature. The observed phenotypes were diverse, but mutations of genes predicted to have related functions in their human orthologs led to similar behavioural differences in worms. As a proof-of-concept, we performed a drug repurposing screen of an FDA approved compound library, and identified two compounds that rescued the behavioural phenotype of a model of UNC80 deficiency. Our results show that a single assay to measure multiple phenotypes can be applied systematically to diverse Mendelian disease models. The relatively short time and low cost associated with creating and phenotyping multiple strains suggests that high-throughput worm tracking could provide a scalable approach to drug repurposing commensurate with the number of Mendelian diseases.

The fruit fly Drosophila melanogaster offers a powerful model to study how diet affects the body and brain. However, existing methods for measuring their food intake often rely on dyes or tags mixed with food, which can be inaccurate due to how the flies absorb and eliminate them. Capillary-based assays like CAFE directly measure consumption, but only work with liquids and shorten fly lifespan. Additionally, capillary assays are incompatible with delivering viscous foods like high-fat diets. Even solidified high-fat diets tend to be sticky death traps for flies. Another longstanding challenge for fly researchers is that dietary restriction in flies involves diluting food, leading to compensatory feeding. To address these shortcomings, we have developed DIETS, a sensitive feeding assay that can be implemented even in low-resource settings. DIETS eliminates the need for labels and directly weighs the solid food consumed by small groups of flies over extended periods of hours to weeks. It allows us to deliver precise amounts of food to flies and implement accurate dietary restrictions. Importantly, DIETS is compatible with studying energy-dense high-fat diets. Using DIETS, we observed that, unlike a high-sugar diet, an isocaloric high-fat diet did not improve the flies ability to withstand starvation, even though they consumed more calories and had higher fat deposition.

We present a highly reproducible method for investigating the startle flight responses of wild type Drosophila melanogaster to light-off stimuli, using the automated Zantiks MWP unit. The built-in, live video-tracking of the Zantiks unit measured distance travelled between frames for 24 flies after light-off stimuli, whilst providing video-recordings of each startle. Using light-off stimuli which elicited peak startling, we found evidence for habituation of the startle response after only a few consecutive trials. Distance travelled on startle trials was reduced when a prepulse stimulus of shorter duration was introduced before the light-off stimulus, providing behavioural evidence for prepulse inhibition (PPI). Deficits in habituation and PPI are linked to various psychiatric disorders and our method holds great potential for use alongside genetic and pharmacological manipulations. Here, we demonstrate the capability of this highly automated, high throughput technology to streamline behavioural research on Drosophila, using a replicable, controlled environment.

Animals can move towards or away from an odorant. Such chemotaxis has been used as a paradigm for learning when coupled with pre-exposure to the sensed odorant. Here we develop an assay for the nematode C. elegans that avoids the typical use of chemical or physical immobilization when measuring the response of worms to odorants. Using two sets of rectangular arenas that are oriented such that worms in one set must move in the opposite direction to worms in the other set for the same response, we found that unfed worms show reproducible movement towards the odorants butanone and benzaldehyde, and away from the odorant nonanone. In addition to the use of opposing orientations to control for gradients of unknown cues outside the arena, we introduce a measure of dispersal to control for locomotion defects and unknown cues within the arena. Since this assay avoids the use of paralytics or physical constraints, it is useful for the analysis of graded responses to a variety of chemicals and the discovery of underlying molecular mechanisms. Using this setup, we found that pre-exposure of unfed worms to butanone to induce an association of starvation with butanone resulted in different extents of such associative learning during different trials - from no learning to learned avoidance. Given this variation in associative learning despite the artificially controlled lab setting, we speculate that in dynamic natural environments such learning might be rare and highlight the challenge in discovering evolutionarily selected mechanisms that could underlie learning in the wild.

