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Understanding virus–host interactions in tissues

Nature Microbiology volume 8, pages 1397–1407 (2023)Cite this article

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Abstract

Although virus–host interactions are usually studied in a single cell type using in vitro assays in immortalized cell lines or isolated cell populations, it is important to remember that what is happening inside one infected cell does not translate to understanding how an infected cell behaves in a tissue, organ or whole organism. Infections occur in complex tissue environments, which contain a host of factors that can alter the course of the infection, including immune cells, non-immune cells and extracellular-matrix components. These factors affect how the host responds to the virus and form the basis of the protective response. To understand virus infection, tools are needed that can profile the tissue environment. This Review highlights methods to study virus–host interactions in the infection microenvironment.

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Fig. 1: Different scales of measuring virus–host interactions.
Fig. 2: Methods to assess the infection microenvironment that require tissue disassociation.
Fig. 3: Considerations for the detection of viral genetic material in scRNA-Seq studies to identify infected cells.

References

  1. Anderson, N. M. & Simon, M. C. The tumor microenvironment. Curr. Biol. 30, R921–R925 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Roma-Rodrigues, C., Mendes, R., Baptista, P. V. & Fernandes, A. R. Targeting tumor microenvironment for cancer therapy. Int. J. Mol. Sci. 20, 840 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Reid, S. P. et al. Ebola virus VP24 binds karyopherin α1 and blocks STAT1 nuclear accumulation. J. Virol. 80, 5156–5167 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Woolsey, C. et al. A VP35 mutant Ebola virus lacks virulence but can elicit protective immunity to wild-type virus challenge. Cell Rep. 28, 3032–3046 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Caballero, I. S. et al. In vivo Ebola virus infection leads to a strong innate response in circulating immune cells. BMC Genomics 17, 707 (2016).

    PubMed  PubMed Central  Google Scholar 

  6. Liu, X. et al. Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus. Genome Biol. 18, 4 (2017).

    PubMed  PubMed Central  Google Scholar 

  7. Speranza, E. et al. Previremic identification of Ebola or Marburg virus infection using integrated host-transcriptome and viral genome detection. mBio 11, e01157-20 (2020).

    PubMed  PubMed Central  Google Scholar 

  8. Wong, H. S. & Germain, R. N. Mesoscale T cell antigen discrimination emerges from intercellular feedback. Trends Immunol. 42, 865–875 (2021).

    CAS  PubMed  Google Scholar 

  9. Germain, R. N. et al. Understanding immunity in a tissue-centric context: combining novel imaging methods and mathematics to extract new insights into function and dysfunction. Immunol. Rev. 306, 8–24 (2022).

    CAS  PubMed  Google Scholar 

  10. Gola, A. et al. Commensal-driven immune zonation of the liver promotes host defence. Nature 589, 131–136 (2021).

    CAS  PubMed  Google Scholar 

  11. Bjarnsholt, T. et al. The importance of understanding the infectious microenvironment. Lancet Infect. Dis. 22, e88–e92 (2022).

    CAS  PubMed  Google Scholar 

  12. Depledge, D. P., Mohr, I. & Wilson, A. C. Going the distance: optimizing RNA-Seq strategies for transcriptomic analysis of complex viral genomes. J. Virol. 93, e01342-18 (2019).

    PubMed  Google Scholar 

  13. 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  Google Scholar 

  14. Sun, X., Sun, S. & Yang, S. An efficient and flexible method for deconvoluting bulk RNA-Seq data with single-cell RNA-Seq data. Cells 8, 1161 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Tsalik, E. L. et al. The host response to viral infections reveals common and virus-specific signatures in the peripheral blood. Front Immunol. 12, 741837 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Wang, R. Y. L., Weng, K. F., Huang, Y. C. & Chen, C. J. Elevated expression of circulating miR876-5p is a specific response to severe EV71 infections. Sci. Rep. 6, 24149 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Speranza, E. et al. T-cell receptor diversity and the control of T-cell homeostasis mark Ebola virus disease survival in humans. J. Infect. Dis. 218, S508–S518 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. Speranza, E. et al. Comparison of transcriptomic platforms for analysis of whole blood from Ebola-infected cynomolgus macaques. Sci. Rep. 7, 14756 (2017).

    PubMed  PubMed Central  Google Scholar 

  20. Warren, S. in Gene Expression Analysis: Methods and Protocols (eds Raghavachari, N. & Garcia-Reyero, N.) 105–120 (Springer New York, 2018).

