Multiplexing across donors has emerged as a popular strategy to increase throughput,reduce costs,overcome technical batch effects,and improve doublet detection in single-cell genomic studies.To eliminate additional ex...Multiplexing across donors has emerged as a popular strategy to increase throughput,reduce costs,overcome technical batch effects,and improve doublet detection in single-cell genomic studies.To eliminate additional experimental steps,endogenous nuclear genome variants are used for demultiplexing pooled single-cell RNA sequencing(scRNA-seq)data by several computational tools.However,these tools have limitations when applied to single-cell sequencing methods that do not cover nuclear genomic regions well,such as single-cell assay for transposase-accessible chromatin with sequencing(scATAC-seq).Here,we demonstrate that mitochondrial germline variants are an alternative,robust,and computationally efficient endogenous barcode for sample demultiplexing.We propose MitoSort,a tool that uses mitochondrial germline variants to assign cells to their donor origins and identify cross-genotype doublets in single-cell genomic datasets.We evaluate its performance by using in silico pooled mitochondrial scATAC-seq(mtscATAC-seq)libraries and experimentally multiplexed data with cell hashtags.MitoSort achieves high accuracy and efficiency in genotype clustering and doublet detection for mtscATAC-seq data,addressing the limitations of current computational techniques tailored for scRNA-seq data.Moreover,MitoSort exhibits versatility,and can be applied to various single-cell sequencing approaches beyond mtscATAC-seq provided that the mitochondrial variants are reliably detected.Furthermore,we demonstrate the application of MitoSort in a case study where B cells from eight donors were pooled and assayed by single-cell multi-omics sequencing.Altogether,our results demonstrate the accuracy and efficiency of MitoSort,which enables reliable sample demultiplexing in various single-cell genomic applications.MitoSort is available at https://github.com/tangzhj/MitoSort.展开更多
基金supported by the National Key R&D Program of China(Grant No.2021YFA1102100)the National Natural Science Foundation of China(Grant Nos.32070644,32293190,32293191)to JX,and the Guangzhou Key Research and Development Program(Grant No.2023B03J1347)to YS and JX.
文摘Multiplexing across donors has emerged as a popular strategy to increase throughput,reduce costs,overcome technical batch effects,and improve doublet detection in single-cell genomic studies.To eliminate additional experimental steps,endogenous nuclear genome variants are used for demultiplexing pooled single-cell RNA sequencing(scRNA-seq)data by several computational tools.However,these tools have limitations when applied to single-cell sequencing methods that do not cover nuclear genomic regions well,such as single-cell assay for transposase-accessible chromatin with sequencing(scATAC-seq).Here,we demonstrate that mitochondrial germline variants are an alternative,robust,and computationally efficient endogenous barcode for sample demultiplexing.We propose MitoSort,a tool that uses mitochondrial germline variants to assign cells to their donor origins and identify cross-genotype doublets in single-cell genomic datasets.We evaluate its performance by using in silico pooled mitochondrial scATAC-seq(mtscATAC-seq)libraries and experimentally multiplexed data with cell hashtags.MitoSort achieves high accuracy and efficiency in genotype clustering and doublet detection for mtscATAC-seq data,addressing the limitations of current computational techniques tailored for scRNA-seq data.Moreover,MitoSort exhibits versatility,and can be applied to various single-cell sequencing approaches beyond mtscATAC-seq provided that the mitochondrial variants are reliably detected.Furthermore,we demonstrate the application of MitoSort in a case study where B cells from eight donors were pooled and assayed by single-cell multi-omics sequencing.Altogether,our results demonstrate the accuracy and efficiency of MitoSort,which enables reliable sample demultiplexing in various single-cell genomic applications.MitoSort is available at https://github.com/tangzhj/MitoSort.