AbstractOptogenetic tools have revolutionized the study of neuronal circuits in Caenorhabditis elegans. The expression of light-sensitive ion channels or pumps under specific promotors allows researchers to modify the behavior of excitable cells. Several optogenetic systems have been developed to spatially and temporally photoactivate light-sensitive actuators in C. elegans. Nevertheless, their high costs and low flexibility have limited wide access to optogenetics. Here, we developed an inexpensive, easy-to-build, and adjustable optogenetics device for use on different microscopes and worm trackers, called the OptoArm. The OptoArm allows for single- and multiple-worm illumination and is adaptable in terms of light intensity, lighting profiles and light-color. We demonstrate the OptoArms power in a population-based study on contributions of motor circuit cells to age-related motility decline. We find that functional decline of cholinergic neurons mirrors motor decline, while GABAergic neurons and muscle cells are relatively age-resilient, suggesting that rate-limiting cells exist and determine neuronal circuit aging.

Drosophila innate response to gravity, geotaxis, has been previously used to assess the impact of aging and disease on motor performance. Despite its rich history, fly geotaxis continues to be largely measured manually and assessed through simplistic metrics. The manual nature of this assay introduces substantial experimental variability while simplistic metrics provide limited analytic insights into the behavior. To address these shortcomings, we have constructed a fully automated, programable apparatus, and developed a multi-object tracking software capable of following sub-second movements of individual flies, thus allowing reproducible, detailed, and quantitative analysis of geotactic behavior. The apparatus triggers and monitors geotaxis of 10 fly cohorts simultaneously, with each cohort consisting of up to 7 flies. The tracking program isolates cohorts and records individual fly coordinate outputs allowing for simultaneous multi-group, multifly tracks per experiment, greatly improving throughput and resolution. The algorithm tracks individual flies during the entire run with [~]97% accuracy, yielding detailed climbing curve, speed, and movement direction with 1/30 second resolution. Our tracking also allows the construction of multi-variable metrics and the detection of transitory movement phenotypes, such as slips and falls, which have thus far been neglected in geotaxis studies due to limited spatio-temporal resolution. Through a combination of automation and robust tracking, the platform is therefore poised to advance Drosophila geotaxis assay into a comprehensive assessment of locomotor behavior.

The genetic tractability, well-mapped circuitry, and diverse behavioral repertoire of the nematode C. elegans make it an ideal model for physiological and behavioral studies. A wide range of methods has been developed for analyzing C. elegans behaviors, evolving with advances in technology such as videography and computer-assisted analysis. However, unlike organisms with distinct body features such as limbs and wings, the contour of C. elegans is rather uniform, posing unique challenges for automated analyses of C. elegans behavior. Here, we introduce LabGym--an open-source, artificial intelligence (AI)-based platform we recently developed--to the C. elegans research community. We trained deep learning models in LabGym capable of automatically categorizing and quantifying multiple user-defined parameters of worm locomotion behavior in multi-worm videos with high accuracy. Furthermore, we demonstrated their efficacy in quantifying locomotion changes in aging worms. Our work offers a cost-effective, user-accessible, and comprehensive approach to behavioral analysis in C. elegans.

Tracking small laboratory animals such as flies, fish, and worms is used for phenotyping in neuroscience, genetics, disease modelling, and drug discovery. Current imaging systems are limited either in spatial resolution or throughput. A system capable of imaging a large number of animals with sufficient resolution to estimate their pose would enable a new class of experiments where detailed behavioural differences are quantified but at a scale where hundreds of treatments can be tested simultaneously. Here we report a new imaging system consisting of an array of six 12-megapixel cameras that can simultaneously record from all the wells of a 96-well plate with a resolution of 80 pixels/mm at 25 frames per second. We show that this resolution is sufficient to estimate the pose of nematode worms including head identification 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 and greatly increases the scale of possible phenotypic screening experiments in C. elegans.

