High Resolution Genome Wide Expression Analysis of Single Myofibers Using SMART-Seq
Bibliographic record
Abstract
ABSTRACT Skeletal muscle is a heterogeneous tissue. Individual myofibers that make up muscle tissue exhibit variation in their metabolic and contractile properties. Although there are biochemical and histological assays to study myofiber heterogeneity, efficient methods to analyze the whole transcriptome of individual myofibers are lacking. We have developed single myofiber RNA-Seq (smfRNA-Seq) to analyze the whole transcriptome of individual myofibers by combining single fiber isolation with Switching Mechanisms at 5’ end of RNA Template (SMART) technology. Our method provides high-resolution genome wide expression profiles of single myofibers. Using smfRNA-Seq, we have analyzed the differences in the transcriptome of young and old myofibers to validate the effectiveness of this new method. Using smfRNA-Seq, we performed comparative gene expression analysis between single myofibers from young and old mice. Our data suggests that aging leads to significant changes in the expression of metabolic and structural genes in myofibers. Our data suggests that smfRNA-Seq is a powerful tool to study developmental, disease and age-related dynamics in the composition of skeletal muscle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".