A multi-channel device for high-density target-selective stimulation and long-term monitoring of cells and subcellular features in C. elegans
Bibliographic record
Abstract
Selective cell ablation can be used to identify neuronal functions in multicellular model organisms such as Caenorhabditis elegans. The optogenetic tool KillerRed facilitates selective ablation by enabling light-activated damage of cell or subcellular components in a temporally and spatially precise manner. However, the use of KillerRed requires stimulating (5 min-1 h), culturing (~24 h) and imaging (often repeatedly) a large number of individual animals. Current manual manipulation methods are limited by their time-consuming, labor-intensive nature, and their usage of anesthetics. To facilitate large-scale selective ablation, culturing, and repetitive imaging, we developed a densely-packed multi-channel device and used it to perform high-throughput neuronal ablation on KillerRed-expressing animals. The ability to load worms in identical locations with high loading efficiency allows us to ablate selected neurons in multiple worms simultaneously. Our device also enables continuous observation of animals for 24 h following KillerRed activation, and allows the animals to be recovered for behavioural assays. We expect this multi-channel device to facilitate a broad range of long-term imaging and selective illumination experiments in neuroscience.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".