Three-Dimensional Label-Free Imaging and Quantification of Migrating Cells during Wound Healing
Bibliographic record
Abstract
ABSTRACT The wound healing assay provides essential information about collective cell migration and cell-to-cell interactions. It is a simple, effective, and widely used tool for observing the effect of numerous chemical treatments on wound healing speed. To perform and analyze a wound healing assay, various imaging techniques have been utilized. However, image acquisition and analysis are often limited in two-dimensional space or require the use of exogenous labeling agents. Here, we present a method for imaging large-scale wound healing assays in a label-free and volumetric manner using optical diffraction tomography (ODT). We performed quantitative high-resolution three-dimensional (3D) analysis of cell migration over a long period without difficulties such as photobleaching or phototoxicity. ODT enables the reconstruction of the refractive index (RI) tomogram of unlabeled cells, which provides both structural and biochemical information about the individual cell at subcellular resolution. Stitching multiple RI tomograms enables long-term (24 h) and large field-of-view imaging (> 800 × 400 μm 2 ) with a lateral resolution of 110 nm. We demonstrated the thickness changes of leading cells and studied the effects of cytochalasin D. The 3D RI tomogram also revealed increased RI values in leading cells compared to lagging cells, suggesting the formation of a highly concentrated subcellular structure. STATEMENT OF SIGNIFICANCE The wound healing assay is a simple but effective tool for studying collective cell migration (CCM) that is widely used in biophysical studies and high-throughput screening. However, conventional imaging and analysis methods only address two-dimensional properties in a wound healing assay, such as gap closure rate. This is unfortunate because biological cells are complex 3D structures, and their dynamics provide significant information about cell physiology. Here, we presented three-dimensional (3D) label-free imaging for wound healing assays and investigated the 3D dynamics of CCM. High-resolution subcellular structures as well as their collective dynamics were imaged and analyzed quantitatively. Our label-free quantitative 3D analysis method provides a unique opportunity to study the behavior of migrating cells during the wound healing process.
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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 | 0.001 | 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".