Identification, Isolation, and Characterization of Fibro-Adipogenic Progenitors (FAPs) and Myogenic Progenitors (MPs) in Skeletal Muscle in the Rat
Bibliographic record
Abstract
Fibro-adipogenic Progenitors (FAPs) are resident interstitial cells in skeletal muscle that, together with myogenic progenitors (MPs), play a key role in muscle homeostasis, injury, and repair. Current protocols for FAPs identification and isolation use flow cytometry/fluorescence-activated cell sorting (FACS) and studies evaluating their function in vivo to date have been undertaken exclusively in mice. The larger inherent size of the rat allows for a more comprehensive analysis of FAPs in skeletal muscle injury models, especially in severely atrophic muscle or when investigators require substantial tissue mass to conduct multiple downstream assays. The rat additionally provides a larger selection of muscle functional assays that do not require animal sedation or sacrifice, thus minimizing morbidity and animal use by enabling serial assessments. The flow cytometry/FACS protocols optimized for mice are species specific, notably restricted by the characteristics of commercially available antibodies. They have not been optimized for separating FAPs from rat or highly fibrotic muscle. A flow cytometry/FACS protocol for the identification and isolation of FAPs and MPs from both healthy and denervated rat skeletal muscle was developed, relying on the differential expression of surface markers CD31, CD45, Sca-1, and VCAM-1. As rat-specific, flow cytometry-validated primary antibodies are severely limited, in-house conjugation of the antibody targeting Sca-1 was performed. Using this protocol, successful Sca-1 conjugation was confirmed, and flow cytometric identification of FAPs and MPs was validated by cell culture and immunostaining of FACS-isolated FAPs and MPs. Finally, we report a novel FAPs time-course in a prolonged (14 week) rat denervation model. This method provides the investigators the ability to study FAPs in a novel animal model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".