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Characterizing cis-regulatory elements using single-cell epigenomics

Nature Reviews Genetics volume 24, pages 21–43 (2023)Cite this article

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Abstract

Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.

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Fig. 1: Epigenomic marks at CREs and their association with gene expression.
Fig. 2: Single-cell epigenomic profiling enables insight into cell type-specific CRE annotation and activity.
Fig. 3: Overview of strategies for barcoding single cells.
Fig. 4: General workflow for the analysis of single-cell epigenomic datasets.

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References

  1. Lee, T. I. & Young, R. A. Transcriptional regulation and its misregulation in disease. Cell 152, 1237–1251 (2013).

    Article  CAS  Google Scholar 

  2. Levine, M., Cattoglio, C. & Tjian, R. Looping back to leap forward: transcription enters a new era. Cell 157, 13–25 (2014).

    Article  CAS  Google Scholar 

  3. Oudelaar, A. M. & Higgs, D. R. The relationship between genome structure and function. Nat. Rev. Genet. 22, 154–168 (2021).

    Article  CAS  Google Scholar 

  4. ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    Article  Google Scholar 

  5. Cramer, P. Organization and regulation of gene transcription. Nature 573, 45–54 (2019).

    Article  CAS  Google Scholar 

  6. Doni Jayavelu, N., Jajodia, A., Mishra, A. & Hawkins, R. D. Candidate silencer elements for the human and mouse genomes. Nat. Commun. 11, 1061 (2020).

    Article  CAS  Google Scholar 

  7. Gisselbrecht, S. S. et al. Transcriptional silencers in Drosophila serve a dual role as transcriptional enhancers in alternate cellular contexts. Mol. Cell 77, 324–337 (2020).

    Article  CAS  Google Scholar 

  8. Pang, B. & Snyder, M. P. Systematic identification of silencers in human cells. Nat. Genet. 52, 254–263 (2020).

    Article  CAS  Google Scholar 

  9. Segert, J. A., Gisselbrecht, S. S. & Bulyk, M. L. Transcriptional silencers: driving gene expression with the brakes on. Trends Genet. 37, 514–527 (2021).

    Article  CAS  Google Scholar 

  10. Batut, P. J. et al. Genome organization controls transcriptional dynamics during development. Science 375, 566–570 (2022).

    Article  CAS  Google Scholar 

  11. Hallikas, O. et al. Genome-wide prediction of mammalian enhancers based on analysis of transcription-factor binding affinity. Cell 124, 47–59 (2006).

    Article  CAS  Google Scholar 

  12. Pennacchio, L. A. & Rubin, E. M. Comparative genomic tools and databases: providing insights into the human genome. J. Clin. Invest. 111, 1099–1106 (2003).

    Article  CAS  Google Scholar 

  13. Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    Article  CAS  Google Scholar 

  14. Boyle, A. P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311–322 (2008).

    Article  CAS  Google Scholar 

  15. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  Google Scholar 

  16. Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

    Article  CAS  Google Scholar 

  17. Albert, I. et al. Translational and rotational settings of H2A.Z nucleosomes across the Saccharomyces cerevisiae genome. Nature 446, 572–576 (2007).

    Article  CAS  Google Scholar 

  18. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

    Article  CAS  Google Scholar 

  19. Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

    Article  CAS  Google Scholar 

  20. Mikkelsen, T. S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007).

    Article  CAS  Google Scholar 

  21. Dekker, J. & Mirny, L. The 3D genome as moderator of chromosomal communication. Cell 164, 1110–1121 (2016).

    Article  CAS  Google Scholar 

  22. Rowley, M. J. & Corces, V. G. Organizational principles of 3D genome architecture. Nat. Rev. Genet. 19, 789–800 (2018).

    Article  CAS  Google Scholar 

  23. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  Google Scholar 

  24. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  CAS  Google Scholar 

  25. Boix, C. A., James, B. T., Park, Y. P., Meuleman, W. & Kellis, M. Regulatory genomic circuitry of human disease loci by integrative epigenomics. Nature 590, 300–307 (2021).

