Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature Communications
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. nature communications
  3. articles
  4. article
Spatial transcriptomics uncovers vasculature-centered cellular interactions driving Japanese encephalitis progression in a mouse model
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 17 March 2026

Spatial transcriptomics uncovers vasculature-centered cellular interactions driving Japanese encephalitis progression in a mouse model

  • Zhihua Ou  (欧芷华)  ORCID: orcid.org/0000-0002-1479-90071,2,3 na1,
  • Zhaoyang Wang  (王朝阳)4,5,6 na1,
  • Qi Chen  (陈旗)2,3,7 na1,
  • Peidi Ren  (任陪娣)2,3 na1,
  • Xiuju He  (何修驹)2,8 na1,
  • Yan Liang  (梁言)4,
  • Ying’an Liang  (梁颖安)2,9,
  • Jiaxuan Wang  (王家轩)2,
  • Sha Liao  (廖莎)8,
  • Dexin Wang  (王德鑫)2,10,
  • Jie Zhao  (赵杰)8,
  • Oujia Zhang  (张偶佳)11,
  • Zhenyu Peng  (彭震宇)  ORCID: orcid.org/0000-0002-8383-59051,
  • Jianxin Su  (苏坚鑫)2,7,
  • Wangsheng Li  (李旺胜)1,12,
  • Guohai Hu  (胡国海)  ORCID: orcid.org/0000-0003-2756-83281,
  • Ao Chen  (陈奥)  ORCID: orcid.org/0000-0002-9699-834013,14,
  • Ziqing Deng  (邓子卿)  ORCID: orcid.org/0000-0001-8726-01601,2,3,
  • Xin Jin  (金鑫)  ORCID: orcid.org/0000-0001-7554-49751,8,15,16,
  • Xun Xu  (徐讯)  ORCID: orcid.org/0000-0002-5338-51731,8,17,
  • Junhua Li  (李俊桦)  ORCID: orcid.org/0000-0001-6784-18731,2,8 &
  • …
  • Gong Cheng  (程功)  ORCID: orcid.org/0000-0001-7447-54884,5,6,18 

Nature Communications , Article number:  (2026) Cite this article

  • 2301 Accesses

  • 19 Altmetric

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Acute inflammation
  • Viral infection

Abstract

The cellular interactions that drive the neuropathology of most neuroinvasive viruses remain elusive. We used Japanese encephalitis virus (JEV) to infect female BALB/c mice and applied Stereo-seq to simultaneously capture host and viral transcriptomes in situ, thereby constructing a comprehensive spatiotemporal atlas of Japanese encephalitis (JE) pathogenesis. Our analysis pinpoints Ly6c2+ monocytes as the primary virus carrier, the most abundant infiltrating immune cell type, and a major source of IFN-γ in the infected mouse brain. Following infection, Ackr1+ endothelial cell activation is linked to blood-brain barrier disruption and immune cell chemotaxis, particularly Ly6c2+ monocytes. The crosstalk between these two cell types appears to orchestrate the pronounced inflammation and cell death, including pyroptosis and necroptosis, which radiate from the vasculature to different brain regions. Disrupting the activation and interactions of these two cell types may help mitigate JE progression. This study also provides a technical framework for investigating the pathogenesis of other neurotropic viruses.

Similar content being viewed by others

Acute neuronal cell death and neuroinflammation per se do not trigger secondary autoimmune encephalitis in mice

Article Open access 27 June 2025

Characterisation of a Japanese Encephalitis virus genotype 4 isolate from the 2022 Australian outbreak

Article Open access 10 May 2024

Neurotropic EV71 causes encephalitis by engaging intracellular TLR9 to elicit neurotoxic IL12-p40-iNOS signaling

Article Open access 11 April 2022

Data availability

All the Stereo-seq data supporting the findings of this study have been deposited in CNGBdb (https://db.cngb.org/cnsa/) under the accession number of STT0000076 (https://db.cngb.org/stomics/). The raw sequencing data have been deposited in the Genome Sequence Archive in National Genomics Data Center129,130, China National Center for Bioinformation under the GSA number of CRA035980 (https://ngdc.cncb.ac.cn/gsa/). The gene expression matrix and associated metadata have been deposited in Zenodo (https://doi.org/10.5281/zenodo.17958448). All the experimental validation data of this study have been deposited in the Figshare database (https://doi.org/10.6084/m9.figshare.30382759) and the Zenodo database (https://doi.org/10.5281/zenodo.18323401). Source data are provided with this paper.

References

  1. Auerswald, H., Maquart, P.-O., Chevalier, V. & Boyer, S. Mosquito vector competence for Japanese encephalitis virus. Viruses 13, 1154 (2021).

    Google Scholar 

  2. Heffelfinger, J. D. et al. Japanese encephalitis surveillance and immunization—Asia and Western Pacific Regions, 2016. MMWR Morb. Mortal. Wkly Rep. 66, 579–583 (2017).

