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Single-cell omics in ageing: a young and growing field

Abstract

Organismal ageing results from interlinked molecular changes in multiple organs over time. The study of ageing at the molecular level is complicated by varying decay characteristics and kinetics—both between and within organs—driven by intrinsic and extracellular factors. Emerging single-cell omics methods allow for molecular and spatial profiling of cells, and probing of regulatory states and cell-fate determination, thus providing promising tools for unravelling the heterogeneous process of ageing and making it amenable to intervention. These new strategies are enabled by advances in genomic, epigenomic and transcriptomic technologies. Combined with methods for proteome and metabolome analysis, single-cell techniques provide multidimensional, integrated data with unprecedented detail and throughput. Here, we provide an overview of the current state, and perspectives on the future, of this emerging field. We discuss how single-cell approaches can advance understanding of mechanisms underlying organismal ageing and aid in the development of interventions for ageing and ageing-associated diseases.

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Fig. 1: Single-cell platforms for studying organismal ageing.
Fig. 2: Building a comprehensive and integrative atlas of human ageing.

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References

  1. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Prakadan, S. M., Shalek, A. K. & Weitz, D. A. Scaling by shrinking: empowering single-cell ‘omics’ with microfluidic devices. Nat. Rev. Genet. 18, 345–361 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Schwartzman, O. & Tanay, A. Single-cell epigenomics: techniques and emerging applications. Nat. Rev. Genet. 16, 716–726 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Wu, A. R., Wang, J., Streets, A. M. & Huang, Y. Single-cell transcriptional analysis. Annu. Rev. Anal. Chem. (Palo Alto Calif) 10, 439–462 (2017).

    Article  CAS  Google Scholar 

  6. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Bennett, M. R. & Hasty, J. Microfluidic devices for measuring gene network dynamics in single cells. Nat. Rev. Genet. 10, 628–638 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  9. Xin, Y. et al. Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc. Natl Acad. Sci. USA 113, 3293–3298 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zilionis, R. et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12, 44–73 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Wadsworth, M. H. II, Hughes, T. K. & Shalek, A. K. Marrying microfluidics and microwells for parallel, high-throughput single-cell genomics. Genome Biol. 16, 129 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Han, X. et al. Mapping the mouse cell atlas by Microwell-seq. Cell 172, 1091–1107.e17 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548, 456–460 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Perié, L. et al. Determining lineage pathways from cellular barcoding experiments. Cell Rep. 6, 617–624 (2014).

    Article  PubMed  CAS  Google Scholar 

  15. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 14, R31 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. The Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Article  CAS  Google Scholar 

  24. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Fu, Y. et al. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. Proc. Natl Acad. Sci. USA 112, 11923–11928 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zong, C., Lu, S., Chapman, A. R. & Xie, X. S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gundry, M., Li, W., Maqbool, S. B. & Vijg, J. Direct, genome-wide assessment of DNA mutations in single cells. Nucleic Acids Res. 40, 2032–2040 (2012).

    Article  CAS  PubMed  Google Scholar 

  31. Hou, Y. et al. Genome analyses of single human oocytes. Cell 155, 1492–1506 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Wang, J., Fan, H. C., Behr, B. & Quake, S. R. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm. Cell 150, 402–412 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zhang, L. et al. Single-cell whole-genome sequencing reveals the functional landscape of somatic mutations in B lymphocytes across the human lifespan. Proc. Natl Acad. Sci. USA 116, 9014–9019 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Knouse, K. A., Wu, J. & Amon, A. Assessment of megabase-scale somatic copy number variation using single-cell sequencing. Genome Res. 26, 376–384 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Pratto, F. et al. Recombination initiation maps of individual human genomes. Science 346, 1256442 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Zhang, L. & Vijg, J. Somatic mutagenesis in mammals and its implications for human disease and aging. Annu. Rev. Genet. 52, 397–419 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. McConnell, M. J. et al. Mosaic copy number variation in human neurons. Science 342, 632–637 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Cai, X. et al. Single-cell, genome-wide sequencing identifies clonal somatic copy-number variation in the human brain. Cell Rep. 8, 1280–1289 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Evrony, G. D. et al. Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151, 483–496 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Abyzov, A. et al. Somatic copy number mosaicism in human skin revealed by induced pluripotent stem cells. Nature 492, 438–442 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Evrony, G. D. et al. Cell lineage analysis in human brain using endogenous retroelements. Neuron 85, 49–59 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230–244 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Kimmerling, R. J. et al. A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat. Commun. 7, 10220 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cheung, P. et al. Single-cell chromatin modification profiling reveals increased epigenetic variations with aging. Cell 173, 1385–1397.e14 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gutin, J. et al. Fine-resolution mapping of TF binding and chromatin interactions. Cell Rep. 22, 2797–2807 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Chen, X., Miragaia, R. J., Natarajan, K. N. & Teichmann, S. A. A rapid and robust method for single cell chromatin accessibility profiling. Nat. Commun. 9, 5345 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

    Article  CAS  PubMed  Google Scholar 

  59. Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324.e18 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gravina, S., Dong, X., Yu, B. & Vijg, J. Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome. Genome Biol. 17, 150 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Perfetto, S. P., Chattopadhyay, P. K. & Roederer, M. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648–655 (2004).

    Article  CAS  PubMed  Google Scholar 

  64. Newell, E. W., Sigal, N., Bendall, S. C., Nolan, G. P. & Davis, M. M. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36, 142–152 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  PubMed  Google Scholar 

  69. Duncan, K. D., Fyrestam, J. & Lanekoff, I. Advances in mass spectrometry based single-cell metabolomics. Analyst 144, 782–793 (2019).

    Article  CAS  PubMed  Google Scholar 

  70. Zhang, L. & Vertes, A. Single-cell mass spectrometry approaches to explore cellular heterogeneity. Angew. Chem. Int. Ed. Engl. 57, 4466–4477 (2018).

    Article  CAS  PubMed  Google Scholar 

  71. Comi, T. J., Do, T. D., Rubakhin, S. S. & Sweedler, J. V. Categorizing cells on the basis of their chemical profiles: progress in single-cell mass spectrometry. J. Am. Chem. Soc. 139, 3920–3929 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Zhu, H. et al. Moderate UV exposure enhances learning and memory by promoting a novel glutamate biosynthetic pathway in the brain. Cell 173, 1716–1727.e17 (2018).

    Article  CAS  PubMed  Google Scholar 

  73. Zhang, W., Qu, J., Liu, G. H. & Belmonte, J. C. I. The ageing epigenome and its rejuvenation. Nat. Rev. Mol. Cell Biol. 21, 137–150 (2020).

    Article  CAS  PubMed  Google Scholar 

  74. Tang, H. et al. Single senescent cell sequencing reveals heterogeneity in senescent cells induced by telomere erosion. Protein Cell 10, 370–375 (2019).

    Article  PubMed  Google Scholar 

  75. Arrojo E Drigo, R. et al. Age mosaicism across multiple scales in adult tissues. Cell Metab. 30, 343–351.e3 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Ximerakis, M. et al. Single-cell transcriptomic profiling of the aging mouse brain. Nat. Neurosci. 22, 1696–1708 (2019).

    Article  CAS  PubMed  Google Scholar 

  77. Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998.e20 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271.e6 (2019).

    Article  CAS  PubMed  Google Scholar 

  79. Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. 9, 539 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Gage, F. H. Adult neurogenesis in mammals. Science 364, 827–828 (2019).

    Article  CAS  PubMed  Google Scholar 

  81. Artegiani, B. et al. A single-cell RNA sequencing study reveals cellular and molecular dynamics of the hippocampal neurogenic niche. Cell Rep. 21, 3271–3284 (2017).

    Article  CAS  PubMed  Google Scholar 

  82. Shi, Z. et al. Single-cell transcriptomics reveals gene signatures and alterations associated with aging in distinct neural stem/progenitor cell subpopulations. Protein Cell 9, 351–364 (2018).

    CAS  PubMed  Google Scholar 

  83. Dulken, B. W. et al. Single-cell analysis reveals T cell infiltration in old neurogenic niches. Nature 571, 205–210 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lodato, M. A. et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359, 555–559 (2018).

    Article  CAS  PubMed  Google Scholar 

  85. Kowalczyk, M. S. et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860–1872 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Grover, A. et al. Single-cell RNA sequencing reveals molecular and functional platelet bias of aged haematopoietic stem cells. Nat. Commun. 7, 11075 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Enge, M. et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171, 321–330.e14 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Bahar, R. et al. Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature 441, 1011–1014 (2006).

    Article  CAS  PubMed  Google Scholar 

  89. Martinez-Jimenez, C. P. et al. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 355, 1433–1436 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Wang, S. et al. Single-cell transcriptomic atlas of primate ovarian aging. Cell 180, 585–600.e19 (2020).

    Article  PubMed  CAS  Google Scholar 

  91. Angelidis, I. et al. An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics. Nat. Commun. 10, 963 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Ma, S. et al. Caloric restriction reprograms the single-cell transcriptional landscape of Rattus norvegicus aging. Cell 180, 984–1001.e22 (2020).

    Article  CAS  PubMed  Google Scholar 

  93. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    Article  PubMed  CAS  Google Scholar 

  94. Baran-Gale, J., Chandra, T. & Kirschner, K. Experimental design for single-cell RNA sequencing. Brief. Funct. Genomics 17, 233–239 (2018).

    Article  CAS  PubMed  Google Scholar 

  95. Krishnaswami, S. R. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat. Protoc. 11, 499–524 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Faridani, O. R. et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).

    Article  CAS  PubMed  Google Scholar 

  97. Fan, X. et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol. 16, 148 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Wang, N. et al. Single-cell microRNA-mRNA co-sequencing reveals non-genetic heterogeneity and mechanisms of microRNA regulation. Nat. Commun. 10, 95 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Chen, J. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566–580 (2017).

    Article  CAS  PubMed  Google Scholar 

  100. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. HuBMAP Consortium. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187–192 (2019).

    Article  CAS  Google Scholar 

  103. Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, e23203 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Clark, S. J. et al. ScNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells e. Nat. Commun. 9, 781 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

The authors thank M. Song, W. Zhang, S. Wang, J. Li and S. Ma for proofreading the manuscript. The authors are also grateful to L. Bai, R. Bai, J. Lu, Q. Chu, Y. Yang, S. K. Ma and M. Schwarz for administrative assistance. This work was supported by the National Key Research and Development Program of China (2018YFC2000100), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16010100), the National Key Research and Development Program of China (2018YFA0107203), the National Natural Science Foundation of China (81921006, 81625009, 91749202, 81861168034, 31671429 and 91949209), the Program of the Beijing Municipal Science and Technology Commission (Z191100001519005), the Beijing Natural Science Foundation (Z190019), the Beijing Municipal Commission of Health and Family Planning (PXM2018_026283_000002), the Advanced Innovation Center for Human Brain Protection (3500-1192012), the Key Research Program of the Chinese Academy of Sciences (KFZD-SW-221), K.C. Wong Education Foundation (GJTD-2019-06) and the State Key Laboratory of Membrane Biology. J.C.I.B. and S.M. were supported by the Moxie Foundation and the Glenn Foundation.

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X.H., S.M., J.Q., J.C.I.B. and G.-H.L. wrote the manuscript.

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Correspondence to Jing Qu, Juan Carlos Izpisua Belmonte or Guang-Hui Liu.

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He, X., Memczak, S., Qu, J. et al. Single-cell omics in ageing: a young and growing field. Nat Metab 2, 293–302 (2020). https://doi.org/10.1038/s42255-020-0196-7

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