Single-cell and spatiotemporal transcriptomic profiling of brain immune infiltration following Venezuelan equine encephalitis virus infection

1 1 Single-cell and spatiotemporal transcriptomic profiling of brain immune infiltration 2 following Venezuelan equine encephalitis virus infection 3 Margarita V. Rangel 1, Aimy Sebastian1, Nicole F. Leon1, Ashlee M. Phillips1, Bria M. Gorman1, 4 Nicholas R. Hum 1, Dina R. Weilhammer1* 5 1Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, 6 California, USA 7 *Corresponding author email: [email protected] 8 9 10 11 12 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 2 13 Abstract 14 Neurotropic alphaviruses such as Venezuelan equine encephalitis virus (VEEV) are critical 15 human pathogens that continually expand to naïve populations and for which there are no 16 licensed vaccines or therapeutics. VEEV is highly infectious via the aerosol route and is a 17 recognized weaponizable biothreat that causes neurological disease in humans. The 18 neuropathology of VEEV has been attributed to an inflammatory immune response in the brain 19 yet the underlying mechanisms and specific immune cell populations involved are not fully 20 elucidated. This study uses single-cell RNA sequencing to produce a comprehensive 21 transcriptional profile of immune cells isolated from the brain over a time course of infection in a 22 mouse model of VEEV. Analyses reveal differentially activated subpopulations of microglia, 23 including a distinct type I interferon-expressing subpopulation. This is followed by the sequential 24 infiltration of myeloid cells and cytotoxic lymphocytes, also comprising subpopulations with 25 unique transcriptional signatures. We identify a subpopulation of myeloid cells that form a 26 distinct localization pattern in the hippocampal region whereas lymphocytes are widely 27 distributed, indicating differential modes of recruitment, including that to specific regions of the 28 brain. Altogether, this study provides a high-resolution analysis of the immune response to 29 VEEV in the brain and highlights potential avenues of investigation for therapeutics that target 30 neuroinflammation in the brain. 31 Author Summary 32 Venezuelan equine encephalitis virus (VEEV) causes brain inflammation in both animals and 33 humans when transmitted by mosquito bite or infectious aerosols. The mechanisms underlying 34 disease caused by VEEV, including the role of the immune response in brain pathology, are not 35 well understood. Here we performed a comprehensive assessment of the immune response to 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 3 36 VEEV in the brain over time using two advanced sequencing techniques. Following infection, 37 immune cells infiltrate the brain in a sequential fashion and display different activation profiles. 38 Different types of immune cells also display strikingly different spatial patterns throughout the 39 brain. This study provides the most comprehensive description of the immune response to VEEV 40 in the brain performed to date and advances our understanding of immune-driven 41 neuropathology and identification of therapeutic targets. 42 Introduction 43 Venezuelan equine encephalitis virus (VEEV) is a mosquito-borne virus within the 44 Alphavirus genus and Togaviridae family that has caused devastating outbreaks of disease 45 among humans and equine in the Americas over recent decades [1-3]. VEEV is maintained in a 46 sylvatic cycle between mosquitos and wild rodents but can spill over and amplify to high titers in 47 equine, leading to epizootic and epidemic outbreaks. VEEV is a New World alphavirus, which 48 are mostly encephalitic, as opposed to the Old World alphaviruses, which cause mostly 49 arthritogenic disease. The virus causes an acute febrile illness that can lead to severe and lethal 50 encephalitic cases with neurological symptoms spanning dizziness, headache, confusion, 51 seizures, and stroke. While the mortality rate overall is <1% in adults and <5% in children, 52 central nervous system (CNS) infections and neurological manifestations can occur in up to 14% 53 of cases and among encephalitic cases, risk of mortality increases up to 10% in adults and 35% 54 in children [4, 5]. In addition to the natural transmission route via mosquito bite, humans are also 55 susceptible to infection via aerosol exposure, as demonstrated by the report of multiple lab- 56 acquired infections [6, 7]. This, paired with the fact that VEEV can be grown to high titers, 57 contributes to its designation as a category B priority pathogen by the National Institutes of 58 Health and as a select agent by the Centers for Disease Control and Prevention. There are no 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 4 59 specific treatments available for VEEV infection nor virus-induced encephalitis. A challenge in 60 treating such infections is an incomplete understanding of the key drivers of pathogenesis, 61 specifically the cellular and molecular changes underlying the progression of damaging 62 inflammation that occurs in the brain. Thus, a temporal and spatial characterization of VEEV 63 pathogenesis in the brain would elucidate the mechanisms of disease and the role of the host 64 immune response, as well as aid the identification of potential therapeutic targets. 