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      Recent advances in single-cell sequencing technologies

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          Abstract

          Single-cell omics sequencing was first achieved for the transcriptome in 2009, which was followed by fast development of technologies for profiling the genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications, etc., in an individual cell. In this review we mainly focus on the recent progress in four topics in the single-cell omics field: single-cell epigenome sequencing, single-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics and third-generation sequencing platform-based single-cell omics sequencing. We also discuss the potential applications and future directions of these single-cell omics sequencing technologies for different biomedical systems, especially for the human stem cell field.

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          Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position.

          Jason Buenrostro, Paul Giresi, Lisa Zaba … (2013)
          We describe an assay for transposase-accessible chromatin using sequencing (ATAC-seq), based on direct in vitro transposition of sequencing adaptors into native chromatin, as a rapid and sensitive method for integrative epigenomic analysis. ATAC-seq captures open chromatin sites using a simple two-step protocol with 500-50,000 cells and reveals the interplay between genomic locations of open chromatin, DNA-binding proteins, individual nucleosomes and chromatin compaction at nucleotide resolution. We discovered classes of DNA-binding factors that strictly avoided, could tolerate or tended to overlap with nucleosomes. Using ATAC-seq maps of human CD4(+) T cells from a proband obtained on consecutive days, we demonstrated the feasibility of analyzing an individual's epigenome on a timescale compatible with clinical decision-making.
          Article 0 comments Cited 1692 times – based on 0 reviews     Review now
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            Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.

            Patrik L. Ståhl, Fredrik Salmén, Sanja Vickovic … (2016)
            Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.
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              mRNA-Seq whole-transcriptome analysis of a single cell.

              Fuchou Tang, Catalin Barbacioru, Yangzhou Wang … (2009)
              Next-generation sequencing technology is a powerful tool for transcriptome analysis. However, under certain conditions, only a small amount of material is available, which requires more sensitive techniques that can preferably be used at the single-cell level. Here we describe a single-cell digital gene expression profiling assay. Using our mRNA-Seq assay with only a single mouse blastomere, we detected the expression of 75% (5,270) more genes than microarray techniques and identified 1,753 previously unknown splice junctions called by at least 5 reads. Moreover, 8-19% of the genes with multiple known transcript isoforms expressed at least two isoforms in the same blastomere or oocyte, which unambiguously demonstrated the complexity of the transcript variants at whole-genome scale in individual cells. Finally, for Dicer1(-/-) and Ago2(-/-) (Eif2c2(-/-)) oocytes, we found that 1,696 and 1,553 genes, respectively, were abnormally upregulated compared to wild-type controls, with 619 genes in common.
              Article 0 comments Cited 776 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Lu Wen:
                ORCID: https://orcid.org/0000-0002-1773-1876
                Fuchou Tang
                Journal
                Journal ID (nlm-ta): Precis Clin Med
                Journal ID (iso-abbrev): Precis Clin Med
                Journal ID (publisher-id): pcm
                Title: Precision Clinical Medicine
                Publisher: Oxford University Press
                ISSN (Print): 2096-5303
                ISSN (Electronic): 2516-1571
                Publication date Collection: March 2022
                Publication date (Electronic): 31 January 2022
                Publication date PMC-release: 31 January 2022
                Volume: 5
                Issue: 1
                Electronic Location Identifier: pbac002
                Affiliations
                Beijing Advanced Innovation Center for Genomics (ICG), Biomedical Pioneering Innovation Center (BIOPIC) , departmentSchool of Life Sciences, Peking University , Beijing 100871, China
                Beijing Advanced Innovation Center for Genomics (ICG), Biomedical Pioneering Innovation Center (BIOPIC) , departmentSchool of Life Sciences, Peking University , Beijing 100871, China
                Author notes
                Correspondence: Lu Wen, wenlu@ 123456pku.edu.cn
                Correspondence: Fuchou Tang, tangfuchou@ 123456pku.edu.cn
                Author information
                https://orcid.org/0000-0002-1773-1876
                Article
                Publisher ID: pbac002
                DOI: 10.1093/pcmedi/pbac002
                PMC ID: 9267251
                PubMed ID: 35821681
                SO-VID: c82068d0-aa07-4ef2-90b2-dc31746888c8
                Copyright © © The Author(s) 2022. Published by Oxford University Press on behalf of the West China School of Medicine & West China Hospital of Sichuan University.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                Date received : 09 November 2021
                Date revision received : 11 January 2022
                Date accepted : 13 January 2022
                Page count
                Pages: 10
                Funding
                Funded by: National Key Research and Development Program of China, DOI 10.13039/501100012166;
                Award ID: 2018YFA0107601
                Categories
                Subject: Review
                Subject: AcademicSubjects/MED00010

                Keywords: single-cell omics,genome sequencing,epigenome sequencing,lineage tracing,third-generation sequencing,human stem cell
                Data availability:
                Keywords: single-cell omics, genome sequencing, epigenome sequencing, lineage tracing, third-generation sequencing, human stem cell

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