Multidimensional profiling of drug‐treated cells by Imaging Mass Cytometry
Bibliographic record
Abstract
In pharmaceutical research, high‐content screening is an integral part of lead candidate development. Measuring drug response in vitro by examining over 40 parameters, including biomarkers, signaling molecules, cell morphological changes, proliferation indices, and toxicity in a single sample, could significantly enhance discovery of new therapeutics. As a proof of concept, we present here a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single‐cell quantitative information visualized as t‐SNE plots and heatmaps. Human breast cancer‐derived cell lines SKBR3, HCC1143, and MCF‐7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF‐7 cells dosed with three target‐specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole, and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3 S28 , Ki‐67, and p4E‐BP1 T37/T46 in classified mitotic cells and anticorrelation with cell surface markers. Our study demonstrates that IMC data expand the number of measured parameters in single cells and brings higher‐dimension analysis to the field of cell‐based screening in early lead compound discovery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".