Evaluation of rapid volume changes of substrate‐adherent cells by conventional microscopy 3D imaging
Bibliographic record
Abstract
Precise measurement of rapid volume changes of substrate-adherent cells is essential to understand many aspects of cell physiology, yet techniques to evaluate volume changes with sufficient precision and high temporal resolution are limited. Here, we describe a novel imaging method that surveys the rapid morphology modifications of living, substrate-adherent cells based on phase-contrast, digital video microscopy. Cells grown on a glass substrate are mounted in a custom-designed, side-viewing chamber and subjected to hypotonic swelling. Side-view images of the rapidly swelling cell, and at the end of the assay, an image of the same cell viewed from a perpendicular direction through the substrate, are acquired. Based on these images, off-line reconstruction of 3D cell morphology is performed, which precisely measures cell volume, height and surface at different points during cell volume changes. Volume evaluations are comparable to those obtained by confocal laser scanning microscopy (DeltaVolume < or = 14%), but our method has superior temporal resolution limited only by the time of single-image acquisition, typically approximately 100 ms. The advantages of using standard phase-contrast microscopy without the need for cell staining or intense illumination to monitor cell volume make this system a promising new tool to investigate the fundamentals of cell volume physiology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".