Skip to main content
SpringerLink
Log in

Sample-multiplexing approaches for single-cell sequencing

  • Review
  • Published:
Cellular and Molecular Life Sciences Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of data and material

Not applicable.

References

  1. Tang F et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6:377–382. https://doi.org/10.1038/nmeth.1315

    Article  CAS  PubMed  Google Scholar 

  2. Shalek AK et al (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498:236–240. https://doi.org/10.1038/nature12172

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tang F et al (2011) Development and applications of single-cell transcriptome analysis. Nat Methods 8:S6-11. https://doi.org/10.1038/nmeth.1557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Deng Q et al (2014) Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science (New York, N.Y.) 343:193–196. https://doi.org/10.1126/science.1245316

    Article  CAS  Google Scholar 

  5. Zhang X et al (2016) Single-cell sequencing for precise cancer research: progress and prospects. Can Res 76:1305–1312. https://doi.org/10.1158/0008-5472.Can-15-1907

    Article  CAS  Google Scholar 

  6. Suvà ML, Tirosh I (2019) Single-cell RNA sequencing in cancer: lessons learned and emerging challenges. Mol Cell 75:7–12. https://doi.org/10.1016/j.molcel.2019.05.003

    Article  CAS  PubMed  Google Scholar 

  7. Wen L, Tang F (2016) Single-cell sequencing in stem cell biology. Genome Biol 17:71. https://doi.org/10.1186/s13059-016-0941-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chen H et al (2019) Revolutionizing immunology with single-cell RNA sequencing. Cell Mol Immunol 16:242–249. https://doi.org/10.1038/s41423-019-0214-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ofengeim D et al (2017) Single-cell RNA sequencing: unraveling the brain one cell at a time. Trends Mol Med 23:563–576. https://doi.org/10.1016/j.molmed.2017.04.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cuevas-Diaz Duran R et al (2017) Single-cell RNA-sequencing of the brain. Clin Transl Med 6:20. https://doi.org/10.1186/s40169-017-0150-9

    Article  PubMed  PubMed Central  Google Scholar 

  11. Mu Q et al (2019) Deciphering brain complexity using single-cell sequencing. Genomics Proteomics Bioinformatics 17:344–366. https://doi.org/10.1016/j.gpb.2018.07.007

    Article  PubMed  PubMed Central  Google Scholar 

  12. Tšuiko O et al (2020) Preimplantation Genetic Testing: single-cell technologies at the forefront of PGT and embryo research. Reproduction (Cambridge, England) 160:A19-a31. https://doi.org/10.1530/rep-20-0102

    Article  CAS  Google Scholar 

  13. Shangguan Y et al (2020) Application of single-cell RNA sequencing in embryonic development. Genomics 112:4547–4551. https://doi.org/10.1016/j.ygeno.2020.08.007

    Article  CAS  PubMed  Google Scholar 

  14. Peng G et al (2020) Using single-cell and spatial transcriptomes to understand stem cell lineage specification during early embryo development. Annu Rev Genomics Hum Genet 21:163–181. https://doi.org/10.1146/annurev-genom-120219-083220

    Article  CAS  PubMed  Google Scholar 

  15. Hedlund E, Deng Q (2018) Single-cell RNA sequencing: technical advancements and biological applications. Mol Aspects Med 59:36–46. https://doi.org/10.1016/j.mam.2017.07.003

    Article  CAS  PubMed  Google Scholar 

  16. Huang X et al (2018) High throughput single cell RNA sequencing, bioinformatics analysis and applications. Adv Exp Med Biol 1068:33–43. https://doi.org/10.1007/978-981-13-0502-3_4

    Article  CAS  PubMed  Google Scholar 

  17. Yasen A et al (2020) Progress and applications of single-cell sequencing techniques. Infect Genet Evol 80:104198. https://doi.org/10.1016/j.meegid.2020.104198

    Article  CAS  PubMed  Google Scholar 

  18. Zheng GX et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049. https://doi.org/10.1038/ncomms14049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Shum EY et al (2019) Quantitation of mRNA transcripts and proteins using the BD Rhapsody™ single-cell analysis system. Adv Exp Med Biol 1129:63–79. https://doi.org/10.1007/978-981-13-6037-4_5

    Article  CAS  PubMed  Google Scholar 

  20. Svensson V et al (2018) Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc 13(4):599–604. https://doi.org/10.1038/nprot.2017.149

    Article  CAS  PubMed  Google Scholar 

  21. Valihrach L et al (2018) Platforms for single-cell collection and analysis. Int J Mol Sci. https://doi.org/10.3390/ijms19030807