  21. Quick, J. et al. Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples. Nat. Protoc. 12, 1261–1276 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang, J., Moore, N. E., Deng, Y. M., Eccles, D. A. & Hall, R. J. MinION nanopore sequencing of an influenza genome. Front. Microbiol. 6, 766 (2015).

    PubMed  PubMed Central  Google Scholar 

  23. Yakovleva, A. et al. Tracking SARS-COV-2 variants using nanopore sequencing in Ukraine in 2021. Sci. Rep. 12, 15749 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC–Seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.21–21.29.29 (2015).

    Google Scholar 

  25. Scott-Browne, J. P. et al. Dynamic changes in chromatin accessibility occur in CD8+ T cells responding to viral infection. Immunity 45, 1327–1340 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Bruzzone, C. et al. SARS-CoV-2 infection dysregulates the metabolomic and lipidomic profiles of serum. iScience 23, 101645 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Schwarz, B. et al. Cutting Edge: Severe SARS-CoV-2 infection in humans is defined by a shift in the serum lipidome, resulting in dysregulation of eicosanoid immune mediators. J. Immunol. 206, 329–334 (2021).

    CAS  PubMed  Google Scholar 

  28. Speranza, E. et al. Age-related differences in immune dynamics during SARS-CoV-2 infection in rhesus macaques. Life Sci. Alliance 5, e202101314 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Roberts, L. M. et al. Pulmonary infection induces persistent, pathogen-specific lipidomic changes influencing trained immunity. iScience 24, 103025 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Barberis, E. et al. Understanding protection from SARS-CoV-2 using metabolomics. Sci. Rep. 11, 13796 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Cui, L. et al. Metabolomics investigation reveals metabolite mediators associated with acute lung injury and repair in a murine model of influenza pneumonia. Sci. Rep. 6, 26076 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Gerner, M. Y., Kastenmuller, W., Ifrim, I., Kabat, J. & Germain, R. N. Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes. Immunity 37, 364–376 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Mathew, D. et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science 369, eabc8511 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Holmes, K. L. Characterization of aerosols produced by cell sorters and evaluation of containment. Cytometry A 79, 1000–1008 (2011).

    PubMed  PubMed Central  Google Scholar 

  35. Robinson, J. P. Flow cytometry: past and future. Biotechniques 72, 159–169 (2022).

    CAS  PubMed  Google Scholar 

  36. Park, L. M., Lannigan, J. & Jaimes, M. C. OMIP-069: forty-color full spectrum flow cytometry panel for deep immunophenotyping of major cell subsets in human peripheral blood. Cytometry A 97, 1044–1051 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Leelatian, N. et al. Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells. eLife 9, e56879 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Lee, J. S. et al. Single-cell transcriptome of bronchoalveolar lavage fluid reveals sequential change of macrophages during SARS-CoV-2 infection in ferrets. Nat. Commun. 12, 4567 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Johnson, M. B. et al. Single-cell analysis reveals transcriptional heterogeneity of neural progenitors in human cortex. Nat. Neurosci. 18, 637–646 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Meyer, M. et al. Attenuated activation of pulmonary immune cells in mRNA-1273-vaccinated hamsters after SARS-CoV-2 infection. J. Clin. Invest. 131, e148036 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Nouailles, G. et al. Temporal omics analysis in Syrian hamsters unravel cellular effector responses to moderate COVID-19. Nat. Commun. 12, 4869 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Friedrichs, V. et al. Landscape and age dynamics of immune cells in the Egyptian rousette bat. Cell Rep. 40, 111305 (2022).

    CAS  PubMed  Google Scholar 

  45. Wang, X., Yu, L. & Wu, A. R. The effect of methanol fixation on single-cell RNA sequencing data. BMC Genomics 22, 420 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Phan, H. V. et al. High-throughput RNA sequencing of paraformaldehyde-fixed single cells. Nat. Commun. 12, 5636 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Logue, J. et al. in Global Virology III: Virology in the 21st Century (eds Shapshak, P. et al.) 437–469 (Springer International Publishing, 2019).

  48. Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Clark, I. C. et al. Microfluidics-free single-cell genomics with templated emulsification. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01685-z (2023).

  50. Tian, Y. et al. Single-cell immunology of SARS-CoV-2 infection. Nat. Biotechnol. 40, 30–41 (2022).

    CAS  PubMed  Google Scholar 

  51. Delorey, T. M. et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 595, 107–113 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Liu, C. et al. Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19. Cell 184, 1836–1857 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Loske, J. et al. Pre-activated antiviral innate immunity in the upper airways controls early SARS-CoV-2 infection in children. Nat. Biotechnol. 40, 319–324 (2022).

