Non-specific interaction of carbon nanotubes with the resazurin assay reagent: Impact on in vitro assessment of nanoparticle cytotoxicity
Bibliographic record
Abstract
In vitro cytotoxicity assays are essential tools in the screening of engineered nanomaterials (NM) for cellular toxicity. The resazurin live cell assay is widely used because it is non-destructive and is well suited for high-throughput platforms. However, NMs, in particular carbon nanotubes (CNT) can interfere in assays through quenching of transmitted light or fluorescence. We show that using the resazurin assay with time-point reading of clarified supernatants resolves this problem. Human lung epithelial (A549) and murine macrophage (J774A.1) cell lines were exposed to NMs in 96-well plates in 200 μL of media/well. After 24 h incubation, 100 μL of supernatant was removed, replaced with resazurin reagent in culture media and aliquots at 10 min and 120 min were transferred to black-wall 96-well plates. The plates were quick-spun to sediment the residual CNTs and fluorescence was top-read (λEx=540 nm, λEm=600 nm). The procedure was validated for CNTs as well as silica nanoparticles (SiNP). There was no indication of reduction of resazurin by the CNTs. Stability of resorufin, the fluorescent product of the resazurin reduction was then assessed. We found that polar CNTs could decrease the fluorescence signal for resorufin, possibly through oxidation to resazurin or hyper-reduction to hydroxyresorufin. This effect can be easily quantified for elimination of the bias. We recommend that careful consideration must be given to fluorimetric/colorimetric in vitro toxicological assessments of optically/chemically active NMs in order to relieve any potential artifacts due to the NMs themselves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".