Abstract PO-021: Single-cell DNA sequencing as a means to directly examine the size and frequency of radiation-induced mutations - An exploratory study
Bibliographic record
Abstract
Abstract This study examines if single-cell DNA sequencing may be used to study the mutational effects of ionizing radiation. As the action of ionizing radiation is a stochastic process, each cell in an irradiated sample experiences its own unique radiation-induced DNA damage. As a result, conventional sequencing methods such as bulk cell sequencing cannot be used to identify individual mutations. In this work, Epstein-Barr virus (EBV) transformed B-lymphoblastoid cells were irradiated with 6 MV X-ray radiation using a medical linear accelerator. Four samples of cells from the same population (400,000 cells/ml) were exposed to sham irradiation (0 Gray [Gy]; control), 0.5 Gy, 1.5 Gy and 3.0 Gy respectively at a common dose rate of about 600 cGy/min. Irradiated samples were incubated for 24 hrs and subsequently underwent single-cell whole-genome DNA sequencing to characterize the radiation impact. Mutational profiles of approximately 500 cells, randomly selected from each sample, were individually analyzed and compared to identify the variation of several mutational parameters as a function of dose. We quantified the copy number variations (CNV) for each cell in our samples. Additionally, we segregated insertion CNVs and deletion CNVs and independently analyzed their dose dependences. We found that the total number of CNVs (insertion and deletion combined) increased with dose, and the number of deletion CNVs consistently increased most. We have repeated the experiment and a new round of single-cell DNA sequencing is underway. If confirmed, our results will demonstrate that the mutational effects of ionizing radiation may be examined directly using single-cell sequencing. Citation Format: Felix Mathew, Jonathan Yeo, Luc Galarneau, Norma Ybarra, Patricia Tonin, Yu Chang Wang, Ioannis Ragoussis, John Kildea. Single-cell DNA sequencing as a means to directly examine the size and frequency of radiation-induced mutations - An exploratory study [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr PO-021.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".