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

Domain: biology
Published: 2026-03-17
Editorial outcome: minor_revision

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 apply 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.

Introduction
Japanese encephalitis (JE) is an acute inflammatory central nervous system (CNS) disease caused by Japanese encephalitis virus (JEV), which is primarily transmitted by the Culex mosquitos1. Local transmission of JEV occurs in 24 countries in East Asia and the Western Pacific, putting over 3 billion people at risk of infection2,3. After infection, the condition can progress to encephalitis, characterized by severe fever, neck stiffness, and seizures. Children under the age of 14 account for 75% of the JE patients3. 20-30% of JE patients die, while 30-50% of survivors develop persistent neurological or psychiatric sequelae4. Although the JEV vaccines are commercially available to reduce JE incidences5, infection risk persists for individuals lacking prior vaccinations6–8. With no virus-specific treatment available, JEV imposes a continuous burden on public health.

Following a skin bite, JEV infects susceptible myeloid immune cells, including dendritic cells, macrophages, and monocytes9–12. At the biting sites, the infected monocytic lineage cells can recirculate to the draining lymph nodes and spleen, where they undergo substantial proliferation and release progeny viruses into the lymphatic system and bloodstream, ultimately resulting in viremia13–16. A key step in JEV infection of the brain is to breach the blood-brain barrier (BBB), and the mechanisms are still debated. The BBB is composed of brain microvascular endothelial cells (BMECs), tight junctions between endothelial cells, the basement membrane, and astrocytic end-feet. It plays a crucial role in maintaining the homeostasis of the brain's internal environment. Some studies suggest that JEV can directly infect BMECs, which subsequently release progeny viruses from the basal side into the brain parenchyma17. Other studies propose that early JEV-induced inflammatory factors affect BMECs by downregulating tight junctions, thus increasing BBB permeability and facilitating viral entry into the brain18,19. Additionally, the virus can infect immune cells (such as macrophages and monocytes), which are then able to cross the damaged BBB and unintentionally transport the virus into the brain, much like the Greek "Trojan Horse", where something harmful is smuggled inside something that appears harmless20. Nevertheless, considerable controversy remains regarding the specific cell types and pathways mediating viral entry into the brain parenchyma.

JEV can infect many brain parenchymal regions such as thalamus, brainstem, and hippocampus21,22. The replication of JEV in neurons can lead to direct neuronal death, while the activated microglia/glial cells and infiltrating inflammatory cells can also damage or kill the neurons by releasing pro-inflammatory cytokines such as IL-6 and TNF-α as well as other cytotoxic factors23,24. Studies have identified monocytes as an essential cell type contributing to JE neuropathology25–27. Inflammatory Ly6chi monocytes have paradoxical roles in viral encephalitis. On one hand, monocytes can differentiate into effector cells and produce reactive oxygen species (ROS) which repress and clear the virus. On the other hand, unbalanced and uncontrolled migration and infiltration of monocytes will facilitate viral neuroinvasion through a "Trojan horse" manner and cause excessive tissue damage28–30. At present, the specific mechanisms underlying monocyte infiltration into brain tissue and their overall contribution to the pathogenesis of encephalitis remain unclear, warranting further in-depth investigation.

As key constituents of the brain’s barriers, the meninges and choroid plexus can serve as gateways for the entry of various neuroinvasive pathogens such as JEV, mediate the infiltration of myeloid immune cells, and participate in orchestrating immune response in the CNS19,31–34. These processes all depend on the engagement of endothelial cells. It is also reported that human brain microvascular endothelial cells (HBMEC) can support JEV replication and promote leukocyte adhesion in vitro17,35. However, there exists a lack of understanding of the in situ interaction networks between the endothelial cells and infiltrating immune cell types within the brain following JEV infection. Moreover, the precise cellular changes associated with JEV-induced tissue damage in the CNS remain largely elusive. Therefore, an integrated and systematic analysis of JE pathogenesis is urgently needed.

Recent investigations into the pathogenesis of neurotropic viruses have been advanced by single-cell RNA sequencing (scRNA-seq), which elucidates the immune functions of microglia, monocytes, and other immune cells in the cerebrospinal fluid and brain tissues24,36–38. However, scRNA-seq requires tissue dissociation, a process that can lead to the loss of low-viability cells and poses significant challenges for the effective isolation of neurons. Consequently, this approach may fail to provide a complete picture of brain pathology. In contrast, spatial transcriptomics can capture transcripts in situ within intact tissue architectures. This technology can therefore elucidate the regional tropism of viral infection, the spatial infiltration patterns of immune cells, the extent of infiltration by various immune subsets, the degree of tissue damage, and the neighborhood interactions between different cell types, providing high-resolution molecular evidence for deciphering the pathogenic mechanisms of neuroinvasive viruses39.

In this study, we construct a female BALB/c mouse infection model and design Stereo-seq chips with JEV probes that can simultaneously detect JEV and host transcriptomes to dissect the spatial and temporal transcriptional features of neurological lesions during JEV acute infection phase, identify the key cells, pathways, and inflammatory factors associated with brain damage, providing clues on the diagnosis and treatment of JE. As a member of the Orthoflavivirus genus, JEV characteristically lacks a polyA tail, posing a detection hurdle for mRNA capture methods, including scRNA-seq and spatial transcriptomics technologies, that predominantly utilize polyT probes40. This work establishes a spatial transcriptomics technical framework that can be adapted to study the immunology of other orthoflaviviruses and neuroinvasive viruses, which will benefit the mechanistic exploration and the discovery of novel therapeutic targets in associated diseases.

Results
Custom Stereo-seq chips reveal the spatial profiles of JEV infection. To explore JEV infection dynamics in the mouse brain, we inoculated female BALB/c mice with 106 PFU of JEV in 200 μl of phosphate-buffered saline (PBS) or 200 μl of PBS in the mock-treated group (Fig. 1a). On the 3rd, 5th, and 7th day post inoculation (dpi), the mouse brains (n=3) were collected for Stereo-seq experiment (Fig. 1a). The selection of these time points was based on key experimental observations as described in methods and shown in the supplementary information (Supplementary Fig. 1a-f): 3 dpi corresponded to the peak of viremia and the initial detection of JEV RNA in the brain (marking viral entry into the CNS); 5 dpi fell in the active phase of intracerebral viral replication and early disease progression (after the onset of clinical signs on 4 dpi); and 7 dpi represented the pre-mortality window (prior to the onset of mortality on 8 dpi) with escalating clinical symptoms and sustained high intracerebral viral load. These time points ensured the capture of critical stages of JEV acute infection. The original Stereo-seq technology utilizes polyT probes to capture mRNA41. However, the mRNA of JEV lacks a polyA tail, which cannot be readily detected. To facilitate the capture of JEV transcripts, we strategically designed a virus-specific probe (VSP) targeting a conserved region in the NS5 gene of multiple orthoflaviviruses (including JEV, Zika virus, and Dengue virus). The VSPs were integrated into the existing polyT probes at a ratio of 1:10 on the Stereo-seq chips (Fig. 1a). Based on our preliminary solution-phase test, this ratio enabled more sensitive detection of JEV than random primers, without compromising the detection of host transcripts. Data from Stereo-seq experiments using JEV-infected mouse brain tissues showed that the captured reads were mainly attributable to polyT and VSPs, with polyT probes capturing approximately 70% of the overall reads, mostly mouse transcripts (Supplementary Fig. 1g). When it comes to the detection of JEV transcripts, the VSPs captured over 80% of the reads (Supplementary Fig. 1g). During viral replication, JEV expresses only a single genomic RNA that serves as mRNA and does not generate multiple distinct transcripts. When quantifying JEV expression, we assumed that each detected viral RNA fragment corresponds to an intact JEV RNA. Although VSPs were included in the chips, the total number of probes that can be anchored on each spot is limited and that the probe density in certain capture spots may be lower than the actual number of viral mRNAs present in the tissue. In addition, steric hindrance may prevent a virus-targeted probe from hybridizing to its cognate mRNA. Consequently, our spatial transcriptomic data provide a qualitative map of JEV distribution rather than an absolute quantification of viral load within each region.

Based on the overall data quality, we chose an analytical unit of bin35, representing a square region of 35 spots x 35 spots, equal to 17.22 μm x 17.22 μm. The median number of genes detected for each bin ranged from 396 to 844, with an average of 671 genes (Supplementary Data 1). The spatial transcriptomics data showed that the JEV genes were expressed in approximately 0.04%, 3.49%, and 4.25% area of the brain tissues on 3, 5, and 7 dpi respectively, reaching a peak on 7 dpi; while no JEV-positive bins were identified in the control samples (Supplementary Data 1). These results were consistent with the JEV load detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR) (Supplementary Fig. 1b), confirming the reliability of the spatial transcriptomic data. To determine the specific regions infected by JEV, we annotated the mouse brain regions using the marker genes and anatomical information from the Allen Brain Atlas (https://atlas.brain-map.org/, Supplementary Data 2). For instance, the thalamus was annotated using marker genes such as Prkcd, Tcf7l2, and Ntng1, while the meninges were annotated by genes such as Tmem212, Igfbp4, Ccdc153, and Ptgds. Our data showed that the thalamus, cerebral cortex, and cerebral nuclei were the most infected regions by JEV (Supplementary Data 3, Fig. 1b,c), consistent with previous reports22,42. On 3 dpi, most brain regions were negative for JEV infection. On 5 dpi, multiple brain regions were JEV positive, but the percentages of virus positive bins in thalamus, cerebral cortex, and cerebral nuclei were less than 20%. On 7 dpi, the JEV positive rates in these brain regions rose to 20%-40%, indicating continuous amplification of JEV in these sites.

We further employed SingleR43 and its accompanying reference dataset to annotate the cell types (Supplementary Fig. 2a, Supplementary Data 4) and characterized the cellular infection profiles (Supplementary Data 5&6). On 3 dpi, monocytes were the dominant JEV positive cells, with an average positive rate of 0.93% (Fig. 1d). On 5 dpi, the highest JEV positive rate was observed in NK cells, 9.65%, followed by dendritic cells (6.12%), microglia (5.25%), macrophages (4.60%), and neurons (3.79%). On 7 dpi, the highest JEV positive rate was observed in microglia (7.60%), followed by NK cells (6.73%). The virus positive rates of NK cells and dendritic cells reached an early peak on 5 dpi. In contrast, the rates in the other cell types showed a rising trend from 3 to 7 dpi, including microglia, macrophages, neurons, T cells, fibroblasts, monocytes, granulocytes, endothelial cells, epithelial cells, and astrocytes (Fig. 1d). Among them, most were brain-resident cells, indicating progressive replication of JEV in the brain parenchyma. Our data suggests that monocytes may be the initial infected cells in the CNS and further spread the virus to the resident cell types in the brain. The phagocytosis of JEV-infected cells and natural viral infection may collectively contribute to the high positive rates of multiple immune cells.

