3D Culture in Functionalized FN‐Silk Networks Facilitate Proliferation, Differentiation and Phenotypic Stability of Mature Human Primary Cells and Stem Cells
Bibliographic record
Abstract
The recombinant functionalized silk protein FN-silk, including a cell adhesion motif from fibronectin, can form networks suitable for 3D culture of adherent cells. Such FN-silk networks have previously been shown to support the growth and differentiation of a wide array of cell types. Herein, we have developed a user-friendly methodology for the creation of free-floating FN-silk networks in 96-well plates with both mature human primary cells and stem cells. We show that human mesenchymal stem cells (hMSC) cultured in FN-silk networks form both cell-cell and cell-matrix contacts, resulting in tissue-mimicking 3D cultures. Viability and expression analysis revealed that hMSC in FN-silk networks have an initial proliferative phase with high cell viability and significantly lower hypoxia and apoptosis, compared to when cultured as scaffold-free spheroids. The FN-silk networks were shown to support differentiation of hMSC into adipocyte-like cells with well-maintained viability during the 3-week-long differentiation period, in contrast to the very poor long-term viability of scaffold-free 3D cultures. Improved adipogenesis was confirmed by lipid droplet staining, quantification of intracellular triglycerides, and secreted adiponectin levels, as well as expression analysis of multiple bona fide adipose markers. Lastly, we show that primary human hepatocytes maintain important functions and phenotypic markers when cultured in FN-silk networks, features that are lost rapidly during conventional 2D culture. We therefore propose FN-silk networks as a valuable scaffold for 3D human cell cultures, providing support for cell proliferation, differentiation, and the maintenance of critical tissue-specific functionality.
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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".