    CAS  PubMed  Google Scholar 

  54. Chua, R. L. et al. COVID-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis. Nat. Biotechnol. 38, 970–979 (2020).

    CAS  PubMed  Google Scholar 

  55. Garcia-Flores, V. et al. Maternal-fetal immune responses in pregnant women infected with SARS-CoV-2. Nat. Commun. 13, 320 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Kotliar, D. et al. Single-cell profiling of Ebola virus disease in vivo reveals viral and host dynamics. Cell 183, 1383–1401 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Zanini, F., Pu, S. Y., Bekerman, E., Einav, S. & Quake, S. R. Single-cell transcriptional dynamics of flavivirus infection. eLife 7, e32942 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. Wyler, E. et al. Single-cell RNA-sequencing of herpes simplex virus 1-infected cells connects NRF2 activation to an antiviral program. Nat. Commun. 10, 4878 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Ratnasiri, K., Wilk, A. J., Lee, M. J., Khatri, P. & Blish, C. A. Single-cell RNA-Seq methods to interrogate virus–host interactions. Semin Immunopathol. 45, 71–89 (2023).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Mulè, M. P., Martins, A. J . & Tsang, J. S. Normalizing and denoising protein expression data from droplet-based single cell profiling. Nat. Commun. 13, 2099 (2022).

    PubMed  PubMed Central  Google Scholar 

  62. Singh, M. et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat. Commun. 10, 3120 (2019).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27, 904–916 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Wimmers, F. et al. The single-cell epigenomic and transcriptional landscape of immunity to influenza vaccination. Cell 184, 3915–3935 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Dohmen, J. et al. Identifying tumor cells at the single-cell level using machine learning. Genome Biol. 23, 123 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Cohen, E. M., Avital, N., Shamay, M. & Kobiler, O. Abortive herpes simplex virus infection of nonneuronal cells results in quiescent viral genomes that can reactivate. Proc. Natl Acad. Sci. USA 117, 635–640 (2020).

    CAS  PubMed  Google Scholar 

  68. Younan, P. et al. Ebola virus-mediated T-lymphocyte depletion is the result of an abortive infection. PLoS Pathog. 15, e1008068 (2019).

    PubMed  PubMed Central  Google Scholar 

  69. Griffin, D. E. Why does viral RNA sometimes persist after recovery from acute infections? PLoS Biol. 20, e3001687 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. van den Elsen, K., Quek, J. P. & Luo, D. Molecular insights into the flavivirus replication complex. Viruses 13, 956 (2021).

    PubMed  PubMed Central  Google Scholar 

  71. O’Neal, J. T. et al. West Nile virus-inclusive single-cell RNA sequencing reveals heterogeneity in the Type I interferon response within single cells. J. Virol. 93, e01778-18 (2019).

    PubMed  PubMed Central  Google Scholar 

  72. Chung, H. et al. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat. Methods 18, 1204–1212 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Bost, P. et al. Host-viral infection maps reveal signatures of severe COVID-19 patients. Cell 181, 1475–1488 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Speranza, E. et al. Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of African green monkeys. Sci. Transl. Med. 13, eabe8146 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Kim, D. et al. The architecture of SARS-CoV-2 transcriptome. Cell 181, 914–921 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Muhlberger, E. Filovirus replication and transcription. Future Virol. 2, 205–215 (2007).

    PubMed  PubMed Central  Google Scholar 

  77. Solignat, M., Gay, B., Higgs, S., Briant, L. & Devaux, C. Replication cycle of chikungunya: a re-emerging arbovirus. Virology 393, 183–197 (2009).

    CAS  PubMed  Google Scholar 

  78. Grant, S. M., Lou, M., Yao, L., Germain, R. N. & Radtke, A. J. The lymph node at a glance—how spatial organization optimizes the immune response. J. Cell Sci. 133, jcs241828 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Stoltzfus, C. R. et al. CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues. Cell Rep. 31, 107523 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Radtke, A. J. et al. A multi-scale, multiomic atlas of human normal and follicular lymphoma lymph nodes. Preprint at bioRxiv https://doi.org/10.1101/2022.06.03.494716 (2022).

  81. Eng, J. et al. A framework for multiplex imaging optimization and reproducible analysis. Commun. Biol. 5, 438 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Chen, H. Y., Palendira, U. & Feng, C. G. Navigating the cellular landscape in tissue: recent advances in defining the pathogenesis of human disease. Computational Struct. Biotechnol. J. 20, 5256–5263 (2022).

