A prioritized medium-throughput screen on human stem cell derived insulin secreting beta cells identifies FGF4, FGF5, FGF8F, FGF19 and FGF21 as protective factors
Bibliographic record
Abstract
Abstract Insulin is produced by pancreatic β cells, whose dysfunction and death are hallmarks of diabetes. Stem cell-derived β-like cells (SCβ cells) are a potential alternative to cadaveric islets for replacement therapy. However, SCβ cells face a multitude of stresses, including hypoxia, hyperglycemia, ER stress, inflammation, and autoimmunity that must be overcome for this treatment to cure diabetes. Pancreatic β cells and SCβ cells reside in complex microtissues (islets or spheroids) and can therefore be potentially modulated by hundreds of autocrine and paracrine signals. Here, we leveraged scRNAseq, bulk RNAseq, and bulk proteomics data from late stage SCβ cells and human islets to map potential ligand-receptor pairs in these tissues and generate a prioritized list of ligands for high-content SCβ cell survival screening. We tracked cell number with Hoechst 33342, cell death using propidium iodide incorporation, and INS activity from INS -EGFP knock-in reporter human embryonic stem cells over several days using high throughput imaging. Members of the fibroblast growth factor (FGF) family significantly prevented cytokine induced cell death, with the top validated hits being FGF4, FGF5, FGF19, FGF21, and FGF8F. These results have the potential to improve SCβ cell survival with pathways that are readily targetable and translatable to the clinic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".