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Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Abstract

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.

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Fig. 1: Adding spatial information to transcriptomes: integration of single-cell and spatial transcriptomics data.
Fig. 2: Common spatial transcriptomics techniques.
Fig. 3: Model workflow integrating scRNA-seq and spatial transcriptomics: the four As.
Fig. 4: Deconvolution and mapping methods.
Fig. 5: Principles used to decode mechanisms of intercellular communication via expression of ligands and receptors in physically proximal cell subpopulations.

References

  1. 1.

    [No authors listed] Method of the Year 2020: spatially resolved transcriptomics. Nat. Methods 18, 1 (2021).

  2. 2.

    Stegle, O., Teichmann, S. & Marioni, J. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

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

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Chen, G., Ning, B. & Shi, T. Single-cell RNA-seq technologies and related computational data analysis. Front. Genet. 10, 317 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

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

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Liao, J., Lu, X., Shao, X., Zhu, L. & Fan, X. Uncovering an organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends Biotechnol. 39, 43–58 (2020).

    PubMed  Article  CAS  Google Scholar 

  8. 8.

    Levsky, J. M., Shenoy, S. M., Pezo, R. C. & Singer, R. H. Single-cell gene expression profiling. Science 297, 836–840 (2002).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Lyubimova, A. et al. Single-molecule mRNA detection and counting in mammalian tissue. Nat. Protoc. 8, 1743–1758 (2013).

    PubMed  Article  CAS  Google Scholar 

  10. 10.

    Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, 1360–1363 (2015).

    Google Scholar 

  12. 12.

    Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Ståhl, P. L. P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    PubMed  Article  CAS  Google Scholar 

  16. 16.

    10× Genomics. Inside Visium spatial capture technology https://pages.10xgenomics.com/rs/446-PBO-704/images/10x_BR060_Inside_Visium_Spatial_Technology.pdf (2019)

  17. 17.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0739-1 (2020).

    Article  PubMed  Google Scholar 

  19. 19.

    Cho, C.-S. et al. Seq-Scope: submicrometer-resolution spatial transcriptomics for single cell and subcellular studies. bioRxiv https://doi.org/10.1101/2021.01.25.427807 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Chen, A. et al. Large field of view-spatially resolved transcriptomics at nanoscale resolution. bioRxiv https://doi.org/10.1101/2021.01.17.427004 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 353–356 (2017).

    Article  CAS  Google Scholar 

  22. 22.

    Ben-Moshe, S. et al. Spatial sorting enables comprehensive characterization of liver zonation. Nat. Metab. 1, 899–907 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Brosch, M. et al. Epigenomic map of human liver reveals principles of zonated morphogenic and metabolic control. Nat. Commun. 9, 4150 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  24. 24.

    Saviano, A., Henderson, N. C. & Baumert, T. F. Review single-cell genomics and spatial transcriptomics: discovery of novel cell states and cellular interactions in liver physiology and disease biology. J. Hepatol. 73, 1219–1230 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167.e15 (2018).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826.e23 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Baccin, C. et al. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat. Cell Biol. 22, 38–48 (2020).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Peng, G. et al. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 36, 681–697 (2016).

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018). This article is an exemplary application of integrating multiplexed ISH with scRNA-seq to reveal the spatial organization and circuitry of neuronal subpopulations pertinent to social behaviours at a single-cell resolution.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30.

    Chen, H. et al. Dissecting mammalian spermatogenesis using spatial transcriptomics. bioRxiv https://doi.org/10.1101/2020.10.17.343335 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Asp, M. et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647–1660 (2019). This analysis generates one of the first organ-wide, human developmental transcriptional atlases with single-cell spatial resolution by integrating scRNA-seq and spatial barcoding to yield optimized in situ sequencing.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Burkhard, S. B. & Bakkers, J. Spatially resolved RNA-sequencing of the embryonic heart identifies a role for Wnt/β-catenin signaling in autonomic control of heart rate. eLife 7, e31515 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 497–514 (2020). This exemplary application of multimodal spatial analysis integrates scRNA-seq with spatial barcoding and multiplexed ion beam imaging (akin to spatial proteomics) to inform an in vivo CRISPR screen that identifies gene networks essential to the function of tumorigenic subpopulations.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Thrane, K., Eriksson, H., Maaskola, J., Hansson, J. & Lundeberg, J. Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma. Cancer Res. 78, 5970–5979 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Chen, W. et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182, 976–991 (2020). This exemplary study characterizes Alzheimer disease-revelant cell types based on biofeature proximity by integrating spatial barcoding and in situ sequencing with histological stainings of disease mouse brain tissue cross-sections, effectively demonstrating how these integrated approaches can help map the spatio-temporal transcriptome at key disease stages.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Maniatis, S. et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364, 89–93 (2019).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. bioRxiv https://doi.org/10.1101/2020.12.08.411686 (2020).

