Live-Cell Migration and Adhesion Turnover Assays
Bibliographic record
Abstract
Fluorescence microscopy has revolutionized the way live-cell imaging is achieved. At the same time, it is also potentially harmful to a living specimen. Therefore, the specimen must be monitored for viability and health before, during, and after imaging sessions. Methods for monitoring cell viability and health will be discussed in this chapter. Another key to successful live-cell imaging is to minimize light exposure as much as possible. A summary of strategies for minimizing light exposure including maximizing the light throughput of the microscope and the sensitivity of light detection is presented. Various fluorescence microscopy techniques are presented with a focus on how the light is delivered to the sample (i.e., light density) and pros and cons for use with living specimens. The reader is also directed to other publications that go into these topics in more detail. Methods are described on how to prepare samples for single cell migration assays, how to measure cell migration rates (e.g., bright-field, semi-automated, and automated), and how to measure focal adhesion turnover rates. Details of how to correct images for background intensity and field-illumination uniformity artifacts for quantitative imaging are also described. Overall, this chapter will be helpful to scientists who are interested in imaging live specimens using fluorescence microscopy techniques. It will be of particular interest to anyone wanting to perform quantitative fluorescence imaging, and wanting to measure cell migration rates, and focal adhesion dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".