A microfluidic mammary gland coculture model using parallel 3D lumens for studying epithelial-endothelial migration in breast cancer
Bibliographic record
Abstract
In breast cancer development, crosstalk between mammary epithelial cells and neighboring vascular endothelial cells is critical to understanding tumor progression and metastasis, but the mechanisms of this dynamic interplay are not fully understood. Current cell culture platforms do not accurately recapitulate the 3D luminal architecture of mammary gland elements. Here, we present the development of an accessible and scalable microfluidic coculture system that incorporates two parallel 3D luminal structures that mimic vascular endothelial and mammary epithelial cell layers, respectively. This parallel 3D lumen configuration allows investigation of endothelial-epithelial crosstalk and its effects of the comigration of endothelial and epithelial cells into microscale migration ports located between the parallel lumens. We describe the development and application of our platform, demonstrate generation of 3D luminal cell layers for endothelial cells and three different breast cancer cell lines, and quantify their migration profiles based on number of migrated cells, area coverage by migrated cells, and distance traveled by individual migrating cells into the migration ports. Our system enables analysis at the single-cell level, allows simultaneous monitoring of endothelial and epithelial cell migration within a 3D extracellular matrix, and has potential for applications in basic research on cellular crosstalk as well as drug development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".