    Article  Google Scholar 

  40. 40.

    Wu, C.-C. et al. Spatially resolved genome-wide transcriptional profiling identifies BMP signaling as essential regulator of zebrafish cardiomyocyte regeneration. Dev. Cell 36, 36–49 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  41. 41.

    Boyd, D. F. et al. Exuberant fibroblast activity compromises lung function via ADAMTS4. Nature 587, 466–471 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Moor, A. E. & Itzkovitz, S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46, 126–133 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  43. 43.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Tarmo, Ä. et al. Splotch: robust estimation of aligned spatial temporal gene expression data. bioRxiv https://doi.org/10.1101/757096 (2019).

    Article  Google Scholar 

  45. 45.

    Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

    Maniatis, S., Petrescu, J. & Phatnani, H. Spatially resolved transcriptomics and its applications in cancer. Curr. Opin. Genet. Dev. 66, 70–77 (2021).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47.

    Balkwill, F. R., Capasso, M. & Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48.

    Whiteside, T. L. The tumor microenvironment and its role in promoting tumor growth. Oncogene 27, 5904–5912 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Fan, J., Slowikowski, K. & Zhang, F. Single-cell transcriptomics in cancer: computational challenges and opportunities. Exp. Mol. Med. 52, 1452–1465 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral hetereogeneity in primary glioblastoma. Science 344, 1396–1402 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Xu, L., He, D. & Bai, Y. Microglia-mediated inflammation and neurodegenerative disease. Mol. Neurobiol. 53, 6709–6715 (2016).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Gulati, A. et al. Association of fibrosis with mortality and sudden cardiac death in patients with nonischemic dilated cardiomyopathy. JAMA 309, 896–908 (2013).

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Haque, A., Engel, J., Teichmann, S. A. & Lönnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 9, 75 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  57. 57.

    Hwang, B., Lee, J. H. & Bang, D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50, 96 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  58. 58.

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

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016). This study exemplifies how scRNA-seq can be wielded to characterize single cells beyond the cell subtype.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Andrews, T. S. & Hemberg, M. Identifying cell populations with scRNASeq. Mol. Asp. Med. 59, 114–122 (2018).

    CAS  Article  Google Scholar 

  65. 65.

    Duò, A., Robinson, M. D. & Soneson, C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Res. 7, 1141 (2020).

    PubMed Central  Article  PubMed  Google Scholar 

  66. 66.

    Butler, A. et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. 68.

    Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020).

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Hu, K. H. et al. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat. Methods 17, 833–843 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat. Methods 11, 190–196 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 1626, 1622–1626 (2017).

    Article  CAS  Google Scholar 

  72. 72.

    Ombrato, L. et al. Metastatic-niche labelling reveals parenchymal cells with stem features. Nature 572, 603–608 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Bodenmiller, B. Multiplexed epitope-based tissue imaging for discovery and healthcare applications. Cell Syst. 2, 225–238 (2016).

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285–302 (2019).

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Rimm, D. L. Next-gen immunohistochemistry. Nat. Methods 20, 436–442 (2014).

    Google Scholar 

  76. 76.

    Levenson, R. M., Borowsky, A. D. & Angelo, M. Immunohistochemistry and mass spectrometry for highly multiplexed cellular molecular imaging. Lab. Investig. 95, 397–405 (2015).

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).

    PubMed  Article  CAS  Google Scholar 

  78. 78.

    Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalinfixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Zrazhevskiy, P. & Gao, X. Quantum dot imaging platform for single-cell molecular profiling. Nat. Commun. 4, 1619 (2013).

