{"paper_id":"32f4c579-7144-46d1-a1c3-b784ca6c5eef","body_text":"1\n1 Single-cell and spatiotemporal transcriptomic profiling of brain immune infiltration \n2 following Venezuelan equine encephalitis virus infection\n3 Margarita V. Rangel 1, Aimy Sebastian1, Nicole F. Leon1, Ashlee M. Phillips1, Bria M. Gorman1, \n4 Nicholas R. Hum 1, Dina R. Weilhammer1* \n5 1Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, \n6 California, USA\n7 *Corresponding author email: weilhammer1@llnl.gov\n8\n9\n10\n11\n12\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n2\n13 Abstract\n14 Neurotropic alphaviruses such as Venezuelan equine encephalitis virus (VEEV) are critical \n15 human pathogens that continually expand to naïve populations and for which there are no \n16 licensed vaccines or therapeutics. VEEV is highly infectious via the aerosol route and is a \n17 recognized weaponizable biothreat that causes neurological disease in humans. The \n18 neuropathology of VEEV has been attributed to an inflammatory immune response in the brain \n19 yet the underlying mechanisms and specific immune cell populations involved are not fully \n20 elucidated. This study uses single-cell RNA sequencing to produce a comprehensive \n21 transcriptional profile of immune cells isolated from the brain over a time course of infection in a \n22 mouse model of VEEV. Analyses reveal differentially activated subpopulations of microglia, \n23 including a distinct type I interferon-expressing subpopulation. This is followed by the sequential \n24 infiltration of myeloid cells and cytotoxic lymphocytes, also comprising subpopulations with \n25 unique transcriptional signatures. We identify a subpopulation of myeloid cells that form a \n26 distinct localization pattern in the hippocampal region whereas lymphocytes are widely \n27 distributed, indicating differential modes of recruitment, including that to specific regions of the \n28 brain. Altogether, this study provides a high-resolution analysis of the immune response to \n29 VEEV in the brain and highlights potential avenues of investigation for therapeutics that target \n30 neuroinflammation in the brain.\n31 Author Summary\n32 Venezuelan equine encephalitis virus (VEEV) causes brain inflammation in both animals and \n33 humans when transmitted by mosquito bite or infectious aerosols. The mechanisms underlying \n34 disease caused by VEEV, including the role of the immune response in brain pathology, are not \n35 well understood. Here we performed a comprehensive assessment of the immune response to \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n3\n36 VEEV in the brain over time using two advanced sequencing techniques. Following infection, \n37 immune cells infiltrate the brain in a sequential fashion and display different activation profiles. \n38 Different types of immune cells also display strikingly different spatial patterns throughout the \n39 brain. This study provides the most comprehensive description of the immune response to VEEV \n40 in the brain performed to date and advances our understanding of immune-driven \n41 neuropathology and identification of therapeutic targets.\n42 Introduction\n43 Venezuelan equine encephalitis virus (VEEV) is a mosquito-borne virus within the \n44 Alphavirus genus and Togaviridae family that has caused devastating outbreaks of disease \n45 among humans and equine in the Americas over recent decades [1-3]. VEEV is maintained in a \n46 sylvatic cycle between mosquitos and wild rodents but can spill over and amplify to high titers in \n47 equine, leading to epizootic and epidemic outbreaks. VEEV is a New World alphavirus, which \n48 are mostly encephalitic, as opposed to the Old World alphaviruses, which cause mostly \n49 arthritogenic disease. The virus causes an acute febrile illness that can lead to severe and lethal \n50 encephalitic cases with neurological symptoms spanning dizziness, headache, confusion, \n51 seizures, and stroke. While the mortality rate overall is <1% in adults and <5% in children, \n52 central nervous system (CNS) infections and neurological manifestations can occur in up to 14% \n53 of cases and among encephalitic cases, risk of mortality increases up to 10% in adults and 35% \n54 in children [4, 5]. In addition to the natural transmission route via mosquito bite, humans are also \n55 susceptible to infection via aerosol exposure, as demonstrated by the report of multiple lab-\n56 acquired infections [6, 7]. This, paired with the fact that VEEV can be grown to high titers, \n57 contributes to its designation as a category B priority pathogen by the National Institutes of \n58 Health and as a select agent by the Centers for Disease Control and Prevention. There are no \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n4\n59 specific treatments available for VEEV infection nor virus-induced encephalitis. A challenge in \n60 treating such infections is an incomplete understanding of the key drivers of pathogenesis, \n61 specifically the cellular and molecular changes underlying the progression of damaging \n62 inflammation that occurs in the brain. Thus, a temporal and spatial characterization of VEEV \n63 pathogenesis in the brain would elucidate the mechanisms of disease and the role of the host \n64 immune response, as well as aid the identification of potential therapeutic targets.\n65 Previous studies have established that inflammation is a key component of VEEV \n66 neuropathogenesis. Severe vascular cuffing, cellular infiltration, edema, and neuronal \n67 karyorrhexis can be observed histologically in VEEV-infected brains [8-10]. An elevation of \n68 pro-inflammatory cytokines (e.g. IL-1, IL-6, IL-12, TNF-, and IFN- and components of \n69 antigen presentation, apoptosis, and antiviral response (e.g. Cxcl9, Cxcl10, Cxcl11, Ccl2, Ccl5, \n70 Ifr7, Ifi27, Oas1b, Fcerg1, Mif, Clusterin, and MHC class II), have been detected in VEEV-\n71 infected mouse brains via histological, cytokine array, and RNA microarray techniques applied \n72 to brain homogenate or total RNA [11-14]. The host immune response plays paradoxical roles \n73 during VEEV infection in that a sufficiently robust response is required for systemic clearance, \n74 meanwhile cell depletion experiments and experiments in immune-compromised mice \n75 demonstrate a dampened response can improve outcome [15, 16]. These findings suggest that \n76 immune invasion in the brain is a large contributor to disease outcome. Thus, defining functions \n77 of discrete populations of immune cells that infiltrate the brain during infection would provide an \n78 opportunity to identify targetable pathways for immunomodulatory therapies. \n79 There is currently an incomplete understanding of the specific activation states of resident \n80 and infiltrating immune cells and the roles they play during the inflammatory response to VEEV \n81 infections. Further, the kinetics of the response and sequential recruitment of these cells has not \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n5\n82 been thoroughly described. Here, we aimed to bridge this gap in knowledge by generating a \n83 high-resolution profiling of the transcriptional activity of individual immune cells in the brain \n84 over a time course of VEEV infection using an established murine model, paired with spatial \n85 transcriptomic analysis during severe neuropathology. We applied single-cell RNA sequencing \n86 (scRNAseq) to immune cells isolated from the brains of C3H/HeN mice infected with VEEV \n87 TC-83 at 2, 4, and 6 dpi. Fluctuations in the immune populations present in the brain were \n88 observed during these key timepoints of disease development. We identified sequentially \n89 emerging subpopulations of microglia and infiltrating myeloid and lymphoid cells with unique \n90 transcriptional profiles. Spatial transcriptomic analysis on brains at 6 dpi revealed differential \n91 distribution of immune cell subtypes, with a subtype of myeloid cells localizing at the periphery \n92 of the cortex and hippocampal region of infected brains whereas other myeloid and lymphoid \n93 groups were more widespread. These results comprise a comprehensive temporal profiling of \n94 transcriptomic activity in the brain in response to VEEV infection that provide insight into key \n95 immune players during the onset of neuroinflammation, information crucial to a fundamental \n96 understanding of VEEV pathogenesis and the potential design of host-based immunomodulatory \n97 therapeutics. Additionally, spatial characterization of key populations during a highly diseased \n98 state highlights specific regions of the brain exhibiting differential immune activity that may be \n99 important to consider for effective delivery of brain-targeting treatments.