ArticleGenome biology2025
Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Single-cell RNA sequencing of circulating tumour cells in colorectal cancer.Molecular biology reports · 2026Article
- Single-cell-marker-based subtyping and multi-level analyses uncover the prognostic effects, dysregulations and therapeutic indicative potential of an eight-gene signature in lung adenocarcinoma.Cancer cell international · 2026Article
- ScQCenrich enables multi-metric quality control for single-cell RNA sequencing.Communications biology · 2026Article
- Multiplexed Transcriptomics for Screening Drug Combinations and Defining the Mechanism of Action of HCC Therapeutics at Single-Cell Resolution.Cell proliferation · 2026Article
- MitoChontrol: Adaptive mitochondrial filtering for robust single-cell RNA sequencing quality control.bioRxiv : the preprint server for biology · 2026Article
- Intratumoral heterogeneity in microsatellite instability status at single-cell resolution.iScience · 2026Article
- Article
- Single-cell mitophagy signature-based artificial intelligence model enhances prediction of prognosis and immunotherapy response in non-small-cell lung cancer.Respiratory research · 2026Article
- Ex vivo drug sensitivity testing predicts treatment outcomes in advanced ovarian cancer.NPJ precision oncology · 2026Article
- Prediction of immunotherapeutic responses by a classifier model based on inflammation-associated tumor microenvironment signatures in colorectal cancer.Discover oncology · 2026Article
- Spotsweeper-py: spatially-aware quality control metrics for spatial omics data in the Python ecosystem.bioRxiv : the preprint server for biology · 2025Article
- Multi-region spatial transcriptomics reveals region specific differences in response to amyloid beta (Aβ) plaque induced changes in Alzheimer's disease (AD).Human genomics · 2025Article
- Spatial transcriptomics of glioblastoma defines biologically and clinically significant reprogramming patterns across unique spatial microenvironments.bioRxiv : the preprint server for biology · 2025Article
- Identification of malignant cells in single-cell transcriptomics data.Communications biology · 2025Review
- Mitochondrial Transfer Between Cancer and T Cells: Implications for Immune Evasion.Antioxidants (Basel, Switzerland) · 2025Review
- SpotSweeper: spatially aware quality control for spatial transcriptomics.Nature methods · 2025Article
- New horizons at the interface of artificial intelligence and translational cancer research.Cancer cell · 2025Review
- Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies.Genome biology · 2025Article
- Interpretable artificial intelligence based on immunoregulation-related genes predicts prognosis and immunotherapy response in lung adenocarcinoma.Frontiers in bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundSingle-cell transcriptomics has transformed our understanding of cellular diversity, yet noise from technical artifacts and low-quality cells can obscure key biological signals. A common practice is filtering out cells with a high percentage of mitochondrial RNA counts (pctMT), typically indicative of cell death. However, commonly used filtering thresholds, primarily derived from studies on healthy tissues, may be overly stringent for malignant cells, which often naturally exhibit higher baseline mitochondrial gene expression.
resultsWe examine nine public single-cell RNA-seq datasets from various cancers, including 441,445 cells from 134 patients, and public spatial transcriptomics data, assessing the viability of malignant cells with high pctMT. Our analysis reveals that malignant cells exhibit significantly higher pctMT than nonmalignant cells, without a notable increase in dissociation-induced stress scores. Malignant cells with high pctMT show metabolic dysregulation, including increased xenobiotic metabolism, relevant to therapeutic response. Analysis of pctMT in cancer cell lines further reveals links to drug resistance. We also observe associations between pctMT and malignant cell transcriptional heterogeneity, as well as patient clinical features.
conclusionsThis study provides insights into the functional characteristics of malignant cells with elevated pctMT, challenging current quality control practices in tumor single-cell RNA-seq analyses and offering potential improvements in data interpretation for future cancer studies.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.