65 Previous studies have established that inflammation is a key component of VEEV 66 neuropathogenesis. Severe vascular cuffing, cellular infiltration, edema, and neuronal 67 karyorrhexis can be observed histologically in VEEV-infected brains [8-10]. An elevation of 68 pro-inflammatory cytokines (e.g. IL-1, IL-6, IL-12, TNF-, and IFN- and components of 69 antigen presentation, apoptosis, and antiviral response (e.g. Cxcl9, Cxcl10, Cxcl11, Ccl2, Ccl5, 70 Ifr7, Ifi27, Oas1b, Fcerg1, Mif, Clusterin, and MHC class II), have been detected in VEEV- 71 infected mouse brains via histological, cytokine array, and RNA microarray techniques applied 72 to brain homogenate or total RNA [11-14]. The host immune response plays paradoxical roles 73 during VEEV infection in that a sufficiently robust response is required for systemic clearance, 74 meanwhile cell depletion experiments and experiments in immune-compromised mice 75 demonstrate a dampened response can improve outcome [15, 16]. These findings suggest that 76 immune invasion in the brain is a large contributor to disease outcome. Thus, defining functions 77 of discrete populations of immune cells that infiltrate the brain during infection would provide an 78 opportunity to identify targetable pathways for immunomodulatory therapies. 79 There is currently an incomplete understanding of the specific activation states of resident 80 and infiltrating immune cells and the roles they play during the inflammatory response to VEEV 81 infections. Further, the kinetics of the response and sequential recruitment of these cells has not 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 5 82 been thoroughly described. Here, we aimed to bridge this gap in knowledge by generating a 83 high-resolution profiling of the transcriptional activity of individual immune cells in the brain 84 over a time course of VEEV infection using an established murine model, paired with spatial 85 transcriptomic analysis during severe neuropathology. We applied single-cell RNA sequencing 86 (scRNAseq) to immune cells isolated from the brains of C3H/HeN mice infected with VEEV 87 TC-83 at 2, 4, and 6 dpi. Fluctuations in the immune populations present in the brain were 88 observed during these key timepoints of disease development. We identified sequentially 89 emerging subpopulations of microglia and infiltrating myeloid and lymphoid cells with unique 90 transcriptional profiles. Spatial transcriptomic analysis on brains at 6 dpi revealed differential 91 distribution of immune cell subtypes, with a subtype of myeloid cells localizing at the periphery 92 of the cortex and hippocampal region of infected brains whereas other myeloid and lymphoid 93 groups were more widespread. These results comprise a comprehensive temporal profiling of 94 transcriptomic activity in the brain in response to VEEV infection that provide insight into key 95 immune players during the onset of neuroinflammation, information crucial to a fundamental 96 understanding of VEEV pathogenesis and the potential design of host-based immunomodulatory 97 therapeutics. Additionally, spatial characterization of key populations during a highly diseased 98 state highlights specific regions of the brain exhibiting differential immune activity that may be 99 important to consider for effective delivery of brain-targeting treatments. 100 Results 101 Single-cell RNA sequencing reveals robust and sequential immune cell infiltration in mouse 102 brains during VEEV infection 103 To understand the progression of the neuroinflammatory immune response to VEEV, we 104 profiled the fluctuations of immune cell populations in the brain and their transcriptional activity 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 6 105 using scRNAseq on immune cells isolated from mouse brains over a time course of VEEV 106 infection. We first confirmed infection dynamics in an established mouse model of VEEV. C3H 107 mice were infected intranasally with 2x10 7 PFU VEEV TC-83 and monitored for signs of disease 108 over six days, encompassing both the early lymphoid phase and central nervous system phase of 109 the typically biphasic disease observed in mice [17, 18]. Signs of disease including ruffled fur 110 and hunched posture were observed in some mice by 4 days post infection (dpi) and by 6 dpi all 111 mice exhibited these symptoms in addition to ataxia, a sign of neurological disease (Fig. 1a). 