Macromolecular crowding regulates matrix composition and gene expression in human gingival fibroblast cultures
Bibliographic record
Abstract
Standard cell cultures are performed in aqueous media with a low macromolecule concentration compared to tissue microenvironment. In macromolecular crowding (MMC) experiments, synthetic polymeric crowders are added into cell culture media to better mimic macromolecule concentrations found in vivo. However, their effect on cultured cells is incompletely understood and appears context-dependent. Here we show using human gingival fibroblasts, a cell type associated with fast and scarless wound healing, that MMC (standard medium supplemented with Ficoll 70/400) potently modulates fibroblast phenotype and extracellular matrix (ECM) composition compared to standard culture media (nMMC) over time. MMC significantly reduced cell numbers, but increased accumulation of collagen I, cellular fibronectin, and tenascin C, while suppressing level of SPARC (Secreted Protein Acidic and Cysteine Rich). Out of the 75 wound healing and ECM related genes studied, MMC significantly modulated expression of 25 genes compared to nMMC condition. MMC also suppressed myofibroblast markers and promoted deposition of basement membrane molecules collagen IV, laminin 1, and expression of LAMB3 (Laminin Subunit Beta 3) gene. In cell-derived matrices produced by a novel decellularization protocol, the altered molecular composition of MMC matrices was replicated. Thus, MMC may improve cell culture models for research and provide novel approaches for regenerative therapy.
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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) | 0.001 | 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".