Single-Cell-Kinetics Approach to Compare Multidrug Resistance-Associated Membrane Transport in Subpopulations of Cells
Bibliographic record
Abstract
Multidrug resistance (MDR) driven by active efflux of drugs from the cells is one of the major obstacles in chemotherapies. Understanding the nature of MDR and designing more efficient chemotherapies requires the comparison of the efflux rate between different subpopulations of cells. Here we propose a single-cell-kinetics approach for such a comparison. In essence, the entire cell population is loaded with a suitable fluorescent substrate for MDR-associated membrane transporters. The kinetics of substrate efflux from individual cells is followed by time-lapse fluorescence microscopy and analyzed at the single-cell level. Microscopy is also used to assign cells to different subpopulations based on differences in morphology or level of staining by molecular probes. The kinetic parameters obtained for individual cells are then averaged for different cell subpopulations and the mean values of these parameters are finally compared between subpopulations. To test our single-cell-kinetics approach, we studied MDR-related efflux for two subpopulations of cultured breast cancer cells: cells in 2N and 4N phases of the cell cycle. The assignment of cells to 2N and 4N subpopulations was done by fluorescent DNA staining after the completion of efflux. By using the single-cell-kinetics approach, we were able to prove for the first time that the rates of MDR-related efflux differ in 2N and 4N phases of the cell cycle. We foresee that this approach will be an important tool in studies of MDR and in designing combination chemotherapies.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".