Bioprinting of Adult Dorsal Root Ganglion (DRG) Neurons Using Laser-Induced Side Transfer (LIST)
Bibliographic record
Abstract
Cell bioprinting technologies aim to fabricate tissuelike constructs by delivering biomaterials layer-by-layer. Bioprinted constructs can reduce the use of animals in drug development and hold promise for addressing the shortage of organs for transplants. Here, we sought to validate the feasibility of bioprinting primary adult sensory neurons using a newly developed laser-assisted cell bioprinting technology, known as Laser-Induced Side Transfer (LIST). We used dorsal root ganglion neurons (DRG; cell bodies of somatosensory neurons) to prepare our bioink. DRG-laden- droplets were printed on fibrin-coated coverslips and their viability, calcium kinetics, neuropeptides release, and neurite outgrowth were measured. The transcriptome of the neurons was sequenced. We found that LIST-printed neurons maintain high viability (Printed: 86%, Control: 87% on average) and their capacity to release neuropeptides (Printed CGRP: 130 pg/mL, Control CGRP: 146 pg/mL). In addition, LIST-printed neurons do not show differences in the expressed genes compared to control neurons. However, in printed neurons, we found compromised neurite outgrowth and lower sensitivity to the ligand of the TRPV1 channel, capsaicin. In conclusion, LIST-printed neurons maintain high viability and marginal functionality losses. Overall, this work paves the way for bioprinting functional 2D neuron assays.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".