Gene expression
High-mito filters can discard living cancer cell states
Open access · cc by · source: Europe PMC
Across ~441k cells from 134 patients, malignant cells often exceed 15% mitochondrial RNA without stress-death signatures and show metabolic programs relevant to therapy.
Key findings
Malignant cells have higher pctMT without higher dissociation-stress scores. HighMT malignant cells show metabolic dysregulation including xenobiotic metabolism and drug-resistance links. In many tumors, 10–50% of samples have ≥2× HighMT fraction in malignant vs TME compartments—so standard filters remove real biology.
Methodology
Yates et al. reanalyzed nine public cancer scRNA-seq datasets (441,445 cells; 134 patients) plus spatial data, comparing pctMT in malignant vs TME compartments, dissociation-stress scores, metabolic pathways, cell-line drug resistance, and clinical associations using a 15% HighMT cutoff.
Limitations
pctMT is not a perfect viability assay; thresholds may still need experiment-specific tuning. Observational public data cannot prove HighMT cells cause resistance in patients.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
High-mito filters can deplete viable metabolically altered malignant cells.
Filtering cells with high mitochondrial content is shown to remove not only debris but also viable metabolically altered malignant cells—QC as biology, not just cleanup.
Evidence for the claim as stated.
Foundation cell models for post-perturbation prediction need careful benchmarks.
Benchmarking foundation cell models for post-perturbation RNA-seq prediction tests how well learned representations generalize after QC and perturbation—downstream of filtering choices.
Scope note — model benchmark ≠ mito-filter depletion experiment
Limits the claim's scope: a different population, assay, or outcome.
Disease atlases depend on QC’d nuclei before cell-state claims.
ICM snRNA-seq profiles ~100k QC’d cardiac nuclei before composition claims—reminding students that atlas biology sits atop filtering decisions.
Scope note — heart atlas composition ≠ mito-filter methods paper
Limits the claim's scope: a different population, assay, or outcome.
A mito-filter methods result, a foundation-model benchmark, and a disease atlas answer different layers of the single-cell stack.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
A mito-filter methods result, a foundation-model benchmark, and a disease atlas answer different layers of the single-cell stack.
Related papers in this topic
Same topic cluster — not a recommendation engine.