Label-free imaging of mammalian cell nucleoli by Raman microspectroscopy
Bibliographic record
Abstract
The nucleolus is a prominent subnuclear structure whose major function is the transcription and assembly of ribosome subunits. The size of the nucleolus varies with the cell cycle, proliferation rate and stress. Changes in nucleolar size, number, chemical composition, and shape can be used to characterize malignant cells. We used spontaneous Raman microscopy as a label-free technique to examine nucleolar spatial and chemical features. Raman images of the 1003 cm(-1) phenylalanine band revealed large, well-defined subnuclear protein structures in MFC-7 breast cancer cells. The 783 cm(-1) images showed that nucleic acids were similarly distributed, but varied more in intensity, forming observable high-intensity regions. High subnuclear RNA concentrations were observed within some of these regions as shown by 809 cm(-1) Raman band images. Principal component analyses of sub-images and library spectra validated the subnuclear presence of RNA. They also revealed that an actin-like protein covaried with DNA within the nucleolus, a combination that accounted for 64% or more of the spectral variance. Embryonic stem cells are another rapidly proliferating cell type, but their nucleoli were not as large or well defined. Estimating the size of the larger MCF-7 nucleolus was used to show the utility of Raman microscopy for morphometric analyses. It was concluded that imaging based on Raman microscopy provides a promising new method for the study of nucleolar function and organization, in the evaluation of drug and experimental effects on the nucleolus, and in clinical diagnostics and prognostics.
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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 | 0.000 | 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 | 0.000 | 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".