    PubMed  Article  CAS  Google Scholar 

  80. 80.

    Lin, J. R., Fallahi-Sichani, M. & Sorger, P. K. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat. Commun. 6, 8390 (2015).

    CAS  PubMed  Article  Google Scholar 

  81. 81.

    Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Damond, N. et al. A map of human type 1 diabetes progression by imaging mass cytometry. Cell Metab. 29, 755–768 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. 84.

    Ali, H. R. et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat. Cancer 1, 163–175 (2020).

    Article  Google Scholar 

  85. 85.

    Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    CAS  PubMed  Article  Google Scholar 

  86. 86.

    Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–695 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 25–36 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  89. 89.

    Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020). This report presents pciSeq for mapping scRNA-seq cell types to multiplexed ISH and in situ sequencing data through probablistic modelling that implements the spatial point process. This is one of very few mapping algorithms specifically tailored towards capture spot deconvolution.

    CAS  PubMed  Article  Google Scholar 

  90. 90.

    Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00830-w (2021).

    Article  PubMed  Google Scholar 

  91. 91.

    Elosua, M. et al. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021). This article presents SPOTlight, to date the only deconvolution technique published in a peer-reviewed journal that is tailored towards deconvolving spatial barcoding capture spots through regression. The article also presents a comprehensive strategy for benchmarking emerging deconvolution methods.

    Article  CAS  Google Scholar 

  92. 92.

    Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020). This report presents stereoscope, an effective approach for deconvolving spatial barcoding capture spots through probabilistic modelling and one of the few spatial barcoding deconvolution strategies published in a peer-reviewed journal to date.

    PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Avila Cobos, F., Vandesompele, J., Mestdagh, P. & De Preter, K. Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics 34, 1969–1979 (2018).

    PubMed  Article  CAS  Google Scholar 

  94. 94.

    Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. 95.

    Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96.

    Dong, M. et al. SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references. Brief. Bioinform. 22, 416–427 (2021).

    CAS  PubMed  Article  Google Scholar 

  97. 97.

    Gong, T. & Szustakowski, J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-seq data. Bioinformatics 29, 1083–1085 (2013).

    CAS  PubMed  Article  Google Scholar 

  98. 98.

    Du, R., Carey, V. & Weiss, S. T. DeconvSeq: deconvolution of cell mixture distribution in sequencing data. Bioinformatics 35, 5095–5102 (2019).

    CAS  PubMed  Article  Google Scholar 

  99. 99.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

    Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  101. 101.

    Aliee, H. & Theis, F. AutoGeneS: automatic gene selection using multi-objective optimization for RNA-seq deconvolution. bioRxiv https://doi.org/10.1101/2020.02.21.940650 (2020).

    Article  Google Scholar 

  102. 102.

    Dong, R., Yuan, GC. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145 (2021).

    PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Kleshchevnikov, V. et al. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics. bioRxiv https://doi.org/10.1101/2020.11.15.378125 (2020).

    Article  Google Scholar 

  104. 104.

    Zhang, X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–D728 (2019).

    CAS  PubMed  Article  Google Scholar 

  105. 105.

    Cao, Y., Wang, X. & Peng, G. SCSA: a cell type annotation tool for single-cell RNA-seq data. Front. Genet. 11, 490 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  106. 106.

    McGinnis, C. S. et al. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat. Methods 16, 619–626 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

    Andersson, A. et al. Spatial deconvolution of HER2-positive breast tumors reveals novel intercellular relationships. bioRxiv https://doi.org/10.1101/2020.07.14.200600 (2020).

    Article  Google Scholar 

  108. 108.

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).

    CAS  PubMed  Article  Google Scholar 

  110. 110.

    Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    CAS  PubMed  Article  Google Scholar 

  111. 111.

    Forcato, M., Romano, O. & Bicciato, S. Computational methods for the integrative analysis of single-cell data. Brief. Bioinform. 22, 20–29 (2021).

    PubMed  Article  Google Scholar 

  112. 112.

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

    Article  CAS  Google Scholar 

  113. 113.

    Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019). This report details the inner workings of the Seurat Integration method, which is an exemplary method for mapping scRNA-seq cell types onto single-cell resolution spatial data. Seurat Integation is one part of a widely used R toolkit for analysing single-cell genomics data.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  115. 115.

    Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1295 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  117. 117.

    Abdelaal, T., Mourragui, S., Mahfouz, A. & Reinders, M. J. T. SpaGE: Spatial Gene Enhancement using scRNA-seq. Nucleic Acids Res. 48, E107–E107 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  118. 118.

    Bishop, C. M. Pattern Recognition and Machine Learning. Oxidation Communications Vol. 27 (Springer, 2004).

  119. 119.

    Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2020).

    PubMed  Article  CAS  Google Scholar 

  120. 120.

    Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    CAS  PubMed  Article  Google Scholar 

  121. 121.

    Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    CAS  PubMed  Article  Google Scholar 

  122. 122.

    Noël, F. et al. Dissection of intercellular communication using the transcriptome-based framework ICELLNET Floriane. Nat. Commun. 12, 1089 (2021).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  123. 123.

    Choi, H. et al. Transcriptome analysis of individual stromal cell populations identifies stroma–tumor crosstalk in mouse lung cancer model. Cell Rep. 10, 1187–1201 (2015).

    CAS  PubMed  Article  Google Scholar 

  124. 124.

    Wang, S., Karikomi, M., Maclean, A. L. & Nie, Q. Cell lineage and communication network inference via optimization for single-cell transcriptomics. Nucleic Acids Res. 47, e66 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  125. 125.

    Cillo, A. R. et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity 52, 183–199.e9 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  126. 126.

    Cabello-Aguilar, S. et al. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 48, e55 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. 127.

    Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  128. 128.

    Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  129. 129.

    Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020). This report presents SpaOTsc, one of the few peer-reviewed methods that formally integrate spatial and scRNA-seq data into an algorithm to decode intercellular communication in tissues. SpaOTsc predicts the maximum signalling range for ligand–receptor pairs through a spatial transcriptomic analysis of each ligand–receptor pair’s target genes.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  130. 130.

    Villani, C. Optimal Transport, Old and New Vol. 338 (Springer, 2009).

  131. 131.

    Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).

    CAS  PubMed  Article  Google Scholar 

  132. 132.

    Ren, X. et al. Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand–receptor mediated self-assembly. Cell Res. 30, 763–778 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  133. 133.

    Arnol, D., Schapiro, D., Bodenmiller, B., Saez-Rodriguez, J. & Stegle, O. Modeling cell–cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 29, 202–211 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  134. 134.

    Tanevski, J., Gabor, A., Flores, R. O. R., Schapiro, D. & Saez-Rodriguez, J. Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data. bioRxiv https://doi.org/10.1101/2020.05.08.084145 (2020).

    Article  Google Scholar 

  135. 135.

    Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020).

    CAS  PubMed  Article  Google Scholar 

  136. 136.

    Liu, D. S., Loh, K. H., Lam, S. S., White, K. A. & Ting, A. Y. Imaging trans-cellular neurexin–neuroligin interactions by enzymatic probe ligation. PLoS ONE 8, e52823 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. 137.

    Pasqual, G. et al. Monitoring T cell-dendritic cell interactions in vivo by intercellular enzymatic labelling. Nature 553, 496–500 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  138. 138.

    He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020). This pioneering work presents ST-Net, one of the first applications of deep learning models for analysing spatial transcriptomic data, and one of the first methods to formally automate analysis of histological images for characterizing the spatial transcriptome.

    CAS  PubMed  Article  Google Scholar 

  139. 139.

    Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. bioRxiv https://doi.org/10.1101/2020.02.28.963413 (2020).

    Article  Google Scholar 

  140. 140.

    Yuan, Y. & Bar-Joseph, Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 21, 300 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  141. 141.

    Xu, Y. & McCord, R. P. CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis. bioRxiv https://doi.org/10.1101/2021.01.12.426400 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014).

    CAS  PubMed  Article  Google Scholar 

  143. 143.

    Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  144. 144.

    Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  145. 145.

    SoRelle, E. D. et al. Spatiotemporal tracking of brain-tumor-associated myeloid cells in vivo through optical coherence tomography with plasmonic labeling and speckle modulation. ACS Nano 13, 7985–7995 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. 146.

    Jung, K. O. et al. Whole-body tracking of single cells via positron emission tomography. Nat. Biomed. Eng. 4, 835–844 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  147. 147.