We next verified the high infection rates of immune cells in the brain using flow cytometry. When considering the percentage of JEV-infected cells within each immune cell type (Supplementary Fig. 2b-e, upper panel), macrophages (16.8% infection rate at 5 dpi) and NK cells (15.4% infection rate at 7 dpi) exhibited the highest JEV infection rates among all infiltrating immune cells in the brain. However, when assessing the proportion of JEV-infected immune cells relative to the total brain cells (Supplementary Fig. 2b-e, lower panel), which better reflected the overall contribution of specific immune cell types to the brain infection, JEV+ monocytes were the predominant infected immune cells, accounting for around 0.08% of the total living cells in the brain at 5 dpi. This evidence indicates that monocytes may be the key immune cell type mediating JEV infection in the brain.

Spatiotemporal immunological landscape during JE progression. To gain a comprehensive view of the cerebral immunological and physiological alterations following JEV infection, we conducted a comparative analysis to identify the temporal transcriptional changes. A total of 225 genes were differentially expressed between the infection and control groups (Supplementary Data 7). Chronologically, 35, 203, and 112 genes were upregulated on 3, 5, and 7 dpi, respectively (Supplementary Fig. 3a), with 95 genes upregulated on both 5 dpi and 7 dpi. Meanwhile, a total of 58 genes were downregulated after infection, with 2, 6, and 56 genes downregulated on 3, 5, and 7 dpi, respectively (Supplementary Fig. 3b). Gene Ontology (GO) enrichment analysis revealed that the upregulated genes in JEV infection groups were mainly associated with immune response (Supplementary Fig. 3c), while the downregulated genes were mostly associated with functional pathways of brain cells including exocytosis, synapse organization, and neurotransmitter transport, indicating functional damage in the brain (Supplementary Fig. 3d). Starting from 3 dpi, innate immune response was initiated to recruit lymphocytes, produce interferons and cytokines, induce cell killing, and inhibit virus replication. We observed an early activation of type I interferons (IFN-α and IFN-β) starting from 3 dpi, followed by the activation of type II interferon (IFN-γ) on 5 dpi (Supplementary Fig. 3c). Stimulated by virus infection, expression levels of multiple Toll-like receptors were enhanced since 3 dpi (Supplementary Fig. 3e), especially Tlr8, Tlr2, Tlr3 and Tlr7. A series of interferon regulatory factors (IRFs) were also upregulated (Supplementary Fig. 3f), such as Irf7, Irf1, Irf8, Irf9, Irf5, and Irf4. The expression of these genes mostly peaked on 5 dpi. Consistently, most of the interferon genes were initially expressed on 3 dpi, peaked on 5 dpi, and were downregulated on 7 dpi (Supplementary Fig. 4a). Consequently, the interferon-inducible genes showed low expression levels on 3 dpi but higher levels on 5 dpi and 7 dpi (Supplementary Fig. 4b). The expressions of representative Tlr, Irf, and Ifn genes were further validated by RT-qPCR (Supplementary Fig. 4c). On 5 dpi, the pathways including Toll-like receptor signaling, ribosome assembly, protein folding, and cytoplasmic translation were uniquely upregulated (Supplementary Fig. 3c), which may be linked to the active replication of JEV in the brain. More immune cell types were activated on 5 dpi, including astrocyte development, glial cell differentiation and migration, chemotaxis of granulocytes and lymphocytes (Supplementary Fig. 3c). On 7 dpi, the immune response was further strengthened by B cell mediated immunity and the activation of myeloid leukocytes and macrophages (Supplementary Fig. 3c).

Spatial mapping of the upregulated immune genes such as Irf7, Ifng, Ifitm3, Isg15, Cxcl10, and Ccl2 revealed significantly high expression levels along the vasculatures in the brain (Fig. 2a). To understand the immune response status of different brain regions, we conducted Gene Set Variation Analysis (GSVA) for each brain sample based on the pro-inflammatory gene set from the GSEA database44–46 (Supplementary Data 8). Our data showed that the meninges and choroid plexus had the highest immune response scores across different infection timepoints, followed by the brain regions with high infection rates such as the thalamus and cerebral cortex (Fig. 2b), especially on 5 and 7 dpi. Immunofluorescent staining revealed enhanced expression of CXCL10, CXCL9, and CCL2 in the meninges (Fig. 2c-e). Hematoxylin and eosin (H&E) staining showed that vascular dilation in the meningeal tissues began at 3 dpi, accompanied by the adhesion of inflammatory cells to the vascular endothelium. At 5 and 7 dpi, vascular dilation became more pronounced, with a significant increase in the number of adherent inflammatory cells (Fig. 2f). The inflammatory response initiated at 3 dpi, peaked at 5 dpi, and decreased at 7 dpi, but still remained higher than that of 3 dpi (Fig. 2b). These observations demonstrate that the cerebral vasculatures, particularly those in the meninges, is involved from the early stages during the inflammatory progression of JE.

At cellular level, comparative temporal analysis of whole Stereo-seq chips demonstrated an increase in the numbers of monocytes, macrophages, T cells, and microglia, in contrast to a progressive decline in the number of neurons (Fig. 3a). We also observed increased signals of endothelial cells, which was probably due to vascular dilation caused by immune infiltration or tissue edema (Fig. 2f), instead of cell proliferation. Flow cytometry results showed that monocytes and T cells were the major immune cells infiltrating into the JEV-infected brain (Fig. 3b). Remarkably, monocytes accounted for over 0.6% of the living cells in the infected brain at 7 dpi (Fig. 3b), potentially dominating the immune response against JEV infection.

IFN-γ is a critical immunomodulatory cytokine that coordinates both innate and adaptive immune responses and also exerts powerful antiviral function47. Studies have also shown that IFN-γ is a key cytokine mediating BBB disruption and immune cell infiltration in the pathogenesis of JE48,49. Therefore, identifying the specific cell types in the brain that secrete IFN-γ would help reveal the drivers of JE immunopathology. To validate the cellular sources of IFN-γ in the JEV-infected mouse brain, we performed flow cytometric analysis. The number of IFN-γ-producing cells increased at 5 and 7 dpi, despite representing only approximately 1% of the total brain cells (Fig. 3c). Notably, at 5 and 7 dpi, when cerebral viral loads were high, immune cells constituted up to 50% of all IFN-γ-producing cells (Fig. 3d). Within the IFN-γ⁺CD45⁺ cell population, T cells accounted for 3.6–5.0% (Fig. 3e), NK cells for 6.3–6.9% (Fig. 3f), and macrophages for 2.5–5.5% (Fig. 3g). Strikingly, monocytes represented the majority, comprising 28.3% and 25.6% of the IFN-γ-producing immune cells at 5 and 7 dpi, respectively (Fig. 3h), suggesting monocytes as the principal source of IFN-γ in the brain during JE.

It is widely recognized that activated T cells and NK cells are the primary sources of IFN-γ, whereas monocytes are generally not considered IFN-γ producers. However, several studies have reported that, under specific stimulatory conditions, monocytes are capable of secreting IFN-γ and may even represent a major source50,51. To further corroborate our transcriptomic and flow cytometry findings, we isolated monocytes from both mouse and human peripheral blood and infected them with JEV in vitro. Compared to uninfected controls, JEV-infected monocytes exhibited significantly increased cellular IFN-γ mRNA and extracellular protein expressions (Fig. 3i-l). These results confirm infiltrating monocytes as a major source of IFN-γ in the brain during JE. Given that the majority of infected mice ultimately succumbed to death (Supplementary Fig. 1e), these IFN-γ-secreting monocytes might contribute to disease exacerbation.

Activation and pro-inflammatory function of Ackr1+ endothelial cells. Our results showed that the meninges were the hotspot of immune response, and the excessive immune infiltration also caused tissue damage (Fig. 2). To further understand the transcriptional changes associated with this pathological process, we extracted the spatial transcriptomics data of meninges from all samples for in-depth investigation. Analysis of differentially expressed genes (DEGs) showed that many inflammatory molecules including Cxcl10 and Ccl2, as well as MHCII-related genes (B2m and H2-K1) were upregulated in samples collected on 5 and 7 dpi (Supplementary Fig. 5a). We further conducted cell clustering and annotations using the bin35 data of meninges (Supplementary Fig. 5b,c). By sub-clustering the Ly6c2+ cells, we identified a cluster of brain endothelial cells (ECs) highly expressing Ackr1 (atypical chemokine receptor 1), defined as Ackr1+ ECs (Fig. 4a). The Ackr1+ ECs highly expressed marker genes that were enriched in angiogenesis and vascular pathways52 (Supplementary Fig. 5d), including Vwf and Igfbp7 (Fig. 4b). Sequential comparative analysis showed an increase in the proportion of Ackr1+ ECs after infection, especially on 5 and 7 dpi, along with significant infiltration of Ly6c2+Lyz2+ monocytes into the meninges on 5 dpi (Fig. 4c). Cell-cell communication53 analysis of the meninges revealed strong interactions between Ackr1+ ECs and Ly6c2+Lyz2+ monocytes via the CCL/CXCL pathway (Fig. 4d,e,f), which was significantly upregulated on 5 dpi and 7 dpi (Fig. 4d). The Ackr1+ ECs may recruit Ly6c2+Lyz2+ monocytes highly expressing Cxcl9, Cxcl10, Ccl7, Ccl5 and Ccl2 (Supplementary Fig. 6a). Moreover, the Ly6c2+Lyz2+ monocytes also expressed a high level of Ackr1, Ccr2, Ccr1, and Ccr5, which may further facilitate the recruitment of monocytes and T cells (Supplementary Fig. 6a). Spatial transcriptomic data displayed strong co-localization signals for ligand-receptor pairs including Ccl7-Ccr2, Ccl2-Ccr2, Ccl5-Ccr1, Ccl2-Ccr5 and Cxcl9/Cxcl10/Ccl7/Ccl5/Ccl2-Ackr1 in the meningeal region on 5 and 7 dpi (Fig. 4g). Further in situ immunofluorescent staining showed increased protein expression levels of ACKR1, CXCL10, CXCL9, and CCL2 in the meninges following JEV infection (Fig. 4h, Supplementary Fig. 6b,c). Notably, cells positive for CXCL10, CXCL9, or CCL2 were situated in close proximity to Ackr1-expressing cells, confirming interactions between Ackr1+ ECs and inflammatory cells. Regulatory factor analysis54 with the cytokines expressed by Ly6c2+Lyz2+ monocytes revealed that the downstream target genes in Ackr1+ ECs included Ccl12, Ccl4, Ccl7, and Ptgs2 (Fig. 4i), which can further promote the production of proinflammatory cytokines by Ackr1+ ECs (Fig. 4j).

