A Screening System for Evaluating Cell Extension Formation, Collagen Compaction, and Degradation in Drug Discovery
Bibliographic record
Abstract
The generation of cell extensions is critical for matrix remodeling in tissue invasion by cancer cells, but current methods for identifying molecules that regulate cell extension formation and matrix remodeling are not well adapted for screening purposes. We applied a grid-supported, floating collagen gel system (~100 Pa stiffness) to examine cell extension formation, collagen compaction, and collagen degradation in a single assay. With the use of cultured diploid fibroblasts, a fibroblast cell line, and two cancer cell lines, we found that compared with attached collagen gels (~2800 Pa), the mean number and length of cell extensions were respectively greater in the floating gels. In assessing specific processes in cell extension formation, compared with controls, the number of cell extensions was reduced by latrunculin B, β1 integrin blockade, and a formin FH2 domain inhibitor. Screening of a kinase inhibitor library (480 compounds) with the floating gel assay showed that compared with vehicle-treated cells, there were large reductions of collagen compaction, pericellular collagen degradation, and number of cell extensions after treatment with SB431542, SIS3, Fasudil, GSK650394, and PKC-412. These data indicate that the grid-supported floating collagen gel model can be used to screen for inhibitors of cell extension formation and critical matrix remodeling events associated with cancer cell invasion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".