112 Weights of infected mice began to decline by 5 dpi and were significantly different versus 113 uninfected controls by 6 dpi (Fig. 1b ). At various timepoints post infection, additional groups of 114 mice were euthanized to measure brain viral load. High infectious viral titers in the brain were 115 detected by 2 dpi and peaked by 4 dpi (Fig. 1c). Histological examination of infected brains was 116 performed at 6 dpi. Hematoxylin and eosin-stained coronal sections of infected brains revealed 117 increased cellularity, edema, and perivascular cuffing (Fig. 1d, e). Our results closely 118 recapitulated previously characterized disease progression and pathological characteristics in this 119 model of VEEV infection [18]. 120 C3H mice were then infected as described above and at 2, 4, and 6 dpi, brain cerebral 121 hemispheres from infected mice and uninfected controls were harvested and processed to 122 perform scRNAseq or spatial transcriptomic analyses (Fig. 1f). For scRNAseq, single-cell 123 suspensions from 3 mice were pooled and mononuclear immune cells were isolated using Percoll 124 gradient centrifugation, as previously described [19]. The following number of cells were 125 profiled for each condition: Uninfected: 2,298, TC-83 2 dpi: 1,698, TC-83 4 dpi: 5,005, and TC- 126 83 6 dpi: 6,488. Unsupervised clustering of the data resulted in 9 clusters that were each assigned 127 to a putative cell-type identity based on their unique profile of differentially expressed genes 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 7 128 (DEGs) encoding cell-type specific markers (Fig. 2a, b). Brains at each timepoint post-infection 129 exhibited unique immune compositions as compared to the uninfected control (Fig. 2c). In 130 uninfected brains, microglia were the predominant immune population detected, as expected. 131 Microglia formed 3 subclusters over the course of infection that we termed resting microglia 132 (MG), activated MG 1, and activated MG 2 (microglia 1, 2, and 3). Activated MG 1 and 2 are 133 representative of a deviation from a homeostatic cell state upon infection. Resting MG compose 134 88% of total immune cells in uninfected mice and are the most enriched in microglia homeostatic 135 markers Cx3cr1 and Tmem119, which are downregulated in activated MG 1 and activated MG 2 136 (Fig. 2b, Supplementary Fig. 1a). At 2 dpi, activated MG 1 and 2 increased in abundance (16% 137 and 3%, respectively) and exhibit enrichment in additional genes indicative of an activated state, 138 including Cd63, Cd72, and Mif (Fig. 2b, D). Activated MG 1 continued to increase in abundance 139 through 4 dpi, detected at a comparable level to resting MG, 16% and 17%, respectively, while 140 activated MG 2 remained at approximately 3%. At 6 dpi, activated MG 1 decreased to 3% and 141 activated MG 2 to less than 1%. Distinct antiviral response gene expression was observed across 142 the microglia subclusters, with activated MG 1 exhibiting the highest expression of viral RNA 143 sensors Ifih1, which encodes melanoma differentiation-associated protein 5 (MDA5) and Ddx58, 144 which encodes retinoic acid-inducible gene I (RIG-I), as well as type I IFN gene Ifnb1 145 (Supplementary Fig. 1a). Following the early shifts in microglial populations, we observed an 146 immense infiltration of myeloid and lymphocyte populations that together comprise a larger 147 proportion of sequenced cells at day 4 and 6 post infection than microglia (Fig. 2c, d ). We 148 additionally confirmed this pattern of myeloid and lymphocyte infiltration using flow cytometry 149 (Fig. 2e, f). 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 8 150 Between uninfected and 2 dpi, the most notable shifts were those in the microglia 151 subclusters, as described earlier, as well as detection of cluster 0, identified as myeloid cells, 152 cluster 2, identified as NK cells, and cluster 6, identified as proliferating NK and T cells. 153 Between 2 and 4 dpi, there was a large increase in myeloid cells (4% to 34%) and NK cells (4% 154 to 16%) and a modest increase in cluster 5, identified as dendritic cells (DC) (3% to 7%). We 155 also identified a small neutrophil cluster which accounted for less than 0.5% of cells at 4 and 6 156 dpi. T cells accounted for ~3% of the immune cells present in the brain at 4 dpi. Between 4 and 6 157 dpi, the myeloid and dendritic cell clusters remain relatively steady (40% and 7%, respectively) 158 while there is an increase in NK cells (16% to 24%) and cluster 4, identified as T cells (3% to 159 18%) (Fig. 2d, e). Notably, it is between day 4 and 6 during which neurological symptoms and 160 severe brain pathology present and weight loss and survival begin to decline. 