Decellularized Extracellular Matrix Composite Hydrogel Bioinks for the Development of 3D Bioprinted Head and Neck in Vitro Tumor Models
Bibliographic record
Abstract
Reinforced extracellular matrix (ECM)-based hydrogels recapitulate several mechanical and biochemical features found in the tumor microenvironment (TME) in vivo. While these gels retain several critical structural and bioactive molecules that promote cell–matrix interactivity, their mechanical properties tend toward the viscous regime limiting their ability to retain ordered structural characteristics when considered as architectured scaffolds. To overcome this limitation characteristic of pure ECM hydrogels, we present a composite material containing alginate, a seaweed-derived polysaccharide, and gelatin, denatured collagen, as rheological modifiers which impart mechanical integrity to the biologically active decellularized ECM (dECM). After an optimization process, the reinforced gel proposed is mechanically stable and bioprintable and has a stiffness within the expected physiological values. Our hydrogel’s elastic modulus has no significant difference when compared to tumors induced in preclinical xenograft head and neck squamous cell carcinoma (HNSCC) mouse models. The bioprinted cell-laden model is highly reproducible and allows proliferation and reorganization of HNSCC cells while maintaining cell viability above 90% for periods of nearly 3 weeks. Cells encapsulated in our bioink produce spheroids of at least 3000 μm 2 of cross-sectional area by day 15 of culture and are positive for cytokeratin in immunofluorescence quantification, a common marker of HNSCC model validation in 2D and 3D models. We use this in vitro model system to evaluate the standard-of-care small molecule therapeutics used to treat HNSCC clinically and report a 4-fold increase in the IC 50 of cisplatin and an 80-fold increase for 5-fluorouracil compared to monolayer cultures. Our work suggests that fabricating in vitro models using reinforced dECM provides a physiologically relevant system to evaluate malignant neoplastic phenomena in vitro due to the physical and biological features replicated from the source tissue microenvironment.
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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".