    Google Scholar 

  3. Campbell, G. L. et al. Estimated global incidence of Japanese encephalitis: a systematic review. Bull. World Health Organ 89, 766–774 (2011). 774A-774E.

    Google Scholar 

  4. Fischer, M. et al. Japanese encephalitis prevention and control: advances, challenges, and new initiatives. in Emerging Infections Vol.8 (eds Michael Scheld, W., Hammer, S. M. & Hughes, J. M.) 93–124. https://doi.org/10.1128/9781555815592.ch6 (ASM Press, 2014).

  5. Quan, T. M., Thao, T. T. N., Duy, N. M., Nhat, T. M. & Clapham, H. Estimates of the global burden of Japanese encephalitis and the impact of vaccination from 2000-2015. eLife 9, e51027 (2020).

    Google Scholar 

  6. Baldwin, Z. et al. The seroprevalence of antibodies to Japanese encephalitis virus in five New South Wales towns at high risk of infection, 2022: a cross-sectional serosurvey. Med. J. Aust. 220, 561–565 (2024).

    Google Scholar 

  7. Wang, G. et al. Peripheral nerve injury associated with JEV infection in high endemic regions, 2016-2020: a multicenter retrospective study in China. Emerg. Microbes Infect. 13, 2337677 (2024).

    Google Scholar 

  8. Zhang, X. et al. Adults in Northwest China experienced the largest outbreak of Japanese encephalitis in history 10 years after the Japanese encephalitis vaccine was included in the national immunization program: A retrospective epidemiological study. J. Med. Virol. 95, e28782 (2023).

    Google Scholar 

  9. Wang, P. et al. DC-SIGN as an attachment factor mediates Japanese encephalitis virus infection of human dendritic cells via interaction with a single high-mannose residue of viral E glycoprotein. Virology 488, 108–119 (2016).

    Google Scholar 

  10. Wang, K. & Deubel, V. Mice with different susceptibility to Japanese encephalitis virus infection show selective neutralizing antibody response and myeloid cell infectivity. PLoS ONE 6, e24744 (2011).

    Google Scholar 

  11. Sharma, K. B., Chhabra, S. & Kalia, M. Japanese encephalitis virus-infected cells. in Virus Infected Cells Vol. 106 (eds Vijayakrishnan, S., Jiu, Y. & Harris, J. R.) 251–281 (Springer International Publishing, 2023).

  12. Yang, K. D. et al. A model to study neurotropism and persistency of Japanese encephalitis virus infection in human neuroblastoma cells and leukocytes. J. Gen. Virol. 85, 635–642 (2004).

    Google Scholar 

  13. Byrne, S. N., Halliday, G. M., Johnston, L. J. & King, N. J. C. Interleukin-1β but not tumor necrosis factor is involved in west nile virus-induced Langerhans cell migration from the skin in C57BL/6 mice. J. Investig. Dermatol. 117, 702–709 (2001).

    Google Scholar 

  14. Wang, P. et al. DC-SIGN promotes Japanese encephalitis virus transmission from dendritic cells to T cells via virological synapses. Virol. Sin. 32, 495–502 (2017).

    Google Scholar 

  15. Malissen, B., Tamoutounour, S. & Henri, S. The origins and functions of dendritic cells and macrophages in the skin. Nat. Rev. Immunol. 14, 417–428 (2014).

    Google Scholar 

  16. Pingen, M., Schmid, M. A., Harris, E. & McKimmie, C. S. Mosquito biting modulates skin response to virus infection. Trends Parasitol. 33, 645–657 (2017).

    Google Scholar 

  17. Lai, C. et al. Endothelial Japanese encephalitis virus infection enhances migration and adhesion of leukocytes to brain microvascular endothelia via MEK-dependent expression of ICAM 1 and the CINC and RANTES chemokines. J. Neurochem. 123, 250–261 (2012).

    Google Scholar 

  18. Chang, C. et al. Disruption of in vitro endothelial barrier integrity by Japanese encephalitis virus-Infected astrocytes. Glia 63, 1915–1932 (2015).

    Google Scholar 

  19. Wang, K. et al. IP-10 promotes blood–brain barrier damage by inducing tumor necrosis factor alpha production in Japanese encephalitis. Front. Immunol. 9, 1148 (2018).

    Google Scholar 

  20. Cain, M. D., Salimi, H., Diamond, M. S. & Klein, R. S. Mechanisms of pathogen invasion into the central nervous system. Neuron 103, 771–783 (2019).

    Google Scholar 

  21. Desai, A., Shankar, S. K., Ravi, V., Chandramuki, A. & Gourie-Devi, M. Japanese encephalitis virus antigen in the human brain and its topographic distribution. Acta Neuropathol. 89, 368–373 (1995).

    Google Scholar 

  22. Fu, T. L., Ong, K. C., Tan, S. H. & Wong, K. T. Japanese encephalitis virus infects the thalamus early followed by sensory-associated cortex and other parts of the central and peripheral nervous systems. J. Neuropathol. Exp. Neurol. 78, 1160–1170 (2019).