\n100 Results\n101 Single-cell RNA sequencing reveals robust and sequential immune cell infiltration in mouse \n102 brains during VEEV infection\n103 To understand the progression of the neuroinflammatory immune response to VEEV, we \n104 profiled the fluctuations of immune cell populations in the brain and their transcriptional activity \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n6\n105 using scRNAseq on immune cells isolated from mouse brains over a time course of VEEV \n106 infection. We first confirmed infection dynamics in an established mouse model of VEEV. C3H \n107 mice were infected intranasally with 2x10 7 PFU VEEV TC-83 and monitored for signs of disease \n108 over six days, encompassing both the early lymphoid phase and central nervous system phase of \n109 the typically biphasic disease observed in mice [17, 18]. Signs of disease including ruffled fur \n110 and hunched posture were observed in some mice by 4 days post infection (dpi) and by 6 dpi all \n111 mice exhibited these symptoms in addition to ataxia, a sign of neurological disease (Fig. 1a). \n112 Weights of infected mice began to decline by 5 dpi and were significantly different versus \n113 uninfected controls by 6 dpi (Fig. 1b ). At various timepoints post infection, additional groups of \n114 mice were euthanized to measure brain viral load. High infectious viral titers in the brain were \n115 detected by 2 dpi and peaked by 4 dpi (Fig. 1c). Histological examination of infected brains was \n116 performed at 6 dpi. Hematoxylin and eosin-stained coronal sections of infected brains revealed \n117 increased cellularity, edema, and perivascular cuffing (Fig. 1d, e). Our results closely \n118 recapitulated previously characterized disease progression and pathological characteristics in this \n119 model of VEEV infection [18]. \n120 C3H mice were then infected as described above and at 2, 4, and 6 dpi, brain cerebral \n121 hemispheres from infected mice and uninfected controls were harvested and processed to \n122 perform scRNAseq or spatial transcriptomic analyses (Fig. 1f). For scRNAseq, single-cell \n123 suspensions from 3 mice were pooled and mononuclear immune cells were isolated using Percoll \n124 gradient centrifugation, as previously described [19]. The following number of cells were \n125 profiled for each condition: Uninfected: 2,298, TC-83 2 dpi: 1,698, TC-83 4 dpi: 5,005, and TC-\n126 83 6 dpi: 6,488. Unsupervised clustering of the data resulted in 9 clusters that were each assigned \n127 to a putative cell-type identity based on their unique profile of differentially expressed genes \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n7\n128 (DEGs) encoding cell-type specific markers (Fig. 2a, b). Brains at each timepoint post-infection \n129 exhibited unique immune compositions as compared to the uninfected control (Fig. 2c). In \n130 uninfected brains, microglia were the predominant immune population detected, as expected. \n131 Microglia formed 3 subclusters over the course of infection that we termed resting microglia \n132 (MG), activated MG 1, and activated MG 2 (microglia 1, 2, and 3). Activated MG 1 and 2 are \n133 representative of a deviation from a homeostatic cell state upon infection. Resting MG compose \n134 88% of total immune cells in uninfected mice and are the most enriched in microglia homeostatic \n135 markers Cx3cr1 and Tmem119, which are downregulated in activated MG 1 and activated MG 2 \n136 (Fig. 2b, Supplementary Fig. 1a). At 2 dpi, activated MG 1 and 2 increased in abundance (16% \n137 and 3%, respectively) and exhibit enrichment in additional genes indicative of an activated state, \n138 including Cd63, Cd72, and Mif (Fig. 2b, D). Activated MG 1 continued to increase in abundance \n139 through 4 dpi, detected at a comparable level to resting MG, 16% and 17%, respectively, while \n140 activated MG 2 remained at approximately 3%. At 6 dpi, activated MG 1 decreased to 3% and \n141 activated MG 2 to less than 1%. Distinct antiviral response gene expression was observed across \n142 the microglia subclusters, with activated MG 1 exhibiting the highest expression of viral RNA \n143 sensors Ifih1, which encodes melanoma differentiation-associated protein 5 (MDA5) and Ddx58, \n144 which encodes retinoic acid-inducible gene I (RIG-I), as well as type I IFN gene Ifnb1 \n145 (Supplementary Fig. 1a). Following the early shifts in microglial populations, we observed an \n146 immense infiltration of myeloid and lymphocyte populations that together comprise a larger \n147 proportion of sequenced cells at day 4 and 6 post infection than microglia (Fig. 2c, d ). We \n148 additionally confirmed this pattern of myeloid and lymphocyte infiltration using flow cytometry \n149 (Fig. 2e, f). \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n8\n150 Between uninfected and 2 dpi, the most notable shifts were those in the microglia \n151 subclusters, as described earlier, as well as detection of cluster 0, identified as myeloid cells, \n152 cluster 2, identified as NK cells, and cluster 6, identified as proliferating NK and T cells. \n153 Between 2 and 4 dpi, there was a large increase in myeloid cells (4% to 34%) and NK cells (4% \n154 to 16%) and a modest increase in cluster 5, identified as dendritic cells (DC) (3% to 7%). We \n155 also identified a small neutrophil cluster which accounted for less than 0.5% of cells at 4 and 6 \n156 dpi. T cells accounted for ~3% of the immune cells present in the brain at 4 dpi. Between 4 and 6 \n157 dpi, the myeloid and dendritic cell clusters remain relatively steady (40% and 7%, respectively) \n158 while there is an increase in NK cells (16% to 24%) and cluster 4, identified as T cells (3% to \n159 18%) (Fig. 2d, e). Notably, it is between day 4 and 6 during which neurological symptoms and \n160 severe brain pathology present and weight loss and survival begin to decline. \n161 Williams et al. recently investigated the dynamics of the host response to VEEV infection \n162 in the mouse brain using bulk RNA sequencing (RNAseq) and histological techniques [20]. We \n163 reanalyzed the bulk RNA-seq data obtained from three different regions of the brain: 1) the main \n164 olfactory bulb (MOB) where robust expression of viral protein was detected as early as 1 dpi, 2) \n165 piriform cortex (PIR), a region with robust viral protein expression by 2 dpi and 3) hippocampus \n166 (HIP), a region showing strong viral protein expression by 4 dpi [20]. This data showed that Ifih1 \n167 and Ddx58 were upregulated immediately after infection in all three regions, which was followed \n168 by upregulation of type I IFN genes including Ifna2, Ifna4, Ifna5 and Ifnb1  (Supplementary \n169 Fig. 1b). Interferon expression decreased over time in all three regions. Consistent with the \n170 decrease in resting microglia observed in our scRNAseq data, a dramatic decrease in the \n171 expression