Investigating ionizing radiation-induced changes in breast cancer cells using stimulated Raman scattering microscopy
Bibliographic record
Abstract
Significance: Altered lipid metabolism of cancer cells has been implicated in increased radiation resistance. A better understanding of this phenomenon may lead to improved radiation treatment planning. Stimulated Raman scattering (SRS) microscopy enables label-free and quantitative imaging of cellular lipids but has never been applied in this domain. Aim: We sought to investigate the radiobiological response in human breast cancer MCF7 cells using SRS microscopy, focusing on how radiation affects lipid droplet (LD) distribution and cellular morphology. Approach: MCF7 breast cancer cells were exposed to either 0 or 30 Gy (X-ray) ionizing radiation and imaged using a spectrally focused SRS microscope every 24 hrs over a 72-hr time period. Images were analyzed to quantify changes in LD area per cell, lipid and protein content per cell, and cellular morphology. Cell viability and confluency were measured using a live cell imaging system while radiation-induced lipid peroxidation was assessed using BODIPY C11 staining and flow cytometry. Results: The LD area per cell and total lipid and protein intensities per cell were found to increase significantly for irradiated cells compared to control cells from 48 to 72 hrs post irradiation. Increased cell size, vacuole formation, and multinucleation were observed as well. No significant cell death was observed due to irradiation, but lipid peroxidation was found to be greater in the irradiated cells than control cells at 72 hrs. Conclusions: This pilot study demonstrates the potential of SRS imaging for investigating ionizing radiation-induced changes in cancer cells without the use of fluorescent labels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".