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  • Introduction to Single-Cell Sequencing

    Single-cell sequencing is a technique used to investigate the genome, transcriptome, epigenome, and other omics of individual cells using high-throughput sequencing. This technology has provided many scientific breakthroughs and continues to be applied across many fields, including microbiology, oncology, immunology, neurobiology, precision medicine, and stem cell research.

    The advancement of single-cell sequencing began in 2009 when Tang et al. investigated the single-cell transcriptomes of individual mouse blastomeres and oocytes1. Since that initial study, the expansion and improvement of cell-isolation techniques, library-preparation schemes, and data analysis methods have led to a significant increase in the amount of research and publications involving single-cell sequencing.

    The popularization of this tool has led to a growing list of over a hundred different single-cell sequencing methods2,3, some of which will be covered in this article.

    Importance of single-cell sequencing
    Single-cell sequencing is essential for studies exploring heterogeneity between cells, determining cell types within a tissue, understanding cell development pathways, identifying uncultivated microbes, and building human cell atlases4.

    While traditional high-throughput sequencing (i.e., bulk sequencing) continues to be a useful tool and has enabled many notable scientific discoveries, bulk sequencing relies on incorporating data from a collection of cells and cell types. In contrast, single-cell sequencing has the capability to distinguish unique genetic differences between cells that are often concealed within the mixed data of bulk sequencing.

    The ability to isolate and sequence single cells is improving our understanding of tumor population structure and evolution5, identifying new types of immune cells6,7, and discovering the transcriptional dynamics of organogenesis8. These discoveries would not have been possible without the resolution provided by single-cell sequencing.

    Basic single-cell sequencing process
    The single-cell sequencing process is composed of four major steps:

    1. Preparation of tissue and isolating cells
    The first step involves carefully removing/dissociating the tissue or groups of cells and then isolating them into individual cells. Many methods for isolation have been developed, including commercial and non-commercial methods. Each isolation technique has advantages and drawbacks that must be considered prior to selection.

    2. Library preparation
    After the cells have been isolated, they must be lysed, and the extracted DNA or RNA will be used for the library preparation. Some cell-isolation methods will incorporate parts of the library prep directly into the isolation step. During library preparation, the DNA or resulting cDNA will be amplified, barcoded/ tagged for identification during data analysis, and then quantified and quality checked.

    3. Sequencing the libraries
    Once the sequencing libraries have been prepared, quantified, and quality checked, they are ready to be loaded onto the appropriate sequencer. Due to single-cell’s popularity and utility, most instruments can perform sequencing with single-cell libraries. The type of instrument used will vary based on the data requirements of the study and the library-prep compatibility.

    4. Data analysis
    After sequencing, the resulting data will undergo demultiplexing, adapter trimming, removal of low-quality reads and bases, and alignments. The remaining steps will differ depending on the scope of the study and the genomic components being investigated. Many analysis tools have been created specifically for analyzing single-cell sequencing data, in addition to published tutorials and guidelines9, 10, 11.







    Single-cell sequencing process
    Figure 1. Primary steps of single-cell sequencing. (A) Tissue or groups of cells are collected and are then isolated into individual cells. (B) The sequencing libraries are prepared from isolated DNA or RNA to investigate the genome, transcriptome, or epigenome based on the requirements of the study. (C) Libraries are loaded onto the appropriate sequencer. (D) The resulting data is analyzed.




    Single-cell sequencing strategies
    Single-cell DNA sequencing (scDNA-seq)
    Among the trillions of cells and hundreds of cell types in the human body12, each cell contains individual genetic differences that can be elucidated using scDNA-seq. Common applications of scDNA-seq involve detecting cell-to-cell heterogeneity through single nucleotide variations (SNV) and copy number variations (CNV). scDNA-seq has been used to advance our understanding of the tumor microenvironment13, somatic mutations in the brain14, and chemotherapy resistance in breast cancer15.

    Workflows involving scDNA-seq are typically less intensive than other methods, but due to the low concentration of DNA, they still require accurate amplification of the genome. There are many techniques used to amplify DNA in this manner, but several of the most common methods are MDA (multiple displacement amplification)16, MALBAC (multiple annealing and looping based amplification cycles)17, DOP-PCR (degenerated oligonucleotide primer-PCR)18, emulsion MDA19, LIANTI (linear amplification via transposon insertion)20, SISSOR (single-stranded sequencing using microfluidic reactors)21, and META-CS (multiplexed end-tagging amplification of complementary strands)22. The accuracy, uniformity, and overall amplification strategies differ for each method and selection may depend on the preparation of samples and scope of the study.