of microglia markers Cx3cr1, Tmem119, P2ry12  and Fcrls was observed while \n172 several markers of infiltrating myeloid cells including Ly6c2 and Plac8 were upregulated \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n9\n173 (Supplementary Fig. 1b). In addition, a significant increase in the expression of NK and T cell \n174 markers over time was also observed in all three regions examined. In alignment with our data, \n175 an increase in NK cell markers was observed between 3 and 7 dpi, followed by a significant \n176 increase in T cell markers between 5 dpi and 7 dpi (Supplementary Fig. 1b). Consistent with \n177 the lack of B cells and few neutrophils observed in our scRNAseq data, the expression of B cell \n178 and neutrophil markers was extremely low, as shown in the MOB at 6 dpi (Supplementary Fig. \n179 1c). We then reanalyzed the expression of interferon and markers of the key infiltrating \n180 populations in all eight parts of the brain isolated by Williams et al., (MOB, PIR, striatum (STR), \n181 motor cortex (MTX), HIP, sensory cortex (STX), thalamus (THA), and cerebral cortex (CBX). \n182 High expression of type I IFN genes was observed earliest in the MOB and PIR, at 3 dpi, \n183 followed by STR, MTX, HIP, STX, and THA across 5 and 6 dpi (Supplementary Fig. 1d). \n184 Ly6c2 was upregulated in the MOB from 3 through 7 dpi whereas it was upregulated from 5 to 7 \n185 dpi in the PIR, STR, MTX, HIP, STX, and THA. NK cell and T cell markers were upregulated \n186 highest from 5 to 7 dpi in the MOB, and PIR, and at 7 dpi in the MTX, HIP, STX, and THA. \n187 Expression of these markers was least changed in the CBX across all timepoints. The early \n188 expression of viral sensing genes and type I IFN genes in the bulk RNAseq data set preceding \n189 high expression of markers of infiltrating cells coincides with our scRNAseq data that implicates \n190 microglia as a key early responder to VEEV infection and early producer of interferon. Both data \n191 sets then indicate a sequential recruitment of myeloid cells followed by lymphocytes at later \n192 timepoints. The bulk RNAseq data set demonstrates that in addition to temporal differences, \n193 there are unique spatial signatures of gene expression that can potentially help define the \n194 progression of the immune response through specific regions of the brain over time and the \n195 extent of infiltration in each of these regions. \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n10\n196 Microglial and myeloid cell subtypes elicit heterogeneous antiviral responses following \n197 VEEV infection\n198 To further examine the transcriptional activity of microglia and infiltrating myeloid \n199 populations, all cells from clusters 0, 1, 3, 5, 7, and 8 (Fig. 2a) were extracted and re-clustered \n200 using an unsupervised clustering approach. This included the following number of cells for each \n201 condition: Uninfected: 2,211, TC-83 2 dpi: 1,527, TC-83 4 dpi: 3,835, and TC-83 6 dpi: 3,398. \n202 Analysis revealed 10 unique cell clusters (Fig. 3a-c ). Microglial subpopulations (cluster 0; \n203 resting MG, cluster 3; activated MG 1, and cluster 5; activated MG 2) clustered apart from \n204 various infiltrating myeloid populations and exhibited enrichment for Cx3cr1, Trem2, and \n205 Tmem119, as well as complement component genes C1qa-c. Cluster 1 and 2 highly expressed \n206 monocyte/macrophage markers Ly6c2 and Plac8, while lacking Tmem119  and were, therefore, \n207 annotated as ‘monocyte/macrophage (Mono/Mac) 1’ and ‘Mono/Mac 2,’ respectively (Fig. 3c, \n208 Supplementary Fig. 2a) [21, 22]. Clusters 4, 7, and 8 were identified as Cd209a+, plasmacytoid, \n209 and Ccr7 + DCs, respectively, based on enrichment of specific genes such as Cd209a and class II \n210 MHC genes Cd74, H2-Aa and H2-Ab1 (cluster 4), Klk1 and Mctp2 (cluster 7) and Ccr7 (cluster \n211 8) (Fig. 3c, Supplementary 2) [23]. Cluster 9 expressed neutrophil markers S100a8 and S100a9 \n212 (Fig. 3c). \n213 To understand the function of the myeloid clusters, we analyzed expression of key \n214 antiviral response genes. Type I IFN genes Ifnb1  and Ifna2 were primarily expressed by \n215 microglia, specifically activated MG 1 (Fig. 3d). Elevated levels of interferon stimulated genes \n216 were observed across other myeloid subclusters, mainly Mono/Mac 1 and 2 and Cd209a + DCs as \n217 early as 2 dpi, but more notably at 4 dpi and through 6 dpi (Fig. 3d, e). Activated MG 1 were \n218 also enriched for inflammatory cytokines Ccl3 and Ccl4, Csf1, a key regulator of macrophage \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n11\n219 differentiation, tumor necrosis factor alpha (Tnf), and Il12b, a cytokine that acts on T and natural \n220 killer cells (Fig. 3g). Ifnb1 and Csf1 elevation was observed by 2 dpi and through 6 dpi, in a \n221 small (< 20) percent of total cells (Fig. 3f), while the other cytokines were observed highly \n222 expressed by greater percentages of total cells and beginning at 4 dpi, through 6 dpi. Activated \n223 MG 2 exhibited elevated Ifna2 expression, though lower than activated MG 1 and not until 6 dpi \n224 (Fig. 3d, Supplementary Fig. 1a), and were enriched for Ccl5, Ccl7, Ccl12, Cxcl16, and Cxcl13 \n225 (Fig. 3g). Mono/Mac 1 shared overlap with activated MG 2 with enrichment for Ccl5 , Ccl7, and \n226 Cxcl16, and additionally expressed high levels of Ccl8. Mono/Mac 2 expressed high levels of \n227 chemokines including Ccl2, Ccl6, Ccl9, Cxcl1, Cxcl2 and Cxcl10, as well as Il15  and Il18, two \n228 interleukins implicated in activation of T/NK cells (Fig. 3g) [24-26]. Mono/Mac 1 also exhibited \n229 elevated expression of Il15 and Il18, but to a lesser extent. Notably, Cd209a + DCs had high \n230 expression of Cxcl9, a prominent mediator of lymphocyte infiltration (Fig. 3g). We validated our \n231 transcriptomic data by measuring protein levels of a subset of cytokines and chemokines \n232 included in our analysis in brain homogenate isolated from infected mice. Consistent with the \n233 observed increase in gene expression, IL-1β, IL-6, CXCL1, CXCL2, CXCL10, CCL5 and IFN-β \n234 were upregulated at the protein level in infected brains (Fig. 3h). \n235 To investigate the relationship between myeloid subclusters, we performed trajectory \n236 analysis, ordering single cells along pseudotime to reconstruct potential differentiation \n237 trajectories (Supplementary Fig. 2b). Mono/Mac and microglia subpopulations clustered \n238 distinctly through each timepoint. Microglia clusters exhibited a shift from resting to activated \n239 subpopulations. By 4 dpi, activated MG 1 partially occupy a shared branch with Mono/Mac \n240 clusters. Meanwhile, activated MG 2 and proliferating cells occupy distinct branches. \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n12\n241 Interestingly, only a small portion of activated MG 1 highly express Ifnb1 at each timepoint \n242 (Supplementary Fig. 2c).  \n243 Infiltrating myeloid cells occupy specific spatial niches in the brain.\n244 Having established that microglia subclusters and infiltrating myeloid cells display \n245 distinct inflammatory profiles in response to VEEV infection, we next asked whether the spatial \n246 distribution of these immune cell subtypes also differ in the brain parenchyma using spatial \n247 transcriptomic analysis performed at 6 dpi. Unsupervised clustering of the spatial transcriptomic \n248 data from uninfected and infected brains resulted in eleven clusters. The majority of these \n249 clusters corresponded to specific anatomical regions in the brain (Fig. 4a-c). Cluster 2 showed \n250 enrichment for myeloid markers such as Ly6c2 and Plac8 and was localized at the periphery of \n251 the cortex and within the hippocampal region (Fig. 4c- f).  