Development and Optimization of a 3D Bioprinted Experimental Model of Skeletal Muscle
Bibliographic record
Abstract
Background Traditional cell culture has been the foundation of biomedical research for over 100 years and remains an essential scientific tool. Yet it is becoming increasingly recognized that biological and technical deficiencies limit the relevance of cell culture to health and disease. These deficiencies are especially relevant to muscle tissues; it is difficult to measure contraction in cultured cells, and physical interactions between cells and the physical environment can modulate tissue‐level function. To fill this gap, we used 3D bioprinting technology to create a free‐floating and bioreactor‐free experimental model of skeletal muscle. The model is designed for contraction to be assessed as muscle shortening against a physiologically relevant auxotonic load. Methods C2C12 mouse skeletal muscle myoblasts were bioprinted at 2.5×10 7 cells/mL in a bioink containing 0.25–1.25% RGD‐coupled‐alginate, 1–2 mg/mL collagen‐I and 0–20 mg/mL fibrinogen using an Aspect Biosystems RX‐1 bioprinter. Cells were printed as a 10 mm diameter ring, with or without an acellular frame providing an auxotonic load opposing contraction. Cells were thrombin treated to polymerise fibrinogen, cultured with or without the antifibrinolytic agent aprotinin (20 μg/mL), and differentiated on day 1–5 in a low serum media with high glucose and insulin. Tissue structure was assessed by live cell phase contract microscopy and filamentous actin immunofluorescence. Cell phenotype was assessed by qPCR for contraction‐relevant genes. Results Cells printed in stiff alginate (≥0.5%) did not form cell‐cell contacts required for myotube differentiation. Soft alginate (<0.5%) allowed cell elongation but rings rapidly collapsed, necessitating the use of a very stiff acellular supporting frame (≥1.0% alginate). Early bioinks including only alginate and collagen promoted myotube formation but fibres tore themselves apart under their own contractile tone. Fibrinogen dose dependently improved fibre integrity, and culturing with 20 g/mL aprotinin dramatically improved overall tissue structure (lifespan >10 days) with extensive formation of elongated, multinucleated and f‐actin‐rich myotubes. Final design parameters of: 0.25% cellular alginate; 1 mg/mL collagen; 20 mg/mL fibrinogen; 1% alginate acellular frame; differentiated on day 3; maintained until day 10, resulted in 30‐fold upregulation of myosin heavy chain 1, with a switch from β‐ to α‐actin and reduction in muscle cadherin expression. These outcomes are entirely consistent with the formation of physically stable contractile myotubes. Conclusion Our 3D bioprinted skeletal muscle represents a novel model for studying muscle biology in vitro , incorporating a physiologically relevant 3D structure, realistic mechanical environment, and the ability to directly measure contraction. The standalone structure requires no specialist bioreactor to produce or maintain, making the model easily accessible to a wide range of biomedical researchers, to better replicate health and disease states. Support or Funding Information Funding: NSERC Discovery Grant (ARW), Research Manitoba Studentship (SS, JO), University of Manitoba Undergraduate Research Award (AK) A freshly 3D bioprinted skeletal muscle construct within a 24 mm diameter ThinCert membrane. The translucent structural (red arrow) and opaque muscle layers (blue arrow) can clearly be seen. The texture of the crosshatching in the structural layer can be seen, providing an auxotonic load opposing muscle contraction. Figure 1 Widefield immunofluorescence of 3D bioprinted skeletal muscle constructs showing actin filaments (red) and nuclei (blue) after 7 days of differentiation in a low serum media with high glucose, insulin and 20 ug/mL aprotinin. There is extensive formation of elongated multinucleated cells, corresponding with >30‐fold increase in myosin heavy chain 1 (MYH1) mRNA abundance, and a switch from β‐ to α‐actin expression. Figure 2
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".