3D bioprinting of bicellular liver lobule-mimetic structures via microextrusion of cellulose nanocrystal-incorporated shear-thinning bioink
Bibliographic record
Abstract
3D bioprinting of living cellular constructs with heterogeneity in cell types and extra cellular matrices (ECMs) matching those of biological tissues remains challenging. Here, we demonstrate that, through bioink material design, microextrusion-based (ME) bioprinting techniques have the potential to address this challenge. A new bioink employing alginate (1%), cellulose nanocrystal (CNC) (3%), and gelatin methacryloyl (GelMA) (5%) (namely 135ACG hybrid ink) was formulated for the direct printing of cell-laden and acellular architectures. The 135ACG ink displayed excellent shear-thinning behavior and solid-like properties, leading to high printability without cell damage. After crosslinking, the ACG gel can also provide a stiff ECM ideal for stromal cell growth. By controlling the degree of substitution and polymer concentration, a GelMA (4%) bioink was designed to encapsulate hepatoma cells (hepG2), as GelMA gel possesses the desired low mechanical stiffness matching that of human liver tissue. Four different versions of to-scale liver lobule-mimetic constructs were fabricated via ME bioprinting, with precise positioning of two different cell types (NIH/3T3 and hepG2) embedded in matching ECMs (135ACG and GelMA, respectively). The four versions allowed us to exam effects of mechanical cues and intercellular interactions on cell behaviors. Fibroblasts thrived in stiff 135ACG matrix and aligned at the 135ACG/GelMA boundary due to durotaxis, while hepG2 formed spheroids exclusively in the soft GelMA matrix. Elevated albumin production was observed in the bicellular 3D co-culture of hepG2 and NIH/3T3, both with and without direct intercellular contact, indicating that improved hepatic cell function can be attributed to soluble chemical factors. Overall, our results showed that complex constructs with multiple cell types and varying ECMs can be bioprinted and potentially useful for both fundamental biomedical research and translational tissue engineering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".