The uninfected brain had significantly \n252 fewer cells from cluster 2 compared to the infected brain at 6 dpi (Fig. 4e, f). Examination of \n253 spatial expression patterns of Ly6c2  and Plac8 revealed a distinct localization of myeloid cells \n254 expressing these genes also in the periphery of the cortex and within the hippocampal region of \n255 infected brains (Fig. 4g). Localized Ly6c was confirmed in these regions via \n256 immunohistochemical analysis (Fig. 4i ). Minimal Ly6c expression was noted in the uninfected \n257 brain and robust signal was detected in the infected brain concentrated around the hippocampal \n258 formation (Fig. 4i). When compared to the localization patterns of Prox1, which appears \n259 enriched at the hippocampal formation, and Nrgn1, within the cortex, the expression of the \n260 myeloid-specific markers appears to distinctly localize at a layer in between the Prox1- and \n261 Nrgn1-high regions, within the hippocampal region (Supplementary Fig. 3d, Fig. 4g). Our re-\n262 analysis of the bulk RNAseq data set generated by Williams et al. also indicated greater immune \n263 infiltration, including myeloid populations, within the hippocampus compared to the cortex \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n13\n264 (Supplementary Fig. 1d). Expression of several upregulated chemokines in the infected brain \n265 including Ccl2, Ccl4, Ccl5, Ccl7 also localized to regions enriched in myeloid cells whereas Tnf  \n266 and interleukins including Il6, Il12b, Il15 and Il18 were detected throughout the brain (Fig. 4c, \n267 Supplementary Fig. 3a, b). Interestingly, Cxcl10 had a more robust expression throughout \n268 regions of the infected brain other than the Ly6c enriched regions, suggesting that Cxcl10 may be \n269 primarily expressed by cells other than the localized myeloid subpopulation (Supplementary \n270 Fig. 3c). Consistent with the scRNAseq data, there was a significant reduction in cells expressing \n271 markers of homeostatic microglia such as Cx3cr1  and Tmem119 and an increase in cells \n272 expressing Cd72, a gene enriched in activated microglia, in infected brains (Fig. 4h). These data \n273 indicate that by 6 dpi, there is widespread activation of microglia and regional specificity in \n274 infiltrating myeloid subpopulations.\n275 Recruited NK and CD8 T cells showed a widespread distribution in the brain parenchyma \n276 and elevated expression of cytotoxic proteins\n277 To gain a more comprehensive understanding of lymphocyte responses to infection, all \n278 cells from lymphocyte clusters (clusters 2, 4 and 6; Fig. 2b) were extracted and reanalyzed. This \n279 included the following number of cells for each condition: Uninfected: 66, TC-83 2 dpi: 89, TC-\n280 83 4 dpi: 987, and TC-83 6 dpi: 2783. Cells were primarily identified as one of two subclusters \n281 of NK cells, referred to as NK cells 1 and 2, CD8 + T cells, CD4+ T cells, or smaller detected \n282 clusters of T cells and cells expressing markers of proliferation (Fig. 5a). At both 4 and 6 dpi, \n283 NK cell clusters composed a greater proportion of total lymphocytes than CD8 + and CD4+ T \n284 cells, which exhibited a steep increase at 6 dpi (Fig. 5b, c). High expression of genes encoding \n285 cytotoxic proteins, including perforin, granzyme B, and FasL, were observed at both 4 and 6 dpi \n286 (Fig. 5d). Perforin and granzyme genes (Prf1 and Gzmb, respectively) were expressed in both \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n14\n287 NK subclusters and CD8 + T cells at both timepoints, with NK cells exhibiting higher expression \n288 (Fig. 5e ). While expression in NK cells was comparable for both timepoints, CD8+ T cells \n289 exhibited higher expression of Prf1 and Gzmb at 6 dpi. Additionally, Ifng expression was \n290 detected in both NK sub clusters and CD8 + T cells, each exhibiting higher expression at 4 dpi. \n291 Production of perforin at 6 dpi was also observed by flow cytometry and IFN- by protein \n292 analysis (Fig. 5g-j). Perforin protein was detected in NK cells and CD8 + T cells, but not CD4+ T \n293 cells (Fig. 5i). Further, perforin protein was significantly higher in the brain than spleen in both \n294 cell types, suggesting unique cytotoxic effector expression in the brain versus systemically. An \n295 increase in the expression of NK cell inhibitory genes such as Klrc1 and Klrd1 was observed in \n296 both NK and CD8 + T cells at 6 dpi (Fig. 5e). This together with the decreased Ifng expression at \n297 6 dpi may indicate NK cell exhaustion around the later timepoint. Spatial transcriptomic analysis \n298 at 6 dpi revealed detection of NK and T cell associated genes throughout the brain with no \n299 observable specific localization patterns, though, Ifng expression sometimes formed concentrated \n300 clusters (Fig. 5f). These results indicate a robust and ubiquitous upregulation of cytotoxic \n301 effectors throughout the brain in response to VEEV infection that coincide with timepoints of \n302 severe neurological dysfunction.\n303 Comparative analysis of infected brain and peripheral blood mononuclear cells reveals \n304 distinct immune landscapes\n305 To compare the immune landscape in the brain to systemic immune changes induced by \n306 VEEV infection, we profiled peripheral blood mononuclear cells (PBMCs) from infected and \n307 uninfected mice at 6 dpi using scRNAseq. Our analysis identified twelve immune cell clusters \n308 including NK cells, T cells, B cells, mono/macs, neutrophils, DCs and plasma cells (Fig. 6a-c ).  \n309 Consistent with our findings in the brain, the proportion of NK cells dramatically increased in the \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n15\n310 blood after infection (Fig. 6d). Mono/Macs also expand in both the infected blood and brain \n311 (Fig. 3c, d and 6d). In contrast to what we observed in the brain, T cells 1, which encompassed \n312 CD4 + T cells and regulatory T cells, decreased and the proportion of CD8+ T cells remained \n313 similar between infected and uninfected blood (Fig. 6d). Neutrophils, which do not infiltrate the \n314 brain, dramatically expanded in the blood after infection and upregulated antiviral response \n315 genes, including Ddx58, Ifih1, Ifit1, Ifit2 and Isg15 (Fig. 6e ). Blood Mono/Macs and DCs also \n316 activated a large number of antiviral response genes in response to infection (Fig. 6e).  \n317 Next, we compared infection-induced transcriptional changes in PBMCs to immune cells \n318 from the brain (Supplementary Fig. 4 a, b). Similar to the brain, blood Mono/Macs from \n319 infected mice activated Plac8, Ly6c2 and cathepsin family proteases (Supplementary Fig. 4c). \n320 We found that cytokines such as Ccl2, Ccl5 and Ccl7  had markedly lower expression in infected \n321 blood Mono/Macs compared to brain Mono/Macs while Ccr2, a receptor involved in myeloid \n322 recruitment to the CNS, had an elevated expression in blood Mono/Macs (Fig. 6f, \n323 Supplementary Fig. 4c) [27]. Peripheral blood NK cells from infected mice expressed perforin, \n324 granzymes, cytokines and inhibitory receptors at a level comparable to NK cells from infected \n325 brains, with slightly higher Gzmb expression in the infected brain (Fig. 6g). Interestingly, \n326 uninfected blood NK cells also had robust expression of Prf1 and Gzma but, not Gzmb. The \n327 expression levels of perforin and granzymes were also upregulated in CD8 + T cells after \n328 infection (Fig. 6h). Unlike uninfected NK cells, uninfected blood CD8 + T cells did not express \n329 Prf1 or Gzma, suggesting that cytotoxic gene expression in CD8+ T cells was infection-induced \n330 (Fig. 6h). Overall, these findings emphasize a tissue-specific upregulation of cytotoxic effectors \n331 as a potential key component of the immune landscape in the brain in response to VEEV \n332 infections.