Transcriptome profiles of isolated murine Achilles tendon proper‐ and peritenon‐derived progenitor cells
Bibliographic record
Abstract
Progenitor cells of the tendon proper and peritenon have unique properties that could impact their utilization in tendon repair strategies. While a few markers have been found to aid in distinguishing progenitors cells from each region, there is great value in identifying more markers. In this study, we hypothesized that RNAseq could be used to improve our understanding of those markers that define these cell types. Transcriptome profiles were generated for pools of mouse Achilles tendon progenitor cells from both regions and catalogues of potential markers were generated. Moreover, common (e.g., glycoprotein, signaling, and proteinaceous extracellular matrix) and unique (e.g., cartilage development versus angiogenesis and muscle contraction) biological processes and molecular functions were described for progenitors from each region. Real-time quantitative PCR of a subset of genes was used to gain insight into the heterogeneity amongst individual progenitor colonies from each region. Markers like Scx, Mkx, Thbs4, and Wnt10a were consistently able to distinguish tendon proper progenitors from peritenon progenitors; expression variability for other genes suggested greater cell type complexity for potential peritenon progenitor markers. This is the first effort to define Achilles tendon progenitor markers by region. Further efforts to investigate the value of these cataloged markers are required by screening more individual colonies of progenitors for more markers. Clinical Significance: Findings from this study advance efforts in the discernment of cell type specific markers for tendon proper and peritenon progenitor cells; insight into marker sets could improve tracking and sorting strategies for these cells for future therapeutic strategies. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1409-1418, 2019.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".