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 Stem cell-derived β-like cells (SCβ cells) are a potential alternative to cadaveric β cells for replacement therapy in type 1 diabetes. However, SCβ cells face a multitude of stresses that must be overcome. Both β cells and SCβ cells reside in complex microtissues and can therefore be modulated by hundreds of autocrine/paracrine signals within these islets or spheroids. Here, we leveraged multi-omics data from late-stage SCβ cells and human islets to map ligand-receptor pairs and generate a prioritized list of ligands for high-content SCβ cell survival screening. Our medium-throughput screen tracked cell number, cell death, and INS production over several days using automated, high-content 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. With these results, there is the potential to improve SCβ cell survival in vitro via readily targetable pathways and to produce a more robust product for translation to the clinic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".