Glycoprotein 2 is a specific cell surface marker of human pancreatic progenitors
Bibliographic record
Abstract
Abstract PDX1 + /NKX6-1 + pancreatic progenitors (PPs) give rise to endocrine cells both in vitro and in vivo. This cell population can be successfully differentiated from human pluripotent stem cells (hPSCs) and hold the potential to generate an unlimited supply of β cells for diabetes treatment. However, the efficiency of PP generation in vitro is highly variable, negatively impacting reproducibility and validation of in vitro and in vivo studies, and consequently, translation to the clinic. Here, we report the use of a proteomics approach to phenotypically characterize hPSC-derived PPs and distinguish these cells from non-PP populations during differentiation. Our analysis identifies the pancreatic secretory granule membrane major glycoprotein 2 (GP2) as a PP-specific cell surface marker. Remarkably, GP2 is co-expressed with NKX6-1 and PTF1A in human developing pancreata, indicating that it marks the multipotent pancreatic progenitors in vivo. Finally, we show that isolated hPSC-derived GP2 + cells generate β-like cells (C-PEPTIDE + /NKX6-1 + ) more efficiently compared to GP2 − and unsorted populations, underlining the potential therapeutic applications of GP2.
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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".