bioRxivBioinformaticsPreprint
Filtering cells with high mitochondrial content removes viable metabolically altered malignant cell populations in cancer single-cell studies
Abstract
BackgroundSingle-cell transcriptomics has transformed our understanding of cellular diversity in biological systems. However, systematic noise, often introduced by low-quality cells, can obscure biological signals if not properly accounted for. Thus, one of the common quality control steps involves filtering out cells with a high percentage of mitochondrial RNA counts (pctMT), as high pctMT typically indicates cell death. Yet, 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. We analyzed public single-cell RNA-seq and spatial data to investigate if malignant cells with high pctMT are viable and functionally significant subpopulations. ResultsWe analyzed nine single-cell RNA-seq datasets from uveal melanoma, breast, lung, kidney, head and neck, prostate, and pancreatic cancers, including 439,507 cells from 151 patients. Malignant cells exhibited significantly higher pctMT than nonmalignant cells without a significant increase in dissociation-induced stress signature scores. Malignant cells with high pctMT showed metabolic dysregulation, including increased xenobiotic metabolism, which is implicated in cancer therapeutic response. Our analysis of pctMT in cancer cell lines uncovered associations with resistance and sensitivity to certain classes of drugs. Additionally, we observed a link between pctMT and malignant cell transcriptional heterogeneity as well as patient clinical features. ConclusionsThis study provides a detailed exploration of the functional characteristics of malignant cells with elevated pctMT, challenging current quality control practices in single-cell RNA-seq analyses of tumors. Our findings have the potential to improve data interpretation and refine the biological conclusions of future cancer studies.
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- Citations
- 2
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