    Single-cell epigenome sequencing
    The human genome is composed of many epigenetic mechanisms that influence cellular functions. Although technically still a component of scDNA-seq, single-cell epigenome studies are used to understand the different epigenetic modifications and their variations between individual cells. These methods have been used to help researchers identify subpopulations of cells23, variability in genome structures24, and chromosomal dynamics during the cell cycle25.

    The method for studying single-cell epigenomics varies by the epigenetic mechanism being investigated. Some of the most widespread strategies include ATAC-seq (assay for transposase-accessible chromatin with sequencing), which allows for epigenomic analysis of chromatin accessibility26,27; single-cell ChIP-seq (chromatin immunoprecipitation followed by sequencing)23 for analyzing binding sites of DNA-associated proteins; and single-cell Hi-C24,28 for identifying chromatin interactions in individual cells. Furthermore, single-cell bisulfite sequencing29 is a common technique used to measure cell-to-cell variability across the DNA methylome. Each strategy offers its own benefits to understanding the differences in epigenetic mechanisms between cells.

    Single-cell RNA sequencing (scRNA-seq)
    The types of available RNA, transcriptional output, and the overall RNA regulation differ across individual cells. scRNA-seq is the most common method of single-cell sequencing, and it is used to investigate the transcriptome of individual cells—a task that was previously impossible with bulk RNA sequencing. The utilization of scRNA-seq has increased our understanding of brain development during neurogenesis30 and predicting drug resistance in cancer31.

    The preparation of samples for scRNA-seq is similar to the methods for scDNA-seq, but they require the conversion of RNA into cDNA before library preparation. There are three primary strategies for transcript coverage when converting RNA into cDNA: full-length transcripts32,33, 5′-ends of transcripts34,35, and 3′-ends of transcripts36,37. The different methods have benefits and limitations and should be chosen based on the requirements for coverage, throughput, and the type of RNA being investigated.

    Conclusion
    The expansion of techniques and the decreasing costs of high-throughput sequencing will increase the utilization and development of single-cell sequencing. Some of the latest approaches include single-cell proteome sequencing, single-cell metabolome sequencing, and single-cell multi-omics. These newer methods, along with the combination of sequencing techniques, have already been applied to enhance our knowledge of disease38 and the cellular landscape39.

    Despite the success of single-cell sequencing, it has limitations and may not always be the best choice for answering specific biological questions. Advanced imaging techniques have become increasingly popular and may be better suited to investigate molecular and cellular interactions between individual live cells40,41. In addition, studies investigating a select number of genes may find that single-cell sequencing is a more expensive option and has lower throughput than is required for the study.



