Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.16.537058v1?rss=1
Authors: Lin, X., Chen, Y., Lin, L., Yin, K., Wang, X., Guo, Y., Wu, Z., Zhang, Y., Li, J., Yang, C., Song, J.
Abstract:
Single-cell RNA-seq (scRNA-seq) analysis of multiple samples separately is expensive and may cause batch effect. Multiplexing several samples for one experiment using exogenous barcodes or demultiplexing scRNA-seq data based on genome-wide RNA mutations is experimentally or computationally challenging and scale-limited, respectively. Mitochondria is a smaller genome set that can provide concise information for individual genotypes. In this study, we developed a computational algorithm "mitoSpliter" that enables sample demultiplexing based on somatic mutations in mitochondrial RNA (mtRNA). We demonstrated that mtRNA variants are computationally capable of demultiplexing large-scale scRNA-seq data. The analysis of peripheral blood mononuclear cells (PBMCs) revealed that mitoSpliter can accurately analyze up to 10 samples and 60K cells in a single experiment in ~6 hours on affordable computational resources. Further application of mitoSpliter to non-small cell lung cancer (NSCLC) cells uncovered the synthetic lethality of TOP2A inhibition and BET chemical degradation in the BET inhibitor-resistant cells, indicating that mitoSpliter will accelerate the application of scRNA-seq assay in biomedical research.
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