NMJ Analyser: a novel method to quantify neuromuscular junction morphology in zebrafish
Bibliographic record
Abstract
MOTIVATION: Neuromuscular junction (NMJ) structural integrity is crucial for transducing motor neuron signals that initiate skeletal muscle contraction. Zebrafish has emerged as a simple and efficient model to study NMJ structural morphology and function in the context of developmental neurobiology and neuromuscular diseases. However, methods to quantify NMJ morphology from voluminous data of NMJ confocal images accurately, rapidly, and reproducibly are lacking. RESULTS: We developed an ImageJ macro called "NMJ Analyser" to automatically and unbiasedly analyse NMJ morphology in zebrafish. From the Z-stack of a zebrafish hemisomite, both presynaptic and postsynaptic fluorescently labeled termini at NMJs are extracted from background signal, with larger clusters of termini being segmented into individual termini using an unbiased algorithm. The program then determines whether each presynaptic terminus is co-localized with a postsynaptic terminus and vice versa, or whether it is orphaned, and tabulates the number of orphan and co-localized pre- and postsynaptic termini. The usefulness of this ImageJ macro plugin will be helpful to quantify NMJ parameters in zebrafish, particularly during development and in disease models of neuromuscular diseases. It can enable high-throughput NMJ phenotypic screens in the drug discovery process for neuromuscular diseases. It could also be further applied to the investigation of NMJ of other developmental systems. AVAILABILITY AND IMPLEMENTATION: NMJ Analyser is available for download at https://github.com/PattenLab/NMJ-Analyser.git.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".