Disruption of the BBB is crucial for immune cell infiltration and virus invasion into the brain. On 5 and 7 dpi, the tight junction associated genes such as Tjp1 and Cldn5 were downregulated (Fig. 5a), which was further validated by immunofluorescent staining (Fig. 5b,c), suggesting the impairment of the BBB. To further assess changes in BBB permeability following JEV infection, we employed the widely used Evans blue assay. The results demonstrated a significant increase in BBB permeability at 5 and 7 dpi (Fig. 5d), indicating that JEV infection markedly disrupts BBB integrity. Moreover, spatial transcriptomics data, immunofluorescent staining and RT-qPCR results demonstrated that the expressions of Ackr1 (Fig. 5a,e) as well as adhesion molecule genes Icam1 (Fig. 5a,e,f), and Vcam1 (Fig. 5a,g,h) were upregulated on 5 and 7 dpi in the meninges, which may promote the transendothelial migration of monocytes and other immune cells28. Notably, besides a small number of viral RNA detected in the ECs (Fig. 1d), we also observed the upregulation of a series of pattern recognition receptors55–57 (PRRs) in different types of ECs, including Tlr1, Tlr2, Tlr3, Tlr4, Tlr6, Tlr7, Tlr13, Ifih1, and Ddx58, suggesting potential JEV infection in the ECs (Fig. 5i). The ECs also displayed higher interferon production activities, as the gene expressions of interferon regulatory factors (Stat3, Stat2, Stat1, Irf7, and Cebpd) and interferon receptors (Ifnar2, Ifngr2), were also upregulated in these cells (Fig. 5i).

To obtain single-cell resolution evidence, we performed cell segmentation on mouse brains collected from the control (n=2) and 5 dpi (n=1) groups and extracted the segmented cells in the meningeal region. Similar to the results based on bin35 data, Ackr1+ ECs were identified in the meningeal region collected on 5 dpi (Supplementary Fig. 7a-c). Regulatory analysis with Pyscenic58 showed that Ackr1+ ECs exhibited activation of a series of specific transcription factors (Supplementary Fig. 7d), among which Irf7, Stat1, Cebpb, Cebpd, Spi1, and Irf1 were highly expressed in the Ackr1+ ECs (Supplementary Fig. 7e), with Irf7 displaying the highest expression level. Cell-cell communication analysis with the segmented cell data further confirmed the interactions between Ackr1+ ECs and Ly6c2+Lyz2+ monocytes through the Cxcl10/Ccl2/Ccl5/Cc7-Ackr1 axes (Supplementary Fig. 7f,g). Remarkably, the single-cell resolution data showed that among the three types of ECs, Ackr1+ ECs significantly upregulated the expression of genes associated with pathogen recognition, interferon production, and cell adhesion (Supplementary Fig. 7h,i). These results reveal the central role of Ackr1+ ECs in receiving and amplifying immune signals during the progression of JE.

Activation and expansion of Ccl12+ microglia. Microglia account for approximately 5%-20% of the total glia cell population and are vital to pathogen defense in the brain59. Indeed, we observed a sharp increase of activated microglia in the mouse brain after JEV infection (Fig. 6a). By examining the expression of pro-inflammatory and anti-inflammatory markers, we observed increased expressions of pro-inflammatory genes including Cd14, Fcgr3, Fcgr2b, Cd40, Cd86, and H2-Ab1 in the microglia, particularly on 5 and 7 dpi (Supplementary Fig. 8a,b). Pro-inflammatory chemokines such as Ccl3, Ccl4, Ccl5, and Ccl12 were also significantly upregulated in microglia on 5 and 7 dpi (Supplementary Fig. 8a), suggesting microglia activation. Ccl12 was specifically expressed in the activated microglia but not in the homeostatic ones (Fig. 6a,b). Therefore, we defined the activated microglia as Ccl12+ microglia. The Ccl12+ microglia were mainly identified in the JEV infection groups, with few homeostatic microglia detected on 5 and 7 dpi (Fig. 6c). RT-qPCR experiment confirmed elevated expressions of Iba1 and Ccl12 (Fig. 6d,e) in the infected mouse brains while immunofluorescent staining revealed co-localization of the corresponding proteins (Fig. 6f). The presence of Ccl12+ microglia was further confirmed by an external scRNA-seq dataset (GSE237915)24 where these cells were also identified and showed a strong correlation with those in our spatial transcriptomics dataset (Supplementary Fig. 8c-e). We further conducted functional enrichment of the DEGs between Ccl12+ microglia and homeostatic microglia. The upregulated genes in Ccl12+ microglia were associated with antiviral response and interferon signaling (Fig. 6g), while the downregulated genes were linked to synapse organization, learning or memory, and cognitive pathway (Fig. 6h). This suggests that Ccl12+ microglia play a role in antiviral responses, but their transcriptional changes are linked to neuropathology. Transcriptional regulation analysis58 showed that transcriptional regulators including Irf7, Stat1, and Cebpd were active in Ccl12+ microglia (Fig. 6i), suggesting their involvement in microglia differentiation. Spatially, the expression of Irf7 not only colocalized with Ccl12+ microglia but also overlapped with Cxcl10 and the meninges (Fig. 6j), indicating a close interplay between immune cell chemotaxis and microglia activation.

Enhanced necroptosis and pyroptosis in the meninges. To understand the cell death mechanism engaged in JE progression, we estimated the transcriptional score of six death pathways including apoptosis, necroptosis, ferroptosis, cuproptosis, pyroptosis, and autophagy (Supplementary Data 8). Enhanced RNA signals were observed for necroptosis and pyroptosis during JEV infection. Genes related to necroptosis and pyroptosis were significantly upregulated in brains collected on 5 and 7 dpi (Fig. 7a,b). The RNA signals of necroptosis and pyroptosis were mainly concentrated in the meningeal regions (Fig. 7c,d), which was further confirmed by in situ immunofluorescent staining and RT-qPCR using the necroptosis marker p-MLKL (encoded by Mlkl, Fig. 7e,f) and the pyroptosis marker GSDMD-N (encoded by Gsdmd, Fig. 7g,h). Multiple cell types upregulated necroptosis and pyroptosis signals including Ackr1+ ECs, CD8+ T cells, Gfap+CD74+ astrocytes, Ly6c2+Lyz2+ monocytes, Ly6c2+Ptgds+ cells, neutrophils, and Saa+ cells (Fig. 7i,j), with Ly6c2+Lyz2+ monocytes infiltrating from the periphery exhibiting the strongest signals, which were further validated by in situ immunofluorescent co-staining of Ly6c2 and necroptosis biomarker p-MLKL (Fig. 7k) or pyroptosis marker GSDMD-N (Fig. 7l). These data demonstrate that meninges were the hotspot of necroptosis and pyroptosis and that Ly6c2+ monocytes were deeply involved in the cell death across multiple brain regions. The release of cellular contents due to necroptosis and pyroptosis could enhance the immune cascade reaction, exacerbating neuroinflammation and tissue damage.

JEV infection induces necroptosis and pyroptosis in neurons. We further focused on the brain regions with a high viral load including the thalamus, cerebral cortex, cerebral nuclei, and hindbrain, to investigate the impact of viral infection on the death of neurons in these regions (Stereo-seq bin35 data). We performed GSVA to evaluate the cell death scores between virus-positive and virus-negative neurons. To avoid potential false positive bins, only bins containing at least two different viral genes or three viral reads were defined as JEV positive. In the thalamus, the JEV-positive Meg3+ Neurons, Oxt+ Neurons, and Tac2+ Neurons exhibited higher pyroptosis and necroptosis signals than their JEV-negative counterparts (Fig. 8a,b). Consistently, genes related to necroptosis (Ripk3, Ripk1, Mlkl, Ripk2, Tnfrsf1a, and Tnf) and pyroptosis (Casp1, Casp4, Casp8, Casp9, and Gsdmd) showed upregulation in the JEV-infected Oxt+ Neurons and Tac2+ Neurons (Fig. 8c). Similarly, Meg3+ Neurons and Pvalb+ Neurons in the cerebral cortex (Supplementary Fig. 9a-f), Oxt+ Neurons and Sst+ Neurons in the cerebral nuclei (Supplementary Fig. 9g-l), as well as Gpr88+ Neurons, Vsnl1+ Neurons and Sncg+Nefl+ Neurons in the hindbrain (Supplementary Fig. 9m-r) all upregulated necroptosis and pyroptosis signals in the virus-positive cells. These results suggest that JEV infection causes loss of neurons through pyroptosis and necroptosis. Although the JEV-negative neurons had lower death signals, the uninfected neurons may also be killed by the bystander effect. Unfortunately, because our virus probes may be insufficient to capture all JEV RNA in each cell, a JEV-negative cell may be JEV-infected, making it hard to define a true bystander cell. Therefore, our detection sensitivity can't support the discrimination between direct virus-induced and bystander cell death. Meanwhile, the necroptosis and pyroptosis signals in the immune cells were upregulated independent of virus infection (Fig. 8b,c and Supplementary Fig. 9d-f,j-l,p-r), suggesting a more complex cell death mechanism of the infiltrating immune cells.

Genes associated with JE neurological disorders. JE patients may experience symptoms such as confusion, aphasia, and paralysis, and the underlying mechanism was not fully explained. While DEG analysis can identify lists of individually altered genes, we would like to find out coordinately expressed gene modules associated with the neurological disorders caused by JEV infection. To achieve this, we used hdWGCNA (high-dimensional Weighted Gene Co-expression Network Analysis) to detect the corresponding functional programs in specific cell types60. We identified modules related to learning, memory, and cognition in multiple brain regions in response to JEV infection, including thalamus, cerebral cortex, cerebral nuclei, and hindbrain. The functional modules related to learning, memory, and cognition were significantly correlated with specific neuron subtypes, which included the Meg3+ Neurons in the thalamus (Module SM4, Fig. 9a, Supplementary Fig. 10a-c), the Meg3+ Neurons in the cerebral cortex (Module SM3, Supplementary Fig. 10d-f), the Tac1+ Neurons in the cerebral nuclei (Module SM3, Supplementary Fig. 10g-i), and the Gpr88+ Neurons in the hindbrain (Module SM6, Supplementary Fig. 10j-l). While the above-mentioned cell types tended to be systemically damaged, the other neurons may also be affected, as the individual functional genes related to learning, memory, cognition, or locomotion were downregulated in various neuron subtypes after JEV infection (Fig. 9b, Supplementary Fig. 11a-c). For instance, Ctnx3+ cells, Meg3+ Neurons, and Oxt+ Neurons of the thalamus all downregulated genes including Gnas, Cck, Snap25, and Atp1a3 (Fig. 9b). Comparative analysis revealed that 21 downregulated genes were shared by the learning, memory, and cognition pathways regardless of brain regions (Fig. 9c). Eleven genes of the learning or memory pathway were downregulated in the virus-positive cells in at least three brain regions, with Atp1a3 and Ndrg4 identified in six regions (Fig. 9d). Meanwhile, ten genes of the locomotory behavior pathway were downregulated in the virus-positive neurons in at least three brain regions, with Atp1a3 and Kcnd2 identified in six regions (Fig. 9e). In summary, JEV infection causes extensive neurological disorders through disrupting the expression of functional genes in the neurons.

