Biology subjects

Guisnet, A.

Publications and source records attributed to Guisnet, A..

3 recordsLinked to original sources

bioRxiv2025

The impact of rearing environment on C. elegans: Phenotypic, transcriptomic and intergenerational responses to 3D enriched habitats

Environmental context profoundly influences organismal biology, yet laboratory studies often rely on simplified conditions that may not fully capture natural phenotypic repertoire. This exploratory study investigated how rearing environment affects various aspects of Caenorhabditis elegans biology by comparing worms cultured in three-dimensional decellularized fruit tissue scaffolds with those raised on standard two-dimensional agar plates. While fat content and feeding rate remained stable across conditions, other life history traits demonstrated varying degrees of plasticity in response to environmental context. We observed that scaffold-grown worms exhibited reduced body size, altered reproductive strategies, and mild enhancements in stress resistance, burrowing ability, swimming kinematics and exploratory behavior. RNA sequencing revealed distinct transcriptional profiles between scaffold-grown and agar-grown worms, with most changes arising within one generation. Some traits showed evidence of intergenerational inheritance. Our findings highlight the sensitivity of C. elegans biology to rearing conditions and underscore the importance of considering environmental context in interpreting laboratory results. This work sets the foundation for future research into the mechanisms underlying environmental adaptation and phenotypic plasticity in model organisms. Summary statementThis study reveals how simple changes in environmental complexity can alter the development, behavior, and gene expression of laboratory animals.

animal behavior and cognition↗

bioRxiv2025

Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity

Quantitative phenotyping of Caenorhabditis elegans is essential across numerous fields, yet data extraction remains a significant analytical bottleneck. Traditional segmentation methods, typically reliant on pixel-intensity thresholding, are highly sensitive to variations in imaging conditions and often fail in the presence of noise, overlaps, or uneven illumination. These failures necessitate meticulous experimental setups, expensive hardware, or extensive manual curation, which reduces throughput and introduces bias. Here, we introduce TWARDIS (Tools for Worm Automated Recognition & Dynamic Imaging System), a modular, Python-based analysis suite that leverages large foundation vision models, specifically the Segment Anything Models (SAM and SAM2) and a fine-tuned vision transformer classifier, to overcome these limitations. We demonstrate the versatility and robustness of our AI compound system across diverse modalities. For static morphological analysis, TWARDIS successfully resolved overlapping worms in noisy images without human intervention, showing a 0.999 correlation with manual segmentation. In behavioral assays (swimming and crawling), the pipeline enabled high-definition postural analysis even in low-resolution, wide-field recordings where the worm occupied only [~]0.25% of the field of view, accurately resolving complex postures without frame rejection. Finally, when applied to calcium imaging of semi-restricted animals, TWARDIS provided precise, frame-by-frame segmentation of neural compartments, reducing the artificial signal flattening common in traditional region-of-interest-based approaches and enabling the extraction of biologically accurate, absolute head positions. The systems hardware-scalable architecture and modular design ensure both current accessibility and future improvements without restructuring. By automating the most time-consuming aspects of image analysis, TWARDIS removes critical bottlenecks and tradeoffs in C. elegans research, enabling researchers to focus on biological questions rather than technical image processing challenges. Author SummaryThe small roundworm Caenorhabditis elegans is widely used by scientists to study fundamental biological questions, such as aging and how the brain works. A crucial part of this research involves analyzing images and videos to measure the worms shape, movement, and neural activity. However, extracting accurate data is a major challenge. Traditional software tools often fail if the lighting is uneven, the image is noisy, or worms overlap. This forces researchers to spend countless hours manually correcting errors or investing in expensive, specialized equipment. To overcome this bottleneck, we developed TWARDIS (Tools for Worm Automated Recognition & Dynamic Imaging System). Our system utilizes recent advances in Artificial Intelligence, harnessing powerful, generalized AI models to automatically and accurately identify the worms, even in challenging images. We demonstrated that TWARDIS reliably analyzes complex behaviors and neural activity without human intervention. Our approach removes the traditional trade-off between data quality and equipment cost, enabling high-precision analysis using simple setups. By automating the most tedious parts of image analysis, TWARDIS allows scientists to focus on biological discovery rather than technical hurdles.

neuroscience↗

bioRxiv2025

A novel epifluorescence microscope design and soft-ware package to record naturalistic behaviour and cell activity in freely moving Caenorhabditis elegans.

Understanding how neural circuits drive behavior requires imaging methods that capture cellular dynamics in freely moving animals. Here, we introduce Wormspy, a compact, flexible, and cost-effective microscope system paired with an open-source software package, specifically designed for high-magnification epifluorescence imaging in Caenorhabditis elegans. By integrating dual-channel fluorescence optics, off-the-shelf components, and a motorized stage, Wormspy enables simultaneous recording of neuronal activity and behavioral dynamics without restraining the animal. Our system incorporates both manual and automated tracking--including DeepLabCut-based feature extraction--to maintain precise centering of the subject, even during complex locomotor behaviors. We demonstrate the utility of Wormspy across multiple paradigms: quantifying body wall muscle calcium transients during locomotion, capturing rapid sensory-evoked responses in the polymodal ASH neuron during aversive stimuli, and resolving food-related activity in the AWCON neuron. Notably, Wormspy further distinguishes subcellular calcium events in the RIA axonal compartments that correlate with head bending kinematics. This versatile platform not only reproduces known phenotypes, such as altered gait in gar-3 mutants, but also uncovers nuanced sensorimotor correlations previously inaccessible with conventional methods. Wormspys modular design and open-source framework lower the technical and financial barriers to high-resolution, behaviorally relevant neural imaging. Our findings establish Wormspy as a robust tool for dissecting the neural underpinnings of behavior in freely moving organisms, with potential applications extending to other small model systems.

neuroscience↗