    Article  PubMed  PubMed Central  Google Scholar 

  22. Regev A et al (2017) The human cell Atlas. Elife. https://doi.org/10.7554/eLife.27041

    Article  PubMed  PubMed Central  Google Scholar 

  23. Li H et al (2021) Fly Cell Atlas: a single-cell transcriptomic atlas of the adult fruit fly. J bioRxiv. https://doi.org/10.1101/2021.07.04.451050

    Article  PubMed  Google Scholar 

  24. Rhee SY et al (2019) Towards building a plant cell Atlas. Trends Plant Sci 24:303–310. https://doi.org/10.1016/j.tplants.2019.01.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wolock SL et al (2019) Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst 8(4):281–291. https://doi.org/10.1016/j.cels.2018.11.005 (e9)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. McGinnis CS et al (2019) DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst 8(4):329-337.e4. https://doi.org/10.1016/j.cels.2019.03.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hicks SC et al (2018) Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics (Oxford, England) 19:562–578. https://doi.org/10.1093/biostatistics/kxx053

    Article  Google Scholar 

  28. Kang HM et al (2018) Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat Biotechnol 36:89–94. https://doi.org/10.1038/nbt.4042

    Article  CAS  PubMed  Google Scholar 

  29. Xu J et al (2019) Genotype-free demultiplexing of pooled single-cell RNA-seq. Genome Biol 20:290. https://doi.org/10.1186/s13059-019-1852-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Huang Y et al (2019) Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. Genome Biol 20:273. https://doi.org/10.1186/s13059-019-1865-2

    Article  PubMed  PubMed Central  Google Scholar 

  31. Heaton H et al (2020) Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat Methods 17:615–620. https://doi.org/10.1038/s41592-020-0820-1

    Article  CAS  PubMed  Google Scholar 

  32. Stoeckius M et al (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14:865–868. https://doi.org/10.1038/nmeth.4380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Peterson VM et al (2017) Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol 35:936–939. https://doi.org/10.1038/nbt.3973

    Article  CAS  PubMed  Google Scholar 

  34. Stoeckius M et al (2018) Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol 19:224. https://doi.org/10.1186/s13059-018-1603-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wu H et al (2019) Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol 30:23–32. https://doi.org/10.1681/asn.2018090912

    Article  CAS  PubMed  Google Scholar 

  36. Slyper M et al (2020) A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med 26:792–802. https://doi.org/10.1038/s41591-020-0844-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ding J et al (2020) Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38:737–746. https://doi.org/10.1038/s41587-020-0465-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Habib N et al (2017) Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods 14:955–958. https://doi.org/10.1038/nmeth.4407

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Nagy C et al (2018) Single-nucleus RNA sequencing shows convergent evidence from different cell types for altered synaptic plasticity in major depressive disorder

  40. Lake BB et al (2018) Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol 36:70–80. https://doi.org/10.1038/nbt.4038

    Article  CAS  PubMed  Google Scholar 

  41. Gaublomme JT et al (2019) Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. Nat Commun 10:2907. https://doi.org/10.1038/s41467-019-10756-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Weber RJ et al (2014) Efficient targeting of fatty-acid modified oligonucleotides to live cell membranes through stepwise assembly. Biomacromol 15:4621–4626. https://doi.org/10.1021/bm501467h

    Article  CAS  Google Scholar 

  43. McGinnis CS et al (2019) MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat Methods 16:619–626. https://doi.org/10.1038/s41592-019-0433-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Fang L et al (2021) CASB: a concanavalin A-based sample barcoding strategy for single-cell sequencing. Mol Syst Biol 17:e10060. https://doi.org/10.15252/msb.202010060

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gehring J et al (2020) Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nat Biotechnol 38:35–38. https://doi.org/10.1038/s41587-019-0372-z

    Article  CAS  PubMed  Google Scholar 

  46. Shin D et al (2019) Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations. Sci Adv 5:eaav2249. https://doi.org/10.1126/sciadv.aav2249

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Srivatsan SR et al (2020) Massively multiplex chemical transcriptomics at single-cell resolution. Science (New York, N.Y.) 367:45–51. https://doi.org/10.1126/science.aax6234

    Article  CAS  Google Scholar 

  48. Guo C et al (2019) Cell Tag Indexing: genetic barcode-based sample multiplexing for single-cell genomics. Genome Biol 20:90. https://doi.org/10.1186/s13059-019-1699-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dixit A et al (2016) Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167:1853-1866.e1817. https://doi.org/10.1016/j.cell.2016.11.038

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Adamson B et al (2016) A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167:1867–1882. https://doi.org/10.1016/j.cell.2016.11.048 (e1821)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jaitin DA et al (2016) Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell 167:1883–1896. https://doi.org/10.1016/j.cell.2016.11.039 (e1815)

