mitoSplitter: A mitochondrial variants- based method for efficient demultiplexing of pooled single- cell RNA- seq

Single cell RNA-seq (scRNA-seq) analysis of multiple samples separately can be costly and lead to batch effects. Exogenous barcodes or genome-wide RNA mutations can be used to demultiplex pooled scRNA-seq data, but they are experimentally or computationally challenging and limited in scope. Mitochondrial genomes are small but diverse, providing concise genotype information. We developed mitoSplitter, an algorithm that demultiplexes samples using mitochondrial RNA (mtRNA) variants, and demonstrated that mtRNA variants can be used to demultiplex large scale scRNA-seq data. Using affordable computational resources, mitoSplitter can accurately analyze 10 samples and 60,000 cells in 6 h. To avoid the batch effects from separated experiments, we applied mitoSplitter to analyze the responses of five non small cell lung cancer cell lines to BET (Bromodomain and extraterminal) chemical degradation in a multiplexed fashion. We found the synthetic lethality of TOP2A inhibition and BET chemical degradation in BET inhibitor- resistant cells. The result indicates that mitoSplitter can accelerate the application of scRNA-seq assays in biomedical research.

Automated summary

mitoSplitter is an algorithm that uses mitochondrial RNA variants to demultiplex samples in large scale scRNA-seq data. It can accurately analyze multiple samples and cells in a short time, and accelerate the application of scRNA-seq assays in biomedical research.