Dose-dependent spatiotemporal responses of mammalian cells to an alkylating agent
Bibliographic record
Abstract
Cultured cell populations are composed of heterogeneous cells, and previous single-cell lineage tracking analysis of individual HeLa cells provided empirical evidence for significant heterogeneity of the rate of cell proliferation and induction of cell death. Nevertheless, such cell lines have been used for investigations of cellular responses to various substances, resulting in incomplete characterizations. This problem caused by heterogeneity within cell lines could be overcome by investigating the spatiotemporal responses of individual cells to a substance. However, no approach to investigate the responses by analyzing spatiotemporal data is currently available. Thus, this study aimed to analyze the spatiotemporal responses of individual HeLa cells to cytotoxic, sub-cytotoxic, and non-cytotoxic doses of the well-characterized carcinogen, N-methyl-N'-nitro-N-nitrosoguanidine (MNNG). Although cytotoxic doses of MNNG are known to induce cell death, the single-cell tracking approach revealed that cell death occurred following at least four different cellular events, suggesting that cell death is induced via multiple processes. We also found that HeLa cells exposed to a sub-cytotoxic dose of MNNG were in a state of equilibrium between cell proliferation and cell death, with cell death again induced through different processes. However, exposure of cells to a non-cytotoxic dose of MNNG promoted growth by reducing the cell doubling time, thus promoting the growth of a sub-population of cells. A single-cell lineage tracking approach could dissect processes leading to cell death in a spatiotemporal manner and the results suggest that spatiotemporal data obtained by tracking individual cells can be used as a new type of bioinformatics data resource that enables the examination of cellular responses to various external substances.
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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".