    Article  CAS  PubMed  Google Scholar 

  52. Datlinger P et al (2017) Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods 14:297–301. https://doi.org/10.1038/nmeth.4177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wagner DE, Klein AM (2017) Genetic screening enters the single-cell era. Nat Methods 14:237–238. https://doi.org/10.1038/nmeth.4196

    Article  CAS  PubMed  Google Scholar 

  54. Aarts M et al (2017) Coupling shRNA screens with single-cell RNA-seq identifies a dual role for mTOR in reprogramming-induced senescence. Genes Dev 31:2085–2098. https://doi.org/10.1101/gad.297796.117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Uzbas F et al (2019) BART-Seq: cost-effective massively parallelized targeted sequencing for genomics, transcriptomics, and single-cell analysis. Genome Biol 20:155. https://doi.org/10.1186/s13059-019-1748-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Datlinger P et al (2021) Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. Nat Methods 18:635–642. https://doi.org/10.1038/s41592-021-01153-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Cao J et al (2017) Comprehensive single-cell transcriptional profiling of a multicellular organism. Science (New York, N.Y.) 357:661–667. https://doi.org/10.1126/science.aam8940

    Article  CAS  Google Scholar 

  58. Cao J et al (2019) The single-cell transcriptional landscape of mammalian organogenesis. Nature 566:496–502. https://doi.org/10.1038/s41586-019-0969-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Rosenberg AB et al (2018) Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science (New York, N.Y.) 360:176–182. https://doi.org/10.1126/science.aam8999

    Article  CAS  Google Scholar 

  60. Shashikant T, Ettensohn CA (2019) Genome-wide analysis of chromatin accessibility using ATAC-seq. Methods Cell Biol 151:219–235. https://doi.org/10.1016/bs.mcb.2018.11.002

    Article  CAS  PubMed  Google Scholar 

  61. Yan F et al (2020) From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis. Genome Biol 21:22. https://doi.org/10.1186/s13059-020-1929-3

    Article  PubMed  PubMed Central  Google Scholar 

  62. Cusanovich DA et al (2015) Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science (New York, N.Y.) 348:910–914. https://doi.org/10.1126/science.aab1601

    Article  CAS  Google Scholar 

  63. Lareau CA et al (2019) Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat Biotechnol 37:916–924. https://doi.org/10.1038/s41587-019-0147-6

    Article  CAS  PubMed  Google Scholar 

  64. Wang K et al (2021) Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility. Mol Cell 81:4319-4332.e4310. https://doi.org/10.1016/j.molcel.2021.09.026

    Article  CAS  PubMed  Google Scholar 

  65. Ng PC, Kirkness EF (2010) Whole genome sequencing. Methods Mol Biol (Clifton, N.J.) 628:215–226. https://doi.org/10.1007/978-1-60327-367-1_12

    Article  CAS  Google Scholar 

  66. Vitak SA et al (2017) Sequencing thousands of single-cell genomes with combinatorial indexing. Nat Methods 14:302–308. https://doi.org/10.1038/nmeth.4154

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Moore LD et al (2013) DNA methylation and its basic function. Neuropsychopharmacology 38:23–38. https://doi.org/10.1038/npp.2012.112

    Article  CAS  PubMed  Google Scholar 

  68. Gouil Q, Keniry A (2019) Latest techniques to study DNA methylation. Essays Biochem 63:639–648. https://doi.org/10.1042/ebc20190027

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Frommer M et al (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci USA 89:1827–1831. https://doi.org/10.1073/pnas.89.5.1827

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Lister R et al (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462:315–322. https://doi.org/10.1038/nature08514

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Smallwood SA et al (2014) Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods 11:817–820. https://doi.org/10.1038/nmeth.3035

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Farlik M et al (2015) Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep 10:1386–1397. https://doi.org/10.1016/j.celrep.2015.02.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Farlik M et al (2016) DNA methylation dynamics of human hematopoietic stem cell differentiation. Cell Stem Cell 19:808–822. https://doi.org/10.1016/j.stem.2016.10.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Angermueller C et al (2016) Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods 13:229–232. https://doi.org/10.1038/nmeth.3728

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Clark SJ et al (2017) Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq). Nat Protoc 12:534–547. https://doi.org/10.1038/nprot.2016.187

    Article  CAS  PubMed  Google Scholar 

  76. Luo C et al (2017) Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science (New York, N.Y.) 357:600–604. https://doi.org/10.1126/science.aan3351

    Article  CAS  Google Scholar 

  77. Mulqueen RM et al (2018) Highly scalable generation of DNA methylation profiles in single cells. Nat Biotechnol 36:428–431. https://doi.org/10.1038/nbt.4112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Lieberman-Aiden E et al (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science (New York, N.Y.) 326:289–293. https://doi.org/10.1126/science.1181369

