Journal
CELLULAR AND MOLECULAR LIFE SCIENCES
Volume 79, Issue 8, Pages -Publisher
SPRINGER BASEL AG
DOI: 10.1007/s00018-022-04482-0
Keywords
Cell Hashing; scRNA-seq; scATAC-seq; Multi-omics; Spatial transcriptomics
Categories
Funding
- Natural Science Foundation of Guangdong Province [2018B030308004, 2019B1515120033]
- National Nature Science Foundation of China [32071452]
- Open Fund Programs of Shenzhen Bay Laboratory [SZBL2020090501003]
- Pearl River Talents Program Local Innovative and Research Teams [2017BT01S131]
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This article discusses the challenges of using single-cell sequencing with multiple samples and proposes sample-multiplexing approaches, including using variant features as sample markers for sample pooling and decoding. Additionally, other single-cell omics methods are introduced, along with selection guidelines.
Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.
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