Characterizing the Immune Cell Transcriptomic Response To Low-Dose Ionizing Radiation
Bibliographic record
Abstract
Abstract Low-dose exposure to ionizing radiation is increasingly common in medical, industrial, and public settings worldwide. Previous studies examining the impacts of low-dose radiation (LDR) exposure on genetic material, cellular responses, and health have been inconclusive and conflicting. The National Academy of Science, Engineering and Medicine has outlined that future LDR research should utilize single-cell omics technologies and computational workflows to define the molecular signatures of LDR exposure. In partnership with the Canadian Nuclear Laboratories (CNL), we have used single-cell RNA sequencing (scRNA-seq) to characterize the in-vivo immune cell response to chronic LDR. Mature adult (18 weeks-old) C57BL/6 female mice were whole-body-exposed to 60Co gamma radiation for 7 days, at dose rates of 0.06 mGy/h or 0.6 mGy/h, to achieve cumulative absorbed doses of 10 mGy (n=6) or 100 mGy (n=6), respectively. scRNA-seq of cells isolated immediately post-irradiation identified subtle candidate transcriptomic changes caused by LDR exposure in splenic and bone marrow cell populations. As the first scRNA-seq study of in-vivo LDR exposure, we reveal a more detailed description of the cellular response to LDR exposure than available in the current literature. Therefore, dissemination of these results will advance radiation biology and will be valuable for evaluating Nuclear Safety guidelines. The research is funded by the CANDU Owner’s Group and the Mitacs Accelerate program.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".