Abstract
In single-cell RNA sequencing (scRNA-seq) data, issues related to the high expression of non-variable RNAs often arise due to organ traits or sample quality. Computational methods, such as SoupX (Young (Gigascience 9:giaa151, 2020)), have been used to solve this problem but it may remove biologically relevant data. This study presents a clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-based method that selectively removes non-variable RNAs. We applied this approach to scRNA-seq data from human intestinal tissues of 17 patients. By targeting non-variable genes, including ribosomal and mitochondrial RNAs, CRISPR-Cas9 treatment effectively reduced their expression, outperforming computational methods in both the number and extent of gene removal. The CRISPR-Cas9 treated samples, sequenced at half the depth compared to untreated samples, maintained comparable sequencing quality, and saturation, demonstrating that this approach can reduce sequencing costs while preserving data quality. Cell type composition and gene expression patterns remained consistent between treated and original datasets, with no unintended gene deletions. Overall, our findings suggest that the CRISPR-Cas9-based method offers a cost-effective solution for improving scRNA-seq data quality, particularly for tissues with high levels of non-variable RNAs, without compromising biological integrity.
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1 Seoul National University Graduate School, Department of Biomedical Sciences, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University College of Medicine, Department of Microbiology and Immunology, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
2 Seoul National University Graduate School, Department of Biomedical Sciences, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University College of Medicine, Department of Microbiology and Immunology, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
3 Seoul National University College of Medicine, Department of Internal Medicine and Liver Research Institute, Medical Research Center, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
4 Seoul National University Graduate School, Department of Biomedical Sciences, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University College of Medicine, Department of Microbiology and Immunology, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University College of Medicine, Genomic Medicine Institute, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University, Interdisciplinary Program in Artificial Intelligence (IPAI), Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)