The Use of a SOX10 Reporter Towards Ameliorating Oligodendrocyte Lineage Differentiation from Human Induced Pluripotent Stem Cells
Bibliographic record
Abstract
Abstract Oligodendrocytes (OLs) are key players in the central nervous system, critical for the formation and maintenance of the myelin sheaths insulating axons, ensuring efficient neuronal communication. In the last decade, the use of human induced pluripotent stem cells (iPSCs) has become essential for recapitulating and understanding the differentiation and role of OLs in vitro . Current methods include overexpression of transcription factors for rapid OL generation, neglecting the complexity of OL lineage development. Alternatively, growth factor-based protocols offer physiological relevance but struggle with efficiency and cell heterogeneity. To address these issues, we created a novel SOX10-P2A-mOrange iPSC reporter line to track and purify oligodendrocyte precursor cells (OPCs). Using this reporter cell line, we analyzed an existing differentiation protocol and shed light on the origin of glial cell heterogeneity. Additionally, we have modified the differentiation protocol, towards enhancing reproducibility, efficiency, and terminal maturity. Our approach not only advances OL biology but holds promise to accelerate research and translational work with iPSC-derived OLs. Main Points The differentiation of iPSCs in Oligodendrocyte Precursor Cells (OPCs) and Oligodendrocytes (OLs) is a notoriously difficult technique and often displays variable efficiency and cellular heterogeneity. We engineered a novel reporter line carrying the fluorescent protein mOrange under the control of the OL-specific transcription factor SOX10 to track, purify and characterize OLs. By experimenting with diverse differentiation media, we improved the generation of SOX10-positive cells. Consequently, these cells exhibited increased consistency and effectiveness in evolving into myelinating OLs.
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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".