Studying Chemotactic Migration in Dunn Chamber: An Example Applied to Adherent Cancer Cells
Bibliographic record
Abstract
Cell migration is a vital process in the development of multicellular organisms. When deregulated, it is involved in many diseases such as inflammation and cancer metastisation. Some cancer cells could be stimulated using chemoattractant molecules, such as growth factor Heregulin β1. They respond to the attractant or repellent gradients through a process known as chemotaxis. Indeed, chemotactic cell motility is crucial in tumour cell dissemination and invasion of distant organs. Due to the complexity of this phenomenon, the majority of available in vitro methods to study the chemotactic motility process have limitations and are mainly based on endpoint assays, such as the Boyden chamber assay. Nevertheless, in vitro time-lapse microscopy represents an interesting opportunity to study cell motility in a chemoattracting gradient, since it generates large volume image-based information, allowing the analysis of cancer cell behaviours. Here, we describe a detailed time-lapse imaging protocol, designed for tracking T47D human breast cancer cell line motility, toward a gradient of Heregulin β1 in a Dunn chemotaxis chamber assay. The protocol described here is readily adapted to study the motility of any adherent cell line, under various conditions of chemoattractant gradients and of pharmacological drug treatments. Moreover, this protocol could be suitable to study changes in cell morphology, and in cell polarity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".