Predicted Interactions between JEV and host factors. With both virus and host transcriptome detected, we sought to explore JEV and host interactions through integrative analysis. Currently, there exist few systematic studies on the protein-protein interactions (PPIs) between JEV and its host, including experimental or computational prediction. We employed an unsupervised sequence embedding method (doc2vec) and random forest classifier to identify potential PPIs between JEV and human61. A total of 588 human proteins interacting with the virus were predicted (Supplementary Data 9). There were 550 proteins that may interact with NS5 and 490 with NS3 (Supplementary Fig. 12a). By performing pathway enrichment analysis on the host proteins interacting with each viral protein, we found that each viral protein may uniquely perturb 2-7 pathways except for C protein whose interacted host proteins always overlapped with those of the other viral proteins (Supplementary Fig. 12b). The NS5 protein affects pathways related to protein stability and cell cycle mitosis, while NS3 specifically hijacks pathways related to endoplasmic reticulum processing of proteins (Supplementary Fig. 12b). The pathways commonly associated with at least seven virus proteins included antiviral response of interferon-stimulated genes, protein localization to organelles, virus infection, and RNA metabolism. Pathway enrichment of the host proteins potentially interacting with all the virus proteins revealed pathways including response to infections, cellular response to interleukin-4, rhythmic process, and HDAC6 interactions in the CNS (Supplementary Fig. 12c, Supplementary Data 10). Among them, the HDAC6 interaction and Parkin ubiquitin proteasome pathway may be involved in nervous system dysfunction. In the Parkin ubiquitin proteasome pathway, members of the HSP70, Tubulin, and 26S ubiquitin proteasome families were predicted to interact extensively with viral proteins.

Because the PPI network was predicted based on genomic data, we wished to test at the transcriptome level, whether the predicted interactions are accompanied by measurable changes in gene expression. We explored two potential strategies: 1) For genes directly implicated in PPI, we asked whether their own transcripts are significantly up- or down-regulated. 2) For genes that act indirectly, we examined the expression of their downstream targets. We first combined PPI results with our spatial transcriptomics data to identify transcriptional changes due to direct virus-host interaction. Because of the low detection level of JEV mRNAs in the spatial transcriptomic data (Supplementary Data 1), the correlation coefficients between host and viral genes were smaller than 0.25 (Supplementary Fig. 13a). Even using a low threshold (|correlation coefficient| >0.2), less than 70 host genes were found to be correlated with virus gene expression in a defined cell type and only 1-3 genes identified in the PPI were included (Supplementary Fig. 13b). When we used the segmented cell data of Sample D5_1, the number of host genes identified in a single cell type correlated with virus replication increased (|correlation coefficient| >0.2) but the overlap with PPI remained minimal (Supplementary Fig. 13c). Similar results were obtained when we conducted correlation analysis (|correlation coefficient| >0.3) using the external scRNA-seq data (GSE237915)24, although the number of overlapped genes increased (Supplementary Fig. 13d,e). Unfortunately, no genes were consistently identified across the three datasets.

While the low capture rate of mRNAs due to technical limitations may mask transcriptomic changes, it’s also possible that the viral proteins may hijack host transcription factors to influence downstream mRNA expression indirectly. For instance, the NS5 protein of Dengue virus can bind the PAF1C complex, thereby affecting the expression of downstream interferon-related genes62. Therefore, we intersected the PPI results with known transcription factors, revealing 59 transcription factors that interact with JEV proteins (Supplementary Fig. 13f) along with their downstream target genes. Because the thalamus has the highest virus infection rate among different brain regions, we decided to conduct host-virus interaction analysis using cells of the thalamus. Based on the bin35 data of the thalamus, 18 target genes correlated with viral gene expressions were identified (Supplementary Fig. 13g, Supplementary Data 11), which were regulated by 13 transcription factors. A total of 158 target genes with expression correlated with viral genes were identified in the external scRNA-seq dataset (GSE237915)24, regulated by 20 transcription factors (Supplementary Fig. 13g, Supplementary Data 11). There were 13 transcription factors and 14 target genes shared by these two datasets (Supplementary Fig. 13g), with NS3 and NS5A interacting with nearly all these 13 transcription factors (Fig. 10a). The regulatory network revealed Nfkb1, Sp1, Stat1, and Stat3 as hub transcription factors and Cdkn1a, Mt1, and Cxcl10 as hub target genes (Fig. 10b). The 14 common target genes were enriched in pathways that positively regulate monocyte chemotaxis and spinal cord injury, among which Cxcl10, Gfap, Ccl2, Mbp, and Tspo are common to both pathways (Fig. 10c). Cxcl10, Gfap, Ccl2, and Tspo showed a positive correlation with viral gene expression (Fig. 10d, Supplementary Fig. 14a), while Mbp displayed a negative correlation (Supplementary Fig. 14a). We further assessed the mRNA expression levels of Gfap, Tspo, and Mbp by RT-qPCR. The expression of Gfap and Tspo began to increase at 3 dpi, reached a peak at 5 dpi, and then slightly decreased at 7 dpi, although levels remained higher than those observed at 3 dpi (Supplementary Fig. 14b,c). In contrast, the expression of Mbp exhibited a continuous decline following JEV infection (Supplementary Fig. 14d). These results strongly suggest that the neuronal death might result in a loss of myelin basic protein (MBP) and cause potential demyelination and gliosis. Luxol Fast Blue (LFB)-Eosin staining of the myelin sheaths showed that the blue staining of myelin in JEV mouse brains collected on 5 and 7 dpi was noticeably reduced, indicating significant myelin loss (Supplementary Fig. 14e). The above results predict key molecules involved in the host response to JEV infection.

Ly6c2+ monocytes and Ackr1+ endothelial cells synergize to fuel JE. Our data suggest a central role of JEV-infected Ly6c2+ monocytes in spreading the virus to the CNS and initiating encephalitis (Fig. 10e). In addition to being the primary viral carriers into the CNS, these cells represent the predominant immune population in the infected brain, where they secrete abundant inflammatory mediators like IFN-γ, orchestrating neuroinflammation and modulating cell deaths, particularly pyroptosis. The vascular network, especially those associated with the meninges and choroid plexus, is distributed throughout the brain, where it normally shields neural tissue and delivers nutrients. Upon infection, this same network is converted into a sensor and amplifier of immune signals: Ackr1+ endothelial cells become inflamed, BBB permeability increases, and both virus and immune mediators disseminate more freely. Intense immune cell infiltration and cell damage follows; once vessels are injured, nutrient transport is impaired and post-acute repair is dampened, culminating in severe neurological deficits and death. During JE development, the interplay between Ly6c2+ monocytes and Ackr1+ endothelial cells plays a dual role, mediating host defense against viral infection while simultaneously driving tissue destruction. The resolution of inflammation targeting these two cell types within the vascular system may be essential in mitigating JE progression.

Methods
Ethics statement. All animal experiments were approved by the Institutional Animal Care and Use Committee of Tsinghua University and conducted following their guidelines (approval number: 22-CG3) and Beijing Genomics Institute, Shenzhen, China (BGI-IRB A21005). Human blood samples for monocyte isolation were collected from two healthy volunteers at Tsinghua University Hospital. Both were Tsinghua University students, aged 18–20, one male and one female, who were physically and mentally healthy, had no history of medication use in the past two months, and participated in this study voluntarily. The collection and use of these samples were approved by the Medical Ethics Committee of Tsinghua University (approval number: THU01-20240062), and written informed consent was obtained from all participants.

Cells and virus. C6/36A cells (Aedes albopictus lava cells) were grown in RPMI 1640 medium (Cat #11875093, Gibco) supplemented with 10% fetal bovine serum (FBS; Cat# 16000-044, Gibco) and maintained at 28°C. Vero cells (African green monkey kidney cells) were raised in minimum essential medium (MEM; Cat# 11095080, Gibco) supplemented with 5% FBS. JEV Beijing strain-1 (JEV) was propagated in C6/36 cells and titrated on Vero cells by plaque assay as previously reported107. The Vero cells and C6/36 cells were purchased from the American Type Culture Collection (ATCC, Cat# CCL81 and CRL-1660).

Mice. Four-week-old female BALB/c mice (BALB/cAnNCrl, Charles River) were obtained from Beijing Vital River Corporation. Female mice were used because they are more docile, easier to handle, and less likely to injure or kill each other when co-housed. The mice were bred and maintained in a specific pathogen-free animal facility at Tsinghua University, which is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC). Specifically, the temperature in the animal facility was maintained at 20–22°C, and the relative humidity was kept at 40%–50%. A 12-hour light/12-hour dark cycle was employed. All mice had ad libitum access to standard chow and sterile water.

Establishment of a murine JE model. Murine models of JE have been well-characterized and widely accepted for investigating JEV infection and JE pathogenesis, with many advantages over other animal models108,109. BALB/c mice are a widely used immunocompetent inbred strain. Upon JEV infection, they display clinical symptoms and brain neuropathology similar to those seen in human JE. Their docile nature and affordability also make them a popular choice for establishing JEV infection models108,110. Typical intraperitoneal challenge doses range from 105 to 108 PFU10,111–113. To better characterize the spatiotemporal features of JE, we chose a higher dose within this range to improve consistency and incidence of JE in our study. Accordingly, four-week-old female BALB/c mice (BALB/cAnNCrl, Charles River) were randomly divided into two groups: a control group and a JEV infection group. Mice in the JEV infection group were intraperitoneally injected with 106 PFU of the JEV Beijing-1 strain in 200 μl of PBS per animal, while mice in the control group received an equal volume of PBS. Body weight, clinical signs and mortality were monitored daily. JEV RNA was detected in the blood as early as 1 dpi, reaching a peak on 3 dpi (Supplementary Fig. 1a). While the detection of JEV RNA in blood at 1 dpi may reflect residual input virus, the peak of viremia marked a period when the virus could more readily invade the brain, establish infection, and induce encephalitis. Consistent with these findings, JEV RNA became detectable in the brain from 3 dpi, with brain viral loads continuing to rise thereafter (Supplementary Fig. 1b). At the same time, disease symptoms were observed in mice starting from 4 dpi (Supplementary Fig. 1c). Both the incidence and severity of illness subsequently increased, characterized by a progression from sluggishness and ruffled fur to hunched posture, limb paralysis, and ultimately death. Infected mice exhibited a decline in body weight, likely attributable to reduced appetite or inability to feed, with the average body weight of the JEV group falling below that of controls from 6 dpi (Supplementary Fig. 1d). This difference became most pronounced by 12 dpi, coinciding with the peak symptom scores (Supplementary Fig. 1c). The JEV-infected mice began to die on 8 dpi, with most deaths occurring between 11 and 13 dpi, reaching a mortality rate of 75% (Supplementary Fig. 1e). H&E staining of the brain tissue further revealed progressive immune cell infiltration, glia cell proliferation and tissue damages from 3 to 7 dpi (Supplementary Fig. 1f), underscoring the exacerbation of neuropathology. Collectively, these data showed a strong correlation between viremia, viral loads in the brain, clinical sign scores, changes in body weight, and survival outcomes, all of which closely parallel viral infection and disease progression within the CNS of JEV-infected mice.

Assessment of clinical signs following JEV infection. To quantitatively evaluate the severity of JEV infection114, clinical signs were scored based on the following scale: 0, healthy; 1, sluggishness; 2, partially ruffled fur; 3, wholly ruffled fur; 4, hunched back; 5, paralysis of one limb; 6, paralysis of two limbs; 7, paralysis of more than two limbs; and 8, death.