\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n16\n333 Discussion\n334 Existing and emerging encephalitic viral infections pose a growing medical challenge. \n335 Viral infection of the brain and subsequent inflammatory immune responses can cause long \n336 lasting or permanent damage leading to serious clinical outcomes and death. Lethal encephalitis \n337 that ensues following VEEV infection is attributed to the host immune response to viral \n338 replication in brain tissue, but it remains unclear which specific components of the immune \n339 response instigate disease. Thus, understanding the processes involved in the recruitment, \n340 differentiation, and function of immune cells in the brain during infection is of high importance \n341 to aid the development of novel therapeutics that target immunopathological responses. Here we \n342 temporally dissected the heterogeneity of the immune response in the brain over a time course of \n343 infection using scRNAseq. We then employed spatial transcriptomic analysis of the brain during \n344 severe disease to investigate the spatial organization of distinct populations within the brain.   \n345 We observed a dynamic response to VEEV in myeloid cells over the course of infection. \n346 The emergence of two subpopulations of microglia was observed by 2 dpi that we termed \n347 activated microglia (MG) 1 and 2. Trajectory analysis revealed their simultaneous expansion, \n348 suggesting they are uniquely activated subpopulations. Notably, activated MG 1 was the primary \n349 cell type expressing genes encoding RIG-I (Ddx58) and MDA5 (Ifih1), PRRs important in \n350 alphavirus sensing, along with type I IFN genes Ifnb1 and Ifna2 throughout infection. Activated \n351 MG 2 did not exhibit elevated Ddx58 or Ifih1 but showed elevated Ifna2 expression at 6 dpi. \n352 Interestingly, within activated MG 1, only a small portion of the population expressed Ifnb1 at \n353 each timepoint. While VEEV primarily infects neurons and astrocytes, it can also infect \n354 microglia to a lesser frequency [10]. It is possible that the unique expression profiles among \n355 microglia may represent infected cells versus bystander cells assuming an antiviral state. \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n17\n356 Alternatively, concurrent molecular pathways could be contributing to limiting Ifnb1 expression. \n357 Further, a lack of upregulation of additional interferon-stimulated genes (ISGs) in the activated \n358 microglia subsets suggests their activation may be orchestrated by other cell types that are \n359 directly sensing virus. \n360 The early and sustained activation of microglia during VEEV infection likely plays a role \n361 in the recruitment of other inflammatory cell types. Activated MG 1 and 2 share expression of \n362 cytokines expressed by disease-associated microglia (DAM) with important roles in the \n363 recruitment of inflammatory monocytes [28-30]. Ly6C hi monocytes have been shown to play \n364 both protective and pathogenic roles upon migration to the brain [31-36]. In our data set, greater \n365 than 30% and 40% of immune cells in the brain are infiltrating myeloid cells by 4 and 6 dpi, \n366 respectively, consisting mostly of two Ly6c2 expressing  subpopulations (Mono/Mac 1 and 2) that \n367 display high induction of numerous ISGs, cytokines, and chemokines. Trajectory analysis \n368 indicated Mono/Mac 1 and 2 cluster separately throughout each timepoint, likely ruling out \n369 either population being a precursor state to the other. Notably, this large myeloid infiltration \n370 coincides with peak viral replication and the appearance of clinical signs and future studies \n371 should investigate the mechanistic role of these cells in VEEV infection.\n372 Other notable myeloid cell populations that responded to infection include neutrophils \n373 and DCs. Both cell populations expanded in the blood and expressed high levels of antiviral \n374 genes in response to infection, yet only DCs were detected at appreciable levels in the brain. The \n375 lack of neutrophils in the brain is notable given their dramatic expansion in the blood and their \n376 significant infiltration of the brain during other neurotropic infections, including with other \n377 neurotropic arboviruses [37]. The expansion of neutrophils in the blood and their high expression \n378 of antiviral response genes may indicate a role in systemic viral clearance. \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n18\n379 Lymphocyte populations exhibited diverse responses upon infection, with subset \n380 populations demonstrating brain-specific cytotoxic signatures. NK cells expanded considerably \n381 in the brains and blood of infected animals, and T cells exhibited a sharp increase in the brain at \n382 6 dpi. Conversely, B cells decreased proportionally in the blood upon infection and did not \n383 infiltrate the brain. Both NK cells and CD8 + T cells from infected animals expressed elevated \n384 levels of cytotoxic genes, with higher expression observed within brain-infiltrating cells. While \n385 blood NK cells exhibited similar levels of Prf1 and Gzma expression as in the brain, brain NK \n386 cells exhibited higher Gzmb. Notably, Gzmb exhibits a lower baseline expression in blood NK \n387 cells than Gzma, therefore the high level of Gzmb observed in the brain following infection \n388 represents a dramatic shift from baseline expression. Furthermore, striking differences in \n389 perforin expression in NK and CD8 + T cells were observed between cells isolated from brains \n390 versus spleens via flow cytometry, with brain populations of both cell types exhibiting \n391 significantly higher expression. Therefore, the highly cytotoxic state of brain infiltrating NK and \n392 CD8 + T cells is a specific quality and may be significantly contributing to immunopathology. \n393 Notably, previous work has implicated NK cells in the pathogenesis of VEEV in mouse models \n394 and the cytopathic effects of NK and T cells have also been implicated in neuroinflammation and \n395 blood brain barrier (BBB) disruption in non-infectious contexts [16, 38, 39]. The abundantly \n396 present cytotoxic lymphocytes are potentially key effectors of damage in the brain and, therefore, \n397 appealing therapeutic targets via blockade of their recruitment or function.\n398 A key feature of our data is the unique spatial localization of infiltrating Mono/Mac cells \n399 within the brain on 6 dpi. A Ly6c2-expressing myeloid cluster was found to occupy a distinct \n400 regional pattern that lined the cortex perimeter and a region surrounding the hippocampal \n401 formation. In contrast, T and NK cells exhibited no clear localization pattern and were \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n19\n402 distributed throughout the brain. These data suggest fundamental differences in the mechanisms \n403 governing myeloid and lymphocyte infiltration into the brain. NK and T cells may require \n404 breakdown of the BBB in order to infiltrate in significant numbers, reflected in their widespread \n405 distribution throughout the brain, whereas myeloid cells may traverse alternate barriers (choroid \n406 plexus, glia limitans) at earlier time points. The mechanisms that control myeloid cell \n407 extravasation into the brain parenchyma across any barrier are under described [40], therefore \n408 additional research into entry mechanisms during viral infection is critical. The relationship \n409 between localized viral replication within the brain and the proximity to clustered immune \n410 populations also warrants further investigation. Notably, concentrated infected/apoptotic neurons \n411 have been observed in the hippocampus in VEEV-infected mice [41]. In turn, the Lyc62-\n412 expressing cluster likely contributes to further