Raman microspectroscopy of live cells under autophagy-inducing conditions
Bibliographic record
Abstract
The role of autophagy in numerous physiological responses triggered by a variety of mechanisms both in states of health and disease has raised considerable interest in this cellular process. However, the current analytical tools to study autophagy are either invasive or require genetic manipulation. Raman microspectroscopy is a potentially quantitative analytical method that has been shown to be useful for the label-free, non-destructive analysis of living biological cells and tissues. We present in this study initial efforts to study autophagy using Raman spectroscopy. The response of adherent mouse and human cancer cells to starvation conditions (glutamine deprivation and amino acid deprivation) was probed by Raman spectroscopy and compared to fluorescence microscopy results using autophagy-specific markers. We also demonstrate the capability of Raman spectroscopy to monitor the recovery dynamics of starved cells and to probe the heterogeneity in the response to starvation that can arise in cell populations. Finally, this work suggests that the 718 cm(-1) Raman line associated with phospholipids may be a useful spectral marker indicative of an autophagic response to starvation stimuli. Overall, this study establishes the utility of Raman spectroscopy to non-invasively detect biologically relevant changes in live cells exposed to conditions known to trigger autophagy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".