Animal infection and tissue sampling. Four-week-old female BALB/c mice (BALB/cAnNCrl, Charles River) were intraperitoneally (i.p.) injected with 106 PFU of JEV in 200 μl of PBS or 200 μL of PBS in control mice. Body weight, clinical sign scores, and survival rates of the mice were monitored daily. Tail blood samples were collected from 1 to 7 dpi to assess viremia. In a parallel cohort experiment, whole brain tissues from JEV-infected and control mice were harvested at 3, 5, and 7 dpi. First, the mice were anesthetized by intraperitoneal injection with 300 μL of 2% (w/v) Avertin solution (Cat# HY-B1372, MCE), followed by cardiac perfusion with ice-cold PBS to thoroughly remove the residual blood. After cardiac perfusion, the whole brain tissues were taken out for immediate cryo-embedding with OCT compound (Cat# 4583, SAKURA) or immersed in 4% paraformaldehyde (PFA) (Cat# P1110, Solarbio) for embedding with paraffin. Afterwards, brain tissues were stored at −80°C for subsequent cryosectioning or at 4°C for paraffin embedding. OCT-embedded and paraffin-embedded brains were sagittally sectioned at 6 μm for immunofluorescence assays and H&E staining. Additionally, OCT-embedded brains were sectioned at 10 μm for Stereo-seq experiments.

Quality control of OCT-embedded samples. For each OCT-embedded sample, 30-40 sections (100-200 μm thick) were cut and harvested to extract total RNA using the RNeasy Mini Kit (Qiagen, USA) according to the manufacturer’s protocol. The RNA integrity number (RIN) was examined by a 2100 Bioanalyzer (Agilent, USA) and samples with RIN≥7 were selected for downstream experiments. For each sample, one tissue section (10 μm thick) with a large coverage along the anterior-posterior axis containing the hippocampus was preserved for Stereo-seq experiment. One adjacent section from each sample was applied to H&E staining (Beyotime Biotechnology, China) for pathohistological observation.

H&E staining. For staining brain sections with OCT embedding, slides were first rinsed with PBS to remove OCT embedding medium. For staining brain sections with paraffin embedding, slides were first dewaxed in xylene, rehydrated in a decreasing concentration gradient of ethanol, and rinsed in deionized water. Afterward, hematoxylin and eosin were used to stain the sections. After staining, the sections were dehydrated in an increasing concentration gradient of ethanol, transparentized with xylene, and sealed with neutral resin.

Immunofluorescent staining. A previously described immunofluorescent staining method was exploited114. Briefly, mouse brain sections were subjected to membrane permeability with 0.1% (v/v) Triton X-100 (Cat# P0096, Beyotime) in PBS for 10 min and blockage with 1% bovine serum albumin (BSA) (Cat# A1933, Sigma) for 1 h; then, they were incubated with primary antibodies at 4°C overnight and incubated with Alexa Fluor 488 or Alexa Fluor 594-labeled secondary antibodies at room temperature for 1 h. The primary antibodies included anti-Claudin-5 antibody (1:300, Cat#AF0130, Affinity Biosciences), anti-ZO-1 (TJP-1) antibody (1:300, Cat# AF5145, Affinity Biosciences), anti-ICAM-1 antibody (1:300, Cat#AF6088, Affinity Biosciences), anti-VCAM-1 antibody (1:300, Cat# DF6082, Affinity Biosciences), anti-CXCL9 antibody(1:300, Cat# PA5-81371, Invitrogen), anti-CXCL10 antibody (1:300, Cat# DF6417, Affinity Biosciences), anti-MCP-1(CCL2) antibody (1:300, Cat# DF7577, Affinity Biosciences), anti-GSDMD-N terminal antibody (1:300, Cat #DF13758, Affinity Biosciences), anti-phospho-MLKL (ser347) antibody (1:300, Cat# AF3903, Affinity Biosciences), anti-DARC (ACKR1) antibody (1:300, Cat# AF6695, R&D systems), anti-mouse Ly6c2 (Ly6C) antibody (1:300, Cat# 128001, BioLegend), anti-IBA1 antibody (1:300, Cat# GB15105-50, Servicebio), and anti-MCP-5 (CCL12) antibody (1:300, Cat# GB112194-50, Servicebio). The fluorophore-labeled secondary antibodies include Alexa Fluor 488-labeled donkey anti-mouse IgG antibody (1:300, Cat# A21202; Thermo), Alexa Fluor 488-labeled donkey anti-sheep IgG antibody (1:300, Cat# A11015, Thermo), Alexa Fluor 488-labeled goat anti-rat IgG antibody (1:300, Cat# A48262TR, Thermo), and Alexa Fluor 594-labeled donkey anti-rabbit IgG antibody (1:300, Cat# A21207, Thermo). The immunofluorescent staining image for a specific target protein was collected under fixed excitation light intensity and exposure time. For each mouse, the most representative immunofluorescent images were shown.

Flow cytometry. At days 3, 5, and 7 post-JEV infection, both virus-infected and control mice (BALB/cAnNCrl, Charles River) were euthanized by intraperitoneal injection with 300 μL of 2% (w/v) Avertin solution followed by transcardial perfusion with PBS. Afterwards, their brains were dissected and dissociated into single-cell suspensions for analysis of infiltrating immune cell populations by flow cytometry. Briefly, mice were first euthanized and transcardially perfused to remove residual blood in vascular vessels. Then, a 100-mg sample from the mid-sagittal section of the brain was finely minced into 0.5–1.0 mm³ fragments and digested in 3 mL of digestion solution containing 1 mg/mL papain (MCE, HY-P1645), 1 mg/mL dispase II (MCE, HY-131577), and 0.5 mg/mL DNase I (MCE, HY-108882) at 37°C for 40 min with gentle rotation. Digestion was terminated by adding pre-chilled Flow Staining Buffer (FSB) (Cat# TNB-4222-L500, TONBO). The cell suspension was then filtered through a 70 μm cell strainer and centrifuged at 500 g for 10 min at 4°C. Cells were washed three times with FSB and resuspended in 100 μL FSB containing relevant antibodies for 30 min incubation at 4°C. Cells were then washed twice with PBS and permeabilized by incubation with Fixation/Permeabilization Solution (Cat# 554722, BD) at 4°C for 20 min. Afterwards, cells were washed twice with PBS and incubated with Elab Fluor Violet 610 Anti-Mouse IFN-γ Antibody [XMG1.2] (1:200, Cat# E-AB-F1101UT, Elabscience) and anti-JEV antibody labeled with Mix-n-Stain CF647 (1:200, Cat# MX647S100, Sigma) at 4°C for 40 min for intracellular staining of IFN-γ and JEV antigens. Afterwards, cells were washed twice with PBS and fixed with 2% PFA. Finally, the fixed cells were analyzed using a Sony Spectral Cell Analyzer ID7000 System. Besides the above two antibodies, other antibodies used in flow cytometry included Ghost Dye UV450 (1:200, Cat# 13-0868-T100, TONBO), FITC anti-mouse CD45 (30-F11) (1:200, Cat# 35-0451-U100, TONBO), violetFluor 450 anti-human/mouse CD11b (M1/70) (1:200, Cat# 75-0112-U100, TONBO), PE-Cyanine7 anti-mouse F4/80 Antigen (BM8.1) (1:200, Cat# 60-4801-U100, TONBO), redFluor 710 anti-mouse Ly-6G (1A8) (1:200, Cat# 80-1276-U100, TONBO), PerCP-Cyanine5.5 anti-mouse Ly-6C (HK1.4) (1:200, Cat# 65-5932-U100, TONBO), PE anti-Mouse CD3e (145-2C11) (1:200, Cat# 50-0031-U025, TONBO), and APC anti-mouse CD49b (HMa2) (1:200, Cat# 20-0491-U025, TONBO). The proportions of immune cells were expressed as percentage (%) of living brain cells (Ghost Dye UV450−). Cell populations were gated as follows: immune cells, CD45+; T cells, CD45+CD3e+; natural killer cells, CD45+CD49b+CD3e−; monocytes, CD45+CD11b+Ly6C+Ly6G−; macrophages, CD45+CD11b+F4/80+. Furthermore, JEV-infected cells were gated as JEV+ and IFN-γ secreting cells were gated as IFN-γ+ (Supplementary Fig. 15).

Reverse transcription quantitative polymerase chain reaction (RT-qPCR). Total RNA was extracted from brain tissue using the AxyPrep Multisource Total RNA Miniprep Kit (Cat# AP-MN-MS-RNA-250, Axygen). Complementary DNA (cDNA) was synthesized from total RNA with the iScript cDNA Synthesis Kit (Cat# 170-8890, Bio-Rad). Quantitative real-time PCR was performed using gene-specific primers (Supplementary Data 12) and the iTaq Universal SYBR Green Supermix (Cat# 1725121, Bio-Rad) on a Bio-Rad CFX96 Touch Real-Time PCR Detection System. Gene expression levels were normalized to Gapdh. Specifically, the genomic copies of JEV in virus-infected mouse brains were quantified by absolute RT-qPCR and expressed as copies per μg total RNA, using a standard curve generated from JEV standards of known copy number.

Evaluation of blood-brain barrier (BBB) permeability in mice. BBB permeability in control and JEV-infected mice (BALB/cAnNCrl, Charles River) at 1, 3, 5, and 7 dpi was assessed using a previously described Evans blue (EB)-based assay114. Briefly, mice were intravenously injected with 200 μL of 1% (w/v) EB solution (E808783; Macklin) in PBS. After a 30 min circulation period, mice were anesthetized with 200 μL of 2% (w/v) Avertin. Subsequently, the mice were transcardially perfused with 30 mL of PBS to thoroughly eliminate residual EB from the circulation. Brains were harvested, weighed, and homogenized in dimethylformamide (DMFA) at a ratio of 200 mg tissue to 500 μL DMFA. The homogenates were incubated at 60°C for 24 hours and then centrifuged at 12,000 g for 10 min. A 100 μL aliquot of the resulting supernatant was transferred to a 96-well plate, and absorbance was measured at 620 nm. The content of EB in the brain tissue was determined based on a standard curve and expressed as μg of EB per g of brain tissue.

Myelin staining in mouse brain. Myelin in mouse (BALB/cAnNCrl, Charles River) brain sections was visualized using a previously described Luxol Fast Blue (LFB) staining protocol115. Briefly, paraffin sections were dewaxed and rehydrated through sequential immersion in xylene I (20 min), xylene II (20 min), absolute ethanol I (10 min), absolute ethanol II (10 min), 95% ethanol (5 min), 90% ethanol (5 min), 80% ethanol (5 min), and 70% ethanol (5 min), followed by rinsing in distilled water. The sections were then incubated in 0.1% LFB solution (Cat# L718215, Macklin) at room temperature overnight. After rinsing with distilled water, differentiation was performed by alternating immersions in 0.1% (w/v) lithium carbonate solution and 70% ethanol under microscopic observation, until a sharp contrast was achieved between the blue-stained white matter and the pale or colorless gray matter. Following a distilled water wash, sections were counterstained with 0.1% eosin for 1 min, rinsed, and air-dried. Finally, slides were cleared in xylene for several minutes, mounted with neutral resin, and imaged using a microscope.

