Dynamic hydrogen peroxide levels reveal a rate-dependent sensitivity in B-cell lymphoma signaling
Bibliographic record
Abstract
Abstract Although in vivo extracellular microenvironments are dynamic, most in vitro studies are conducted under static conditions. Here, we exposed diffuse large B-cell lymphoma (DLBCL) cells to gradient increases in the concentration of hydrogen peroxide (H2O2), thereby capturing some of the dynamics of the tumour microenvironment. Subsequently, we measured the phosphorylation response of B-cell receptor (BCR) signalling proteins CD79a, SYK and PLCγ2 at a high temporal resolution via single-cell phospho-specific flow cytometry. We demonstrated that the cells respond bimodally to static extracellular H2O2, where the percentage of cells that respond is mainly determined by the concentration. Computational analysis revealed that the bimodality results from a combination of a steep dose–response relationship and cell-to-cell variability in the response threshold. Dynamic gradient inputs of varying durations indicated that the H2O2 concentration is not the only determinant of the signalling response, as cells exposed to more shallow gradients respond at lower H2O2 levels. A minimal model of the proximal BCR network qualitatively reproduced the experimental findings and uncovered a rate-dependent sensitivity to H2O2, where a lower rate of increase correlates to a higher sensitivity. These findings will bring us closer to understanding how cells process information from their complex and dynamic in vivo environments.
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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 | 0.000 |
| 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) | 0.001 | 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".