161 Williams et al. recently investigated the dynamics of the host response to VEEV infection 162 in the mouse brain using bulk RNA sequencing (RNAseq) and histological techniques [20]. We 163 reanalyzed the bulk RNA-seq data obtained from three different regions of the brain: 1) the main 164 olfactory bulb (MOB) where robust expression of viral protein was detected as early as 1 dpi, 2) 165 piriform cortex (PIR), a region with robust viral protein expression by 2 dpi and 3) hippocampus 166 (HIP), a region showing strong viral protein expression by 4 dpi [20]. This data showed that Ifih1 167 and Ddx58 were upregulated immediately after infection in all three regions, which was followed 168 by upregulation of type I IFN genes including Ifna2, Ifna4, Ifna5 and Ifnb1 (Supplementary 169 Fig. 1b). Interferon expression decreased over time in all three regions. Consistent with the 170 decrease in resting microglia observed in our scRNAseq data, a dramatic decrease in the 171 expression of microglia markers Cx3cr1, Tmem119, P2ry12 and Fcrls was observed while 172 several markers of infiltrating myeloid cells including Ly6c2 and Plac8 were upregulated 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 9 173 (Supplementary Fig. 1b). In addition, a significant increase in the expression of NK and T cell 174 markers over time was also observed in all three regions examined. In alignment with our data, 175 an increase in NK cell markers was observed between 3 and 7 dpi, followed by a significant 176 increase in T cell markers between 5 dpi and 7 dpi (Supplementary Fig. 1b). Consistent with 177 the lack of B cells and few neutrophils observed in our scRNAseq data, the expression of B cell 178 and neutrophil markers was extremely low, as shown in the MOB at 6 dpi (Supplementary Fig. 179 1c). We then reanalyzed the expression of interferon and markers of the key infiltrating 180 populations in all eight parts of the brain isolated by Williams et al., (MOB, PIR, striatum (STR), 181 motor cortex (MTX), HIP, sensory cortex (STX), thalamus (THA), and cerebral cortex (CBX). 182 High expression of type I IFN genes was observed earliest in the MOB and PIR, at 3 dpi, 183 followed by STR, MTX, HIP, STX, and THA across 5 and 6 dpi (Supplementary Fig. 1d). 184 Ly6c2 was upregulated in the MOB from 3 through 7 dpi whereas it was upregulated from 5 to 7 185 dpi in the PIR, STR, MTX, HIP, STX, and THA. NK cell and T cell markers were upregulated 186 highest from 5 to 7 dpi in the MOB, and PIR, and at 7 dpi in the MTX, HIP, STX, and THA. 187 Expression of these markers was least changed in the CBX across all timepoints. The early 188 expression of viral sensing genes and type I IFN genes in the bulk RNAseq data set preceding 189 high expression of markers of infiltrating cells coincides with our scRNAseq data that implicates 190 microglia as a key early responder to VEEV infection and early producer of interferon. Both data 191 sets then indicate a sequential recruitment of myeloid cells followed by lymphocytes at later 192 timepoints. The bulk RNAseq data set demonstrates that in addition to temporal differences, 193 there are unique spatial signatures of gene expression that can potentially help define the 194 progression of the immune response through specific regions of the brain over time and the 195 extent of infiltration in each of these regions. 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 10 196 Microglial and myeloid cell subtypes elicit heterogeneous antiviral responses following 197 VEEV infection 198 To further examine the transcriptional activity of microglia and infiltrating myeloid 199 populations, all cells from clusters 0, 1, 3, 5, 7, and 8 (Fig. 2a) were extracted and re-clustered 200 using an unsupervised clustering approach. This included the following number of cells for each 201 condition: Uninfected: 2,211, TC-83 2 dpi: 1,527, TC-83 4 dpi: 3,835, and TC-83 6 dpi: 3,398. 202 Analysis revealed 10 unique cell clusters (Fig. 3a-c ). Microglial subpopulations (cluster 0; 203 resting MG, cluster 3; activated MG 1, and cluster 5; activated MG 2) clustered apart from 204 various infiltrating myeloid populations and exhibited enrichment for Cx3cr1, Trem2, and 205 Tmem119, as well as complement component genes C1qa-c. Cluster 1 and 2 highly expressed 206 monocyte/macrophage markers Ly6c2 and Plac8, while lacking Tmem119 and were, therefore, 207 annotated as ‘monocyte/macrophage (Mono/Mac) 1’ and ‘Mono/Mac 2,’ respectively (Fig. 3c, 208 Supplementary Fig. 2a) [21, 22]. Clusters 4, 7, and 8 were identified as Cd209a+, plasmacytoid, 209 and Ccr7 + DCs, respectively, based on enrichment of specific genes such as Cd209a and class II 210 MHC genes Cd74, H2-Aa and H2-Ab1 (cluster 4), Klk1 and Mctp2 (cluster 7) and Ccr7 (cluster 211 8) (Fig. 3c, Supplementary 2) [23]. Cluster 9 expressed neutrophil markers S100a8 and S100a9 212 (Fig. 3c). 