Label-free identification and characterization of murine hair follicle stem cells located in thin tissue sections with Raman micro-spectroscopy
Bibliographic record
Abstract
Stem cells offer tremendous opportunities for regenerative medicine. Over the past decade considerable research has taken place to identify and characterize the differentiation states of stem cells in culture. Raman micro-spectroscopy has emerged as an ideal technology since it is fast, nondestructive, and does not require potentially toxic dyes. Raman spectroscopy systems can also be incorporated into confocal microscope imaging systems allowing spectra to be obtained from below the tissue surface. Thus there is significant potential for monitoring stem cells in living tissue. Stem cells that reside in hair follicles are suitable for testing this possibility since they are close to the skin surface, and typically clustered around the bulge area. One of the first steps needed would be to obtain Raman micro-spectra from stem cells located in thin sections of tissue, and then see whether these spectra are clearly different from those of the surrounding differentiated cells. To facilitate this test, standard 5 μm thick sections of murine skin tissue were stained to identify the location of hair follicle stem cells and their progeny. Raman spectra were then obtained from adjacent cells in a subsequent unstained 10 μm thick section. The spectra revealed significant differences in peak intensities associated with nucleic acids, proteins, lipids and amino acids. Statistical analyses of the Raman micro-spectra identified stem cells with 98% sensitivity and 94% specificity, as compared with a CD34 immunostaining gold standard. Furthermore analyses of the spectral variance indicated differences in cellular dynamics between the two cell groups. This study shows that Raman micro-spectroscopy has a potential role in identifying adult follicle stem cells, laying the groundwork for future applications of hair follicle stem cells and other somatic stem cells in situ.
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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 | 0.000 | 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".