    Rodriques, S. G. et al. RNA timestamps identify the age of single molecules in RNA sequencing. Nat. Biotechnol. 39, 320–325 (2021).

    CAS  PubMed  Article  Google Scholar 

  148. 148.

    Crick, F. Central dogma. Nature 227, 561–563 (2008).

    Article  Google Scholar 

  149. 149.

    Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).

    CAS  PubMed  Article  Google Scholar 

  150. 150.

    Deng, Y. et al. Spatial epigenome sequencing at tissue scale and cellular level. bioRxiv https://doi.org/10.1101/2021.03.11.434985 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Payne, A. C. et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371, eaay3446 (2021).

    CAS  PubMed  Article  Google Scholar 

  152. 152.

    Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  153. 153.

    Su, J. H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-scale imaging of the 3D organization and transcriptional activity of chromatin. Cell 182, 1641–1659.e26 (2020).

    CAS  PubMed  Article  Google Scholar 

  154. 154.

    Hendriks, G.-J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  155. 155.

    Helmink, B. et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 577, 549–555 (2020).

    CAS  PubMed  Article  Google Scholar 

  156. 156.

    Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  157. 157.

    Regev, A. et al. Science forum: the Human Cell Atlas. eLife 6, e27041 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  158. 158.

    Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    CAS  PubMed  Article  Google Scholar 

  159. 159.

    Bakken, T. E. et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367–375 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  160. 160.

    Fan, Z., Chen, R. & Chen, X. SpatialDB: a database for spatially resolved transcriptomes. Nucleic Acids Res. 48, D233–D237 (2020). This article is one of the first publications describing a database directly geared towards aggregating spatial transcriptomic data.

    CAS  PubMed  Article  Google Scholar 

  161. 161.

    Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  162. 162.

    Asp, M., Bergenstråhle, J. & Lundeberg, J. Spatially resolved transcriptomes — next generation tools for tissue exploration. BioEssays 42, 1–16 (2020).

    Article  Google Scholar 

  163. 163.

    Zhuang, X. Spatially resolved single-cell genomics and transcriptomics by imaging. Nat. Methods 18, 18–22 (2021).

    CAS  PubMed  Article  Google Scholar 

  164. 164.

    Waylen, L. N., Nim, H. T., Martelotto, L. G. & Ramialison, M. From whole-mount to single-cell spatial assessment of gene expression in 3D. Commun. Biol. 3, 602 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  165. 165.

    Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  166. 166.

    Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

The authors thank members of the Khavari, Nolan, Altman, Zou and Lundeberg laboratories for helpful discussions. This work was supported by the US Veterans Affairs Office of Research and Development I01BX00140908, National Institutes of Health, National Cancer Institute (NIH/NCI) CA142635, NIH, National Institute for Arthritis and Musculoskeletal and Skin Diseases (NIH/NIAMS) AR43799 and AR49737 (P.A.K.), and a Physician-Scientist Training Award from the Damon Runyon Cancer Research Foundation (A.L.J.).

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S.K.L. researched the literature and wrote the article. M.G.G., A.L.J. and P.A.K. contributed substantially to discussions of the content. All authors reviewed and/or edited the manuscript.

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Correspondence to Paul A. Khavari.

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Nature Reviews Genetics thanks O. Bayraktar and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Single-cell RNA sequencing

(scRNA-seq). A method that sequences RNA transcripts (primarily mRNA) isolated from each cell of a tissue, thereby characterizing individual cells’ transcriptomes; aggregated data characterize the gene expression distribution of tissue cell subpopulations.

Intercellular communication

Communication between cells, typically through ligand–receptor interaction. Juxtracrine denotes direct cellular contact for signalling molecules to be passed between cells, whereas paracrine refers to diffusion of signalling molecules from a sender to a receiver cell.

Spatial transcriptomics

A method that localizes mRNA transcripts to precise spatial locations (single cells or capture spots, formerly regions) in a tissue. In this Review, the term refers broadly to all spatial transcriptomics methods, not exclusively spatial barcoding.

Bulk RNA-seq

A method that sequences a mixture of RNA transcripts (primarily mRNA) from the whole tissue to generate an average expression level for each gene across all cells sequenced.