The ability to generate and recall memory is a behavior that is evolutionarily conserved across the animal kingdom from humans to jellyfish. Memory not only allows previous experiences to inform future decision making, but it also amasses information essential to life, such as memory of quality food sources, shelter, and predator-related associations. Associative memory forms a relationship between two or more distinct and initially unrelated stimuli and can be defined by its temporal characteristics, such as short- and long-term duration, as well as the memory being appetitive or aversive, generating approach or avoidance behavior, respectively. Since its introduction as a memory model in the 1970s, the fruit fly, Drosophila melanogaster, has emerged as a powerful tool for the investigation of memory-related processes. While a variety of memory paradigms have been used extensively in Drosophila, such as appetitive and aversive olfactory memory, the use of appetitive visual memory remains infrequent. A previous study introduced a visual short-term memory (STM) paradigm that could be used for the study of both appetitive and aversive visual memory in Drosophila. However, this protocol required 50+ flies per condition, with three conditions per experiment, and 15 or more replications were frequently used to assess memory. As a result, this paradigm requires substantial numbers of flies, time, and is impractical for large genetic screens. Here, building upon this previous work, we describe an optimized appetite visual STM paradigm in freely moving Drosophila. Using recently published data on sexual dimorphism, innate color preferences, and borrowing practices from related appetitive assays, we have established an approach that minimizes confounding factors, such as sexually dimorphic starvation survival and sucrose preference, as well as pre-training color preference variation between groups. In doing so, we present an appetitive visual STM paradigm requiring substantially fewer replicates and numbers of flies to produce significant learning.

Most insects, including agricultural pests and disease vectors, rely on olfaction for key innate behaviors. Consequently, there is growing interest in studying insect olfaction to gain insights into odor-driven behavior and to support efforts in vector control. Calcium imaging using GCaMP fluorescence is widely used to identify olfactory receptor neurons (ORNs) responsive to ethologically relevant odors. However, accurate interpretation of GCaMP signals in the antenna requires understanding both response uniformity within an ORN population and how calcium signals relate to spike activity. To address this, we optimized a dual-modality recording method combining single-sensillum electrophysiology and widefield imaging for Drosophila ORNs. Calcium imaging showed that homotypic ab2A neurons exhibit similar odor sensitivity, consistent with spike recordings, indicating that a single ORNs response can reliably represent its homotypic counterparts. Furthermore, concurrent dual recordings revealed that peak calcium responses are linearly correlated with spike activity, regardless of imaging site (soma or dendrites), GCaMP variant, odorant, or fly age. These findings validate the use of somatic calcium signals as a reliable proxy for spike activity in fly ORNs and provide a foundation for future large-scale surveys of spike-calcium response relationships across diverse ORN types.

Bang-sensitive (BS) Drosophila mutants exhibit a stereotyped pattern of seizure behavior after mechanical disturbances. We previously identified mutations in the julius seizure (jus) gene, formerly CG14509, can induce BS seizures. However, the behavioral manifestations of the seizure phenotype of the various jus mutants have not been fully characterized. Here, we developed a machine learning pipeline featuring LASC (Long short-term memory and Attention mechanism for Sequence Classification) for automatic phenotyping of jus mutant videos. LASC achieves 90% classification accuracy in distinguishing five phases: paralysis (P), tonic seizure (T), spasm (S), clonic seizure (C), and recovery (R). Applying the trained LASC model to multiple jus lines showed they use a common repertoire of seizure stages and followed the general P[->]T[->]S[->]C[->]R progression, but each genotype exhibited unique patterns of stage duration and transition probabilities. Remarkably, stage usage patterns are distinct among the mutant genotypes. These findings establish that while all jus mutants adhere to stereotyped behavioral rules, each allele generates a distinct signature in stage usage. This work demonstrates how advanced behavioral quantification can reveal previously hidden relationships between gene mutation and complex motor outputs. More broadly, the complete pipeline presented here can pave the way for high-throughput, automated drug screening for epilepsy.

