Three-Dimensional Quantification of Spheroid Degradation-Dependent Invasion and Invadopodia Formation
Bibliographic record
Abstract
Invadopodia are actin-rich, proteolytic structures that enable cancer cell to invade into the surrounding tissues. Several in vitro invasion assays have been used in the literature ranging from directional quantitative assays to complex three-dimensional (3D) analyses. One of the main limitations of these assays is the lack of quantifiable degradation-dependent invasion in a three-dimensional (3D) environment that mimics the tumor microenvironment. In this article, we describe a new invasion and degradation assay based on the currently available tumor spheroid model that allows long-term high-resolution imaging of the tumor, precise quantification, and visualization of matrix degradation and multichannel immunocytochemistry. By incorporating a degradation marker (DQ-Green BSA) into a basement-membrane matrix, we demonstrate the ability to quantitate cancer cell-induced matrix degradation in 3D. Also, we describe a technique to generate histological sections of the tumor spheroid allowing the detection of invadopodia formation in the 3D tumor spheroid. This new technique provides a clear advantage for studying cancer in vitro and will help address critical questions regarding the dynamics of 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".