Microextrusion Printing Cell-Laden Networks of Type I Collagen with Patterned Anisotropy and Geometry
Bibliographic record
Abstract
Abstract Type I collagen self-assembles into three-dimensional (3D) fibrous networks. These dynamic viscoelastic materials can be remodeled in response to mechanical and chemical cues to form anisotropic networks, the structure of which influences tissue development, homeostasis, and disease progression. Conventional approaches for fabricating anisotropic networks of type I collagen are often limited to unidirectional alignment over small areas. Here, we describe a new approach for engineering cell-laden anisotropic networks of type I collagen fibers using 3D microextrusion printing of a collagen-Matrigel ink. By adding molecular crowders, we demonstrate hierarchical control of 3D-printed collagen with the ability to spatially pattern collagen fiber anisotropy and geometry. Our data suggest that collagen anisotropy results from a combination of molecular crowding in the ink and shear and extensional flows present during 3D-printing. We demonstrate that human breast cancer cells cultured on 3D-printed collagen orient along the direction of collagen fiber alignment. We also demonstrate the ability to simultaneously bioprint epithelial cell clusters and control the alignment and geometry of collagen fibers surrounding cells in the bioink. The resulting cell-laden constructs consist of epithelial cell clusters fully embedded in aligned networks of collagen fibers. We foresee that cell-laden collagen-Matrigel constructs with spatially-patterned anisotropy and geometry will be broadly useful for the fields of developmental biology, tissue engineering, and regenerative medicine.
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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".