A live-cell, high-content imaging survey of 206 endogenous factors across five stress conditions reveals context-dependent survival effects in mouse primary beta cells
Bibliographic record
Abstract
AIMS/HYPOTHESIS: Beta cell death is a hallmark of diabetes. It is not known whether specific cellular stresses associated with type 1 or type 2 diabetes require specific factors to protect pancreatic beta cells. No systematic comparison of endogenous soluble factors in the context of multiple pro-apoptotic conditions has been published. METHODS: Primary mouse islet cells were cultured in conditions mimicking five type 1 or type 2 diabetes-related stresses: basal 5 mmol/l glucose, cytokine cocktail (25 ng/ml TNF-α, 10 ng/ml IL-1β, 10 ng/ml IFN-γ), 1 μmol/l thapsigargin, 1.5 mmol/l palmitate and 20 mmol/l glucose (all in the absence of serum). We surveyed the effects of a library of 206 endogenous factors (selected based on islet expression of their receptors) on islet cell survival through multi-parameter, live-cell imaging. RESULTS: Our survey pointed to survival factors exhibiting generalised protective effects across conditions meant to model different types of diabetes and stages of the diseases. For example, our survey and follow-up experiments suggested that OLFM1 is a novel protective factor for mouse and human beta cells across multiple conditions. Most strikingly, we also found specific protective survival factors for each model stress condition. For example, semaphorin4A (SEMA4A) was toxic to islet cells in the serum-free baseline and serum-free 20 mmol/l glucose conditions, but protective in the context of lipotoxicity. Rank product testing supported the consistency of our observations. CONCLUSIONS/INTERPRETATION: Collectively, our survey reveals previously unidentified islet cell survival factors and suggest their potential utility in individualised medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".