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Updated: Jul 16, 2025
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Simultaneous Mapping and Quantitation of Ribonucleotides in Human Mitochondrial DNA
Published on: November 14, 2017
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mitoSplitter: A mitochondrial variants-based method for efficient demultiplexing of pooled single-cell RNA-seq
Xinrui Lin1, Yingwen Chen2, Li Lin2
1Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, People's Republic of China.
Summary
This study introduces mitoSplitter, a new algorithm for demultiplexing pooled single-cell RNA sequencing (scRNA-seq) data using mitochondrial RNA variants. This method efficiently reduces costs and batch effects in large-scale scRNA-seq experiments.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Analyzing multiple single-cell RNA sequencing (scRNA-seq) samples separately is expensive and introduces batch effects.
- Existing demultiplexing methods using exogenous barcodes or RNA mutations are computationally or experimentally challenging.
- Mitochondrial genomes offer small, diverse genetic information suitable for genotype-based demultiplexing.
Purpose of the Study:
- To develop and validate an algorithm, mitoSplitter, for demultiplexing pooled scRNA-seq data using mitochondrial RNA (mtRNA) variants.
- To demonstrate the efficiency and accuracy of mitoSplitter for large-scale scRNA-seq analysis.
- To apply mitoSplitter to investigate cancer cell line responses to BET chemical degradation, avoiding batch effects.
Main Methods:
- Developed the mitoSplitter algorithm to identify and utilize mtRNA variants for sample demultiplexing.
- Applied mitoSplitter to analyze pooled scRNA-seq data from 10 samples and 60,000 cells, achieving accurate results within 6 hours using affordable computational resources.
- Utilized the algorithm to study the response of five non-small cell lung cancer cell lines to BET chemical degradation in a multiplexed manner.
Main Results:
- MitoSplitter accurately demultiplexed large-scale scRNA-seq data using mtRNA variants.
- The algorithm processed 10 samples and 60,000 cells in 6 hours with minimal computational resources.
- Analysis of BET chemical degradation in lung cancer cell lines revealed synthetic lethality between TOP2A inhibition and BET degradation in resistant cells.
Conclusions:
- MitoSplitter provides an efficient, cost-effective, and accurate method for demultiplexing pooled scRNA-seq data.
- The algorithm effectively mitigates batch effects inherent in separate sample analyses.
- MitoSplitter can accelerate the application of scRNA-seq in biomedical research, as demonstrated by its use in cancer drug response studies.
