Assessing Differentiation Status of Human Embryonic Stem Cells Noninvasively Using Raman Microspectroscopy
Bibliographic record
Abstract
Raman microspectroscopy is an attractive approach for chemical imaging of biological specimens, including live cells, without the need for chemi-selective stains. Using a microspectrometer, near-infrared Raman spectra throughout the range 663 cm(-1) to 1220 cm(-1) were obtained from colonies of CA1 human embryonic stem cells (hESCs) and CA1 cells that had been stimulated to differentiate for 3 weeks by 10% fetal bovine serum on gelatin. Distributions and intensities of spectral bands attributed to proteins varied significantly between undifferentiated and differentiated cells. Importantly, compared to proteins and lipids, the band intensities of nucleic acids were dominant in undifferentiated cells with a dominance-reversal in differentiated cells. Thus, we could identify intensity ratios of particular protein-related bands (e.g., 757 cm(-1) tryptophan) to nucleic acid bands (784 cm(-1) DNA/RNA composite) that were effective in discriminating between spectra of undifferentiated and differentiated cells. We observed no discernible negative effects due to the laser exposure in terms of morphology, proliferation, or pluripotency of the stem cells. We conclude that Raman microscopy and complementary data processing procedures provide a rapid, noninvasive approach that can distinguish hESCs from differentiated cells. This is the first report to identify specific Raman markers for the differentiation status of hESCs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".