    CAS  Google Scholar 

  83. Hickey, J. W. et al. Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging. Nat. Methods 19, 284–295 (2022).

    CAS  PubMed  Google Scholar 

  84. Frederico, B., Chao, B., Lawler, C., May, J. S. & Stevenson, P. G. Subcapsular sinus macrophages limit acute gammaherpesvirus dissemination. J. Gen. Virol. 96, 2314–2327 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Reynoso, G. V. et al. Zika virus spreads through infection of lymph node-resident macrophages. Cell Rep. 42, 112–126 (2023).

    Google Scholar 

  86. Hickman, H. D. et al. Anatomically restricted synergistic antiviral activities of innate and adaptive immune cells in the skin. Cell Host Microbe 13, 155–168 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Greenberg, A. et al. Quantification of viral and host biomarkers in the liver of rhesus macaques: a longitudinal study of Zaire Ebolavirus strain Kikwit (EBOV/Kik). Am. J. Pathol. 190, 1449–1460 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Radtke, A. J. et al. IBEX: a versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues. Proc. Natl Acad. Sci. USA 117, 33455–33465 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Jiang, S. et al. Rhesus macaque CODEX multiplexed immunohistochemistry panel for studying immune responses during Ebola infection. Front. Immunol. 12, 729845 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Rendeiro, A. F. et al. The spatial landscape of lung pathology during COVID-19 progression. Nature 593, 564–569 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Berry, S. et al. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. Science 372, eaba2609 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555–565 (2022).

    CAS  PubMed  Google Scholar 

  93. Lee, M. Y. et al. CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images. BMC Bioinform. 23, 46 (2022).

    Google Scholar 

  94. Method of the Year 2020: spatially resolved transcriptomics. Nat. Methods 18, 1 (2021).

  95. Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).

    CAS  PubMed  Google Scholar 

  97. Tian, L., Chen, F. & Macosko, E. Z. The expanding vistas of spatial transcriptomics. Nat. Biotechnol. 41, 773–782 (2022).

  98. Acheampong, K. K. et al. Multiplexed detection of SARS-CoV-2 genomic and subgenomic RNA using in situ hybridization. Preprint at bioRxiv https://doi.org/10.1101/2021.08.11.455959 (2021).

  99. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    PubMed  PubMed Central  Google Scholar 

  100. He, J. et al. In situ single-cell transcriptomic imaging in formalin-fixed paraffin-embedded tissues with MERSCOPE. Cancer Res. 83, 4195 (2023).

    Google Scholar 

  101. Mantri, M. et al. Spatiotemporal transcriptomics reveals pathogenesis of viral myocarditis. Nat. Cardiovascular Res. 1, 946–960 (2022).

    Google Scholar 

  102. Gracia Villacampa, E. et al. Genome-wide spatial expression profiling in formalin-fixed tissues. Cell Genomics 1, 100065 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Kulasinghe, A. et al. Profiling of lung SARS-CoV-2 and influenza virus infection dissects virus-specific host responses and gene signatures. Eur. Respiratory J. 59, 2101881 (2022).

    CAS  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Altboum, Z. et al. Digital cell quantification identifies global immune cell dynamics during influenza infection. Mol. Syst. Biol. 10, 720 (2014).

    PubMed  PubMed Central  Google Scholar 

  108. Rooijers, K. et al. Simultaneous quantification of protein-DNA contacts and transcriptomes in single cells. Nat. Biotechnol. 37, 766–772 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Xi, N. M. & Li, J. J. Benchmarking computational doublet-detection methods for single-cell RNA sequencing data. Cell Syst. 12, 176–194 (2021).

    CAS  PubMed  Google Scholar 

  110. Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Radtke, A. J. et al. IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues. Nat. Protoc. 17, 378–401 (2022).

    CAS  PubMed  Google Scholar 

  112. Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Tsujikawa, T. et al. Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. Cell Rep. 19, 203–217 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Stack, E. C., Wang, C., Roman, K. A. & Hoyt, C. C. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70, 46–58 (2014).

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  118. Christopher R. Merritt CR. et. al. High multiplex, digital spatial profiling of proteins and RNA in fixed tissue using genomic detection methods. Preprint at bioRxiv https://doi.org/10.1101/559021 (2019).

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Acknowledgements

This work was supported by Cleveland Clinic Lerner Research Institute start-up funds.

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    Emily Speranza

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Speranza, E. Understanding virus–host interactions in tissues. Nat Microbiol 8, 1397–1407 (2023). https://doi.org/10.1038/s41564-023-01434-7

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