High-Throughput Technology: Green Fluorescent Protein to Monitor Cell Death
Bibliographic record
Abstract
Reliable assessment of cell death is now pivotal to many research programs aiming at generating new antitumor compounds or at screening cDNA libraries to identify genes with pro- or antiapoptotic functions. Such approaches need to rely on reproducible, easy handling, and rapid microplate-based cytotoxicity assays that are amenable to high-throughput screening technologies. We describe here a method for the direct measurement of cell death, based on the detection of a decrease in fluorescence observed following death induction in cells stably expressing enhanced green fluorescent protein (EGFP). Our data clearly show that such a decrease in EGFP fluorescence after cell death induction happens in various cell types, including those routinely used in anticancer drug screening (i.e., murine and human, lymphoid, fibroblastic, or epithelial cell lines). Moreover, the decrease in EGFP fluorescence is observed in cells induced to die by a variety of apoptosis-inducing agents, such as glucocorticoids (dexamethasone), DNA- damaging agents (etoposide, cisplatin), microtubule disorganizers (paclitaxel), protein kinase C inhibitors (staurosporine), or a caspase-independent apoptotic stimulus (CD45 crosslinking). A decrease in fluorescence can be assessed either by flow cytometry or with a fluorescence microplate reader. The kinetics and specificity of this EGFP-based assay were comparable with those of other conventional techniques used to detect cell death. This novel EGFP-based microplate assay combines sensitivity and rapidity and is amenable to high-throughput setups, making it an assay of choice for evaluation of cell cytotoxicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".