Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies
Abstract Background Single-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), t...
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BMC
2025-04-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03559-w |
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| author | Josephine Yates Agnieszka Kraft Valentina Boeva |
| author_facet | Josephine Yates Agnieszka Kraft Valentina Boeva |
| author_sort | Josephine Yates |
| collection | DOAJ |
| description | Abstract Background Single-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. Results We 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. Conclusions This 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. |
| format | Article |
| id | doaj-art-c4e508fc69ef4171873866fa528c0bf1 |
| institution | OA Journals |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
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| series | Genome Biology |
| spelling | doaj-art-c4e508fc69ef4171873866fa528c0bf12025-08-20T02:28:05ZengBMCGenome Biology1474-760X2025-04-0126112610.1186/s13059-025-03559-wFiltering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studiesJosephine Yates0Agnieszka Kraft1Valentina Boeva2Department of Computer Science, Institute for Machine Learning, ETH ZürichDepartment of Computer Science, Institute for Machine Learning, ETH ZürichDepartment of Computer Science, Institute for Machine Learning, ETH ZürichAbstract Background Single-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. Results We 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. Conclusions This 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.https://doi.org/10.1186/s13059-025-03559-wMT-RNAData qualitySingle-cell RNA-seqCancerDrug resistanceMetabolism |
| spellingShingle | Josephine Yates Agnieszka Kraft Valentina Boeva Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies Genome Biology MT-RNA Data quality Single-cell RNA-seq Cancer Drug resistance Metabolism |
| title | Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies |
| title_full | Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies |
| title_fullStr | Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies |
| title_full_unstemmed | Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies |
| title_short | Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies |
| title_sort | filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single cell studies |
| topic | MT-RNA Data quality Single-cell RNA-seq Cancer Drug resistance Metabolism |
| url | https://doi.org/10.1186/s13059-025-03559-w |
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