On-chip functional neuroimaging with mechanical stimulation in<i>Caenorhabditis elegans</i>larvae for studying development and neural circuits
Bibliographic record
Abstract
Mechanosensation is fundamentally important for the abilities of an organism to experience touch, hear sounds, and maintain balance. Caenorhabditis elegans is a powerful system for studying mechanosensation as this worm is well suited for in vivo functional imaging of neurons. Many years of research using labor-intensive methods have generated a wealth of knowledge about mechanosensation in C. elegans, and the recent microfluidic-based platforms continue to push the boundary for this field. However, developmental aspects of sensory biology, including mechanosensation, are still not fully understood. One current bottleneck is the difficulty in assaying larvae because they are much smaller than adult worms. Microfluidic devices with features small enough for larvae, especially actuators for the delivery of mechanical stimulation, are difficult to design and fabricate. Here, we present a series of automatic microfluidic platforms that allow for in vivo functional imaging of C. elegans responding to controlled mechanical stimulation at different developmental stages. Using a novel fabrication method, we designed highly deformable pneumatically actuated on-chip structures that can deliver mechanical stimulation to larval worms. The PDMS actuator allows for quantitatively controlled mechanical stimulation of both gentle and harsh touch neurons, by simply changing the actuation pressure, which makes this device easily translatable to other labs. We validated the design and utility of our systems with studies of the functional role of mechanosensory neurons in developing worms; we showed that gentle and harsh touch neurons function similarly in early larvae as they do in the adult stage, which would not have been possible previously. Finally, we investigated the effect of a sleep-like state on neuronal responses by imaging C. elegans in the lethargus state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".