inflammation, as these cells exhibited high \n413 expression of chemokines, including Ccl2, Ccl4, and Ccl7. The role of each cell population and \n414 features such as their localization, expression profiles, and temporal infiltration during VEEV \n415 pathogenesis are of keen interest for follow on studies. \n416 In summary, this study provides a comprehensive profiling of transcriptional activity of \n417 immune cells in the brain during viral encephalitis. We define the heterogeneity of the immune \n418 response in the brain at three timepoints post-VEEV infection, tracing the activation of resident \n419 cells, infiltration of cells from the periphery, and the emergence of various subpopulations. We \n420 compared the brain gene expression data to that of PBMCs to underscore brain-specific changes. \n421 Spatial transcriptomics identified localization of an infiltrating myeloid population to discrete \n422 regions of the brain, and furthermore highlighted distinct localization patterns of myeloid cells \n423 and lymphocytes. Future studies should utilize the data provided here to further define the \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n20\n424 immunopathogenic components of the response to VEEV versus those that are protective to \n425 inform the development of vaccine and therapeutic candidates. \n426 Materials and Methods\n427 Cells and virus \n428 Vero E6 cells were obtained from the American Type Culture Collection (ATCC) and \n429 maintained in Dulbecco’s modified Eagle’s medium (DMEM, Thermo Fisher) supplemented \n430 with 10% fetal bovine serum (FBS, ATCC) supplemented with 100 units/mL penicillin and 100 \n431 μg/mL streptomycin (Thermo Fisher) at 37 °C in 5% CO2. Venezuelan equine encephalitis virus \n432 (VEEV) strain TC-83 (NR-63) was obtained from the NIH Biodefense and Emerging Infections \n433 Research Resources Repository, NIAID, NIH. VEEV stocks were propagated in Vero E6 cells \n434 and harvested via clarification of cell culture supernatant by centrifugation. Titers of viral stocks \n435 were determined by standard plaque assay consisting of a methyl crystalline cellulose overlay \n436 and crystal violet staining [42].\n437 Mouse infections\n438 All animal work was approved by the Lawrence Livermore National Laboratory Institutional \n439 Animal Care and Use Committee under protocol #310. All animals were housed in an \n440 Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC)-\n441 accredited facility. C3H/HeN mice (strain ID 025) were obtained from Charles River. 5–10-\n442 week-old female mice were used in all experiments. Groups of mice were inoculated intranasally \n443 with 2x10 7 PFU TC-83 while under anesthesia (4-5% isoflurane in 100% oxygen). Mice were \n444 monitored daily for signs of morbidity and animals were humanely euthanized upon signs of \n445 severe disease by CO 2 asphyxiation. For tissue harvest, animals were anesthetized under \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n21\n446 isoflurane and the whole animal was perfused with 20 mL sterile PBS containing 50,000 U/L \n447 sodium heparin via the left ventricle.\n448 Brain tissue isolation and preparation \n449 Brains for flow cytometric, scRNAseq, cytokine, and viral titer analysis were isolated from \n450 infected mice on days 2, 4, or 6 post-infection. Preparation of brain tissue was performed as \n451 previously described [19]. Following euthanasia and perfusion, brains were removed and placed \n452 in 1 mL digestion buffer (PBS pH 7.4 (Thermo Fisher) + collagenase (Worthington) + DNase I \n453 (Roche) to a final concentration of 3 mg/mL and 0.5 mg/mL, respectively) on ice in a 1.5 mL \n454 tube. Brains were finely diced into 1-2 mm 3 pieces with scissors and tissue was digested at 37 °C \n455 for 30 min in a total of 5 mL digestion buffer. A cell suspension was generated by gentle \n456 pipetting followed by passage through a 70 μm cell strainer. The cell strainer was rinsed with \n457 PBS supplemented with 5% FBS to a total volume of 20 mL. Aliquots of this suspension were \n458 stored at -80 °C for cytokine and viral titer analysis. The remaining suspension was subjected to \n459 Percoll gradient centrifugation to purify mononuclear immune cells for flow cytometric analysis \n460 or RNA sequencing as previously described [19].\n461 Single-cell RNA sequencing of brain samples and data analysis\n462 Single-cell suspensions of mononuclear immune cells from uninfected mouse brains and infected \n463 brains were prepared as described above. Cells were counted on a Countess II automated cell \n464 counter prior to single-cell sequencing preparation using Chromium Single-cell 3ʹ GEM, Library \n465 & Gel Bead Kit v3 (10x Genomics Cat # 1000075) on a 10× Genomics Chromium Controller \n466 following manufacturers’ protocol. Subsequently, libraries were sequenced on Illumina NextSeq \n467 2000. The Cell Ranger Single-Cell Software Suite (10x Genomics) was then used to perform \n468 sample demultiplexing, barcode processing, and single-cell gene counting. After demultiplexing \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n22\n469 the sequencing files with “cellranger mkfastq”, “cellranger count” was used to perform \n470 alignment to mouse reference transcriptome (mm10), barcode processing and gene counting. \n471 Further analysis was performed using Seurat [43]. First, cells with fewer than 500 detected genes \n472 per cell or mitochondrial content greater than 10% and genes that were expressed by fewer than \n473 5 cells were filtered out. Potential doublets and CD45 - cells were also removed. After pre-\n474 processing, we performed data normalization, scaling, and identified 2000 most variable \n475 features. Then, anchors for data integration were identified using the ‘FindIntegrationAnchors()’ \n476 function. Next, these anchors were passed to the ‘IntegrateData()’ function and a new integrated \n477 matrix with all four datasets was generated. After data integration, data was scaled, and the \n478 dimensionality of the data was reduced by principal component analysis (PCA). Subsequently, \n479 cells were grouped into an optimal number of clusters for de novo cell type discovery using \n480 Seurat’s ‘FindNeighbors()’ and ‘FindClusters()’ functions. A non-linear dimensional reduction \n481 was then performed via uniform manifold approximation and projection (UMAP) and various \n482 cell clusters were identified and visualized. Genes differentially expressed between clusters were \n483 identified using ‘FindMarkers()’ function implemented in Seurat. Gene ontology (GO) \n484 enrichment analysis was performed using ToppGene Suite [44] and heatmaps were generated \n485 using custom R scripts. Single-cell pseudo-time trajectories of immune cell subpopulations were \n486 constructed with Monocle [45] as described before [46].\n487 Single-cell RNA sequencing of blood samples and data analysis \n488 Single-cell suspensions of PBMCs were isolated from uninfected mice and mice at 6-days post-\n489 infection. Sequencing libraries were generated using Chromium Single-cell 3ʹ  GEM, Library & \n490 Gel Bead Kit v3 (10x Genomics Cat # 1000075) on a 10× Genomics Chromium Controller \n491 following manufacturers’ protocol. Subsequently, libraries were sequenced on Illumina NextSeq \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n23\n492 2000.  Sequencing files were demultiplexed using “cellranger mkfastq”. Subsequently, \n493 “cellranger count” was used to perform alignment to mouse reference transcriptome (mm10), \n494 barcode processing and gene counting. Further analysis was performed using Seurat [43] as \n495 described above.