Live cell painting: New nontoxic dye to probe cell physiology in high content screening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it