213 To understand the function of the myeloid clusters, we analyzed expression of key 214 antiviral response genes. Type I IFN genes Ifnb1 and Ifna2 were primarily expressed by 215 microglia, specifically activated MG 1 (Fig. 3d). Elevated levels of interferon stimulated genes 216 were observed across other myeloid subclusters, mainly Mono/Mac 1 and 2 and Cd209a + DCs as 217 early as 2 dpi, but more notably at 4 dpi and through 6 dpi (Fig. 3d, e). Activated MG 1 were 218 also enriched for inflammatory cytokines Ccl3 and Ccl4, Csf1, a key regulator of macrophage 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 11 219 differentiation, tumor necrosis factor alpha (Tnf), and Il12b, a cytokine that acts on T and natural 220 killer cells (Fig. 3g). Ifnb1 and Csf1 elevation was observed by 2 dpi and through 6 dpi, in a 221 small (< 20) percent of total cells (Fig. 3f), while the other cytokines were observed highly 222 expressed by greater percentages of total cells and beginning at 4 dpi, through 6 dpi. Activated 223 MG 2 exhibited elevated Ifna2 expression, though lower than activated MG 1 and not until 6 dpi 224 (Fig. 3d, Supplementary Fig. 1a), and were enriched for Ccl5, Ccl7, Ccl12, Cxcl16, and Cxcl13 225 (Fig. 3g). Mono/Mac 1 shared overlap with activated MG 2 with enrichment for Ccl5 , Ccl7, and 226 Cxcl16, and additionally expressed high levels of Ccl8. Mono/Mac 2 expressed high levels of 227 chemokines including Ccl2, Ccl6, Ccl9, Cxcl1, Cxcl2 and Cxcl10, as well as Il15 and Il18, two 228 interleukins implicated in activation of T/NK cells (Fig. 3g) [24-26]. Mono/Mac 1 also exhibited 229 elevated expression of Il15 and Il18, but to a lesser extent. Notably, Cd209a + DCs had high 230 expression of Cxcl9, a prominent mediator of lymphocyte infiltration (Fig. 3g). We validated our 231 transcriptomic data by measuring protein levels of a subset of cytokines and chemokines 232 included in our analysis in brain homogenate isolated from infected mice. Consistent with the 233 observed increase in gene expression, IL-1β, IL-6, CXCL1, CXCL2, CXCL10, CCL5 and IFN-β 234 were upregulated at the protein level in infected brains (Fig. 3h). 235 To investigate the relationship between myeloid subclusters, we performed trajectory 236 analysis, ordering single cells along pseudotime to reconstruct potential differentiation 237 trajectories (Supplementary Fig. 2b). Mono/Mac and microglia subpopulations clustered 238 distinctly through each timepoint. Microglia clusters exhibited a shift from resting to activated 239 subpopulations. By 4 dpi, activated MG 1 partially occupy a shared branch with Mono/Mac 240 clusters. Meanwhile, activated MG 2 and proliferating cells occupy distinct branches. 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 12 241 Interestingly, only a small portion of activated MG 1 highly express Ifnb1 at each timepoint 242 (Supplementary Fig. 2c). 243 Infiltrating myeloid cells occupy specific spatial niches in the brain. 244 Having established that microglia subclusters and infiltrating myeloid cells display 245 distinct inflammatory profiles in response to VEEV infection, we next asked whether the spatial 246 distribution of these immune cell subtypes also differ in the brain parenchyma using spatial 247 transcriptomic analysis performed at 6 dpi. Unsupervised clustering of the spatial transcriptomic 248 data from uninfected and infected brains resulted in eleven clusters. The majority of these 249 clusters corresponded to specific anatomical regions in the brain (Fig. 4a-c). Cluster 2 showed 250 enrichment for myeloid markers such as Ly6c2 and Plac8 and was localized at the periphery of 251 the cortex and within the hippocampal region (Fig. 4c- f). The uninfected brain had significantly 252 fewer cells from cluster 2 compared to the infected brain at 6 dpi (Fig. 4e, f). Examination of 253 spatial expression patterns of Ly6c2 and Plac8 revealed a distinct localization of myeloid cells 254 expressing these genes also in the periphery of the cortex and within the hippocampal region of 255 infected brains (Fig. 4g). Localized Ly6c was confirmed in these regions via 256 immunohistochemical analysis (Fig. 4i ). Minimal Ly6c expression was noted in the uninfected 257 brain and robust signal was detected in the infected brain concentrated around the hippocampal 258 formation (Fig. 4i). When compared to the localization patterns of Prox1, which appears 259 enriched at the hippocampal formation, and Nrgn1, within the cortex, the expression of the 260 myeloid-specific markers appears to distinctly localize at a layer in between the Prox1- and 261 Nrgn1-high regions, within the hippocampal region (Supplementary Fig. 3d, Fig. 4g). Our re- 262 analysis of the bulk RNAseq data set generated by Williams et al. also indicated greater immune 263 infiltration, including