Smaller scale, same impact: replicating high-throughput phenotypic profiling in a medium-throughput lab for use in chemical risk assessment
Bibliographic record
Abstract
Cell Painting visualizes toxicity-induced morphological changes by staining cellular structures with fluorescent dyes. Coupled with high-content imaging and analysis software, Cell Painting allows high-throughput phenotypic profiling (HTPP) to quantify phenotypic changes and estimate points of departure for toxicity assessments. Regulatory agencies have applied HTPP in 384-well plates for chemical hazard screening. In this study, established protocols for 384-well plates were adapted for use in 96-well plates to increase accessibility for laboratories with lower throughput. U-2 OS human osteosarcoma cells in 96-well plates were exposed to 12 phenotypic reference compounds for 24 h before fixation and staining with fluorescent dyes (golgi apparatus, endoplasmic reticulum, nucleic acids, cytoskeleton, mitochondria). Four independent chemical exposures across eight concentrations generated four biological replicates. Stained cells were imaged on an Opera Phenix, a high-content imaging system, and the Columbus analysis software extracted numerical values for 1300 morphological features. Features were normalized to control cells, followed by principal component analysis and a calculation of Mahalanobis for each treatment concentration. Mahalanobis distances were modeled to calculate benchmark concentrations (BMC) for chemicals. Most BMCs differed by less than one order of magnitude across experiments, demonstrating intra-laboratory consistency. Compared to published BMCs, ten compounds had comparable BMCs in both plate formats. In addition, we observed a significant inverse relationship between seeding density and Mahalanobis distances, suggesting that experimental factors like cell density may influence BMCs. Overall, we demonstrate that Cell Painting is adaptable across formats and laboratories, supporting efforts to develop and validate it as a complementary new approach methodology to existing toxicity tests.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".