    Google Scholar 

  23. Ghoshal, A. et al. Proinflammatory mediators released by activated microglia induces neuronal death in Japanese encephalitis. Glia 55, 483–496 (2007).

    Google Scholar 

  24. Yang, L. et al. Single-cell RNA sequencing reveals the immune features and viral tropism in the central nervous system of mice infected with Japanese encephalitis virus. J. Neuroinflammation 21, 76 (2024).

    Google Scholar 

  25. Kim, J. H. et al. CCL 2, but not its receptor, is essential to restrict immune privileged central nervous system-invasion of Japanese encephalitis virus via regulating accumulation of CD 11b + Ly-6C hi monocytes. Immunology 149, 186–203 (2016).

    Google Scholar 

  26. Patil, A. M. et al. Type I IFN signaling limits hemorrhage-like disease after infection with Japanese encephalitis virus through modulating a prerequisite infection of CD11b+Ly-6C+ monocytes. J. Neuroinflammation 18, 136 (2021).

    Google Scholar 

  27. Howe, C. L., LaFrance-Corey, R. G., Goddery, E. N., Johnson, R. K. & Mirchia, K. Neuronal CCL2 expression drives inflammatory monocyte infiltration into the brain during acute virus infection. J. Neuroinflammation 14, 238 (2017).

    Google Scholar 

  28. Terry, R. L. et al. Inflammatory monocytes and the pathogenesis of viral encephalitis. J. Neuroinflammation 9, 270 (2012).

    Google Scholar 

  29. de Vries, L. & Harding, A. T. Mechanisms of neuroinvasion and neuropathogenesis by pathologic flaviviruses. Viruses 15, 261 (2023).

    Google Scholar 

  30. Zou, S.-S. et al. Brain microvascular endothelial cell-derived HMGB1 facilitates monocyte adhesion and transmigration to promote JEV neuroinvasion. Front. Cell. Infect. Microbiol. 11, 701820 (2021).

    Google Scholar 

  31. Xu, H. et al. The choroid plexus synergizes with immune cells during neuroinflammation. Cell 187, 4946–4963.e17 (2024).

    Google Scholar 

  32. Rua, R. & McGavern, D. B. Advances in meningeal immunity. Trends Mol. Med. 24, 542–559 (2018).

    Google Scholar 

  33. Myint, K. S. Neuropathogenesis of Japanese encephalitis in a primate model. PLoS Negl. Trop. Dis. 8, 2980 (2014).

    Google Scholar 

  34. Lauer, A. N., Tenenbaum, T., Schroten, H. & Schwerk, C. The diverse cellular responses of the choroid plexus during infection of the central nervous system. Am. J. Physiol.-Cell Physiol. 314, C152–C165 (2018).

    Google Scholar 

  35. Shwetank, Date, O. S., Kim, K. S. & Manjunath, R. Infection of human endothelial cells by japanese encephalitis virus: increased expression and release of soluble HLA-E. PLoS ONE 8, e79197 (2013).

    Google Scholar 

  36. Uyar, O. et al. Single-cell transcriptomics of the ventral posterolateral nucleus-enriched thalamic regions from HSV-1-infected mice reveal a novel microglia/microglia-like transcriptional response. J. Neuroinflammation 19, 81 (2022).

    Google Scholar 

  37. Qiu, M. et al. Decoding dengue’s neurological assault: insights from single-cell CNS analysis in an immunocompromised mouse model. J. Neuroinflammation 22, 62 (2025).

    Google Scholar 

  38. Ding, X. et al. Temporally resolved single-cell RNA sequencing reveals protective and pathological responses during herpes simplex virus CNS infection. J. Neuroinflammation 22, 146 (2025).

    Google Scholar 

  39. Piwecka, M., Rajewsky, N. & Rybak-Wolf, A. Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat. Rev. Neurol. 19, 346–362 (2023).

    Google Scholar 

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

    Google Scholar 

  41. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).

    Google Scholar 

  42. Han, W. et al. Precise localization and dynamic distribution of Japanese encephalitis virus in the rain nuclei of infected mice. PLoS Negl. Trop. Dis. 15, e0008442 (2021).

    Google Scholar 

  43. Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

    Google Scholar 

  44. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. Usa. 102, 15545–15550 (2005).

    Google Scholar 

  45. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Google Scholar 

  46. Castanza, A. S. et al. Extending support for mouse data in the Molecular Signatures Database (MSigDB). Nat. Methods 20, 1619–1620 (2023).

    Google Scholar 

  47. Kak, G., Raza, M. & Tiwari, B. K. Interferon-gamma (IFN-γ): exploring its implications in infectious diseases. Biomol. Concepts 9, 64–79 (2018).

    Google Scholar 

  48. Tripathi, A. et al. Lack of interferon (IFN) regulatory factor 8 associated with restricted IFN-γ response augmented Japanese encephalitis virus replication in the mouse brain. J. Virol. 95, e0040621 (2021).

