Single-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) are a potential unlimited source of insulin-producing β cells for diabetes treatment. A greater understanding of how β cells form during embryonic development will improve current hESC differentiation protocols. All pancreatic endocrine cells, including β cells, are derived from Neurog3-expressing endocrine progenitors. This study characterizes the single-cell transcriptomes of 6,905 mouse embryonic day (E) 15.5 and 6,626 E18.5 pancreatic cells isolated from Neurog3-Cre; Rosa26 mT/mG embryos, allowing for enrichment of endocrine progenitors (yellow; tdTomato + EGFP) and endocrine cells (green; EGFP). Using a NEUROG3-2A-eGFP CyT49 hESC reporter line (N5-5), 4,462 hESC-derived GFP+ cells were sequenced. Differential expression analysis revealed enrichment of markers that are consistent with progenitor, endocrine, or previously undescribed cell-state populations. This study characterizes the single-cell transcriptomes of mouse and hESC-derived endocrine progenitors and serves as a resource (https://lynnlab.shinyapps.io/embryonic_pancreas) for improving the formation of functional β-like cells from hESCs.
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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".