    Article  CAS  Google Scholar 

  79. Ramani V et al (2016) Understanding spatial genome organization: methods and insights. Genomics Proteomics Bioinformatics 14:7–20. https://doi.org/10.1016/j.gpb.2016.01.002

    Article  PubMed  PubMed Central  Google Scholar 

  80. Eagen KP (2018) Principles of chromosome architecture revealed by Hi-C. Trends Biochem Sci 43:469–478. https://doi.org/10.1016/j.tibs.2018.03.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Kong S, Zhang Y (2019) Deciphering Hi-C: from 3D genome to function. Cell Biol Toxicol 35:15–32. https://doi.org/10.1007/s10565-018-09456-2

    Article  CAS  PubMed  Google Scholar 

  82. Ramani V et al (2017) Massively multiplex single-cell Hi-C. Nat Methods 14:263–266. https://doi.org/10.1038/nmeth.4155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Hasin Y et al (2017) Multi-omics approaches to disease. Genome Biol 18:83. https://doi.org/10.1186/s13059-017-1215-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Chappell L et al (2018) Single-sell (Multi)omics technologies. Annu Rev Genomics Hum Genet 19:15–41. https://doi.org/10.1146/annurev-genom-091416-035324

    Article  CAS  PubMed  Google Scholar 

  85. Mimitou EP et al (2019) Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat Methods 16:409–412. https://doi.org/10.1038/s41592-019-0392-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Cao J et al (2018) Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science (New York, N.Y.) 361:1380–1385. https://doi.org/10.1126/science.aau0730

    Article  CAS  Google Scholar 

  87. Mimitou EP et al (2021) Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat Biotechnol 39:1246–1258. https://doi.org/10.1038/s41587-021-00927-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Swanson E et al (2021) Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. Elife. https://doi.org/10.7554/eLife.63632

    Article  PubMed  PubMed Central  Google Scholar 

  89. Hartmann FJ et al (2018) A universal live cell barcoding-platform for multiplexed human single cell analysis. Sci Rep 8(1):10770. https://doi.org/10.1038/s41598-018-28791-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Mylka V et al (2022) Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq. Genome Biol 23(1):55. https://doi.org/10.1186/s13059-022-02628-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Cheng J et al (2021) Multiplexing methods for simultaneous large-scale transcriptomic profiling of samples at single-cell resolution. Adv Sci (Weinheim, Baden-Wurttemberg, Germany) 8:e2101229. https://doi.org/10.1002/advs.202101229

    Article  CAS  Google Scholar 

  92. Rodriques SG et al (2019) Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 363(6434):1463–1467. https://doi.org/10.1126/science.aaw1219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Vickovic S et al (2019) High-definition spatial transcriptomics for in situ tissue profiling. Nat Methods 16(10):987–990. https://doi.org/10.1038/s41592-019-0548-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Liu Y et al (2020) High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183(6):1665-1681.e18. https://doi.org/10.1016/j.cell.2020.10.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Kaya-Okur HS et al (2019) CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun 10:1930. https://doi.org/10.1038/s41467-019-09982-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Kai He from Southern Medical University, and Yanfang Lu from Henan Provincial People’s Hospital for suggestion in the preparation of this manuscript.

Funding

This work was supported by the grants from the Natural Science Foundation of Guangdong Province (Major Basic Cultivation Project 2018B030308004, and Major Projects of Basic and Applied Basic Research 2019B1515120033), the National Nature Science Foundation of China (32071452), the Open Fund Programs of Shenzhen Bay Laboratory (SZBL2020090501003), and the Pearl River Talents Program Local Innovative and Research Teams (2017BT01S131).

Author information

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China

    Yulong Zhang, Siwen Xu, Zebin Wen, Jinyu Gao & Xinghua Pan

  2. Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China

    Yulong Zhang, Siwen Xu, Zebin Wen, Jinyu Gao & Xinghua Pan

  3. Shenzhen Bay Laboratory, Shenzhen, Guangdong, China

    Yulong Zhang, Shuang Li & Xinghua Pan

  4. SequMed BioTechnology, Inc., Guangzhou, Guangdong, China

    Siwen Xu

  5. Department of Genetics, Yale University School of Medicine, New Haven, CT, 06520-8005, USA

    Sherman M. Weissman

Authors
  1. Yulong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siwen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zebin Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinyu Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sherman M. Weissman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinghua Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the review conception and design. Material preparation, data collection, and analysis were performed by YZ, SX, ZW, and JG. The first draft of the manuscript was written by YZ and SX, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xinghua Pan.

Ethics declarations

Conflict of interest

There are no conflicts of interest to declare.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Xu, S., Wen, Z. et al. Sample-multiplexing approaches for single-cell sequencing. Cell. Mol. Life Sci. 79, 466 (2022). https://doi.org/10.1007/s00018-022-04482-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00018-022-04482-0

Keywords

Search

Navigation