Enhanced efficiency of nonviral direct neuronal reprogramming on topographical patterns
Bibliographic record
Abstract
Nonviral direct neuronal reprogramming holds significant potential in the fields of tissue engineering and regenerative medicine. However, the issue of low reprogramming efficiency poses a major barrier to its application. We propose that topographical cues, which have been applied successfully to enhance lineage-directed differentiation and multipotent stem cell transdifferentiation, could improve nonviral direct neuronal reprogramming efficiency. To investigate, we used a polymer-BAM (Brn2, Ascl1, Myt1l) factor transfection polypex to reprogram primary mouse embryonic fibroblasts. Using a multiarchitecture chip, we screened for patterns that may improve transfection and/or subsequent induced neuron reprogramming efficiency. Selected patterns were then investigated further by analyzing β-tubulin III (TUJ1) and microtubule-associated protein 2 (MAP2) protein expression, cell morphology and electrophysiological function of induced neurons. Certain hierarchical topographies, with nanopatterns imprinted on micropatterns, significantly improved the percentage of TUJ1+ and MAP2+ cells. It is postulated that the microscale base pattern enhances initial BAM expression while the nanoscale sub-pattern promotes subsequent maturation. This is because the base pattern alone increased expression of TUJ1 and MAP2, while the nanoscale pattern was the only pattern yielding induced neurons capable of firing multiple action potentials. Nanoscale patterns also produced the highest fraction of cells showing spontaneous synaptic activity. Overall, reprogramming efficiency with one dose of polyplex on hierarchical patterns was comparable to that of five doses without topography. Thus, topography can enhance nonviral direct reprogramming of fibroblasts into induced neurons.
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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) | 0.001 | 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 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".