Modulating Myogenesis: An Optimized <i>In Vitro</i> Assay to Pharmacologically Influence Primary Myoblast Differentiation
Bibliographic record
Abstract
The intentional pharmacological manipulation of myogenesis is an important technique for understanding the underlying mechanisms of muscle differentiation and disease etiology. Using the pharmacological agent metformin as an example molecule, we present a systematic approach to examine the impact of pharmacological agents on the myogenic program. This consists of optimizing the in vitro differentiation of primary myoblast cells followed by the generation of a dose-response curve for a respective pharmaceutical. To assess myogenic differentiation, we utilized three approaches (incorporating both transcriptional and protein techniques) to assess the effects of biologically active agents on the in vitro differentiation of primary myogenic progenitors. First, the immunofluorescent visualization of myosin heavy chain (MYHC), which is expressed in differentiated myofibers, is used to obtain the fusion index, a quantitative read-out of differentiation efficiency. Second, quantitative reverse transcription PCR (RT-qPCR) reveals the expression of myogenic factors (Pax7, Myf5, Myod, Myog, Myh2) at the transcript level. Third, western blotting is used to assess the protein expression levels of the myogenic markers (PAX7, MYF5, MYOD, MYOG, and MYHC). By monitoring the expression of these various myogenic factors during the differentiation process, the relative cellular state and differentiation status between samples can be determined. Combined, these approaches enable the successful assessment of the impact of pharmacological agents on myogenic differentiation. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Immunofluorescence assay for qualitative and quantitative assessment of pharmacological agents on in vitro myogenic differentiation Support Protocol 1: Evaluating myogenic gene expression by RT-qPCR Support Protocol 2: Evaluating myogenic protein expression by western blot.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".