Fibroblast and myeloid cells with high mitochondrial RNA content represent biologically significant populations within the OA synovium
Bibliographic record
Abstract
OBJECTIVES: Standard scRNA-seq QC often excludes cells with high mitochondrial RNA content (pMT), assuming they reflect low viability or dissociation-induced stress. Emerging evidence suggests high-pMT cells may represent disease-relevant cell populations. This study investigates how exclusion of high-pMT cells affects the transcriptional landscape of knee OA synovium and explores their potential role in disease pathobiology. DESIGN: Seven published human and mouse scRNA-seq datasets were reanalyzed using QC thresholds and quantile-based filtering to include high-pMT cells. Analyses included pMT distribution, dissociation-induced stress scores, cell death-related and mitochondrial apoptotic gene signatures. Dissociation-induced stress was assessed using a gene set from three published studies, while cell death and mitochondrial signatures were derived from established GSEA gene sets. Focusing on a single human knee OA synovial dataset stratified by pain (GSE248453), differential gene expression and pathway enrichment were compared between conventionally filtered and high-pMT inclusive pipelines. RESULTS: Across all datasets, pMT showed no correlation with dissociation-induced stress scores (R= -0.036). High-pMT cells showed no association with apoptosis pathways, suggesting that they are not actively undergoing cell death. High-pMT cells primarily localized to fibroblast and myeloid subsets. High-pMT synovial fibroblasts were enriched in ECM remodeling processes, while high-pMT myeloid cells were linked to inflammatory signaling and immune activation. CONCLUSIONS: These findings suggest high-pMT cells are viable and potentially disease relevant, with their exclusion possibly obscuring key aspects of OA pathophysiology. This highlights the necessity of context-specific QC strategies in OA research and further exploration of high-pMT fibroblast and myeloid populations as potential disease drivers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".