Isolation of human and murine peripheral monocytes. Human and murine (BALB/cAnNCrl, Charles River) monocytes were isolated from peripheral blood using a monocyte isolation kit (Cat# P8680, Solarbio). Briefly, peripheral blood samples were diluted 1:1 with PBS. In a sterile centrifuge tube, a gradient interface was formed by layering Reagent A followed by Reagent D (volume ratio 3:2). The diluted blood was carefully overlaid onto the gradient. Following centrifugation at 600g for 30 min at room temperature, the white mononuclear cell layer was aspirated and washed with PBS. Cells were resuspended in RPMI1640 medium (Cat# 22400089, Gibco) with 2% FBS (Cat# 16000-044, Gibco), seeded into 96-well plates (10^5 cells/well), and incubated at 37°C for 2 h. After gentle shaking and supernatant removal, cells were washed twice with PBS. Adherent cells were identified as monocytes.

Measurement of IFN-γ levels in JEV-infected human and murine monocytes. Human and murine (BALB/cAnNCrl, Charles River) monocytes were infected with JEV at a multiplicity of infection (MOI) of 1 for 2 hours. Following infection, cells were washed PBS and incubated in fresh RPMI1640 medium supplemented with 2% FBS for an additional 48 hours. Subsequently, cell culture supernatants and lysates were collected. IFN-γ levels in the supernatants were quantified using ELISA kits (Cat# E-EL-M0048, Elabscene; Cat# KIT11725A, Sinobio) following the manufacturer's instructions. The RNA levels of IFN-γ in cell lysates were determined by RT-qPCR.

Stereo-seq library preparation and sequencing. To capture the orthoflaviviruses, we synthesized a virus-specific probe (VSP) targeting a conserved region in the NS5 gene of JEV (5’->3’: AACATGATGGGAAAAAGAGA). The VSPs were mixed with polyT probes at a ratio of 1:10 on the Stereo-seq chips. The spatial transcriptomics experiment was performed using the STOmics gene expression assay kit (BGI Research Shenzhen, China). The frozen brain tissue sections were placed onto the Stereo-seq chip surface patterned with DNA nanoballs (DNBs, i.e., spot) and incubated at 37ºC for 3 minutes. Then, the sections were fixed in methanol and incubated for 40 minutes. After that, the sections were stained with nucleic acid dye (Thermo Fisher, Q10212) and imaging was performed with a Motic microscope scanner (Motic, China) at the channel of FITC. After washing off the staining solution, the sections were permeabilized for 12 minutes in an incubator at 37°C to allow mRNA in situ hybridization. Reverse transcription (RT) was performed at 42°C for 1 hour following mRNA capture. The RT products were subsequently subjected to cDNA release enzyme treatment overnight at 55°C. Then the released cDNA was purified using DNA clean beads and amplified with PCR mix. The PCR products were used for library construction and finally sequenced with a 50 + 100 bp strategy on an MGI DNBSEQ T series sequencer (MGI, China).

Preliminary processing of Stereo-seq data. Stereo-seq raw data were automatically processed using the SAW pipeline116, where the reads were sequentially processed by barcode demultiplexing, adapter filtering, reference mapping, deduplication, and quantification. The reference genome was a combination of the mouse (Mus_musculus, GRCm38) and the JEV (Accession Number: L48961) reference genome. To enhance resolution and ensure that each analytical unit contains more than 200 genes, we merged adjacent DNBs to one bin35 (35 x 35 DNBs, i.e., 17.22 x 17.22 μm). For quality control, the bins with UMIs fewer than 100 and those genes expressed in fewer than three cells were removed. Then the data were normalized, logarithmically transformed, and scaled by Scanpy (version 1.9.1)117. Dimension reduction was performed using PCA. Unsupervised clustering of bins was performed using leiden with default parameters in Scanpy117. Data statistics are shown in Supplementary Data 1.

Cell segmentation. Cell segmentation analysis was performed using StereoCell (version 1.2.6) with default parameters118. The program used the multiple Fast Fourier Transform weighted stitching algorithm (MFWS) to splice the morphologic nucleus staining images of the tissue sections. Briefly, image splicing and registration were performed based on "track lines" (the marking lines designed on the three-dimensional Stereo-seq chip). The morphologic staining image, the corresponding spatial gene expression map, and a deep learning model (U-Net) were used to predict the mask of the cell nucleus to obtain single-cell segmentation. The algorithm (MLCG) based on cell morphology and Gaussian Mixture Model (GMM) was used to obtain the gene expression profile at the single-cell level.

Annotation of cell types in Stereo-seq data. We used SingleR (version 2.0.0) to annotate the cell types of bin35 data, employing the reference datasets provided by SingleR43, which contained both neural cells and immune cells.

Annotation of brain region in Stereo-seq chips. The bin clusters were annotated based on the in situ expression patterns of the brain region marker genes as well as the anatomical information from the Allen Brain Atlas (https://atlas.brain-map.org/). For a cluster spanning two brain regions, it was named by the combination of the two respective brain regions. The clusters covering multiple brain regions sporadically were annotated as Unknown.

Cell annotation of specific brain regions. The expression data of a specific brain region from every chip were extracted and merged using Seurat (version 4.3.0)119. The standard analysis procedures were performed on the integrated gene count matrix, including data normalization, identification of the top 2000 highly variable genes, PCA dimension reduction, batch correction by Harmony (version 1.0)120, and clustering. It is worth noting that to avoid the influence of viral genes on clustering, we excluded viral genes from the highly variable gene list. We then used the `FindAllMarkers` function (log fc.threshold=0.25 and assay="Spatial") to find marker genes for each cluster and used these marker genes for manual cell annotation of these clusters. For secondary clustering of specific cell types of interest (such as immune cells), a similar approach was used by first subsetting the cell population of interest and then repeating the above steps.

Determination of virus infection profile. According to the brain region annotation results, the proportion of virus-positive bins within each brain region was calculated. To filter out possible false positive bins, only bins that contained at least two JEV genes or three viral reads were deemed as JEV-positive.

Identification of differentially expressed genes (DEGs). After normalization, we merged all 12 samples using Seurat's `merge` function and removed batch effects. Differential expression analysis was conducted to identify genes with significant expression changes across the four timepoints: CTRL, D3, D5 and D7. For each pairwise comparison (e.g., D3 vs. CTRL), Seurat's `FindMarkers` function was employed using a Wilcoxon rank sum test. Genes were considered differentially expressed based on a threshold of adjusted p-value < 0.05 and a log2 fold change > |0.25|.

Gene Ontology (GO) enrichment analysis. Lists of DEGs were used as input for the `clusterProfiler` R package (version 4.6.0) to conduct GO enrichment analysis121,122. GO terms in the categories "Biological Process", "Molecular Function", and "Cellular Component" were considered. The `enrichGO` function was utilized to identify overrepresented GO terms in the DEG lists. The Benjamini-Hochberg procedure was applied for multiple testing corrections and the GO terms with a corrected p-value < 0.05 were considered significant. Enrichment analysis and visualization were performed with clusterProfiler121,122. Online website Metascape (https://metascape.org/) was also used for pathway enrichment analysis, after inputting the genes as well as choosing their matching species (H. sapiens or M. musculus), all the analysis was conducted with default parameters.

Gene set scoring analysis. The expression scores for gene sets of specific biological pathways were calculated using the AddModuleScore function in Seurat119 and GSVA (Gene Set Variation Analysis)123. AddModuleScore calculated a score for each bin or cell by obtaining the average expression levels of selected genes and subtracting the aggregated expression of control feature sets that were randomly selected. GSVA analysis was also used to determine whether a gene set of interest was enriched in certain cell types. The scores of interested gene sets were obtained using the “gsva’’ function in the GSVA (version 1.46.0) package with default parameters. The hallmarks for apoptosis, necroptosis, ferroptosis, cuproptosis, pyroptosis, and autophagy were acquired from published articles (Supplementary Data 8)88,124–126. The other gene sets or pathways were acquired from GSEA (https://www.gsea-msigdb.org/gsea/msigdb/mouse/genesets.jsp) website or GO Database. The gene sets used for AddModuleScore and GSVA were the same.

Cell-cell communication analysis. We performed cell-cell interaction analysis using CellChat (version 2.1.0), taking into account the spatial information of bins/cells53. The analysis was conducted using a Mouse database20. We extracted the gene expression profile of each sample from the integrated dataset. The cell type information, expression matrix information, and cell position coordinates of each sample were input into CellChat. Since the diameter of bin35 is approximately 17.5 μm, the parameter "spot.diameter" was set to 17.5. For single-cell resolution data, the "spot.diameter" parameter was set to 10. Next, we utilized the "computeCommunProb" function ("type='truncatedMean'", "trim=0.1", "distance.use=TRUE", and "interaction.range=250") to infer cell-cell interactions between cell clusters. We retained interactions detected in at least 3 bins/cells of each cell cluster. After completing the analysis for each sample, the results were merged to compare across samples, including the ligands involved in cell interactions and their associated pathways. NicheNet (version 1.1.0) was used to infer the target genes influenced by interacting cells based on prior knowledge of potential ligand-receptor and ligand-target resources54.

Identification of microglia subpopulations. We selected those bins that contain the inflammatory associated genes including Cd14, Adgre1, Aif1, Cd68, Cd163, Cx3cr1, Fcrls, Itgam, Itgax, P2ry12, Sall1, Tmem119, Ccr2, and Trem2 for cluster analysis by Seurat. We further used marker genes for microglia (C1qa, C1qb, and Ccl12) to identify clusters with microglial signals and performed a secondary clustering. Finally, we adjusted the resolution to identify subtypes of Ccl12 microglia by differential expression of Il2b, Spp1, and Cxcl13. All the above analyses were conducted in Seurat119.

Gene co-expression module analysis. Gene co-expression module analysis was performed using hdWGCNA (version 0.2.17)60 on the integrated dataset combining the same brain regions from different samples. In brief, this process involved the use of the hdWGCNA function "MetacellsByGroups" to construct metacells separately for each sample and each cell groups, with each metacell encompassing 25 bins. For all cell groups, the following functions were sequentially executed using default parameters: “TestSoftPowers”, “ConstructNetwork”, “ModuleEigengenes”, and “ModuleConnectivity”. After that, the "GetModules" function was employed to extract the genes within each module, and pathway enrichment analysis was conducted on the module genes to determine their functions. Modules enriched in pathways related to learning, memory, cognition, and movement or locomotory behavior were selected, and the corresponding genes for these pathways were identified. Further examination of the expression patterns of these functional genes in different groups was performed within the Seurat object127.

