Satellite cell number and cell cycle kinetics in response to acute myotrauma in humans: immunohistochemistry<i>versus</i>flow cytometry
Bibliographic record
Abstract
In humans, muscle satellite cell (SC) enumeration is an important measurement used to determine the myogenic response to various stimuli. To date, the standard practice for enumeration is immunohistochemistry (IHC) using antibodies against common SC markers (Pax7, NCAM). Flow cytometry (FC) analysis may provide a more rapid and quantitative determination of changes in the SC pool with potential for additional analysis not easily achievable with standard IHC. In this study, FC analysis revealed that the number of Pax7(+) cells per milligram isolated from 50 mg of fresh tissue increased 36% 24 h after exercise-induced muscle injury (300 unilateral maximal eccentric contractions). IHC analysis of Pax7 and neural cell adhesion molecule (NCAM) appeared to sufficiently and similarly represent the expansion of SCs after injury (28-36% increase). IHC and FC data illustrated that Pax7 was the most widely expressed SC marker in muscle cross-sections and represented the majority of positive cells, while NCAM was expressed to a lesser degree. Moreover, FC and IHC demonstrated a similar percentage change 24 h after injury (36% increase, Pax7; 28% increase, NCAM). FC analysis of isolated SCs revealed that the number of Pax7(+) cells per milligram in G(2)/M phase of the cell cycle increased 202% 24 h after injury. Number of cells per milligram in G(0)/G(1) and cells in S-phase increased 32% and 59% respectively. Here we illustrate the use of FC as a method for enumerating SC number on a per milligram tissue basis, providing a more easily understandable relation to muscle mass (vs. percentage of myonuclei or per myofibre). Although IHC is a powerful tool for SC analysis, FC is a fast, reliable and effective method for SC quantification as well as a more informative method for cell cycle kinetics of the SC population in humans.
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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 | 0.001 | 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".