    Google Scholar 

  49. Li, F. Viral infection of the central nervous system and neuroinflammation precede blood-brain barrier disruption during Japanese encephalitis virus infection. J. Virol. 89, 5602–5614 (2015).

    Google Scholar 

  50. Kraaij, M. D. et al. Human monocytes produce interferon-gamma upon stimulation with LPS. Cytokine 67, 7–12 (2014).

    Google Scholar 

  51. Frey, T. et al. Monocyte production of IFN-γ is interleukin-12 dependent in a model of mevalonate kinase deficiency. J. Interferon Cytokine Res. 39, 364–374 (2019).

    Google Scholar 

  52. Hu, C. et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 51, D870–D876 (2023).

    Google Scholar 

  53. Jin, S., Plikus, M. V. & Nie, Q. CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics. Nat. Protoc. https://doi.org/10.1038/s41596-024-01045-4 (2024).

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

    Google Scholar 

  55. Fadnis, P. R., Ravi, V., Desai, A., Turtle, L. & Solomon, T. Innate immune mechanisms in japanese encephalitis virus infection: effect on transcription of pattern recognition receptors in mouse neuronal cells and brain tissue. Viral Immunol. 26, 366–377 (2013).

    Google Scholar 

  56. Arpaia, N. & Barton, G. M. Toll-like receptors: key players in antiviral immunity. Curr. Opin. Virol. 1, 447–454 (2011).

    Google Scholar 

  57. Li, D. & Wu, M. Pattern recognition receptors in health and diseases. Sig Transduct. Target. Ther. 6, 291 (2021).

    Google Scholar 

  58. Van De Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).

    Google Scholar 

  59. Lawson, L. J., Perry, V. H., Dri, P. & Gordon, S. Heterogeneity in the distribution and morphology of microglia in the normal adult mouse brain. Neuroscience 39, 151–170 (1990).

    Google Scholar 

  60. Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3, 100498 (2023).

    Google Scholar 

  61. Yang, X., Yang, S., Li, Q., Wuchty, S. & Zhang, Z. Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method. Comput. Struct. Biotechnol. J. 18, 153–161 (2020).

    Google Scholar 

  62. Shah, P. S. et al. Comparative flavivirus-host protein interaction mapping reveals mechanisms of dengue and Zika virus pathogenesis. Cell 175, 1931–1945.e18 (2018).

    Google Scholar 

  63. WHO Japanese encephalitis vaccines: WHO position paper, February 2015—Recommendations. Vaccine 34, 302–303 (2016).

    Google Scholar 

  64. Mathur, A., Arora, K. L. & Chaturvedi, U. C. Host defence mechanisms against Japanese encephalitis virus infection in mice. J. Gen. Virol. 64, 805–811 (1983).

    Google Scholar 

  65. Larena, M., Regner, M., Lee, E. & Lobigs, M. Pivotal role of antibody and subsidiary contribution of CD8+ T cells to recovery from infection in a murine model of Japanese encephalitis. J. Virol. 85, 5446–5455 (2011).

    Google Scholar 

  66. Song, Z.-R. et al. The slow progression of Japanese encephalitis in aged mice is likely associated to B cell recruitment in the brain. Virol. Sin. 40, 546–559 (2025).

    Google Scholar 

  67. Srivastava, S. et al. Degradation of Japanese encephalitis virus by neutrophils. Int J. Exp. Pathol. 80, 17–24 (1999).

    Google Scholar 

  68. Lindsay, H. G., Hendrix, C. J., Gonzalez Murcia, J. D., Haynie, C. & Weber, K. S. The role of atypical chemokine receptors in neuroinflammation and neurodegenerative disorders. Int. J. Mol. Sci. 24, 16493 (2023).

    Google Scholar 

  69. Minten, C. et al. DARC shuttles inflammatory chemokines across the blood-brain barrier during autoimmune central nervous system inflammation. Brain 137, 1454–1469 (2014).

    Google Scholar 

  70. Marchetti, L. et al. ACKR1 favors transcellular over paracellular T-cell diapedesis across the blood-brain barrier in neuroinflammation in vitro. Eur. J. Immunol. 52, 161–177 (2022).

    Google Scholar 

  71. Lee, E.-S. et al. Inflammatory risk contributes to post-COVID endothelial dysfunction through anti-ACKR1 autoantibody. Life Sci. Alliance 7, e202402598 (2024).

    Google Scholar 

  72. Pham, A. M., Langlois, R. A. & tenOever, B. R. Replication in cells of hematopoietic origin is necessary for dengue virus dissemination. PLoS Pathog. 8, e1002465 (2012).

    Google Scholar 

  73. Quicke, K. M. et al. Zika virus infects human placental macrophages. Cell Host Microbe 20, 83–90 (2016).

    Google Scholar 

  74. Ma, Y. et al. Avian flavivirus infection of monocytes/macrophages by extensive subversion of host antiviral innate immune responses. J. Virol. 93, e00978-19 (2019).

