Tang Zhongjie, Zhang Weixing, Shi Peiyu, Li Sijun, Li Xinhui, Li Yueming, Xu Yicong, Shu Yaqing, Hu Zheng, Xu Jin
State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China.
Department of Neurology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.
Genomics Proteomics Bioinformatics. 2024 Dec 3;22(5). doi: 10.1093/gpbjnl/qzae073.
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.
跨供体复用已成为一种流行策略，用于提高通量、降低成本、克服技术批次效应并改善单细胞基因组研究中的 doublet 检测。为了消除额外的实验步骤，几种计算工具使用内源性核基因组变异对汇集的单细胞 RNA 测序（scRNA-seq）数据进行解复用。然而，当应用于不能很好覆盖核基因组区域的单细胞测序方法时，这些工具存在局限性，例如用于转座酶可及染色质测序的单细胞分析（scATAC-seq）。在这里，我们证明线粒体种系变异是用于样本解复用的一种替代、稳健且计算高效的内源性条形码。我们提出了 MitoSort 工具，它使用线粒体种系变异将细胞分配到其供体来源，并在单细胞基因组数据集中识别跨基因型 doublet。我们通过使用计算机模拟汇集的线粒体 scATAC-seq（mtscATAC-seq）文库和带有细胞标签的实验复用数据来评估其性能。MitoSort 在 mtscATAC-seq 数据的基因型聚类和 doublet 检测中实现了高精度和高效率，解决了当前为 scRNA-seq 数据量身定制的计算技术的局限性。此外，MitoSort 具有通用性，只要线粒体变异能够可靠检测，它就可以应用于 mtscATAC-seq 之外的各种单细胞测序方法。此外，我们在一个案例研究中展示了 MitoSort 的应用，其中将来自八个供体的 B 细胞汇集并用单细胞多组学测序进行分析。总之，我们的结果证明了 MitoSort 的准确性和效率，它能够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 获得。
