Analyzing the Effects of Stromal Cells on the Recruitment of Leukocytes from Flow
Bibliographic record
Abstract
Stromal cells regulate the recruitment of circulating leukocytes during inflammation through cross-talk with neighboring endothelial cells. Here we describe two in vitro "vascular" models for studying the recruitment of circulating neutrophils from flow by inflamed endothelial cells. A major advantage of these models is the ability to analyze each step in the leukocyte adhesion cascade in order, as would occur in vivo. We also describe how both models can be adapted to study the role of stromal cells, in this case mesenchymal stem cells (MSC), in regulating leukocyte recruitment. Primary endothelial cells were cultured alone or together with human MSC in direct contact on Ibidi microslides or on opposite sides of a Transwell filter for 24 hr. Cultures were stimulated with tumor necrosis factor alpha (TNFα) for 4 hr and incorporated into a flow-based adhesion assay. A bolus of neutrophils was perfused over the endothelium for 4 min. The capture of flowing neutrophils and their interactions with the endothelium was visualized by phase-contrast microscopy. In both models, cytokine-stimulation increased endothelial recruitment of flowing neutrophils in a dose-dependent manner. Analysis of the behavior of recruited neutrophils showed a dose-dependent decrease in rolling and a dose-dependent increase in transmigration through the endothelium. In co-culture, MSC suppressed neutrophil adhesion to TNFα-stimulated endothelium. Our flow based-adhesion models mimic the initial phases of leukocyte recruitment from the circulation. In addition to leukocytes, they can be used to examine the recruitment of other cell types, such as therapeutically administered MSC or circulating tumor cells. Our multi-layered co-culture models have shown that MSC communicate with endothelium to modify their response to pro-inflammatory cytokines, altering the recruitment of neutrophils. Further research using such models is required to fully understand how stromal cells from different tissues and conditions (inflammatory disorders or cancer) influence the recruitment of leukocytes during inflammation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".