    Google Scholar 

  75. Spiteri, A. G. et al. Temporal tracking of microglial and monocyte single-cell transcriptomics in lethal flavivirus infection. Acta Neuropathol. Commun. 11, 60 (2023).

    Google Scholar 

  76. Chauhan, S. et al. Japanese encephalitis virus infected human monocyte-derived dendritic cells activate a transcriptional network leading to an antiviral inflammatory response. Front. Immunol. 12, 638694 (2021).

    Google Scholar 

  77. Getts, D. R. et al. Targeted blockade in lethal West Nile virus encephalitis indicates a crucial role for very late antigen (VLA)-4-dependent recruitment of nitric oxide-producing macrophages. J. Neuroinflammation 9, 246 (2012).

    Google Scholar 

  78. Ge, S. et al. The CCL2 synthesis inhibitor bindarit targets cells of the neurovascular unit, and suppresses experimental autoimmune encephalomyelitis. J. Neuroinflammation 9, 171 (2012).

    Google Scholar 

  79. Mirolo, M. et al. Impact of the anti-inflammatory agent bindarit on the chemokinome: selective inhibition of the monocyte chemotactic proteins. Eur. Cytokine Netw. 19, 119–122 (2008).

    Google Scholar 

  80. Grassia, G. et al. The anti-inflammatory agent bindarit inhibits neointima formation in both rats and hyperlipidaemic mice. Cardiovasc. Res. 84, 485–493 (2009).

    Google Scholar 

  81. Chiu, T.-M. et al. CCR8/CCL1 and CXCR3/CXCL10 axis-mediated memory T-cell activation in patients with recalcitrant drug-induced hypersensitivity. Br. J. Dermatol. 192, 293–305 (2025).

    Google Scholar 

  82. Casanova, J.-L., MacMicking, J. D. & Nathan, C. F. Interferon-γ and infectious diseases: lessons and prospects. Science 384, eadl2016 (2024).

    Google Scholar 

  83. Li, Q. & Barres, B. A. Microglia and macrophages in brain homeostasis and disease. Nat. Rev. Immunol. 18, 225–242 (2018).

    Google Scholar 

  84. Liao, C. L. et al. Effect of enforced expression of human bcl-2 on Japanese encephalitis virus-induced apoptosis in cultured cells. J. Virol. 71, 5963–5971 (1997).

    Google Scholar 

  85. Mukherjee, S. et al. Japanese encephalitis virus induces human neural stem/progenitor cell death by elevating GRP78, PHB and hnRNPC through ER stress. Cell Death Dis. 8, e2556–e2556 (2017).

    Google Scholar 

  86. Zhu, W. et al. Ferroptosis contributes to JEV-induced neuronal damage and neuroinflammation. Virol. Sin. S1995820X23001566. https://doi.org/10.1016/j.virs.2023.12.004 (2023).

  87. Wang, Z.-Y., Zhen, Z.-D., Fan, D.-Y., Wang, P.-G. & An, J. Transcriptomic analysis suggests the M1 polarization and launch of diverse programmed cell death pathways in japanese encephalitis virus-infected macrophages. Viruses 12, 356 (2020).

    Google Scholar 

  88. Yu, P. et al. Pyroptosis: mechanisms and diseases. Sig Transduct. Target. Ther. 6, 128 (2021).

    Google Scholar 

  89. Wei, Y. et al. GSDME-mediated pyroptosis promotes the progression and associated inflammation of atherosclerosis. Nat. Commun. 14, 929 (2023).

    Google Scholar 

  90. Wei, C. et al. Brain endothelial GSDMD activation mediates inflammatory BBB breakdown. Nature. https://doi.org/10.1038/s41586-024-07314-2 (2024).

  91. Hu, J. J. et al. FDA-approved disulfiram inhibits pyroptosis by blocking gasdermin D pore formation. Nat. Immunol. 21, 736–745 (2020).

    Google Scholar 

  92. Xu, M. et al. A systematic integrated analysis of brain expression profiles reveals YAP1 and other prioritized hub genes as important upstream regulators in Alzheimer’s disease. Alzheimers Dement. 14, 215–229 (2018).

    Google Scholar 

  93. You, Y. et al. ATP1A3 as a target for isolating neuron-specific extracellular vesicles from human brain and biofluids. Sci. Adv. 9, eadi3647 (2023).

    Google Scholar 

  94. Liu, H. et al. Biomarker identification for gender specificity of Alzheimer’s disease based on the glial transcriptome profiles. J. Vis. Exp. https://doi.org/10.3791/66552 (2024).

  95. Yamamoto, H. et al. NDRG4 protein-deficient mice exhibit spatial learning deficits and vulnerabilities to cerebral ischemia. J. Biol. Chem. 286, 26158–26165 (2011).

    Google Scholar 

  96. Wen, L. et al. NDRG4 prevents cerebral ischemia/reperfusion injury by inhibiting neuronal apoptosis. Genes Dis. 6, 448–454 (2019).

