Culture Strategy Determines the Differentiation Status of Sweat Gland Cells
Bibliographic record
Abstract
Reliable methods for the isolation and culture of human eccrine sweat gland cells (SGCs) are essential for studying glandular biology and developing tissue-engineered skin substitutes (TESs) that restore full skin function. However, maintaining the glandular phenotype of SGCs in vitro remains a major challenge. In this study, we present an optimized isolation protocol combining enzymatic digestion with mechanical separation to improve SGC yield and purity, while also enabling keratinocyte isolation from a single human skin biopsy. We then evaluated two culture strategies, 2D monolayers and 3D spheroids, to determine their impact on SGC identity and proliferation. While 2D culture supported cell expansion, SGCs and keratinocytes exhibited highly similar marker expression profiles, with the absence of functional SGC markers (AQP5, α-SMA) reflecting a shift toward less differentiated phenotypes. In contrast, SGCs cultured in 3D spheroids preserved the expression of SGC-specific markers (AQP5, K18, α-SMA), distinguishing them from keratinocytes; however, their growth and structural organization were suboptimal under these 3D conditions. Moreover, SGCs expanded in 2D did not regain their glandular features when reintroduced into 3D culture, suggesting potential limitations in phenotype recovery. These results highlight the need for improved culture systems that maintain SGC identity while supporting expansion. Advancing such methods is a critical step toward integrating functional sweat glands into TESs and achieving complete skin regeneration for clinical applications.
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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".