Protocol for high-throughput viability screening and gene expression of human EndoC-βH5 β cells and pancreatic islets
Bibliographic record
Abstract
Viability assays allow the assessment of toxicity induced by any treatment of interest. Here, we present a protocol to evaluate cell viability in human EndoC-βH5 β cells and human pancreatic islets using a high-throughput fluorescence viability assay. We describe the steps for cell and islet culture, reagent dilution, viability assay preparation, and performance. The protocol is compatible with RNA isolation and gene expression analysis on the same cells, and we detail procedures for reagent washout and profiling gene expression. For complete details on the use and execution of this protocol, please refer to Rampazzo Morelli et al. 1 • Protocol to measure relative cell viability of EndoC-βH5 β cells and human islets • Instructions for culturing EndoC-βH5 cells and human islets for the viability assay • Steps for reagent dilution, viability assay setup, and performance • Guidance on reagent removal for gene expression analysis from the same cells Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Viability assays allow the assessment of toxicity induced by any treatment of interest. Here, we present a protocol to evaluate cell viability in human EndoC-βH5 β cells and human pancreatic islets using a high-throughput fluorescence viability assay. We describe the steps for cell and islet culture, reagent dilution, viability assay preparation, and performance. The protocol is compatible with RNA isolation and gene expression analysis on the same cells, and we detail procedures for reagent washout and profiling gene expression.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.034 |
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".