    References
    1. Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods. 2009;6(5):377-382. doi:10.1038/nmeth.1315
    2. Kashima Y, Sakamoto Y, Kaneko K, Seki M, Suzuki Y, Suzuki A. Single-cell sequencing techniques from individual to multiomics analyses. Experimental & Molecular Medicine. 2020;52(9):1419-1427. doi:10.1038/s12276-020-00499-2
    3. Adil A, Kumar V, Jan AT, Asger M. Single-Cell Transcriptomics: Current methods and challenges in data acquisition and analysis. Frontiers in Neuroscience. 2021;15. doi:10.3389/fnins.2021.591122
    4. Regev A, Teichmann A, Lander E, et al. Human cell atlas meeting participants. 2017 Science Forum: The Human Cell Atlas eLife 6:e27041 https://doi.org/10.7554/eLife.27041
    5. Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472(7341):90-94. doi:10.1038/nature09807
    6. Wilk AJ, Rustagi A, Zhao NQ, et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nature Medicine. 2020;26(7):1070-1076. doi:10.1038/s41591-020-0944-y
    7. Aran D, Looney AP, Liu L, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology. 2019;20(2):163-172. doi:10.1038/s41590-018-0276-y
    8. Cao J, Spielmann M, Qiu X, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019;566(7745):496-502. doi:10.1038/s41586-019-0969-x
    9. Andrews TS, Kiselev VY, McCarthy D, Hemberg M. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nature Protocols. 2021;16(1):1-9. doi:10.1038/s41596-020-00409-w
    10. Stuart T, Satija R. Integrative single-cell analysis. Nature Reviews Genetics. 2019;20(5):257-272. doi:10.1038/s41576-019-0093-7
    11. Baek S, Lee I. Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation. Computational and Structural Biotechnology Journal. 2020;18(2001-0370):1429-1439. doi:10.1016/j.csbj.2020.06.012
    12. Sender R, Fuchs S, Milo R. Revised estimates for the number of human and bacteria cells in the body. PLOS Biology. 2016;14(8):e1002533. doi:10.1371/journal.pbio.1002533
    13. Lim B, Lin Y, Navin N. Advancing cancer research and medicine with single-cell genomics. Cancer Cell. 2020;37(4):456-470. doi:10.1016/j.ccell.2020.03.008
    14. Lodato MA, Walsh CA. Genome aging: somatic mutation in the brain links age-related decline with disease and nominates pathogenic mechanisms. Human Molecular Genetics. 2019;28(R2):R197-R206. doi:10.1093/hmg/ddz191
    15. Kim C, Gao R, Sei E, et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell. 2018;173(4):879-893.e13. doi:10.1016/j.cell.2018.03.041
    16. Dean FB, Hosono S, Fang L, et al. Comprehensive human genome amplification using multiple displacement amplification. Proceedings of the National Academy of Sciences. 2002;99(8):5261-5266. doi:10.1073/pnas.082089499
    17. Zong C, Lu S, Chapman AR, Xie XS. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science. 2012;338(6114):1622-1626. doi:10.1126/science.1229164
    18. Telenius H, Carter NP, Bebb CE, Nordenskjöld M, Ponder BA, Tunnacliffe A. Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. Genomics. 1992;13(3):718-725. doi:10.1016/0888-7543(92)90147-k
    19. Fu Y, Li C, Lu S, et al. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. Proceedings of the National Academy of Sciences. 2015;112(38):11923-11928. doi:10.1073/pnas.1513988112
    20. Chen C, Xing D, Tan L, et al. Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI). Science. 2017;356(6334):189-194. doi:10.1126/science.aak9787
    21. Chu WK, Edge P, Lee HS, et al. Ultraaccurate genome sequencing and haplotyping of single human cells. Proceedings of the National Academy of Sciences. 2017;114(47):12512-12517. doi:10.1073/pnas.1707609114
    22. Xing D, Tan L, Chang CH, Li H, Xie XS. Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands. Proceedings of the National Academy of Sciences. 2021;118(8). doi:10.1073/pnas.2013106118
    23. Rotem A, Ram O, Shoresh N, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nature Biotechnology. 2015;33(11):1165-1172. doi:10.1038/nbt.3383
    24. Nagano T, Lubling Y, Stevens TJ, et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502(7469):59-64. doi:10.1038/nature12593
    25. Nagano T, Lubling Y, Várnai C, et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature. 2017;547(7661):61-67. doi:10.1038/nature23001
    26. Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature Methods. 2013;10(12):1213-1218. doi:10.1038/nmeth.2688
    27. Fang R, Preissl S, Li Y, et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nature Communications. 2021;12(1):1337. doi:10.1038/s41467-021-21583-9
    28. Lieberman-Aiden E, van Berkum NL, Williams L, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326(5950):289-293. doi:10.1126/science.1181369
    29. Smallwood SA, Lee HJ, Angermueller C, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nature Methods. 2014;11(8):817-820. doi:10.1038/nmeth.3035
    30. Zhong S, Zhang S, Fan X, et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature. 2018;555(7697):524-528. doi:10.1038/nature25980
    31. Shaffer SM, Dunagin MC, Torborg SR, et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature. 2017;546(7658):431-435. doi:10.1038/nature22794
    32. Sasagawa Y, Nikaido I, Hayashi T, et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biology. 2013;14(4). doi:10.1186/gb-2013-14-4-r31
    33. Ramsköld D, Luo S, Wang YC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnology. 2012;30(8):777-782. doi:10.1038/nbt.2282
    34. Islam S, Kjallquist U, Moliner A, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Research. 2011;21(7):1160-1167. doi:10.1101/gr.110882.110
    35. Kouno T, Moody J, Kwon ATJ, et al. C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution. Nature Communications. 2019;10(1). doi:10.1038/s41467-018-08126-5
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