FACS Analysis and Immunohistochemical Analysis of Human Myogenic Stem Cell Number and Cell‐cycle Kinetics in Response to Acute Myotrauma
Bibliographic record
Abstract
In humans, myogenic stem cell (SC) enumeration is an important measurement used to determine the SC response in vivo to various physiological stimuli. The current standard for enumeration is immunohistochemistry (IHC) with antibodies against common SC markers (i.e. Pax7, NCAM). Fluorescent activated cell sorting (FACS) analysis may provide a more accurate determination of changes in the SC pool and provide additional analysis unachievable with IHC. FACS analysis revealed Pax7+ cells/mg isolated from 50mg fresh tissue increased 36% 24h after injury. The number of Pax7+ cells/mg in G2/M phase of the cell cycle increased 202% after 24h and cells/mg in G1/G0 and S‐phase increased 32% and 59% respectively. IHC data illustrated, in relation to N‐CAM or C‐Met alone, Pax7 alone was expressed on a greater number of cells. Furthermore, all 3 markers appear to sufficiently and similarly report SC expansion after injury (26–36%). Here we illustrate the use of FACS as a precise method of enumerating SC number on a per milligram tissue basis, providing a more easily understandable relation to muscle mass as opposed to number of myonuclei or fiber number. Although IHC is a powerful tool for SC analysis, FACS is an objective, reliable and effective method for SC quantification and can provide additional information such as cell‐cycle kinetics more accurately than IHC alone.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".