    Google Scholar 

  97. Qu, M. et al. Histone deacetylase 6’s function in viral infection, innate immunity, and disease: latest advances. Front Immunol. 14, 1216548 (2023).

    Google Scholar 

  98. Banerjee, I. et al. Influenza A virus uses the aggresome processing machinery for host cell entry. Science 346, 473–477 (2014).

    Google Scholar 

  99. Zan, J. et al. Rabies virus infection induces microtubule depolymerization to facilitate viral RNA synthesis by upregulating HDAC6. Front. Cell. Infect. Microbiol. 7, 146 (2017).

    Google Scholar 

  100. Zhu, J., Coyne, C. B. & Sarkar, S. N. PKC alpha regulates Sendai virus-mediated interferon induction through HDAC6 and β-catenin: PKCα-HDAC6-β-catenin controls interferon induction. EMBO J. 30, 4838–4849 (2011).

    Google Scholar 

  101. Youn, G. S., Cho, H., Kim, D., Choi, S. Y. & Park, J. Crosstalk between HDAC6 and Nox2-based NADPH oxidase mediates HIV-1 Tat-induced pro-inflammatory responses in astrocytes. Redox Biol. 12, 978–986 (2017).

    Google Scholar 

  102. Ripamonti, C. et al. HDAC inhibition as potential therapeutic strategy to restore the deregulated immune response in severe COVID-19. Front. Immunol. 13, 841716 (2022).

    Google Scholar 

  103. Adhya, D., Dutta, K., Kundu, K. & Basu, A. Histone deacetylase inhibition by Japanese encephalitis virus in monocyte/macrophages: a novel viral immune evasion strategy. Immunobiology 218, 1235–1247 (2013).

    Google Scholar 

  104. Lu, C.-Y. et al. Tubacin, an HDAC6 selective inhibitor, reduces the replication of the Japanese encephalitis virus via the decrease of viral RNA synthesis. Int. J. Mol. Sci. 18, 954 (2017).

    Google Scholar 

  105. Klein, R. S. et al. Neuroinflammation during RNA viral infections. Annu. Rev. Immunol. 37, 73–95 (2019).

    Google Scholar 

  106. Fekete, R. et al. Microglia control the spread of neurotropic virus infection via P2Y12 signalling and recruit monocytes through P2Y12-independent mechanisms. Acta Neuropathol. 136, 461–482 (2018).

    Google Scholar 

  107. Wang, R. et al. Maternal immunization with a DNA vaccine candidate elicits specific passive protection against post-natal Zika virus infection in immunocompetent BALB/c mice. Vaccine 36, 3522–3532 (2018).

    Google Scholar 

  108. Frank, J. C., Song, B.-H. & Lee, Y.-M. Mice as an animal model for Japanese encephalitis virus research: mouse susceptibility, infection route, and viral pathogenesis. Pathogens 12, 715 (2023).

    Google Scholar 

  109. Sarkar, S. & Heise, M. T. Mouse models as resources for studying infectious diseases. Clin. Ther. 41, 1912–1922 (2019).

    Google Scholar 

  110. Bharucha, T. et al. Mouse models of Japanese encephalitis virus infection: a systematic review and meta-analysis using a meta-regression approach. PLoS Negl. Trop. Dis. 16, e0010116 (2022).

    Google Scholar 

  111. Saxena, V., Mathur, A., Krishnani, N. & Dhole, T. N. Kinetics of cytokine profile during intraperitoneal inoculation of Japanese encephalitis virus in BALB/c mice model. Microbes Infect. 10, 1210–1217 (2008).

    Google Scholar 

  112. Yang, H. et al. Peripheral nerve injury induced by Japanese encephalitis virus in C57BL/6 mouse. J. Virol. 97, e0165822 (2023).

    Google Scholar 

  113. Ma, X., Ju, L., Zheng, J., Li, Z. & Gao, L. Japanese encephalitis virus infection modulates gut microbiota of immunocompetent mice. Vet. Microbiol 311, 110779 (2025).

    Google Scholar 

  114. Wang, Z.-Y. et al. Axl deficiency promotes the neuroinvasion of Japanese encephalitis virus by enhancing IL-1α production from pyroptotic macrophages. J. Virol. 94, e00602-20 (2020).

    Google Scholar 

  115. Yang, Z.-Z. et al. Inhibition of GZMB activity ameliorates cognitive dysfunction by reducing demyelination in diabetic mice. Free Radic. Biol. Med. 225, 53–62 (2024).

    Google Scholar 

  116. Gong, C. et al. SAW: An efficient and accurate data analysis workflow for stereo-seq spatial transcriptomics. GigaByte 2024, gigabyte111 (2024).

    Google Scholar 

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

    Google Scholar 

  118. Zhang, B. et al. Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images. GigaByte 2024, gigabyte110 (2024).

    Google Scholar 

  119. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Google Scholar 

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

    Google Scholar 

  121. Xu, S. et al. Using clusterProfiler to characterize multiomics data. Nat. Protoc. https://doi.org/10.1038/s41596-024-01020-z (2024).