myeloid populations, within the hippocampus compared to the cortex 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 13 264 (Supplementary Fig. 1d). Expression of several upregulated chemokines in the infected brain 265 including Ccl2, Ccl4, Ccl5, Ccl7 also localized to regions enriched in myeloid cells whereas Tnf 266 and interleukins including Il6, Il12b, Il15 and Il18 were detected throughout the brain (Fig. 4c, 267 Supplementary Fig. 3a, b). Interestingly, Cxcl10 had a more robust expression throughout 268 regions of the infected brain other than the Ly6c enriched regions, suggesting that Cxcl10 may be 269 primarily expressed by cells other than the localized myeloid subpopulation (Supplementary 270 Fig. 3c). Consistent with the scRNAseq data, there was a significant reduction in cells expressing 271 markers of homeostatic microglia such as Cx3cr1 and Tmem119 and an increase in cells 272 expressing Cd72, a gene enriched in activated microglia, in infected brains (Fig. 4h). These data 273 indicate that by 6 dpi, there is widespread activation of microglia and regional specificity in 274 infiltrating myeloid subpopulations. 275 Recruited NK and CD8 T cells showed a widespread distribution in the brain parenchyma 276 and elevated expression of cytotoxic proteins 277 To gain a more comprehensive understanding of lymphocyte responses to infection, all 278 cells from lymphocyte clusters (clusters 2, 4 and 6; Fig. 2b) were extracted and reanalyzed. This 279 included the following number of cells for each condition: Uninfected: 66, TC-83 2 dpi: 89, TC- 280 83 4 dpi: 987, and TC-83 6 dpi: 2783. Cells were primarily identified as one of two subclusters 281 of NK cells, referred to as NK cells 1 and 2, CD8 + T cells, CD4+ T cells, or smaller detected 282 clusters of T cells and cells expressing markers of proliferation (Fig. 5a). At both 4 and 6 dpi, 283 NK cell clusters composed a greater proportion of total lymphocytes than CD8 + and CD4+ T 284 cells, which exhibited a steep increase at 6 dpi (Fig. 5b, c). High expression of genes encoding 285 cytotoxic proteins, including perforin, granzyme B, and FasL, were observed at both 4 and 6 dpi 286 (Fig. 5d). Perforin and granzyme genes (Prf1 and Gzmb, respectively) were expressed in both 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 14 287 NK subclusters and CD8 + T cells at both timepoints, with NK cells exhibiting higher expression 288 (Fig. 5e ). While expression in NK cells was comparable for both timepoints, CD8+ T cells 289 exhibited higher expression of Prf1 and Gzmb at 6 dpi. Additionally, Ifng expression was 290 detected in both NK sub clusters and CD8 + T cells, each exhibiting higher expression at 4 dpi. 291 Production of perforin at 6 dpi was also observed by flow cytometry and IFN- by protein 292 analysis (Fig. 5g-j). Perforin protein was detected in NK cells and CD8 + T cells, but not CD4+ T 293 cells (Fig. 5i). Further, perforin protein was significantly higher in the brain than spleen in both 294 cell types, suggesting unique cytotoxic effector expression in the brain versus systemically. An 295 increase in the expression of NK cell inhibitory genes such as Klrc1 and Klrd1 was observed in 296 both NK and CD8 + T cells at 6 dpi (Fig. 5e). This together with the decreased Ifng expression at 297 6 dpi may indicate NK cell exhaustion around the later timepoint. Spatial transcriptomic analysis 298 at 6 dpi revealed detection of NK and T cell associated genes throughout the brain with no 299 observable specific localization patterns, though, Ifng expression sometimes formed concentrated 300 clusters (Fig. 5f). These results indicate a robust and ubiquitous upregulation of cytotoxic 301 effectors throughout the brain in response to VEEV infection that coincide with timepoints of 302 severe neurological dysfunction. 303 Comparative analysis of infected brain and peripheral blood mononuclear cells reveals 304 distinct immune landscapes 305 To compare the immune landscape in the brain to systemic immune changes induced by 306 VEEV infection, we profiled peripheral blood mononuclear cells (PBMCs) from infected and 307 uninfected mice at 6 dpi using scRNAseq. Our analysis identified twelve immune cell clusters 308 including NK cells, T cells, B cells, mono/macs, neutrophils, DCs and plasma cells (Fig. 6a-c ). 309 Consistent with our findings in the brain, the proportion of NK cells dramatically increased in the 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 15 310 blood after infection (Fig. 6d). Mono/Macs also expand in both the infected blood and brain 311 (Fig. 3c, d and 6d). In contrast to what we observed in the brain, T cells 1, which encompassed 312 CD4 + T cells and regulatory T cells, decreased and the proportion of CD8+ T cells remained 313 similar between infected and uninfected blood (Fig. 6d). Neutrophils, which do not infiltrate the 314 brain, dramatically expanded in the blood after infection and upregulated antiviral response 315 genes, including Ddx58, Ifih1, Ifit1, Ifit2 and Isg15 (Fig. 6e ). Blood Mono/Macs and DCs also 316 activated a large number of antiviral response genes in response to infection (Fig. 6e). 