High-plex RNA imaging

(HPRI). A targeted spatial transcriptomics method that localizes and quantifies RNA transcripts through multiplexed fluorescent microscopy imaging. Can typically target up to ~100–200 genes simultaneously in intact tissue sections.

Spatial barcoding

Spatial transcriptomic methods that use a microarray of poly-T oligonucleotides to ‘capture’ mRNA transcripts of tissue cross-sections, typically followed by next-generation sequencing.

Coverage

In the context of single-cell or spatial transcrtipomics assays, the number of distinct genes that are represented from captured RNA molecules.

Capture spots

Individual coordinates or capture locations on the microarray used to ‘capture’ mRNA transcripts for spatial barcoding and identified by a DNA barcode; each capture spot generally captures mRNA from multiple cells.

Resolution

In spatial data, the distance between spatial coordinates that identify the source of molecules. For spatial barcoding, higher resolution refers to a smaller distance between capture spot coordinates with smaller capture spot diameter.

Depth

In the context of single-cell or spatial transcriptomics assays, the number of unique RNA molecules captured for a particular gene.

Fluorescent-activated cell sorting

(FACS). A cell sorting technique based on flow cytometry for isolating and identifying different cell types using fluorescent antibodies targeting known cell type-specific cell surface proteins.

Microdissection

A method, such as laser-capture microdissection (LCM), that finely isolates different tissue parts from cryo-sections. LCM-seq is a spatial transcriptomics method that couples LCM with RNA sequencing.

Deconvolution

The process of predicting cell-type proportions at a given spatial barcoding capture spot based on its mRNA mixture. Cell types are typically derived from single-cell RNA sequencing profiles of the tissue.

Pseudo-time analysis

Computational ordering of single cells, namely of single-cell RNA sequencing, based on a gradual evolution of their transcriptomes to measure progress through a given biological process (for example, differentiation or proliferation)

Mapping

The process of assigning a single-cell RNA sequencing (scRNA-seq)-based cell type to each cell spatially resolved by high-plex RNA imaging assays; secondarily, predicting the spatial location of each scRNA-seq cell based on its transcriptome

Statistical regression

In the context of deconvolution, linear regression models are fit to determine to what extent each cell type explains the gene expression values for each capture spot.

Bayesian statistical framework

In the deconvolution context, statistical models that rely on inferences about the distribution of transcripts for each cell type to yield a probability that a mixture of transcripts can be explained by a specific single-cell RNA sequencing cell type.

Maximum a posteriori

A Bayesian estimate of an unknown conditional. In the context of spatial transcriptomics integration methods, the maximum a posteriori estimate most often refers to the cell-type distribution given the gene distribution at a capture spot.

Enrichment

Refers to a particular class of genes that is over-represented in a large set of genes. In the deconvolution and mapping contexts, this class is often the cell type.

Mismatch

Incongruity between cell types detected as present in single-cell RNA sequencing and spatial transcriptomics data that can complicate spatial barcoding deconvolution, but less so high-plex RNA imaging mapping.

Mutual nearest neighbour

A robust algorithm for clustering single cells between two different single-cell transcriptomic data sets (that is, single-cell RNA sequencing with high-plex RNA imaging) based on the cell subtype.

Dimensionality reduction

A mathematical technique to represent high-dimensional data in lower dimensions. Mostly used in transcriptomic analysis for visualization of cell subtype clustering in 2D, sometimes 3D, space to establish subtypes.

Non-negative matrix factorization

A method commonly used in bioinformatics for dimensionality reduction of gene expression data as the non-negativity constraint reflects that genes are either expressed or not and cannot be negatively expressed.

Factor loading

In the context of transcriptomics, correlation coefficients between known gene transcript levels and latent cell subtype information. Can be plotted in low-dimensional space prior to joint clustering of single-cell RNA sequencing and high-plex RNA imaging data.

Impute

In the context of spatial transcriptomics, the computational process of determining unknown gene expression values of spatially resolved single cells (high-plex RNA imaging) with corresponding values from mapped single-cell RNA sequencing data.

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Longo, S.K., Guo, M.G., Ji, A.L. et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 22, 627–644 (2021). https://doi.org/10.1038/s41576-021-00370-8

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