A novel epifluorescence microscope design and software package to record naturalistic behaviour and cell activity in freely moving <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
ABSTRACT 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 AWC ON 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. Wormspy’s 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".