Sci-Fri AM: Mountain - 04: Label-free Raman spectroscopy of single tumour cells detects early radiation-induced glycogen synthesis associated with increased radiation resistance
Bibliographic record
Abstract
Purpose: To use label-free Raman spectroscopy (RS) for early treatment monitoring of tumour cell radioresistance. Methods: Three human tumour cell lines, two radioresistant (H460, SF2 = 0.57 and MCF7, SF2 = 0.70) and one radiosensitive (LNCaP, SF2 = 0.36), were irradiated with single fractions of 2, 4, 6, 8 or 10 Gy. In additional experiments, H460 and MCF7 cells were irradiated under co-treatment with the anti-diabetic drug metformin, a known radiosensitizing agent. Treated and control cultures were analyzed with RS daily for 3 days post-treatment. Single-cell Raman spectra were acquired from 20 live cells per sample, and experiments were repeated in triplicate. The combined data sets were analyzed with principal component analysis using standard algorithms. Cells from each culture were also subjected to standard assays for viability, proliferation, cell cycle, and radiation clonogenic survival. Results: The radioresistant cells (H460, MCF7) exhibited a RS molecular radiation response signature, detectable as early as 1 day post-treatment, of which radiation-induced glycogen synthesis is a significant contributor. The radiosensitive cells (LNCaP) exhibited negligible glycogen synthesis. Co-treatment with metformin in MCF7 cells blocked glycogen synthesis, reduced viability and proliferation, and increased radiosensitivity. Conversely, metformin co-treatment in H460 cells did not produce these same effects; importantly, both radiation-induced synthesis of glycogen and radiosensitivity were unaffected. Conclusions: Label-free RS can detect early glycogen synthesis post-irradiation, a previously undocumented metabolic mechanism associated with tumour cell radioresistance that can be targeted to increase radiosensitivity. RS monitoring of intratumoral glycogen may provide new opportunities for personalized combined modality radiotherapy treatments.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.014 |
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".