A Unique 3D In Vitro Cellular Invasion Assay
Bibliographic record
Abstract
Three-dimensional (3D) cell culture techniques using a bioreactor have been used to co-culture various breast cancer cell lines. Comparisons between 3D co-cultures containing different proportions of breast cancer cell lines have been made with respect to cluster size, cell surface marker distribution, and Ki67 expression. Furthermore, an observed difference in invasion through collagen between co-cultures has been briefly reported. However, these assays have not yet been developed into a quantifiable methodology to assess the effects of drugs and/or microenvironments on cellular invasion. From a cancer perspective, two important aspects of cellular invasion that are often left out of in vitro assays are considerations about the 3D structural heterogeneity of the primary tumor and the ability of cells to migrate in all directions. Accordingly, we have taken advantage of the methodology previously described for 3D cell culture techniques and have developed a 3D invasion assay using cell clusters that can be used to assess the effects of different drugs and treatment conditions on cancer cell invasion. We also describe a novel whole-mount technique that permits fluorescence-based immunolocalization of proteins through the entire tumorsphere, without the need for sectioning. Our assay provides a simple, inexpensive, and physiologically relevant context to study cellular invasion in vitro, in a way that recapitulates an in vivo milieu.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".