Improved 3D cellular morphometry of Caenorhabditis elegans embryos using a refractive index matching medium
Bibliographic record
Abstract
Cell shape change is one of the driving forces of animal morphogenesis, and the model organism Caenorhabditis elegans has played a significant role in analyzing the underlying mechanisms involved. The analysis of cell shape change requires quantification of cellular shape descriptors, a method known as cellular morphometry. However, standard C. elegans live imaging methods limit the capability of cellular morphometry in 3D, as spherical aberrations generated by samples and the surrounding medium misalign optical paths. Here, we report a 3D live imaging method for C. elegans embryos that minimized spherical aberrations caused by refractive index (RI) mismatch. We determined the composition of a refractive index matching medium (RIMM) for C. elegans live imaging. The 3D live imaging with the RIMM resulted in a higher signal intensity in the deeper cell layers. We also found that the obtained images improved the 3D cell segmentation quality. Furthermore, our 3D cellular morphometry and 2D cell shape simulation indicated that the germ cell precursor P4 had exceptionally high cortical tension. Our results demonstrate that the RIMM is a cost-effective solution to improve the 3D cellular morphometry of C. elegans. The application of this method should facilitate understanding of C. elegans morphogenesis from the perspective of cell shape changes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".