Variability in Raman Spectra of Single Human Tumor Cells Cultured <i>in vitro</i>: Correlation with Cell Cycle and Culture Confluency
Bibliographic record
Abstract
In this work we investigate the capability of Raman microscopy (RM) to detect inherent sources of biochemically based spectral variability between single cells of a human tumor cell line (DU145) cultured in vitro. Principal component analysis (PCA) is used to identify differences in single-cell Raman spectra. These spectral differences correlate with (1) cell cycle progression and (2) changing confluency of a cell culture during the first 3 to 4 days after sub-culturing. Cell cycle regulatory drugs are used to synchronize the cell cycle progression of cell cultures, and flow cytometry is used to determine the cell cycle distribution of cell cultures at the time of Raman analysis. Spectral variability arising from cell cycle progression is (1) expressed as varying intensities of protein and nucleic acid features relative to lipid features, (2) well correlated with known biochemical changes in cells as they progress through the cell cycle, and (3) shown to be the most significant source of inherent spectral variability between cells. Furthermore, the specific biomolecules responsible for the observed spectral variability due to both cell cycle progression and changes in cell culture confluency can be identified in the first and second components of principal component analysis (PCA). Our characterization of the inherent sources of variability in Raman spectra of single human cells will be useful for understanding subtle spectral differences in RM studies of single cells.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".