317 Next, we compared infection-induced transcriptional changes in PBMCs to immune cells 318 from the brain (Supplementary Fig. 4 a, b). Similar to the brain, blood Mono/Macs from 319 infected mice activated Plac8, Ly6c2 and cathepsin family proteases (Supplementary Fig. 4c). 320 We found that cytokines such as Ccl2, Ccl5 and Ccl7 had markedly lower expression in infected 321 blood Mono/Macs compared to brain Mono/Macs while Ccr2, a receptor involved in myeloid 322 recruitment to the CNS, had an elevated expression in blood Mono/Macs (Fig. 6f, 323 Supplementary Fig. 4c) [27]. Peripheral blood NK cells from infected mice expressed perforin, 324 granzymes, cytokines and inhibitory receptors at a level comparable to NK cells from infected 325 brains, with slightly higher Gzmb expression in the infected brain (Fig. 6g). Interestingly, 326 uninfected blood NK cells also had robust expression of Prf1 and Gzma but, not Gzmb. The 327 expression levels of perforin and granzymes were also upregulated in CD8 + T cells after 328 infection (Fig. 6h). Unlike uninfected NK cells, uninfected blood CD8 + T cells did not express 329 Prf1 or Gzma, suggesting that cytotoxic gene expression in CD8+ T cells was infection-induced 330 (Fig. 6h). Overall, these findings emphasize a tissue-specific upregulation of cytotoxic effectors 331 as a potential key component of the immune landscape in the brain in response to VEEV 332 infections. 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 16 333 Discussion 334 Existing and emerging encephalitic viral infections pose a growing medical challenge. 335 Viral infection of the brain and subsequent inflammatory immune responses can cause long 336 lasting or permanent damage leading to serious clinical outcomes and death. Lethal encephalitis 337 that ensues following VEEV infection is attributed to the host immune response to viral 338 replication in brain tissue, but it remains unclear which specific components of the immune 339 response instigate disease. Thus, understanding the processes involved in the recruitment, 340 differentiation, and function of immune cells in the brain during infection is of high importance 341 to aid the development of novel therapeutics that target immunopathological responses. Here we 342 temporally dissected the heterogeneity of the immune response in the brain over a time course of 343 infection using scRNAseq. We then employed spatial transcriptomic analysis of the brain during 344 severe disease to investigate the spatial organization of distinct populations within the brain. 345 We observed a dynamic response to VEEV in myeloid cells over the course of infection. 346 The emergence of two subpopulations of microglia was observed by 2 dpi that we termed 347 activated microglia (MG) 1 and 2. Trajectory analysis revealed their simultaneous expansion, 348 suggesting they are uniquely activated subpopulations. Notably, activated MG 1 was the primary 349 cell type expressing genes encoding RIG-I (Ddx58) and MDA5 (Ifih1), PRRs important in 350 alphavirus sensing, along with type I IFN genes Ifnb1 and Ifna2 throughout infection. Activated 351 MG 2 did not exhibit elevated Ddx58 or Ifih1 but showed elevated Ifna2 expression at 6 dpi. 352 Interestingly, within activated MG 1, only a small portion of the population expressed Ifnb1 at 353 each timepoint. While VEEV primarily infects neurons and astrocytes, it can also infect 354 microglia to a lesser frequency [10]. It is possible that the unique expression profiles among 355 microglia may represent infected cells versus bystander cells assuming an antiviral state. 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 17 356 Alternatively, concurrent molecular pathways could be contributing to limiting Ifnb1 expression. 357 Further, a lack of upregulation of additional interferon-stimulated genes (ISGs) in the activated 358 microglia subsets suggests their activation may be orchestrated by other cell types that are 359 directly sensing virus. 