Differentiation of Human Induced Pluripotent Stem Cells into Mature and Myelinating Schwann Cells
Bibliographic record
Abstract
In the peripheral nervous system, Schwann cells (SCs) play a crucial role in axonal growth, metabolic support of neurons, and the production of myelin sheaths. Expansion of SCs after extraction from human or animal nerves is a long and often low-yielding process. We established a rapid cell culture method using a defined serum-free medium to differentiate human induced pluripotent stem cells (iPSCs) into SCs in only 21 days. The SC identity was characterized by expression of SRY-Box Transcription factor 10 (SOX10), S100b, glial fibrillary acidic protein (GFAP), P75, growth-associated protein 43 (GAP43), and early growth response 2 (EGR2) markers. The SC purity reached 87% as assessed by flow cytometry using the specific SOX10 marker, and 69% based on S100b expression. When SCs were cocultured with iPSC-derived motor neurons two-dimensionally or three-dimensionally (3D), they also expressed the markers of myelin MBP, MPZ, and gliomedin. Likewise, when they were seeded on the opposite side of a porous collagen sponge from motor neurons in the 3D model, they were able to migrate through it and colocalize with motor axons after 8 weeks of maturation. Moreover, they were shown by transmission electron microscopy to form myelin sheaths around motor axons. These results suggest that the use of autologous iPSC-derived SCs for clinical applications such as the repair of peripheral nerve damage, the treatment of spinal cord injuries, or for demyelinating diseases could be a valuable option. Impact Statement Peripheral nerve injuries can cause the complete paralysis of the upper or lower limbs, which considerably reduces the quality of life of patients. To repair this injury, many approaches have been developed by tissue engineering. Combining biomaterials with Schwann cells (SCs) has been shown to be an effective solution for stimulating nerve regeneration. However, the challenge faced concerns the strategy for obtaining autologous SCs to treat patients. A promising approach is to differentiate them from the patient's own cells, previously induced into pluripotent stem cells. We propose a fast culture method to generate functional SCs differentiated from induced pluripotent stem cells.
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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) | 0.002 | 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".