SMART approaches for genome-wide analyses of skeletal muscle stem and niche cells
Bibliographic record
Abstract
Muscle stem cells (MuSCs) also called satellite cells are the building blocks of skeletal muscle, the largest tissue in the human body which is formed primarily of myofibers. While MuSCs are the principal cells that directly contribute to the formation of the muscle fibers, their ability to do so depends on critical interactions with a vast array of nonmyogenic cells within their niche environment. Therefore, understanding the nature of communication between MuSCs and their niche is of key importance to understand how the skeletal muscle is maintained and regenerated after injury. MuSCs are rare and therefore difficult to study in vivo within the context of their niche environment. The advent of single-cell technologies, such as switching mechanism at 5’ end of the RNA template (SMART) and tagmentation based technologies using hyperactive transposase, afford the unprecedented opportunity to perform whole transcriptome and epigenome studies on rare cells within their niche environment. In this review, we will delve into how single-cell technologies can be applied to the study of MuSCs and muscle-resident niche cells and the impact this can have on our understanding of MuSC biology and skeletal muscle regeneration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".