Truncated CD19 as a selection marker for the isolation of stem cell-derived β-cells
Bibliographic record
Abstract
Stem cell-derived β-cells (SCβ-cell) are a renewable and scalable alternative to cadaveric islets as a cell-replacement therapy for type 1 diabetes (T1D). However, heterogeneity within SCβ-cell cultures remains problematic for graft safety and function. Magnetic selection of SCβ-cells expressing a unique cell-surface marker may help deplete undesirable cell types and facilitate functional maturation. Here, we explored the transmembrane glycoprotein CD19 as a potential cell-surface marker for the enrichment of insulin-expressing SCβ-cells. Using CRISPR/Cas9 technology, we created a knock-in add-on of CD19-mScarlet downstream of insulin (INS) coding sequence exon 2 in human embryonic stem cells (hESCs). We developed and optimized a magnetic-activated cell sorting protocol for CD19-mScarlet-expressing cells, forming enriched SCβ-cell clusters with improved glucose-stimulated C-peptide secretion. This strategy holds promise to facilitate large-scale production of functional SCβ-cells for disease modeling and cell-replacement therapy.
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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 | 0.001 |
| 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".