Macromolecular crowding enhances fibrillin-1 deposition in the extracellular matrix
Bibliographic record
Abstract
Biochemical and biophysical factors need consideration when modelling in vivo cellular behaviour using in vitro cell culture systems. One underappreciated factor is the high concentration of macromolecules present in vivo, which is typically not simulated under standard cell culture conditions. This disparity is especially relevant when studying biochemical processes that govern extracellular matrix (ECM) deposition, which may be altered due to dilution of secreted macromolecules by the relatively large volumes of culture medium required for cell maintenance in vitro. Macromolecular crowding (MMC) utilises the addition of inert macromolecules to cell culture medium to mimic such high concentration environments found in vivo. The present study induced MMC using the sucrose polymer Ficoll and examined whether fibrillin-1 deposition by human lung fibroblasts could be augmented. Fibrillin-1 forms extracellular microfibrils, which are versatile scaffolds required for elastic fibre formation, deposition of other ECM proteins and growth factor regulation. Pathogenic variants in the fibrillin-1 gene (FBN1) cause Marfan syndrome, where ECM deposition of fibrillin-1 can be compromised. Using immunocytochemistry, significantly enhanced fibrillin-1 deposition was observed when lung fibroblasts were cultured under MMC conditions. MMC also augmented fibrillin-1 deposition in Marfan syndrome patient-derived skin fibroblasts in a cell line- and likely FBN1 variant-specific manner. The ability of MMC to increase fibrillin-1 deposition suggested potential applications for tissue-engineering approaches, e.g. to generate tendon or vascular tissues, where fibrillin-1 microfibrils and elastic fibres are key determinants of their biomechanical properties. Moreover, it suggested the potency of MMC to better mimic in vivo ECM environments in cell culture studies.
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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 | 0.000 |
| 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) | 0.000 | 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".