A versatile cancer cell trapping and 1D migration assay in a microfluidic device
Bibliographic record
Abstract
Highly migratory cancer cells often lead to metastasis and recurrence and are responsible for the high mortality rates in many cancers despite aggressive treatment. Recently, the migratory behavior of patient-derived glioblastoma multiforme cells on microtracks has shown potential in predicting the likelihood of recurrence, while at the same time, antimetastasis drugs have been developed which require simple yet relevant high-throughput screening systems. However, robust in vitro platforms which can reliably seed single cells and measure their migration while mimicking the physiological tumor microenvironment have not been demonstrated. In this study, we demonstrate a microfluidic device which hydrodynamically seeds single cancer cells onto stamped or femtosecond laser ablated polystyrene microtracks, promoting 1D migratory behavior due to the cells’ tendency to follow topographical cues. Using time-lapse microscopy, we found that single U87 glioblastoma multiforme cells migrated more slowly on laser ablated microtracks compared to stamped microtracks of equal width and spacing (p < 0.05) and exhibited greater directional persistence on both 1D patterns compared to flat polystyrene (p < 0.05). Single-cell morphologies also differed significantly between flat and 1D patterns, with cells on 1D substrates exhibiting higher aspect ratios and less circularity (p < 0.05). This microfluidic platform could lead to automated quantification of single-cell migratory behavior due to the high predictability of hydrodynamic seeding and guided 1D migration, an important step to realizing the potential of microfluidic migration assays for drug screening and individualized medicine.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".