Abstract 269: Collagen Topographical Patterning Modulates Endothelial Cell Morphology, Gene Expression and Function
Bibliographic record
Abstract
Endothelial cells (ECs) exposed to laminar shear stress align in the direction of blood flow and express low levels of adhesion molecules. However, it is unknown whether these biological responses result from shear stress per se, or if they can be recapitulated using topography cues from the underlying extracellular matrix (ECM). We hypothesized that collagen topographical patterning can regulate EC assembly and function in the absence of shear stress. We fabricated collagen-coated polydimethylsiloxane microchannels (30μm wide) to induce EC alignment. When grown on microchannels, the ECs underwent striking re-organization of their actin cytoskeleton (aligned within 10 degrees of the microchannel direction) and their focal adhesions (Fig A). Mimicking the reported EC responses to laminar shear stress, aligned ECs were more quiescent (reduced Ki67 expression), and less adhesive for monocytes (with 50% reductions in the expression of intercellular adhesion molecule 1, and in monocyte adhesion in a functional binding assay). DNA microarrays revealed a transcriptional signature of 600 genes that were differentially expressed by ECs cultured on the patterned substrates (Fig B), including genes previously not associated with EC function such as histones (HIST1H2AH, HIST2H2BF) and heat shock proteins (HSPB7, HSPB9). These results demonstrate that topographical cues from the underlying ECM are potent regulators of EC morphology and function and mimic the effects of laminar shear-stress. This work highlights the importance of cell-ECM interactions in maintenance of EC phenotype and has implications in the design of vascular conduits to minimize atherogenesis.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".