Surface Tension Guided Hanging-Drop: Producing Controllable 3D Spheroid of High-Passaged Human Dermal Papilla Cells and Forming Inductive Microtissues for Hair-Follicle Regeneration
Why this work is in the frame
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Bibliographic record
Abstract
Human dermal papilla (DP) cells have been studied extensively when grown in the conventional monolayer. However, because of great deviation from the real in vivo three-dimensional (3D) environment, these two-dimensional (2D) grown cells tend to lose the hair-inducible capability during passaging. Hence, these 2D caused concerns have motivated the development of novel 3D culture techniques to produce cellular microtissues with suitable mimics. The hanging-drop approach is based on surface tension-based technique and the interaction between surface tension and gravity field that makes a convergence of liquid drops. This study used this technique in a converged drop to form cellular spheroids of dermal papilla cells. It leads to a controllable 3Dspheroid model for scalable fabrication of inductive DP microtissues. The optimal conditions for culturing high-passaged (P8) DP spheroids were determined first. Then, the morphological, histological and functional studies were performed. In addition, expressions of hair-inductive markers including alkaline phosphatase, α-smooth muscle actin and neural cell adhesion molecule were also analyzed by quantitative RT-PCR, immunostaining and immunoblotting. Finally, P8-DP microtissues were coimplanted with newborn mouse epidermal cells (EPCs) into nude mice. Our results indicated that the formation of 3D microtissues not only endowed P8-DP microtissues many similarities to primary DP, but also confer these microtissues an enhanced ability to induce hair-follicle (HF) neogenesis in vivo. This model provides a potential to elucidate the native biology of human DP, and also shows the promising for the controllable and scalable production of inductive DP cells applied in future follicle regeneration.
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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) | 0.001 | 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 it