Transcription factor (TF) analysis. Transcription factor regulation analysis was performed using Pyscenic (version 0.12.1)58. In brief, the analysis involved three main steps. Firstly, grnboost2 was employed to identify co-expression networks of transcription factors and genes based on the input cell-gene expression matrix. Secondly, RcisTarget was utilized to search for significantly enriched motifs within each co-expression module, based on its internal gene-motif and motif-transcription factor databases58. The significantly enriched motifs are selected for each module and used to predict target genes. By integrating the TF-gene modules and the predicted target genes, a gene regulatory network module (regulons) was constructed, which included both transcription factors and target genes. Thirdly, AUCell was employed to evaluate the activity of each regulon in each cell/bin, assessing the regulatory activity of each transcription factor and its target genes in different cellular contexts128.

scRNA-seq data and analysis. Single-cell data were obtained from the GEO dataset GSE23791524, which contains a total of 8 samples. We excluded one sample from the healthy group due to the failure in reading the matrix of the sample ‘GSM7656311’. We sequentially read the expression matrices of the remaining 7 samples using Seurat (version 4.3.0)127, retained the cells with mitochondrial ratio below 25% as well as gene number larger than 200, and then merged the 7 samples into an integrated Seurat object using “Merge”. The merged Seurat objects were then normalized, high-variable genes selected, and PCA downscaled according to the default parameters, and the batch effect was removed by harmony (version 1.0)120. Clustering was performed using 'FindClusters', obtaining 13 clusters at a resolution of 0.05. The 'FindAllMarkers' function was utilized to find the marker genes for each cluster to perform cellular annotation, and a total of 11 major cell types were annotated. After normalization, the number of virus reads (labeled as “jev-p3”) in each cell ranged between 0 and 8. Cells with a normalized virus gene count >3.5 were set as virus-positive.

Correlation assessment between scRNA-seq and Stereo-seq datasets. The microglial subsets from scRNA-seq and spatial transcriptomics were analyzed separately using the “FindAllMarkers” function to identify differentially expressed genes, with an adjusted p-value < 0.05 and log fold change (logFC) > 0.25. The enrichment scores for the microglial subtypes across both datasets were calculated using the “enricher” function from the “clusterProfiler” package to assess the correlation between the two datasets121,122.

Host and virus interaction analysis. We used an unsupervised sequence embedding encoding method (doc2vec) combined with a random forest classifier61 to predict the interactions between the 12 proteins encoded by JEV (Accession Number: L48961) and 20,433 human proteins reviewed by SwissProt. To obtain high-confidence results, the specificity parameter was set to 0.95. The database of mouse transcription factor information was collected from the SCENIC software (https://github.com/aertslab/pySCENIC/tree/master/resources), while the information of transcription factors and their target genes was obtained from TRRUST database (https://www.grnpedia.org/trrust/).