  122. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Google Scholar 

  123. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform. 14, 7 (2013).

    Google Scholar 

  124. Mishra, A. P. et al. Programmed cell death, from a cancer perspective: an overview. Mol. Diagn. Ther. 22, 281–295 (2018).

    Google Scholar 

  125. Seo, J., Nam, Y. W., Kim, S., Oh, D.-B. & Song, J. Necroptosis molecular mechanisms: recent findings regarding novel necroptosis regulators. Exp. Mol. Med. 53, 1007–1017 (2021).

    Google Scholar 

  126. Xie, Y. et al. Ferroptosis: process and function. Cell Death Differ. 23, 369–379 (2016).

    Google Scholar 

  127. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).

    Google Scholar 

  128. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Google Scholar 

  129. Zhang, S. et al. The GSA family in 2025: A broadened sharing platform for multi-omics and multimodal data. Genom. Proteom. Bioinform. 23, qzaf072 (2025).

    Google Scholar 

  130. CNCB-NGDC Members and Partners Database resources of the national genomics data center, China National Center for Bioinformation in 2025. Nucleic Acids Res. 53, D30–D44 (2025).

    Google Scholar 

Download references

Acknowledgements

This study was supported by grants from the Science and Technology Project of Southwest United Graduate School of Yunnan (202302AO370010 to G.C.), Shenzhen Medical Research Fund (B2404002 and B2402011 to G.C.), Yunnan Major Scientific and Technological Projects (202502AU100001 to G.C.), the National Key Research and Development Plan of China (2023YFA1801000 to G.C.), the National Natural Science Foundation of China (82341118 and 82341082 to Z.O., 82502710 to Z.W., 32188101 and 82422049 to G.C.), the Shenzhen San Ming Project for Prevention and Research on Vector-borne Diseases (SZSM202211023 to G.C.), the New Cornerstone Science Foundation through the New Cornerstone Investigator Program to G.C., and the XPLORER PRIZE to G.C. We thank China National GeneBank for providing sequencing services for this project. We would also like to thank DCS Cloud (https://cloud.stomics.tech) for providing the computational resources and software support.

Author information

Author notes
  1. These authors contributed equally: Zhihua Ou, Zhaoyang Wang, Qi Chen, Peidi Ren and Xiuju He

Authors and Affiliations

  1. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China

    Zhihua Ou  (欧芷华), Zhenyu Peng  (彭震宇), Wangsheng Li  (李旺胜), Guohai Hu  (胡国海), Ziqing Deng  (邓子卿), Xin Jin  (金鑫), Xun Xu  (徐讯) & Junhua Li  (李俊桦)

  2. Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China

    Zhihua Ou  (欧芷华), Qi Chen  (陈旗), Peidi Ren  (任陪娣), Xiuju He  (何修驹), Ying’an Liang  (梁颖安), Jiaxuan Wang  (王家轩), Dexin Wang  (王德鑫), Jianxin Su  (苏坚鑫), Ziqing Deng  (邓子卿) & Junhua Li  (李俊桦)

  3. BGI Research, Beijing, China

    Zhihua Ou  (欧芷华), Qi Chen  (陈旗), Peidi Ren  (任陪娣) & Ziqing Deng  (邓子卿)

  4. New Cornerstone Science Laboratory, Tsinghua University-Peking University Joint Center for Life Sciences, School of Basic Medical Sciences, Beijing Key Laboratory of Viral Infectious Diseases, Tsinghua University, Beijing, China

    Zhaoyang Wang  (王朝阳), Yan Liang  (梁言) & Gong Cheng  (程功)

  5. Institute of Infectious Diseases, Shenzhen Bay Laboratory, Shenzhen, China

    Zhaoyang Wang  (王朝阳) & Gong Cheng  (程功)

  6. Institute of Pathogenic Organisms, Shenzhen Center for Disease Control and Prevention, Shenzhen, China

    Zhaoyang Wang  (王朝阳) & Gong Cheng  (程功)

  7. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Qi Chen  (陈旗) & Jianxin Su  (苏坚鑫)

  8. BGI Research, Shenzhen, China

    Xiuju He  (何修驹), Sha Liao  (廖莎), Jie Zhao  (赵杰), Xin Jin  (金鑫), Xun Xu  (徐讯) & Junhua Li  (李俊桦)

  9. Department of Immunology and Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China

    Ying’an Liang  (梁颖安)

  10. NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

    Dexin Wang  (王德鑫)

  11. National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Oujia Zhang  (张偶佳)

  12. Shenzhen Proof-of-Concept Center of Digital Cytopathology, BGI Research, Shenzhen, China

    Wangsheng Li  (李旺胜)

  13. BGI Research, Chongqing, China

    Ao Chen  (陈奥)

  14. JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing, China

    Ao Chen  (陈奥)

  15. School of Medicine, South China University of Technology, Guangzhou, China

    Xin Jin  (金鑫)

