生物
计算生物学
染色质
异质性
线粒体DNA
遗传学
单细胞分析
单细胞测序
细胞
基因
表型
外显子组测序
作者
Caleb A. Lareau,Vincent Liu,Christoph Muus,Samantha D. Praktiknjo,Lena Nitsch,Pauline Kautz,Katalin Sándor,Yajie Yin,Jacob C. Gutierrez,Karin Pelka,Ansuman T. Satpathy,Aviv Regev,Vijay G. Sankaran,Leif S. Ludwig
出处
期刊:Nature Protocols
[Springer Nature]
日期:2023-02-15
卷期号:18 (5): 1416-1440
被引量:20
标识
DOI:10.1038/s41596-022-00795-3
摘要
Natural sequence variation within mitochondrial DNA (mtDNA) contributes to human phenotypes and may serve as natural genetic markers in human cells for clonal and lineage tracing. We recently developed a single-cell multi-omic approach, called ‘mitochondrial single-cell assay for transposase-accessible chromatin with sequencing’ (mtscATAC-seq), enabling concomitant high-throughput mtDNA genotyping and accessible chromatin profiling. Specifically, our technique allows the mitochondrial genome-wide inference of mtDNA variant heteroplasmy along with information on cell state and accessible chromatin variation in individual cells. Leveraging somatic mtDNA mutations, our method further enables inference of clonal relationships among native ex vivo-derived human cells not amenable to genetic engineering-based clonal tracing approaches. Here, we provide a step-by-step protocol for the use of mtscATAC-seq, including various cell-processing and flow cytometry workflows, by using primary hematopoietic cells, subsequent single-cell genomic library preparation and sequencing that collectively take ~3–4 days to complete. We discuss experimental and computational data quality control metrics and considerations for the extension to other mammalian tissues. Overall, mtscATAC-seq provides a broadly applicable platform to map clonal relationships between cells in human tissues, investigate fundamental aspects of mitochondrial genetics and enable additional modes of multi-omic discovery. This protocol for concomitant high-throughput mitochondrial DNA genotyping and accessible chromatin profiling of single cells allows paired analysis of clonal relationships and cell states.
科研通智能强力驱动
Strongly Powered by AbleSci AI