Live cell painting: New nontoxic dye to probe cell physiology in high content screening
Bibliographic record
Abstract
High-content imaging approaches, in combination with the use of perturbing agents such as small molecules or CRISPR-driven gene editing, have widely contributed to the identification of new therapeutic compounds. Thanks to recent advances in image-analysis methods, the use of high-content screens is increasingly gaining popularity and thus accelerating the discovery of new therapeutics. However, due to the lack of fully biocompatible fluorescent markers, large-scale high-content screens are mostly performed on fixed cells, which complicates the monitoring of changes in cell physiology over time. Here we present a novel fluorescent nontoxic dye that displays intensity and staining pattern changes in response to different physiological states. With multiparametric image analysis, these unique properties allow not only for the detection of distinct phenotypic fingerprints, but also for the quantification of more traditional disease-relevant phenotypes such as apoptosis, autophagy, ER stress and more. Since the dye only gets fluorescent when incorporated into cellular membranes, it is typically used without washing steps, therefore making it ideal to include in automation workflows. In this work, we present relevant data on its biocompatibility and its potential to quantitatively assess subtle cellular phenotypes. Applications such as live kinetic imaging, and live image-based morphological profiling are also discussed. The rich information this fluorescent probe provides facilitates unbiased quantitative phenotypic analysis at larger scale, and ultimately paves the way for more discoveries of new therapeutic agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".