Determination of the Relationship between Expression and Functional Activity of Multidrug Resistance-Associated Protein 1 using Scanning Electrochemical Microscopy
Bibliographic record
Abstract
Cancer cells can develop multidrug resistance (MDR) after prolonged exposure to chemotherapeutic drugs, which is a severe impediment to successful treatment. MDR is typically associated with transmembrane proteins mediating efflux of administered drugs, thereby keeping their intracellular concentration below the threshold required to kill cells. Although expression assays based on flow cytometry and immunostaining have shown that multidrug resistance-associated protein 1 (MRP1) is prevalent in many cancer types, the functional activity of this efflux pump is more difficult to elucidate, especially at the single-cell level. Herein, we report the measurement of MRP1 functional activity in individual cancer cells using scanning electrochemical microscopy (SECM). Cells were cultured onto plastic substrates containing selective adhesion sites. Optical microscopy and SECM revealed that cells adapt to the underlying surface, while MRP1 functional activity increases once the dimensions of the adhesive islands become smaller than those of the cell itself. Time-lapse SECM imaging revealed a suitable window of 30 min to complete each measurement before the cell undergoes blebbing, which is associated with a considerable increase in functional activity. Distinct cell populations were produced by performing a doxorubicin drug challenge on two parental cell lines (e.g., wild-type HeLa cells and MRP1-overexpressing HeLa-R cells). Expression and functional activity of MRP1 were determined using flow cytometry and SECM, and our findings show that these parameters do not directly correlate. This suggests that functional activity may represent a powerful indicator of a cancer cell's response to chemotherapeutic treatment and should improve our understanding of efflux mechanisms based on MRP1.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".