Immunophenotyping of the human bulge region: the quest to define useful <i>in situ</i> markers for human epithelial hair follicle stem cells and their niche
Bibliographic record
Abstract
Since the discovery of epithelial hair follicle stem cells (eHFSCs) in the bulge of human hair follicles (HFs) an important quest has started: to define useful markers. In the current study, we contribute to this by critically evaluating corresponding published immunoreactivity (IR) patterns, and by attempting to identify markers for the in situ identification of human eHFSCs and their niche. For this, human scalp skin cryosections of at least five different individuals were examined, employing standard immunohistology as well as increased sensitivity methods. Defined reference areas were compared by quantitative immunohistochemistry for the relative intensity of their specific IR. According to our experience, the most useful positive markers for human bulge cells turned out to be cytokeratin 15, cytokeratin 19 and CD200, but were not exclusive, while beta1 integrin and Lhx2 IR were not upregulated by human bulge keratinocytes. Absent IR for CD34, connexin43 and nestin on human bulge cells may be exploited as negative markers. alpha6 integrin, fibronectin, nidogen, fibrillin-1 and latent transforming growth factor (TGF)-beta-binding protein-1 were expressed throughout the connective tissue sheath of human HFs. On the other hand, tenascin-C was upregulated in the bulge and may thus constitute a component of the bulge stem cell niche of human HFs. These immunophenotyping results shed further light on the in situ expression patterns of claimed follicular 'stem cell markers' and suggest that not a single marker alone but only the use of a limited corresponding panel of positive and negative markers may offer a reasonable and pragmatic compromise for identifying human bulge stem cells in situ.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".