  16. Shenzhen Key Laboratory of Transomics Biotechnologies, BGI Research, Shenzhen, China

    Xin Jin  (金鑫)

  17. Guangdong Provincial Key Laboratory of Genome Read and Write, BGI Research, Shenzhen, China

    Xun Xu  (徐讯)

  18. Southwest United Graduate School, Kunming, China

    Gong Cheng  (程功)

Authors
  1. Zhihua Ou  (欧芷华)
    View author publications

    Search author on:PubMed Google Scholar

  2. Zhaoyang Wang  (王朝阳)
    View author publications

    Search author on:PubMed Google Scholar

  3. Qi Chen  (陈旗)
    View author publications

    Search author on:PubMed Google Scholar

  4. Peidi Ren  (任陪娣)
    View author publications

    Search author on:PubMed Google Scholar

  5. Xiuju He  (何修驹)
    View author publications

    Search author on:PubMed Google Scholar

  6. Yan Liang  (梁言)
    View author publications

    Search author on:PubMed Google Scholar

  7. Ying’an Liang  (梁颖安)
    View author publications

    Search author on:PubMed Google Scholar

  8. Jiaxuan Wang  (王家轩)
    View author publications

    Search author on:PubMed Google Scholar

  9. Sha Liao  (廖莎)
    View author publications

    Search author on:PubMed Google Scholar

  10. Dexin Wang  (王德鑫)
    View author publications

    Search author on:PubMed Google Scholar

  11. Jie Zhao  (赵杰)
    View author publications

    Search author on:PubMed Google Scholar

  12. Oujia Zhang  (张偶佳)
    View author publications

    Search author on:PubMed Google Scholar

  13. Zhenyu Peng  (彭震宇)
    View author publications

    Search author on:PubMed Google Scholar

  14. Jianxin Su  (苏坚鑫)
    View author publications

    Search author on:PubMed Google Scholar

  15. Wangsheng Li  (李旺胜)
    View author publications

    Search author on:PubMed Google Scholar

  16. Guohai Hu  (胡国海)
    View author publications

    Search author on:PubMed Google Scholar

  17. Ao Chen  (陈奥)
    View author publications

    Search author on:PubMed Google Scholar

  18. Ziqing Deng  (邓子卿)
    View author publications

    Search author on:PubMed Google Scholar

  19. Xin Jin  (金鑫)
    View author publications

    Search author on:PubMed Google Scholar

  20. Xun Xu  (徐讯)
    View author publications

    Search author on:PubMed Google Scholar

  21. Junhua Li  (李俊桦)
    View author publications

    Search author on:PubMed Google Scholar

  22. Gong Cheng  (程功)
    View author publications

    Search author on:PubMed Google Scholar

Contributions

G.C., J.L., Z.O., and Z.W. conceived the study and designed the research. Z.W. constructed the animal models, collected samples, and performed all validation experiments, including flow cytometry, qPCR, and immunofluorescence assays. Y.L. and O.Z. assisted Z.W. with the validation experiments. Z.O., P.R., S.L., A.C., and J.Z. designed and optimized the orthoflavivirus-specific Stereo-seq chips. P.R., Q.C., and Ying’an L. conducted Stereo-seq experiments. Z.P., W.L., and G.H. performed sequencing of Stereo-seq libraries. Z.O., Q.C., X.H., Ying’an L., J.W., D.W., and J.S. carried out bioinformatic analyses. Z.O., Z.W., Q.C., and X.H. wrote and revised the manuscript. G.C., J.L., Z.O., X.X., X.J., and Z.D. supervised the project and revised the manuscript.

Corresponding authors

Correspondence to Junhua Li  (李俊桦) or Gong Cheng  (程功).

Ethics declarations

Competing interests

X.X., A.C., and S.L. are the co-inventors of Stereo-seq technology. Employees of BGI have stock holdings in BGI. The other authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Juan Quintana and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

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

Supplementary information

Supplementary Information (download PDF )

Description of Additional Supplementary Files (download PDF )

Supplementary Data 1–12 (download ZIP )

Reporting Summary (download PDF )

Transparent Peer Review file (download PDF )

Source data

Source Data (download XLSX )

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ou, Z., Wang, Z., Chen, Q. et al. Spatial transcriptomics uncovers vasculature-centered cellular interactions driving Japanese encephalitis progression in a mouse model. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70047-5

Download citation

  • Received: 24 April 2025

  • Accepted: 09 February 2026

  • Published: 17 March 2026

  • DOI: https://doi.org/10.1038/s41467-026-70047-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Videos
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • Aims & Scope
  • Editors
  • Journal Information
  • Open Access Fees and Funding
  • Calls for Papers
  • Editorial Values Statement
  • Journal Metrics
  • Editors' Highlights
  • Contact
  • Editorial policies
  • Top Articles

Publish with us

  • For authors
  • For Reviewers
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Nature Communications (Nat Commun)

ISSN 2041-1723 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing