Raman Microscopy of Human Embryonic Stem Cells Exposed to Heat and Cold Stress
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) have large nucleus-to-cytoplasm ratios and nucleic acid spectral bands are prominent in their characteristic Raman signatures. Under normal conditions, the major variations in these signatures are due to changes in glycogen content, but how these signatures vary in response to different external conditions is largely unknown. In this study we investigated the influences of temperature variations on hESC Raman signatures. At 32 °C, compared to the 37 °C control condition, cell proliferation rates were markedly reduced and glycogen Raman band intensities were elevated. In addition, at both temperatures, an inverse relationship between cell proliferation rates (i.e., onset of exponential growth phase vs. end of exponential phase) and glycogen Raman band intensities was observed. This relationship suggested a role for glycogen in the energy metabolism of hESC self-renewal. Protein and lipid spectral variations were small and co-varied with those of nucleic acids, suggesting that they were related to changes in cellular dimensions occurring during the cell cycle. When the temperature was elevated to 39 °C, increased glycogen band intensities, compared to controls, were also observed. In addition, spectral evidence of differentiation emerged that was supported by reduced SSEA-3 expression. Taken together, these results demonstrated that heat and cold stress had quite different effects on the characteristic Raman signatures of hESCs. Thus, Raman spectroscopy can be used to detect deviation from optimal culturing temperatures and therefore it could be of considerable value in the routine and noninvasive determination of hESC culture quality.
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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".