Visualization of the Skeletal Muscle Stem Cell Niche in Fiber Bundles
Bibliographic record
Abstract
Skeletal muscle stem cells (MuSCs) reside in a complex niche composed of the muscle fiber plasma membrane and the laminin-rich basal lamina surrounded by the microvasculature, as well as different supportive cell types such as fibro-adipogenic progenitors residing in the interstitial extracellular matrix. Within the first few hours after tissue damage, MuSCs undergo cytoskeletal rearrangements and transcriptional changes that prime the cells for activation. Due to their time-consuming nature, enzymatic methods for liberation of single muscle fibers with fully quiescent MuSCs are challenging. Moreover, during enzymatic digestion, important niche components including the microvasculature and the collagenous interstitial matrix are destroyed. Here, we provide a method for the visualization of MuSCs on muscle fibers in their intact niche. Our method relies on mechanical teasing of fiber bundles from fixed skeletal muscles. We demonstrate that teased muscle fiber bundles allow the investigator to capture a representative snapshot of the MuSC niche in skeletal muscle, and outline how stem cell morphology and different microenvironmental components can be visualized. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Isolation of fiber bundles Basic Protocol 2: Immunofluorescence staining of MuSCs on fiber bundles Support Protocol: Preparation of Sylgard dishes.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".