Similar to other animals, the fly, Drosophila melanogaster, changes its foraging strategy from exploration to exploitation upon encountering a nutrient-rich food source. However, the impact of metabolic state or taste/nutrient value on exploration vs. exploitation decisions in flies is poorly understood. Here, we developed a one-source foraging assay that uses automated video tracking coupled with high-resolution measurements of food ingestion to investigate the behavioral variables flies use when foraging for food with different taste/caloric values and when in different metabolic states. We found that flies alter their foraging and ingestive behaviors based on their hunger state and the concentration of the sucrose solution. Interestingly, sugar-blind flies did not transition from exploration to exploitation upon finding a high-concentration sucrose solution, suggesting that taste sensory input, as opposed to post-ingestive nutrient feedback, plays a crucial role in determining the foraging decisions of flies. Using a Generalized Linear Model (GLM), we showed that hunger state and sugar volume ingested, but not the nutrient or taste value of the food, influence flies radial distance to the food source, a strong indicator of exploitation. Our behavioral paradigm and theoretical framework offer a promising avenue for investigating the neural mechanisms underlying state and value-based foraging decisions in flies, setting the stage for systematically identifying the neuronal circuits that drive these behaviors.

Sleep, a state of quiescence associated with growth and restorative processes, is conserved across species. Invertebrates including the nematode Caenorhabditis elegans exhibit sleep-like states during development and periods of satiety and stress. Here we describe two methods to study behavior and associated neural activity during sleep and awake states in adult C. elegans. A large microfluidic device facilitates population-wide assessment of long-term sleep behavior over 12 h, including effects of fluid flow, oxygen, feeding, odors, and genetic perturbations. Smaller devices allow simultaneous recording of sleep behavior and neuronal activity, and a closed-loop sleep detection system delivers chemical stimuli to individual animals to assess sleep-dependent changes to neural responses. Sleep increased the arousal threshold to aversive chemical stimulation, yet sensory neuron (ASH) and first-layer interneuron (AIB) responses were unchanged. This localizes adult sleep-dependent neuromodulation within interneurons presynaptic to the AVA premotor interneurons, rather than afferent sensory circuits.

The fruit fly, Drosophila melanogaster, is a widely used model species in biomedical research. Despite its importance, conducting manual experiments with individual fruit flies can be challenging and time-consuming, especially for studies of individual fly behaviors. Such studies often involve cumbersome preparatory steps, such as manually tethering a fly and then positioning it within an experimental setup1,2. These procedures commonly require the fly to be anesthetized, and, before behavioral assessments begin, the fly must recover from anesthesia. Hence, the introduction of automated phenotyping and behavioral assays would expedite important aspects of fly research, by minimizing manual handling of flies and decreasing the net time needed for experiments. Here, we introduce FlyMAX (Fly Manipulation and Autonomous eXperimentation), an autonomous robotic system for manipulating adult flies without use of anesthesia. FlyMAX collects individual flies from a standard vial, analyzes them with computer vision, and achieves a throughput of >1,000 flies per day for high-throughput inspection and characterization assays. Robotic handling had no detectable adverse effects on fly longevity or our assessments of fly health. Moreover, the behavioral performance of flies, especially of males, was better and less variable than of flies handled manually. Our system employs deep learning-based machine vision for real-time assessments of picking quality and fly phenotypes. This enables fully pipelined, autonomous experimentation for behavioral assays with individual flies in controlled environments, which was previously infeasible. Overall, FlyMAX constitutes a promising technology to enhance the efficiency and reproducibility of research with flies and other insects in fields such as genetics, neuroscience, and drug screening.

