MitoSort:利用内源性线粒体变异对汇集的单细胞基因组学数据进行稳健的解复用。

Zhongjie Tang, Weixing Zhang, Peiyu Shi, Sijun Li, Xinhui Li, Yueming Li, Yicong Xu, Yaqing Shu, Zheng Hu, Jin Xu
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引用次数: 0

摘要

在单细胞基因组研究中,为提高通量、降低成本、克服技术批次效应和改善双倍检测,跨供体复用已成为一种流行的策略。为了省去额外的实验步骤,一些计算工具利用内源性核基因组变体对汇集的单细胞 RNA 测序(scRNA-seq)数据进行解复用。然而,当这些工具应用于不能很好覆盖核基因组区域的单细胞测序方法时,如单细胞转座酶可接触染色质测序(scATAC-seq),就会受到限制。在这里,我们证明线粒体种系变异是一种可供选择的、稳健的、计算效率高的内源条形码,可用于样本解复用。我们提出的 MitoSort 是一种利用线粒体种系变异将细胞分配到其来源供体并在单细胞基因组学数据集中识别交叉基因型双倍体的工具。我们使用线粒体scATAC-seq(mtscATAC-seq)文库和带有细胞标签的实验多重数据对其性能进行了评估。MitoSort在mtscATAC-seq数据的基因型聚类和双重检测方面实现了高准确度和高效率,解决了当前针对scRNA-seq数据定制的计算技术的局限性。此外,MitoSort 还具有多功能性,可应用于 mtscATAC-seq 之外的各种单细胞测序方法,前提是线粒体变异得到可靠检测。此外,我们还在一个案例研究中展示了 MitoSort 的应用,该案例研究汇集了来自 8 个供体的 B 细胞,并通过单细胞多组学测序进行了检测。总之,我们的研究结果证明了 MitoSort 的准确性和高效性,它能在各种单细胞基因组应用中实现可靠的样本解复用。MitoSort可在https://github.com/tangzhj/MitoSort。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MitoSort: Robust Demultiplexing of Pooled Single-cell Genomics Data Using Endogenous Mitochondrial Variants.

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 of origin and identify cross-genotype doublets in single-cell genomics 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 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.

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