Statistical analyses. Details of statistical testing for each analysis are included in the corresponding figure legends. For sequencing data, the R packages “ggpubr” (version 0.5.0) and “rstatix” (version 0.7.2) were used for statistical analysis. The two-sided paired t test or Wilcoxon test was conducted for two paired group comparisons respectively for normally distributed data or not normally distributed data. Spearman test was performed for correlation analysis. For experimental validation data, quantitative data that were normally distributed with homogenous variance are presented as mean ± SEM, whereas data not conforming to a normal distribution are presented as medians. All statistical analyses were performed using GraphPad Prism (version 10.1.2). Comparisons between two groups were conducted using either an unpaired two-tailed t-test (parametric and homogenous variance) or the Mann-Whitney test (nonparametric or parametric but heterogenous variance), as appropriate. For comparisons involving three or more groups, one-way analysis of variance (ANOVA), two-way ANOVA (for murine viremia), and post hoc multiple t-tests were employed. Survival curves were compared using the log-rank test. Statistical significance was defined as p < 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary: Ou et al. utilized spatial transcriptomics to examine gene expression dynamics in mouse brains infected with JEV at 3, 5, and 7 d post-infection. By employing both a polyT probe for host transcripts and a JEV-specific probe targeting the NS5 region, the authors reported temporal and spatial mapping of viral and host gene expression. Their bioinformatics analyses revealed widespread viral dissemination across multiple brain regions, including the thalamus and cortex. Monocytes were identified as the initial targets of infection, followed by involvement of various cell types, including endothelial cells. The study described dynamic, time-dependent transcriptional changes, with immune-related gene upregulation particularly enriched in vascular endothelial cells. The authors propose that Ackr1⁺ endothelial cells facilitate immune cell recruitment and amplify inflammation, while Ccl12⁺ microglia are activated and expand in response to infection. They also reported increased necroptosis and pyroptosis in the meninges and neurons, contributing to tissue damage. Additionally, host factors potentially associated with JEV-induced neurological dysfunction were suggested. Overall, the authors propose a model in which monocytes and endothelial cells synergistically drive disease progression.\n\nComments:\n1) Descriptive nature of the study: While the study employs a comprehensive suite of bioinformatics tools, the conclusions are based entirely on transcriptomic data. The functional roles of key cell types (e.g., Ackr1⁺ endothelial cells and Ccl12⁺ microglia) are inferred rather than experimentally validated. Functional assays are essential to confirm the biological relevance of these findings, particularly for publication in a high-impact journal.\n2) Novelty of the proposed model: The proposed model—emphasizing endothelial activation, blood-brain barrier disruption, monocyte infiltration, and immune-mediated cell death—aligns with existing paradigms in JEV pathogenesis. The manuscript would benefit from a more explicit discussion of how these findings refine or extend current models.\n3) Route of virus inoculation: The use of intraperitoneal inoculation raises concerns, as it does not mimic the natural route of JEV transmission via mosquito bite. The authors should justify this choice and discuss its implications for disease modeling and relevance to natural infection.\n4) Spatial resolution and validation: The spatial resolution of the transcriptomics platform may not be sufficient to resolve cell-type-specific expression in complex brain regions. Key spatial findings should be validated using other techniques such as in situ hybridization or immunohistochemistry.\n5) Sample size and reproducibility: The study includes only three mice per time point, which limits statistical power and raises concerns about reproducibility. Have the authors validated their findings in independent cohorts?\n6) Cell type annotation: Cell type identification based on transcriptomic signatures can be ambiguous, especially in infected tissues where gene expression is altered. Were annotations cross-validated using canonical markers?\n7) JEV probe and viral RNA detection: Lines 111–115: This reviewer is somewhat confused. JEV expresses only its genomic RNA as mRNA and does not produce multiple distinct mRNAs. Also, the limitations of spatial transcriptomics in quantifying viral load should be acknowledged.\n8) Translational relevance: Findings from a mouse model may not fully translate to human JEV infection due to species-specific differences in immune responses and brain architecture. Are any human data or comparative analyses included to support translational relevance?\n9) Taxonomic nomenclature: Line 90: The manuscript should reflect the updated taxonomy, noting that JEV is now classified under the Orthoflavivirus genus, in accordance with current nomenclature recommendations.", "reviewer_2": "In the paper,” Spatial transcriptomics elucidates the central role of cerebral vasculatures in the progression of Japanese encephalitis” Ou et al investigate the temporal dynamics of the immune response in the brain following peripheral JEV infection in a mouse model using spatial transcriptomics. The basic experimental setup is sound, including appropriate controls, the different time points and three biological replicates per time point. They detect virus infection in monocytes the brain at 3 days post i.p. inoculation and the virus then spread to other cell types during day 5 and 7. The results show the dynamics and transient nature of interferon and immune responses in the brain, highlights the importance of the meninges and identifies several infiltrating/activated cell populations that likely play an important role in driving immunopathology during JEV infection. This study is well performed and adds significantly to the current knowledge about flavivirus infection in the CNS of mice. While the experimental setup and analysis is sound, the study is missing some complementary experiments to validate key observations to further strengthen the conclusions. For example, fluorescence imaging of brain slices or FACS analysis of infiltrating immune cells. These analyses are important to verify the transcriptional findings as RNA and protein levels are not always comparable. Additionally, if the study was better put into the context of similar studies for other neurotropic viruses this would add meaning to the results and facilitate for the reader to understand the importance of the results presented.\n\nMajor:\n- Please add clinical characteristics (weight loss/symptoms) for the model used (mouse strain, inoculation route and infectious dose) since now it is difficult to correlate how the observations correlate with disease progression, especially the apparent reduction in inflammatory response seen between day 5 and day 7.\n- Complementary experiments validating some of the key observations are necessary.\no Please validate the immune cell infiltration in the brain Fig 2e with FACS analysis.\no Please show the Necroptosis and Pyroptosis in brain slices with immunofluorescence imaging.\no Verify the high infection rate of the immune cells by FACS analysis.\no Quantify the IFN response with Q-PCR over time to see that the slices are representing the brain.\no Verify the localization of tight junction proteins, cell adhesion molecules and chemokine receptors over time using fluorescent imaging of brain slices.\n- On line 227 authors write that disruption of the BBB is crucial for immune cell infiltration and virus invasion. However, there is no data supporting this claim although RNA data indicate it. Please investigate the BBB integrity with fluorescent dextran or Evans blue 3, 4, 5, and 6 days post infection in combination with FACS analysis of infiltrating immune cells and viral load with q-PCR.\n- The study and results need to be better put into the larger context of neurotropic virus infection both in the introduction and the discussion. For example, the virus distribution and cellular tropism (Fig 1c-d), the role/importance of inflammatory response in the meninges and the specific cell populations highlighted to potentially be driving the inflammatory response. There are several reports that the choroid plexus is highly responsive to virus infection by upreglating type I IFNs.\n\nMinor:\nRow 66-67: “Without 67 timely treatment, the condition progresses to encephalitis…”, makes it sound like treatment can prevent encephalitis, rephrase, or clarify which treatment with reference.\nRow 92-97: Mention the model system used (mice).\nRow 116-117: Add rout of infection in the main text line, for clarity.\nRow 123-125: Mention if any cells were positive for JEV in the control as QC/validation of the method/threshold selected.\nRow 191-194: Add statistical test and indicators of significance in the figure (Fig 2e) to support this statement.\nRow 194-196: “We also observed increased signals of endothelial cells (Fig. 2e), 195 which was probably due to vascular dilation caused by immune infiltration or tissue 196 edema, instead of cell proliferation” – elaborate what this statement is based on.\nRow 199-200: “JE progression” indicates worsened disease, requires clinical scoring or similar for the mice, else rephrase to immune response.\nRow 210-213: Cell population mentioned as significant in the text differs from those labeled as significant in the figure (Fig 3c)\nRow 220-222: Strengthen some of these observation with e.g. IF (Fig 3c).\nRow 287-288: “Only necroptosis and pyroptosis were found to be associated with JEV infection (Fig. 5a-d, Extended Data Fig. 6a-d)”, unclear which figure is showing the association only with necroptosis and pyroptosis. Method/statistical test to measure association?\nRow 299-300: Mentions different regions to be highly infected than previously (row 131), hindbrain instead of cerebral cortex.\nRow 369-383: Difficult section with no clear message/result.\nRow 409- 434: Can this section be shortened and more result-focused, briefly describing how the results support the suggested model.\nFig 1a: Reduce repetition of methodology.\nFig 1b: consider including a sketch of a brain slice to highlight some of the main brain regions mentioned in the text to guide the reader. Can the slices be rotated/aligned in a way to make the data more accessible?\nFig 2a-b: Why is the pathway analysis for upregulated genes divided by timepoint (2a) but downregulated genes all time points are grouped together (2b)?\nFig 2c: Important figure that is very difficult to read/interpret. Consider making the scale the same across all conditions for one gene and have the legend only once. Make text/legends larger/readable. Highlight in the figure legend that the coloring represent the brain parts as described in Fig1b.\nFig2f: Avoid implying results in the figure legend. Why is only one sample chosen here, and additionally a sample with a much higher intensity flow for the CCL pathway than the other two samples from that time point (D7_1, Fig 2d). Would it not be better to pool all samples from d5 and/or d7 or make an average?\nFig 7c: Change the colouring, the yellow letters on white background is very hard to see.\nFigures general: Ensure all text is readable and improve the style formatting of headings/legends, e.g. change “_” for spaces, capitalize the first letter etc.", "reviewer_3": "The manuscript presented by Zhihua Ou and colleagues presents for the first time a spatial atlas of the neuropathology caused by the Japanese Encephalitis Virus. Firstly, I would like to commend the authors for providing such as concise and well-presented manuscript, with some novel insights derived from their dataset. I would also like to apologise for the time that took me to review this manuscript (personal circumstances precluded me from doing this sooner).\n\nThis is an incredible resource for the scientific community, as it provides novel insights about the expression pattern of candidate genes of interest that could be the focus of much needed therapeutic applications. The comments below are geared towards increasing the impact of the findings presented here, and I hope the authors find it useful. My major recommendations are to include independent validation of potential key findings (e.g., immune infiltrates, changes in vascular permeability, gliosis, demyelination) to increase the impact of the manuscript. These are listed in my questions below:\n\nLine 105: How was the 1:10 ratio chosen to create the virus-compatible Stereo-seq chip?\nLine 130: Does the accumulation of viral transcripts in these brain regions correlate with what is known in the field? In other words, can these observations be used to benchmark the newly developed platform?\nFigure 1d: Is it not surprising to find adipocyte signatures in the brain? Can these cells be mesenchymal progenitors wrongly annotated as such? What are the markers genes used to identify the cell types in this figure? As far as I know there are not adipocytes in the brain parenchyma.\nFigure 2a: The gene signatures observed here are quite consistent with potential adaptive immune cell infiltrates into the brain parenchyma, which is not surprising due to the viral infection going on. Is this known in JEV infection? If not, I think a validation of these findings by flow cytometry and/or IFA would be advantageous and would certainly elevate the paper. In this regard, it would be interesting to determine whether: i) the dynamics of T and NK cell accumulation in the brain parenchyma (as shown in Figure 2e), and ii) which of these subsets produce IFNg?\nFigure 2c: It is interesting to see that the genes associated with lymphocyte trafficking and recruitment (Cxcl10, Ccr2, Ccl5) seem to precede IFN-mediated signatures. Have the authors try to run a pseudotime analysis to better resolve the order of events leading to the robust IFN signature observed in the brain?\nLine 199: The choroid plexus also seems to display inflammatory signatures, in particular at d7pi (Figure 2e). Given the possibility that infected cells might exploit both routes to colonise the brain parenchyma, I wonder if the authors have compared meninges vs choroid plexus to determine region-specific differences, or lack of?\nLine 220: I find it intriguing to see so many chemokine interactions with Acrk1, which is thought to bind to CXCL12. Is this expected? Have the authors considered validating these findings using imaging techniques (e.g., IFA or RNAscope) to determine if Cxcl9/10+ cells cluster around Ackr1+ ECs in JEV-infected brain specimens?\nGiven that this manuscript is likely to set a standard for others in the field trying to replicate these findings, or use similar methods for their own studies, I’d encourage the authors to include a supplementary figure highlighting the optimisation workflow. For instance, if possible, I’d like to see the permeabilization time course and the bioanalyzer profile of the resulting RNA/libraries, etc. This could be really informative to the scientific community\nLine 289: The choroid plexus also shows cell death signatures observed in the meninges, but overall, the signals shown in figure 5b and 5d are not the most convincing are certainly not limited to the regions described in the text. For instance, there is a clear enrichment for pyroptosis-related genes in the brainstem area and the midbrain, whereas the necrosis seems more prominent in the external capsule. I believe this is further discussed in figure 5. I believe that validating these findings in independent experiments would be extremely informative to determine whether various types of cell death occur in a region-specific manner.\nLine 405: The results presented here are indeed quite interesting and might suggest that the neuronal death results in a loss of MBP (and potential demyelination) and gliosis. Given that this is critical to understand the neuropathology of JEV infection, I think some validation using independent samples of i) demyelination using Luxol Fast blue (or related) and ii) gliosis would be critical.\nFigure 7: Does the vascular changes observed at the transcriptional level correlate with changes in vascular permeability? I think experiments using labelled dextran with various molecular weights would help clarify whether the overall vascular permeability changes in response to infection.\nLine 413: gene symbols in this line are not italicised. Please amend."}, "author_response": "Dear reviewer, thank you very much for this constructive feedback. In accordance with your and the other reviewers’ recommendations, we have now performed a very comprehensive validation of the key sequencing findings using multiple experimental techniques, specifically including:\n(1) validate the infiltration (Fig. 2e), JEV infection (Supplementary Fig. 2b-e), and IFN-γ secretion (Fig. 2f-k) of different types of immune cells in the mouse brain using flow cytometry;\n(2) validate the IFN-γ secretion function of monocytes by conducting JEV infection experiment on cells isolated from human and mouse PBMCs (Fig. 2l-o);\n(3) confirm meningeal inflammation using immunofluorescence assay (Fig. 2c, Supplementary Fig. 5a,b);\n(4) confirm the activation of Ackr1 endothelial cells, the down-regulation of tight junction proteins, and the up-regulation of crucial immune cytokines using immunofluorescence assay (Fig. 3h,k,m, Supplementary Fig. 7b,c,e,f);\n(5) confirm the dynamic alteration of blood-brain barrier permeability using Evans blue staining (Fig. 3l);\n(6) confirm the activation of Ccl12 microglial cells using immunofluorescence assay (Fig. 4f);\n(7) verify the necroptosis and pyroptosis of immune cells using immunofluorescent co-staining of specific biomarkers (Fig. 5c,f,i,l);\n(8) confirm the pathological damage including neuron cell death, vasodilation with inflammatory cells adhering to the vascular endothelial cells, myelin loss, and gliosis in brain using H&E staining (Supplementary Fig. 1f) and Luxol Fast Blue-Eosin staining (Supplementary Fig. 15e);\n(9) validate the dynamic expression of viral genes, interferons and down-stream transduction genes, chemokine genes, cell death and other damage releated genes in JEV-infected mouse brains using RT-qPCR (Fig. 3n, Fig. 4d-e, Fig. 5d-j, Supplementary Fig. 4c, Supplementary Fig. 7g, Supplementary Fig. 15b-d).\nThe newly acquired data have been incorporated into the revised manuscript and are highlighted in red. For further details, please refer to the point-by-point responses addressing the comments from other reviewers. We sincerely thank you for your valuable feedback, which have significantly improved the reliability, coherence, and overall readability of our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Summary\nI would like to commend the authors for their thorough and thoughtful revision, which has substantially strengthened the manuscript. The additional experimental validation, expanded discussion, and clarifications have addressed nearly all of the initial concerns, resulting in a version that is considerably more rigorous and compelling. I offer below a set of points that the authors may consider in further revision.\n\nComments\n1) The study used BALB/c mice inoculated with 10^6 PFU of the Nakayama strain via the intraperitoneal route. While many strains (e.g., BALB/c, C57BL/6, and C3H/He) are susceptible to JEV infection, the use of such a high dose in BALB/c mice warrants further discussion. It would be helpful if the authors could elaborate on the rationale for this choice and comment on how results might differ with other strains.\n2) The data suggest that JEV RNA was not detected in granulocytes or B cells. It would be valuable to discuss whether this finding implies that these cell types are not susceptible to JEV infection.\n3) Please clarify the distinction between Ly6c⁺ and Ly6c2⁺, and consider using one consistent designation throughout the manuscript if appropriate.\n4) Virus taxonomy terminology should be standardized. Please use Orthoflavivirus, orthoflaviviruses, and orthoflaviviral consistently throughout.\n5) Lines 73–93: The paragraph describing the current working hypothesis of JEV pathogenesis could be revised for clarity. The findings of this study align well with existing concepts of viral amplification, brain invasion, and pathology, while specifying the involvement of endothelial cells and macrophages.\n6) Line 130: The detection of JEV RNA in blood at 1 day post-infection may reflect residual input virus, given the high inoculum (10^6 PFU), which can persist for about 2–4 days without replication. Please clarify this point.\n7) Lines 161–189: JEV expresses only one mRNA, the genomic RNA. Please revise the paragraph to reflect this fact accurately.\n8) Lines 226–233: The statements referring to Supplementary Fig. 2b (L229, L233) do not appear to be supported by the data. Please review and correct.\n9) Line 297: The emphasis on IFN-γ is not explained or justified. A brief rationale for focusing on this cytokine would be helpful.\n10) Line 303: Please revise “NK cells (Fig. 2i) for 6.3–6.9%” to “NK cells for 6.3–6.9% (Fig. 2i).”\n11) Line 319: The statement “the intense involvement of the meninges in JE progression (Fig. 2a–d)” would benefit from further elaboration.\n12) Lines 443–464: Could the authors clarify whether they can differentiate direct virus-induced cell death from bystander cell death?\n13) One of the most important findings appears to be the specific involvement of Ly6c⁺ monocytes and Ackr1⁺ endothelial cells in disease progression. It would be valuable to elaborate on whether this observation can be translated to human JE or other neuroinvasive orthoflaviviruses. Testing the functional importance of these cell types or their interactions would further strengthen the study.\n14) Lines 465–490: This section is somewhat difficult to follow. Please revise for clarity.\n15) Lines 491–570: The section describing virus–host interactions should explicitly state that these are “predicted” protein-protein interactions.\n16) Lines 592–594: This reviewer feels that the use of terms such as “groundbreaking” and “unprecedented” is overstated. I recommend toning down this language here and throughout the manuscript to maintain a balanced and objective tone.\n17) Line 595: The authors state, “Demonstrating JEV's dual neuroinvasion pathways while mapping region-specific viral tropism across distinct brain areas.” Could the authors clarify what is meant by “dual neuroinvasion pathways” and specify the nature of the “region-specific viral tropism”?\n18) Figures (e.g., Fig. 1): Abbreviations for brain regions make interpretation difficult. Please spell out the names of brain regions in figures and throughout the manuscript.\n19) Supplementary Fig. 2b: Please verify whether the y-axis labeling is correct.\n20) The section “Establishment of a murine JE model” distracts from the main points of the paper. Consider moving this section to the Supporting Information.", "reviewer_2": "I am very satisfied with the revision, and all my comments have been addressed. Only two minor points that I would like the authors to change.\nFirst, please add example FACS plots showing the gating strategy for the different cell populations mentioned in line 928-931, as supplemental figure.\nSecondly, line 294 and line 668 both states that 60% of the living cells in the flow cytometry analysis is monocytes however, the panel Fig 2e shows 0.6% monocytes day 7.", "reviewer_3": "Many thanks to the authors for addressing my questions. The revised manuscript is certainly much improved and I am sure will be of interest to a broad readership."}, "author_response": "Thank you very much for your recognition of our revised manuscript and for your valuable suggestions for further improvement. We have addressed each of your comments point by point, and we kindly ask you to review our revisions."}]

Decision letter