    Article  CAS  Google Scholar 

  26. Meuleman, W. et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature 584, 244–251 (2020).

    Article  CAS  Google Scholar 

  27. Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  Google Scholar 

  28. Grubert, F. et al. Landscape of cohesin-mediated chromatin loops in the human genome. Nature 583, 737–743 (2020).

    Article  CAS  Google Scholar 

  29. Calderon, D. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat. Genet. 51, 1494–1505 (2019).

    Article  CAS  Google Scholar 

  30. Zhang, K. et al. A single-cell atlas of chromatin accessibility in the human genome. Cell 184, 5985–6001 (2021). This paper describes the integrated sciATAC-seq analysis of a large panel of adult and fetal human tissue to catalog cCREs in 222 cell clusters.

    Article  CAS  Google Scholar 

  31. Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022).

    Article  CAS  Google Scholar 

  32. Luo, C., Hajkova, P. & Ecker, J. R. Dynamic DNA methylation: in the right place at the right time. Science 361, 1336–1340 (2018).

    Article  CAS  Google Scholar 

  33. Chen, Z. & Zhang, Y. Role of mammalian DNA methyltransferases in development. Annu. Rev. Biochem. 89, 135–158 (2020).

    Article  CAS  Google Scholar 

  34. Wu, X. & Zhang, Y. TET-mediated active DNA demethylation: mechanism, function and beyond. Nat. Rev. Genet. 18, 517–534 (2017).

    Article  CAS  Google Scholar 

  35. Yin, Y. et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, eaaj2239 (2017).

    Article  Google Scholar 

  36. He, Y. & Ecker, J. R. Non-CG methylation in the human genome. Annu. Rev. Genomics Hum. Genet. 16, 55–77 (2015).

    Article  CAS  Google Scholar 

  37. Miura, F., Enomoto, Y., Dairiki, R. & Ito, T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 40, e136 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014). This paper describes the first single-cell bisulfite sequencing approach.

    Article  CAS  Google Scholar 

  40. Farlik, M. et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386–1397 (2015).

    Article  CAS  Google Scholar 

  41. Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    Article  CAS  Google Scholar 

  42. Luo, C. et al. Robust single-cell DNA methylome profiling with snmC-seq2. Nat. Commun. 9, 3824 (2018).

    Article  Google Scholar 

  43. Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 598, 103–110 (2021).

    Article  CAS  Google Scholar 

  44. Liu, H. et al. DNA methylation atlas of the mouse brain at single-cell resolution. Nature 598, 120–128 (2021). This paper describes application of snmC-seq2 to mouse brain tissues resulting in identification 161 brain cell types and cell-type specific CREs.

    Article  CAS  Google Scholar 

  45. Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013).

    Article  CAS  Google Scholar 

  46. Shareef, S. J. et al. Extended-representation bisulfite sequencing of gene regulatory elements in multiplexed samples and single cells. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00910-x (2021).

    Article  Google Scholar 

  47. Mulqueen, R. M. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36, 428–431 (2018).

    Article  CAS  Google Scholar 

  48. Luo, C. et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genom. https://doi.org/10.1016/j.xgen.2022.100107 (2022).

    Article  Google Scholar 

  49. Zaret, K. S. Pioneer transcription factors initiating gene network changes. Annu. Rev. Genet. 54, 367–385 (2020).

    Article  CAS  Google Scholar 

  50. Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015).

    Article  CAS  Google Scholar 

  51. Gao, W. et al. Multiplex indexing approach for the detection of DNase I hypersensitive sites in single cells. Nucleic Acids Res. 49, e56 (2021).

    Article  CAS  Google Scholar 

  52. 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  Google Scholar 

  53. Xu, W. et al. A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility. Nat. Protoc. 16, 4084–4107 (2021).

    Article  CAS  Google Scholar 

  54. Graybuck, L. T. et al. Enhancer viruses for combinatorial cell-subclass-specific labeling. Neuron 109, 1449–1464 (2021).

    Article  CAS  Google Scholar 

  55. Mich, J. K. et al. Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex. Cell Rep. 34, 108754 (2021).

    Article  CAS  Google Scholar 

  56. Mezger, A. et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat. Commun. 9, 3647 (2018).

    Article  Google Scholar 

  57. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015). This paper introduces scATAC-seq using microfluidics.

    Article  CAS  Google Scholar 

  58. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019). This paper describes a droplet-based scATAC-seq procedure.

    Article  CAS  Google Scholar 

  59. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019). This paper introduces the combination of combinatorial indexing with droplet-based approaches for scATAC-seq.

    Article  CAS  Google Scholar 

  60. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015). This paper introduces scATAC-seq using combinatorial indexing.

    Article  CAS  Google Scholar 

  61. Wang, K. et al. Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility. Mol. Cell 81, 4319–4332 (2021).

    Article  CAS  Google Scholar 

  62. De Rop, F. et al. HyDrop enables droplet based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads. Elife https://doi.org/10.7554/eLife.73971 (2022).

    Article  Google Scholar 

  63. Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science https://doi.org/10.1126/science.aba7612 (2020). This paper describes three-round indexing for sciATAC-seq to generate a fetal tissue single-cell chromatin accessibility atlas.

    Article  Google Scholar 

  64. Sinnamon, J. R. et al. The accessible chromatin landscape of the murine hippocampus at single-cell resolution. Genome Res. 29, 857–869 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  66. Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).

    Article  CAS  Google Scholar 

  67. Li, Y. E. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021). This paper describes the application of snATAC-seq to mouse brain tissues resulting in identification of 160 brain cell types and cell-type specific CREs.

    Article  CAS  Google Scholar 

  68. LaFave, L. M. et al. Epigenomic state transitions characterize tumor progression in mouse lung adenocarcinoma. Cancer Cell 38, 212–228 (2020).

    Article  CAS  Google Scholar 

  69. Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018).

    Article  CAS  Google Scholar 

  70. Thornton, C. A. et al. Spatially mapped single-cell chromatin accessibility. Nat. Commun. 12, 1274 (2021).

    Article  CAS  Google Scholar 

  71. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  CAS  Google Scholar 

  72. Mulqueen, R. M. et al. High-content single-cell combinatorial indexing. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00962-z (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  74. Allis, C. D. & Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500 (2016).

    Article  CAS  Google Scholar 

  75. Heintzman, N. D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311–318 (2007).

    Article  CAS  Google Scholar 

  76. Cenik, B. K. & Shilatifard, A. COMPASS and SWI/SNF complexes in development and disease. Nat. Rev. Genet. 22, 38–58 (2021).

    Article  CAS  Google Scholar 

  77. Calo, E. & Wysocka, J. Modification of enhancer chromatin: what, how, and why? Mol. Cell 49, 825–837 (2013).

    Article  CAS  Google Scholar 

  78. Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015). This paper describes the first scChIP-seq protocol.

    Article  CAS  Google Scholar 

  79. Grosselin, K. et al. High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nat. Genet. 51, 1060–1066 (2019).

    Article  CAS  Google Scholar 

  80. Ai, S. et al. Profiling chromatin states using single-cell itChIP-seq. Nat. Cell Biol. 21, 1164–1172 (2019).

    Article  CAS  Google Scholar 

  81. Schmid, M., Durussel, T. & Laemmli, U. K. ChIC and ChEC; genomic mapping of chromatin proteins. Mol. Cell 16, 147–157 (2004).

    CAS  Google Scholar 

  82. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. Elife https://doi.org/10.7554/eLife.21856 (2017).

    Article  Google Scholar 

  83. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    Article  Google Scholar 

  84. Ku, W. L. et al. Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nat. Methods 16, 323–325 (2019).

    Article  CAS  Google Scholar 

  85. Hainer, S. J., Boskovic, A., McCannell, K. N., Rando, O. J. & Fazzio, T. G. Profiling of pluripotency factors in single cells and early embryos. Cell 177, 1319–1329 (2019).

    Article  CAS  Google Scholar 

  86. Ku, W. L., Pan, L., Cao, Y., Gao, W. & Zhao, K. Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing. Genome Res. 31, 1831–1842 (2021).

    Article  Google Scholar 

  87. Carter, B. et al. Mapping histone modifications in low cell number and single cells using antibody-guided chromatin tagmentation (ACT-seq). Nat. Commun. 10, 3747 (2019).

    Article  Google Scholar 

  88. Wang, Q. et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol. Cell 76, 206–216 (2019).

    Article  CAS  Google Scholar 

  89. Wu, S. J. et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat. Biotechnol. 39, 819–824 (2021). Together with Bartosovic et al. (2021), this paper describes droplet-based scCUT&Tag.

    Article  CAS  Google Scholar 

  90. Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 39, 825–835 (2021). Together with Wu et al. (2021), this paper describes droplet-based scCUT&Tag.

    Article  CAS  Google Scholar 

  91. Bartlett, D. A. et al. High-throughput single-cell epigenomic profiling by targeted insertion of promoters (TIP-seq). J. Cell Biol. https://doi.org/10.1083/jcb.202103078 (2021).

    Article  Google Scholar 

  92. Gibcus, J. H. et al. A pathway for mitotic chromosome formation. Science https://doi.org/10.1126/science.aao6135 (2018).

    Article  Google Scholar 

  93. Zhang, H. et al. Chromatin structure dynamics during the mitosis-to-G1 phase transition. Nature 576, 158–162 (2019).

    Article  CAS  Google Scholar 

  94. Davidson, I. F. & Peters, J. M. Genome folding through loop extrusion by SMC complexes. Nat. Rev. Mol. Cell Biol. 22, 445–464 (2021).

    Article  CAS  Google Scholar 

  95. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013). This paper describes the first scHi-C protocol.

    Article  CAS  Google Scholar 

  96. Stevens, T. J. et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature 544, 59–64 (2017).

    Article  CAS  Google Scholar 

  97. Flyamer, I. M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017).

    Article  CAS  Google Scholar 

  98. Tan, L., Xing, D., Chang, C. H., Li, H. & Xie, X. S. Three-dimensional genome structures of single diploid human cells. Science 361, 924–928 (2018).

    Article  CAS  Google Scholar 

  99. Tan, L. et al. Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development. Cell 184, 741–758 (2021).

    Article  CAS  Google Scholar 

  100. Nagano, T. et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017).

    Article  CAS  Google Scholar 

  101. Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

    Article  CAS  Google Scholar 

  102. Ramani, V. et al. Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells. Methods 170, 61–68 (2020).

    Article  CAS  Google Scholar 

  103. Arrastia, M. V. et al. Single-cell measurement of higher-order 3D genome organization with scSPRITE. Nat. Biotechnol. 40, 64–73 (2022).

    Article  CAS  Google Scholar 

  104. Zhu, C., Preissl, S. & Ren, B. Single-cell multimodal omics: the power of many. Nat. Methods 17, 11–14 (2020).

    Article  CAS  Google Scholar 

  105. Elmentaite, R., Dominguez Conde, C., Yang, L. & Teichmann, S. A. Single-cell atlases: shared and tissue-specific cell types across human organs. Nat. Rev. Genet. https://doi.org/10.1038/s41576-022-00449-w (2022).

    Article  Google Scholar 

  106. Hu, Y. et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol. 17, 88 (2016).

    Article  Google Scholar 

  107. Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016).

    Article  CAS  Google Scholar 

  108. Bian, S. et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362, 1060–1063 (2018).

    Article  CAS  Google Scholar 

  109. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018). This paper describes the first single-cell co-assay for ATAC-seq and RNA expression.

    Article  CAS  Google Scholar 

  110. 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  Google Scholar 

  111. Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019).

    Article  CAS  Google Scholar 

  112. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020).

    Article  CAS  Google Scholar 

  113. Xing, Q. R. et al. Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility. Genome Res. 30, 1027–1039 (2020).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  115. Plongthongkum, N., Diep, D., Chen, S., Lake, B. B. & Zhang, K. Scalable dual-omics profiling with single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-seq2). Nat. Protoc. 16, 4992–5029 (2021).

    Article  CAS  Google Scholar 

  116. Xiong, H., Luo, Y., Wang, Q., Yu, X. & He, A. Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions. Nat. Methods 18, 652–660 (2021). Together with Zhu et al. (2021), this paper introduces joint profiling of histone modifications and RNA from the same cell using combinatorial indexing.

    Article  CAS  Google Scholar 

  117. Zhu, C. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat. Methods 18, 283–292 (2021). Together with Xiong et al. (2021), this paper introduces joint profiling of histone modifications and RNA from the same cell using combinatorial indexing.

    Article  CAS  Google Scholar 

  118. Sun, Z. et al. Joint single-cell multiomic analysis in Wnt3a induced asymmetric stem cell division. Nat. Commun. 12, 5941 (2021).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  120. Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00927-2 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  122. Chen, X. et al. Joint single-cell DNA accessibility and protein epitope profiling reveals environmental regulation of epigenomic heterogeneity. Nat. Commun. 9, 4590 (2018).

    Article  Google Scholar 

  123. Fiskin, E. et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01065-5 (2021).

    Article  Google Scholar 

  124. Zhang, B. et al. Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01250-0 (2022).

    Article  Google Scholar 

  125. Chen, A. F. et al. NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells. Nat. Methods https://doi.org/10.1038/s41592-022-01461-y (2022). This paper describes combined profiling of nuclear transcription factor protein levels, chromatin accessibility and transcriptomes.

    Article  Google Scholar 

  126. 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  Google Scholar 

  127. Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. Elife https://doi.org/10.7554/eLife.23203 (2017).

    Article  Google Scholar 

  128. Gu, C., Liu, S., Wu, Q., Zhang, L. & Guo, F. Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes. Cell Res. 29, 110–123 (2019).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  130. Wang, Y. et al. Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos. Nat. Commun. 12, 1247 (2021).

    Article  CAS  Google Scholar 

  131. Tedesco, M. et al. Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin. Nat. Biotechnol. 40, 235–244 (2022).

    Article  CAS  Google Scholar 

  132. Gopalan, S., Wang, Y., Harper, N. W., Garber, M. & Fazzio, T. G. Simultaneous profiling of multiple chromatin proteins in the same cells. Mol. Cell 81, 4736–4746.e5 (2021).

    Article  CAS  Google Scholar 

  133. Li, G. et al. Joint profiling of DNA methylation and chromatin architecture in single cells. Nat. Methods 16, 991–993 (2019). Together with Lee et al. (2019), this paper describes the first methods for single-cell joint profiling of chromatin architecture and DNA methylation.

    Article  CAS  Google Scholar 

  134. Lee, D. S. et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019). Together with Li et al. (2019), this paper describes the first methods for single-cell joint profiling of chromatin architecture and DNA methylation.

    Article  CAS  Google Scholar 

  135. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  Google Scholar 

  136. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  CAS  Google Scholar 

  137. Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021).

    Article  CAS  Google Scholar 

  138. Bravo Gonzalez-Blas, C. et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. Methods 16, 397–400 (2019). The papers by Stuart et al.135, Granja et al.136, Fang et al.137 and Bravo Gonzalez-Blas et al.138 describe software packages for scATAC-seq analysis.

    Article  CAS  Google Scholar 

  139. Xiong, L. et al. SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nat. Commun. 10, 4576 (2019).

    Article  Google Scholar 

  140. Forgy, E. W. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21, 768–769 (1965).

    Google Scholar 

  141. Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) 226–231 (AAAI Press, 1996).

  142. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).

    Article  Google Scholar 

  143. Traag, V. A. Faster unfolding of communities: speeding up the Louvain algorithm. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 92, 032801 (2015).

    Article  CAS  Google Scholar 

  144. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  145. McInnes, L., John, H. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.03426 (2018).

    Article  Google Scholar 

  146. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).

    Article  CAS  Google Scholar 

  147. Thibodeau, A. et al. AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data. Genome Biol. 22, 252 (2021).

    Article  CAS  Google Scholar 

  148. Pierce, S. E., Granja, J. M. & Greenleaf, W. J. High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer. Nat. Commun. 12, 2969 (2021).

    Article  CAS  Google Scholar 

  149. Danese, A. et al. EpiScanpy: integrated single-cell epigenomic analysis. Nat. Commun. 12, 5228 (2021). This paper describes a software package for single-cell epigenomic analysis.

    Article  CAS  Google Scholar 

  150. Zhang, H. et al. Fast alignment and preprocessing of chromatin profiles with Chromap. Nat. Commun. 12, 6566 (2021).

    Article  CAS  Google Scholar 

  151. de Boer, C. G. & Regev, A. BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization. BMC Bioinformatics 19, 253 (2018).

    Article  Google Scholar 

  152. Zamanighomi, M. et al. Unsupervised clustering and epigenetic classification of single cells. Nat. Commun. 9, 2410 (2018).

    Article  Google Scholar 

  153. Baker, S. M., Rogerson, C., Hayes, A., Sharrocks, A. D. & Rattray, M. Classifying cells with Scasat, a single-cell ATAC-seq analysis tool. Nucleic Acids Res. 47, e10 (2019).

    Article  Google Scholar 

  154. Ji, Z., Zhou, W. & Ji, H. Single-cell regulome data analysis by SCRAT. Bioinformatics 33, 2930–2932 (2017).

    Article  CAS  Google Scholar 

  155. Fowlkes, C., Belongie, S., Chung, F. & Malik, J. Spectral grouping using the Nystrom method. IEEE Trans. Pattern Anal. Mach. Intell. 26, 214–225 (2004).

    Article  Google Scholar 

  156. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  Google Scholar 

  157. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  Google Scholar 

  158. Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    Article  CAS  Google Scholar 

  159. Kang, J. B. et al. Efficient and precise single-cell reference atlas mapping with Symphony. Nat. Commun. 12, 5890 (2021).

    Article  CAS  Google Scholar 

  160. Argelaguet, R., Cuomo, A. S. E., Stegle, O. & Marioni, J. C. Computational principles and challenges in single-cell data integration. Nat. Biotechnol. 39, 1202–1215 (2021).

    Article  CAS  Google Scholar 

  161. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  Google Scholar 

  162. Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 111 (2020).

    Article  Google Scholar 

  163. Jin, S., Zhang, L. & Nie, Q. scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles. Genome Biol. 21, 25 (2020).

    Article  Google Scholar 

  164. Kim, H. J., Lin, Y., Geddes, T. A., Yang, J. Y. H. & Yang, P. CiteFuse enables multi-modal analysis of CITE-seq data. Bioinformatics 36, 4137–4143 (2020).

    Article  CAS  Google Scholar 

  165. Gayoso, A. et al. Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nat. Methods 18, 272–282 (2021).

    Article  CAS  Google Scholar 

  166. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).

    Article  CAS  Google Scholar 

  167. Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16, 695–698 (2019).

    Article  CAS  Google Scholar 

  168. Jansen, C. et al. Building gene regulatory networks from scATAC-seq and scRNA-seq using linked self organizing maps. PLoS Comput. Biol. 15, e1006555 (2019).

    Article  Google Scholar 

  169. Welch, J. D., Hartemink, A. J. & Prins, J. F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 18, 138 (2017).

    Article  Google Scholar 

  170. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  171. Haeussler, M. et al. The UCSC genome browser database: 2019 update. Nucleic Acids Res. 47, D853–D858 (2019).

    Article  CAS  Google Scholar 

  172. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  Google Scholar 

  173. Li, D., Hsu, S., Purushotham, D., Sears, R. L. & Wang, T. WashU epigenome browser update 2019. Nucleic Acids Res. 47, W158–W165 (2019).

    Article  CAS  Google Scholar 

  174. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  Google Scholar 

  175. Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871.e8 (2018).

    Article  CAS  Google Scholar 

  176. Yu, M. et al. SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data. Nat. Methods https://doi.org/10.1038/s41592-021-01231-2 (2021).

    Article  Google Scholar 

  177. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  Google Scholar 

  178. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  Google Scholar 

  179. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  Google Scholar 

  180. Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).

    Article  CAS  Google Scholar 

  181. Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).

    Article  CAS  Google Scholar 

  182. Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).

    Article  CAS  Google Scholar 

  183. Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535–1548.e16 (2018).

    Article  CAS  Google Scholar 

  184. Muto, Y. et al. Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney. Nat. Commun. 12, 2190 (2021).

    Article  CAS  Google Scholar 

  185. Duong, T. E. et al. A single-cell regulatory map of postnatal lung alveologenesis in humans and mice. Cell Genom. https://doi.org/10.1016/j.xgen.2022.100108 (2022).

    Article  Google Scholar 

  186. Zhang, Z. et al. Single nucleus transcriptome and chromatin accessibility of postmortem human pituitaries reveal diverse stem cell regulatory mechanisms. Cell Rep. 38, 110467 (2022).

    Article  CAS  Google Scholar 

  187. Rai, V. et al. Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Mol. Metab. 32, 109–121 (2020).

    Article  CAS  Google Scholar 

  188. Wang, A. et al. Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. Elife https://doi.org/10.7554/eLife.62522 (2020).

    Article  Google Scholar 

  189. Ziffra, R. S. et al. Single-cell epigenomics reveals mechanisms of human cortical development. Nature 598, 205–213 (2021).

    Article  CAS  Google Scholar 

  190. Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 184, 5053–5069.e23 (2021).

    Article  CAS  Google Scholar 

  191. Morabito, S. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat. Genet. 53, 1143–1155 (2021).

    Article  CAS  Google Scholar 

  192. Fasolino, M. et al. Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes. Nat. Metab. 4, 284–299 (2022).

    Article  CAS  Google Scholar 

  193. Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).

    Article  CAS  Google Scholar 

  194. Hocker, J. D. et al. Cardiac cell type-specific gene regulatory programs and disease risk association. Sci. Adv. https://doi.org/10.1126/sciadv.abf1444 (2021).

    Article  Google Scholar 

  195. Chiou, J. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature 594, 398–402 (2021).

    Article  CAS  Google Scholar 

  196. Ord, T. et al. Single-cell epigenomics and functional fine-mapping of atherosclerosis GWAS loci. Circ. Res. 129, 240–258 (2021).

    Article  Google Scholar 

  197. Sheng, X. et al. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat. Genet. 53, 1322–1333 (2021).

    Article  CAS  Google Scholar 

  198. Chiou, J. et al. Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk. Nat. Genet. 53, 455–466 (2021).

    Article  CAS  Google Scholar 

  199. Orchard, P. et al. Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits. Genome Res. https://doi.org/10.1101/gr.268482.120 (2021).

    Article  Google Scholar 

  200. Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022).

    Article  CAS  Google Scholar 

  201. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  Google Scholar 

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Acknowledgements

We apologize to those authors whose work we fail to include in this Review due to space constraints. Research in the Ren lab is funded by the Ludwig Institute and the National Institutes of Health (NIH) grants 1UM1HG009402, 1U19MH114831, 1U01MH121282, 1R01AG066018, R01AG067153, U01DA052769, 1UM1HG011585, RF1MH124612, 1R56AG069107, R01EY031663, 1U01HG012059, R24AG073198 and RF1MH128838. The Center for Epigenomics was supported, in part, by the UC San Diego School of Medicine and by NIH grants R01EY030591, U01HL148867, U01DK120429 and R01HD102534. The Gaulton lab is funded by NIH grants DK114650, DK120429, DK122607, DK105554 and HG012059.

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Authors and Affiliations

  1. Center for Epigenomics, University of California San Diego, La Jolla, CA, USA

    Sebastian Preissl & Bing Ren

  2. Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Sebastian Preissl

  3. Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA

    Kyle J. Gaulton

  4. Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA

    Bing Ren

  5. Ludwig Institute for Cancer Research, La Jolla, CA, USA

    Bing Ren

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The authors contributed equally to all aspects of the article.

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Correspondence to Sebastian Preissl, Kyle J. Gaulton or Bing Ren.

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Competing interests

B.R. is a shareholder and consultant of Arima Genomics, Inc., and a co-founder of Epigenome Technologies, Inc. K.J.G. is a consultant of Genentech and a shareholder in Vertex Pharmaceuticals and Neurocrine Biosciences. These relationships have been disclosed to and approved by the UCSD Independent Review Committee. S.P. declares no competing interests.

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Supplementary information

Glossary

Cis-regulatory elements

(CREs). Non-coding DNA sequences that regulate transcription of genes located on the same chromosome. They include enhancers, promoters, insulators, silencing elements and tethering elements. Different classes of CREs can be identified using a combination of molecular markers, including chromatin accessibility and epigenetic modifications.

Promoters

CREs located at the transcriptional start site of a gene.

Enhancers

CREs that can activate target gene expression from a large genomic distance, ranging from several kilobases to even millions of base pairs. They can be found either upstream or downstream of the target gene promoter.

Insulators

CREs that prevent an enhancer from activating a target gene when placed between the enhancer and gene promoter but not when placed outside. An insulator also refers to a boundary element that can prevent the spreading of heterochromatin into euchromatic regions.

Silencer elements

CREs that can be located close or distal to the transcriptional start site of the target gene. Silencers are bound by repressive transcription factors to inactivate gene expression.

Tethering elements

CREs that can bring together promoters and enhancers for gene activation.

Chromatin

A complex of DNA and histone proteins. The basic unit of chromatin is the nucleosome.

Histone modifications

Covalent modifications to histone proteins, such as methylation, acetylation, phosphorylation, ubiquitylation and sumoylation, that take place at lysine, serine, threonine, arginine and other residues. Histone modifications are catalysed by a diverse panel of enzymes referred to as writers, removed by a different set of proteins known as erasers, and recognized by chromatin-binding proteins known as readers. Activity of CREs is directly linked to distinct histone modifications due to the activities of writers, erasers and readers.

Epigenome

The combined features that enable stable propagation of different gene expression patterns from the same genome sequence. These include methylation of DNA at cytosine bases (mC), chemical modification of the histone proteins, chromatin accessibility and higher-order chromatin structures.

Tagmentation

The process by which double-stranded DNA is cleaved by the transposase Tn5, creating short DNA fragments that are simultaneously tagged with PCR adapters. Tagmentation using Tn5 preferentially occurs at accessible or open chromatin and this property is used in ATAC-seq and other related assays.

3D-chromatin organization

Folding of the chromatin fibres inside the nucleus governs the spatial proximity between genes and CREs. While complex and variable between cells, the chromatin organization exhibits certain common features, including A/B compartments, topologically associating domains and loops.

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Preissl, S., Gaulton, K.J. & Ren, B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat Rev Genet 24, 21–43 (2023). https://doi.org/10.1038/s41576-022-00509-1

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