3D Bioprinting Pluripotent Stem Cell Derived Neural Tissues Using a Novel Fibrin Bioink Containing Drug Releasing Microspheres
Bibliographic record
Abstract
3D bioprinting combines cells with a supportive bioink to fabricate multiscale, multi-cellular structures that imitate native tissues. Here, we demonstrate how our novel fibrin-based bioink formulation combined with drug releasing microspheres can serve as a tool for bioprinting tissues using human induced pluripotent stem cell (hiPSC)-derived neural progenitor cells (NPCs). Microspheres, small spherical particles, can provide a controlled release rate for drugs like guggulsterone, shown to promote hiPSC differentiation into dopaminergic neurons, making them a valuable tool for tissue engineering. We printed dome shaped structures with a 1 cm diameter using the Aspect Biosystems RX1 bioprinter with our novel bioink consisting of fibrin, alginate and genipin containing guggulsterone microspheres crosslinked by a mixture of calcium chloride, chitosan and thrombin. Cell viability one day post printing was over 90% for the cells printed using our bioink containing guggulsterone microspheres that increased to 95%, 7 days after printing. The bioprinted tissues expressed the early neuronal marker, TUJ1 and the early midbrain marker, forkhead/winged helix transcription factor (FOXA2) (Forkhead Box A2) after 15 days of culture. These bioprinted neural tissues expressed TUJ1, (15 ± 1.3%), the dopamine marker, tyrosine hydroxylase (TH) (8 ± 0.6%) and other glial markers such as glial fibrillary acidic protein (GFAP) (15 ± 3.5%) and oligodendrocyte progenitor marker (O4) (4 ± 0.9%) as showed by flow cytometry after 30 days. Also, relative gene expression by quantitative polymerase chain reaction (qPCR) showed expression of TUBB3 (TUJ1) and specific midbrain dopaminergic neurons Nuclear receptor related 1 protein (NURR1), LIM Homeobox Transcription Factor 1 Beta (LMX1B), TH, and Paired Box 6 (PAX6) in these tissues after 30 days. In conclusion, we have demonstrated that 3D bioprinting pluripotent stem cell derived neural tissues using a microsphere-laden bioink can promote the differentiation of neural tissue when used to bioprint hiPSC-derived NPCs.
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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) | 0.000 | 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".