\n496 Bulk RNAseq data analysis \n497 We used publicly available data [20] to identify VEEV infection-induced changes in different \n498 regions of the brain. Raw counts were downloaded from Gene Expression Omnibus (GEO, \n499 accession ID: GSE213725). Data was then normalized using the Trimmed Mean of M-values \n500 (TMM) normalization method implemented in edgeR [47]. For differential expression analysis, \n501 the limma package [48] was employed in conjunction with the voom transformation [49]. \n502 Heatmaps for genes of interest were generated using the pheatmap package [50], and a boxplot \n503 was created using the ggplot2 package [51], both in R (version 4.3.1) [52].\n504 Flow cytometry\n505 Cells were incubated for 30 min on ice in 100 μl Hank’s balanced salt solution (Thermo \n506 Fisher) + 2% FBS with Fc block (1:100 dilution, clone 2.4G2; BD Biosciences) along with the \n507 following antibodies, each diluted 1:500: CD45 APC-Cy7 (clone 30-F11; BD Biosciences), \n508 CD11b PE-CF594 (clone M1/70; BD Biosciences), CD3 PerCP-Cy5.5 (clone 17A2; BD \n509 Biosciences), CD4 BV650 (clone GK1.5; BD Biosciences), CD8 FITC (53-6.7; BioLegend), \n510 CD49b PE (clone DX5; BD Biosciences), and Perforin PE/Dazzle 594 (clone S16009A; \n511 BioLegend). Cells were then fixed using BD Cytofix/Cytoperm (BD Biosciences) according to \n512 manufacturer’s instructions. Flow cytometry was performed using a FACSAria Fusion and data \n513 were analyzed using FlowJo software. Microglia, other myeloid lineage, and lymphocytes were \n514 resolved using CD45 and CD11b expression, with microglia identified as CD45 int CD11bint, \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n24\n515 other myeloid as CD45 hi CD11bhi, and lymphocytes as CD45hi CD11b– as previously described \n516 [19].\n517 Cytokine analysis\n518 Cytokines were quantified using LEGENDplex TM multiplex bead-based assay (BioLegend) using \n519 the mouse anti-virus response panel according to manufacturer’s instructions. Flow cytometry of \n520 the beads was performed using a FACSAria Fusion and data were analyzed using BioLegend’s \n521 cloud-based analysis software available at https://legendplex.qognit.com.\n522 Immunohistochemical staining of mouse brains\n523 Brains for immunohistochemical staining were collected from infected mice at 6 dpi. Upon \n524 euthanasia and perfusion as described above, mice were perfused with 20 mL 10% neutral \n525 buffered formalin (NBF). Isolated brains were subsequently immersed in 10% NBF at 4° C for 3 \n526 days with gentle agitation. Following fixation, brains were paraffin-embedded and cut into 5 μm \n527 thick sections. All sections were dewaxed with xylene and hydrated with alcohol. Citrate or Tris-\n528 EDTA were used for antigen retrieval, and hydrogen peroxide (ab64218, Abcam) was used to \n529 block endogenous peroxidase. After blocking non-specific sites with CAS-block (008120, \n530 Thermo Fisher Scientific), sections were incubated with Ly6c primary antibody (ab314120, \n531 Abcam) and secondary antibody (ab6720, Abcam). 3,3′-diaminobenzidine (DAB) kit (ab64238, \n532 Abcam) was used for visualization, and hematoxylin was used to stain the nuclei. All sections \n533 were rinsed with distilled water and sealed with permount (sp15-100, Fisher Scientific).\n534 Spatial transcriptomics\n535 Spatial transcriptomic analysis of a VEEV-infected brain collected 6 days post-infection was \n536 conducted using 10x Visium technology (10x Genomics). 5 µm brain sections were mounted \n537 onto charged slides, deparaffinized, and stained with hematoxylin and eosin. The brain sections \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n25\n538 were imaged at 4x magnification using an ECHO Revolve microscope and the tiles were stitched \n539 together using Affinity Photo (Serif). The brain sections were then destained and decrosslinked \n540 to release RNA sequestered by formalin fixation. Next, the sections were incubated with mouse-\n541 specific probes targeting the whole transcriptome (PN-1000365, 10x Genomics), allowing for the  \n542 hybridization and ligation of each probe pair. Gene expression probes were released from the \n543 tissue and captured by spatially barcoded oligonucleotides on the Visium slide surface using the \n544 Visium CytAssist instrument. Gene expression libraries were then prepared from each tissue \n545 section and sequenced using the Illumina NextSeq 2000. \n546 After sequencing, the data and histology images were processed with the Space Ranger software \n547 (10x Genomics) and Seurat [43]. After demultiplexing the sequencing files with “spaceranger \n548 mkfastq”, the fastq files and the brightfield image of the tissue were provided to “spaceranger \n549 count” and read alignment, tissue detection, fiducial detection, and barcode/UMI counting were \n550 performed. Seurat was used then used for filtering (nFeature_Spatial > 500 & nCount_Spatial > \n551 500 & percent.mt < 30), data normalization, dimensionality reduction, clustering, identification \n552 of spatially variable genes, and visualization. For each sample, after filtering, the data was \n553 normalized using “SCTransform()” function implemented in Seurat. Dimensionality reduction \n554 and clustering analysis was performed using “RunPCA()”, “FindNeighbors()”, “FindClusters ()” \n555 and RunUMAP() functions. We then applied the “FindAllMarkers()” function to identify genes \n556 enriched in each cluster. We also performed integrative analysis of uninfected and infected brain \n557 samples using the “SelectIntegrationFeatures()”, “PrepSCTIntegration()”, \n558 “FindIntegrationAnchors()”, and “IntegrateData ()” functions, followed by dimensionality \n559 reduction using PCA and UMAP. Clusters were visualized in UMAP space using DimPlot() and \n560 overlaid on the tissue image using “SpatialDimPlot()” function.\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n26\n561 Statistical analyses\n562 Statistical significance was determined using Prism Version 10.2.3 (347) (GraphPad, La Jolla, \n563 CA). Specific tests and p values are indicated in the figure legends.\n564 Data Availability\n565 The single cell RNA-sequencing and spatial data have been deposited at the NCBI Gene \n566 Expression Omnibus (GEO), accession numbers GSE274566 and GSE275201, respectively.\n567 Acknowledgements and Funding. \n568 Funding for this research was provided by internal Lawrence Livermore National Laboratory \n569 Directed Research and Development funds (22-ERD-038 to D.R.W.). The funders had no role in \n570 study design, data collection and analysis, decision to publish, or preparation of the manuscript. \n571 This work was performed under the auspices of the U.S. Department of Energy by Lawrence \n572 Livermore National Security, LLC, Lawrence Livermore National Laboratory under Contract \n573 DE-AC52-07NA27344.\n574 Author Contributions. \n575 M.V.R., A.S., N.R.H, and D.R.W. conceived the project and designed experiments; M.V.R., \n576 N.F.L., A.M.P, N.R.H, and D.R.W. performed experiments; A.S. and B.M.G.  analyzed \n577 sequencing data; D.R.W. supervised the project. M.V.R. and D.R.W. wrote the original draft, \n578 and all authors were involved in manuscript review and editing.\n579 Competing Interests Statement\n580 The authors declare no competing interests.\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. 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Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential \n719 expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-40. Epub \n720 20091111. doi: 10.1093/bioinformatics/btp616. PubMed PMID: 19910308; PubMed Central \n721 PMCID: PMCPMC2796818.\n722 48. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential \n723 expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. \n724 2015;43(7):e47. Epub 20150120. doi: 10.1093/nar/gkv007. PubMed PMID: 25605792; PubMed \n725 Central PMCID: PMCPMC4402510.\n726 49. Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model \n727 analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. Epub 20140203. doi: \n728 10.1186/gb-2014-15-2-r29. PubMed PMID: 24485249; PubMed Central PMCID: \n729 PMCPMC4053721.\n730 50. Kolde R. Pheatmap: pretty heatmaps. R package version 1, 726. 2019.\n731 51. Wickam H. ggplot2: Elegant Graphics for Data Analysis: Springer Cham; 2016.\n732 52. Team RC. R: A Language and Environment for Statistical Computing. R Foundation for \n733 Statistical Computing, Vienna. 2023.\n734\n735 Figure Legends\n736 Fig. 1: VEEV TC-83 Infection in C3H mice. 5–8-week-old C3H mice were infected \n737 intranasally with 2e7 PFU of VEEV TC-83 and symptoms (a) and weight loss (b) were \n738 monitored. Data are shown as mean  standard error of mean (SEM) (n=4-12). Experiments were \n739 repeated at least twice. Two-tailed p values were calculated using 2-way ANOVA with \n740 Bonferroni’s correction for multiple comparisons. Brain viral titers were evaluated (c). Two-\n741 tailed p values were calculated using Kruskal-Wallis and Dunn’s multiple comparisons test. \n742 Signs of pathology in the brain were observed by H&E histological staining (d, e); yellow arrows \n743 denote vascular cuffing. (f) Schematic representation of experimental workflow for scRNAseq \n744 and spatial transcriptomic analysis of VEEV-infected brains. Following VEEV infection as \n745 described above, the cerebral cortex of 3 uninfected mice or 3 infected mice harvested at 2, 4, \n746 and 6 dpi were processed to yield a single cell suspension of mononuclear immune cells that \n747 were analyzed by scRNAseq. Alternatively, uninfected and infected cerebral cortices harvested \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n31\n748 at 6 dpi were fixed and processed for analysis by spatial transcriptomics. Created with \n749 BioRender.com.\n750 Fig. 2: Temporal profiling of immune cells in the brain by scRNAseq in VEEV infection. \n751 (a) Uniform Manifold Approximation and Projection (UMAP) visualization of cell clusters \n752 identified in each experimental group. Cell clusters are color-coded and numbered. (b) Dot plot \n753 showing the expression of select cell-type markers. Dot size represents the fraction of cells of the \n754 indicated cluster expressing the markers and the intensity of color represents the average marker \n755 expression level in that cluster. (c) The relative proportion of cell types within brains of each \n756 experimental group. (d) Line graph representation of the change in proportion of prominent cell \n757 types over time in uninfected and infected brains. (e) Flow cytometric analysis of cell types in \n758 uninfected and infected brains. Representative CD11b vs CD45 plots are shown with gates \n759 identifying lymphocytes (CD11b -, CD45hi), myeloid cells (CD11bhi, CD45hi), and microglia \n760 (CD11b hi, CD45low) (f) Percentages of lymphocytes and myeloid cells. Data are shown as mean \n761  SEM (n= 4-7). Experiments were repeated at least twice. Two-tailed p values were calculated \n762 using one-way ANOVA with Tukey’s correction for multiple comparisons.\n763 Fig. 3: Analysis of myeloid populations in the brain in by scRNAseq in VEEV infection. \n764 UMAP visualization of myeloid sub-clusters colored by identification (a) or experimental group \n765 (b). (c) Heat map showing expression level of selected cell type markers in each cluster. (d ) Heat \n766 map of IFN ⍺/β response genes. (e ) Violin plots showing the expression of select ISGs across \n767 experimental groups. (f) Dot plot showing expression level of select inflammatory signaling \n768 genes across experimental groups. Dot size represents the fraction of cells in the indicated group \n769 expressing the indicated gene and the intensity of color represents the average marker expression \n770 level in that group. (g) Heat map showing expression level of select inflammatory signaling \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n32\n771 genes across key myeloid populations. (h) Protein levels of select immune mediators measured \n772 using BioLegend LEGENDplex multiplex immunoassay. Data are shown as mean   SEM (n=4). \n773 Two-tailed p values were calculated using unpaired Student’s t test.\n774 Fig. 4: Spatial transcriptomic profiling of myeloid populations in the brain during VEEV \n775 infection. 5-m coronal sections through the hippocampus were derived from uninfected mice or \n776 infected mice at 6 dpi. Sections were used for either ST (a-h) or immunostaining (i). Distribution \n777 of transcriptionally unique populations detected in uninfected (a) or infected (b) brains. \n778 Populations are numbered and color-coded. (c) Annotation of major brain regions. MB= \n779 midbrain, HPF= hippocampal formation, CTX= cortex. (d) Dot plot showing expression levels \n780 of identifying markers of cell types across the brain. Dot size represents the fraction of cells in \n781 the indicated cluster expressing the markers and the intensity of color represents the average \n782 marker expression level in that cluster. Distribution of cluster 2 is shown in uninfected (e) or \n783 infected (f) brains. Distribution of select myeloid gene and cytokine expression (g) and \n784 microglial gene expression (h ). (i) Representative images of Ly6C staining in the hippocampus \n785 (magnification = 20).\n786 Fig. 5: Analysis of lymphocyte populations in the brain by scRNAseq and spatial \n787 transcriptomics. UMAP visualization of lymphocyte clusters colored by identity (a) or \n788 experimental group (b). (c) Proportions of lymphocyte populations. (d ) Violin plots showing \n789 expression of cytotoxic genes across experimental groups. (e) Violin plots showing expression of \n790 select NK and T cells genes within the indicated subpopulations. Colors indicate timepoint post \n791 infection. (f) Spatial distribution of select NK and T cell genes in uninfected or infected brains at \n792 6 dpi. (g) Representative flow cytometry plots showing perforin expression in NK and T cell \n793 populations in the brain at 6 dpi. Quantification of NK and T cells in the brain is shown in (h) \n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n33\n794 and perforin in the brain versus spleen in (i). (j) Protein analysis of IFN- in uninfected or \n795 infected brains at 6 dpi. Data in h-j are shown as mean  standard error of mean (SEM) (n=4-7). \n796 Two-tailed p values were calculated using 2-way ANOVA with Bonferroni’s correction for \n797 multiple comparisons (h, i) or unpaired Student’s t test (j).\n798 Fig. 6: Comparative scRNAseq analysis immune cells in the brain and PBMCs during \n799 VEEV infection. UMAP visualization cell clusters in the blood at 6 dpi colored by identity (a) \n800 or treatment (b). c Expression of identifying markers across clusters. d Relative proportions of \n801 populations. e  Heat map showing expression of select antiviral response genes across cell \n802 populations in uninfected versus infected blood. Comparison of the expression of select genes in \n803 mono/mac (f), NK cell (g) and CD8 + T cell (h) populations in the brain versus blood.\n804\n805\n806\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n34\n807 Figures\n808 Fig. 1\n809\n810\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n35\n811 Fig. 2\n812\n813\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n36\n814\n815 Fig. 3\n816\n817\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n37\n818\n819 Fig. 4\n820\n821\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n38\n822 Fig. 5\n823\n824\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint \n\n39\n825 Fig. 6\n826\n827\n105 and is also made available for use under a CC0 license. \n(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC \nThe copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint","source_license":"Public-Domain","license_restricted":false}