Protocol for 3D Bioprinting Mesenchymal Stem Cell–derived Neural Tissues Using a Fibrin-based Bioink
Bibliographic record
Abstract
Three-dimensional bioprinting utilizes additive manufacturing processes that combine cells and a bioink to create living tissue models that mimic tissues found in vivo. Stem cells can regenerate and differentiate into specialized cell types, making them valuable for research concerning degenerative diseases and their potential treatments. 3D bioprinting stem cell-derived tissues have an advantage over other cell types because they can be expanded in large quantities and then differentiated to multiple cell types. Using patient-derived stem cells also enables a personalized medicine approach to the study of disease progression. In particular, mesenchymal stem cells (MSC) are an attractive cell type for bioprinting because they are easier to obtain from patients in comparison to pluripotent stem cells, and their robust characteristics make them desirable for bioprinting. Currently, both MSC bioprinting protocols and cell culturing protocols exist separately, but there is a lack of literature that combines the culturing of the cells with the bioprinting process. This protocol aims to bridge that gap by describing the bioprinting process in detail, starting with how to culture cells pre-printing, to 3D bioprinting the cells, and finally to the culturing process post-printing. Here, we outline the process of culturing MSCs to produce cells for 3D bioprinting. We also describe the process of preparing Axolotl Biosciences TissuePrint - High Viscosity (HV) and Low Viscosity (LV) bioink, the incorporation of MSCs to the bioink, setting up the BIO X and the Aspect RX1 bioprinters, and necessary computer-aided design (CAD) files. We also detail the differentiation of 2D and 3D cell cultures of MSC to dopaminergic neurons, including media preparation. We have also included the protocols for viability, immunocytochemistry, electrophysiology, and performing a dopamine enzyme-linked immunosorbent assay (ELISA), along with the statistical analysis. Graphical overview.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".