A robust in vitro model for trans-lymphatic endothelial migration
Bibliographic record
Abstract
Trans-endothelial migration (TEM) is essential for leukocyte circulation. While much is known about trans-blood endothelial migration, far less is known about trans-lymphatic endothelial migration. We established an in vitro system to evaluate lymphatic TEM for various cell types across primary mouse and human lymphatic endothelial cells (LEC), and validated the model for the murine LEC cell line SVEC4-10. T cells exhibited enhanced unidirectional migration from the basal (abluminal) to the apical (luminal) surface across LEC, whereas for blood endothelial cells (BEC) they migrated similarly in both directions. This preferential, vectorial migration was chemotactic toward many different chemoattractants and dose-dependent. Stromal protein fibers, interstitial type fluid flow, distribution of chemokines in the stromal layer, and inflammatory cytokines influenced LEC phenotype and leukocyte TEM. Activated and memory CD4 T cells, macrophages, and dendritic cell (DC) showed chemoattractantΔdriven vectorial migration, while CD8 T cell migration across LEC was not. The system was further validated for studying cancer cell transmigration across lymphatic endothelium. This model for lymphatic TEM for various migrating and endothelial cell types possesses the capacity to be high-throughput, highly reproducible and integrate the complexities of lymphatic biology, stromal variability, chemoattractant distribution, and fluid flow.
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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) | 0.001 | 0.000 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".