Comparative analyses of locomotor behavior and cellular electrical properties between wild-type and mutant C. elegans are crucial for exploring the gene basis of behaviors and the underlying cellular mechanisms. Although many tools have been developed by research labs and companies, their application is often hindered by implementation difficulties or lack of features specifically suited for C. elegans. Track-A-Worm 2.0 addresses these challenges with three key components: WormTracker, SleepTracker, and Action Potential (AP) Analyzer. WormTracker accurately quantifies a comprehensive set of locomotor and body bending metrics, reliably distinguish between the ventral and dorsal sides, continuously tracks the animal using a motorized stage, and seamlessly integrates external devices, such as a light source for optogenetic stimulation. SleepTracker detects and quantifies sleep-like behavior in freely moving animals. AP Analyzer assesses the resting membrane potential, afterhyperpolarization level, and various AP properties, including threshold, amplitude, mid-peak width, rise and decay times, and maximum and minimum slopes. Importantly, it addresses the challenge of AP threshold quantification posed by the absence of a pre-upstroke inflection point. Track-A-Worm 2.0 is potentially a valuable tool for many C. elegans research labs due to its powerful functionality and ease of implementation.

Drosophila melanogaster serves as a powerful model for studying neurodegenerative diseases, often employing the GAL4-UAS system for targeted gene expression. Electroretinograms (ERGs) provide a robust in vivo functional readout of neuronal integrity and are increasingly used to assess disease progression and therapeutic interventions in these models. However, the genetic background upon which these models are built, particularly the widely used w1118 white-eyed mutant, can significantly influence baseline ERG characteristics. This study systematically characterizes ERG responses in wild-type Canton S (CS), w1118, and a w1118line carrying a UAS-hPLD1 construct (which includes a mini-white gene). We demonstrate profound differences in ERG amplitudes, waveforms, and responses to varying light stimuli (intensity and duration) between these genotypes, as well as significant sex-specific variations. Notably, w1118 flies exhibit markedly larger ERG amplitudes compared to CS, while the hPLD1 line shows partial compensation. We also introduce a novel quadrant-based analysis of the receptor potential, revealing distinct "fingerprints" for each genotype. These findings underscore that the w1118 background is not electrophysiologically neutral and can intrinsically alter neuronal responses. This has critical implications for interpreting ERG data from neurodegeneration models, as these background effects could mask or mimic disease-related changes. Researchers must consider these baseline differences and potential sex-specific effects to accurately attribute observed ERG phenotypes to the gene or condition under investigation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/626125v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1b655f0org.highwire.dtl.DTLVardef@1c412e7org.highwire.dtl.DTLVardef@1b52fe3org.highwire.dtl.DTLVardef@5af680_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIERG profiles differ significantly between CS, w1118, and w1118; UAS-hPLD1 flies. C_LIO_LIThe w1118 background, common in GAL4-UAS studies, exhibits distinct ERG features. C_LIO_LISex-specific differences in ERG responses are prominent and genotype-dependent. C_LIO_LIUAS-hPLD1 insertion (with mini-white) partially alters the w1118 ERG phenotype. C_LIO_LIResults caution the interpretation of ERG data in w1118 neurodegeneration models. C_LI

Drosophila melanogaster is a leading genetic model for studying the neural regulation of sleep. Sleep is associated with changes in behavior and physiological state that are largely conserved across species. The investigation of sleep in flies has predominantly focused on behavioral readouts of sleep because physiological measurements, including changes in brain activity and metabolic rate are less accessible. We have previously used stop-flow indirect calorimetry to measure whole body metabolic rate in single flies and have shown that in flies, like mammals, metabolic rate is reduced during sleep. Here, we describe a modified version of this system that allows for efficient and highly sensitive acquisition of CO2 output from single flies. We also describe a modification that allows for simultaneous acquisition of CO2 and O2 levels, providing a respiratory quotient that quantifies how metabolic stores are utilized. Finally, we show that sleep-dependent changes in metabolic rate are diminished in aging flies, supporting the notion that sleep quality is reduced as flies age. Taken together, the use of indirect calorimetry provides a physiological measure of sleep with broad applications to genetic studies in flies.