360 The early and sustained activation of microglia during VEEV infection likely plays a role 361 in the recruitment of other inflammatory cell types. Activated MG 1 and 2 share expression of 362 cytokines expressed by disease-associated microglia (DAM) with important roles in the 363 recruitment of inflammatory monocytes [28-30]. Ly6C hi monocytes have been shown to play 364 both protective and pathogenic roles upon migration to the brain [31-36]. In our data set, greater 365 than 30% and 40% of immune cells in the brain are infiltrating myeloid cells by 4 and 6 dpi, 366 respectively, consisting mostly of two Ly6c2 expressing subpopulations (Mono/Mac 1 and 2) that 367 display high induction of numerous ISGs, cytokines, and chemokines. Trajectory analysis 368 indicated Mono/Mac 1 and 2 cluster separately throughout each timepoint, likely ruling out 369 either population being a precursor state to the other. Notably, this large myeloid infiltration 370 coincides with peak viral replication and the appearance of clinical signs and future studies 371 should investigate the mechanistic role of these cells in VEEV infection. 372 Other notable myeloid cell populations that responded to infection include neutrophils 373 and DCs. Both cell populations expanded in the blood and expressed high levels of antiviral 374 genes in response to infection, yet only DCs were detected at appreciable levels in the brain. The 375 lack of neutrophils in the brain is notable given their dramatic expansion in the blood and their 376 significant infiltration of the brain during other neurotropic infections, including with other 377 neurotropic arboviruses [37]. The expansion of neutrophils in the blood and their high expression 378 of antiviral response genes may indicate a role in systemic viral clearance. 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 18 379 Lymphocyte populations exhibited diverse responses upon infection, with subset 380 populations demonstrating brain-specific cytotoxic signatures. NK cells expanded considerably 381 in the brains and blood of infected animals, and T cells exhibited a sharp increase in the brain at 382 6 dpi. Conversely, B cells decreased proportionally in the blood upon infection and did not 383 infiltrate the brain. Both NK cells and CD8 + T cells from infected animals expressed elevated 384 levels of cytotoxic genes, with higher expression observed within brain-infiltrating cells. While 385 blood NK cells exhibited similar levels of Prf1 and Gzma expression as in the brain, brain NK 386 cells exhibited higher Gzmb. Notably, Gzmb exhibits a lower baseline expression in blood NK 387 cells than Gzma, therefore the high level of Gzmb observed in the brain following infection 388 represents a dramatic shift from baseline expression. Furthermore, striking differences in 389 perforin expression in NK and CD8 + T cells were observed between cells isolated from brains 390 versus spleens via flow cytometry, with brain populations of both cell types exhibiting 391 significantly higher expression. Therefore, the highly cytotoxic state of brain infiltrating NK and 392 CD8 + T cells is a specific quality and may be significantly contributing to immunopathology. 393 Notably, previous work has implicated NK cells in the pathogenesis of VEEV in mouse models 394 and the cytopathic effects of NK and T cells have also been implicated in neuroinflammation and 395 blood brain barrier (BBB) disruption in non-infectious contexts [16, 38, 39]. The abundantly 396 present cytotoxic lymphocytes are potentially key effectors of damage in the brain and, therefore, 397 appealing therapeutic targets via blockade of their recruitment or function. 398 A key feature of our data is the unique spatial localization of infiltrating Mono/Mac cells 399 within the brain on 6 dpi. A Ly6c2-expressing myeloid cluster was found to occupy a distinct 400 regional pattern that lined the cortex perimeter and a region surrounding the hippocampal 401 formation. In contrast, T and NK cells exhibited no clear localization pattern and were 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 19 402 distributed throughout the brain. These data suggest fundamental differences in the mechanisms 403 governing myeloid and lymphocyte infiltration into the brain. NK and T cells may require 404 breakdown of the BBB in order to infiltrate in significant numbers, reflected in their widespread 405 distribution throughout the brain, whereas myeloid cells may traverse alternate barriers (choroid 406 plexus, glia limitans) at earlier time points. The mechanisms that control myeloid cell 407 extravasation into the brain parenchyma across any barrier are under described [40], therefore 408 additional research into entry mechanisms during viral infection is critical. The relationship 409 between localized viral replication within the brain and the proximity to clustered immune 410 populations also warrants further investigation. Notably, concentrated infected/apoptotic neurons 411 have been observed in the hippocampus in VEEV-infected mice [41]. In turn, the Lyc62- 412 expressing cluster likely contributes to further inflammation, as these cells exhibited high 413 expression of chemokines, including Ccl2, Ccl4, and Ccl7. The role of each cell population and 414 features such as their localization, expression profiles, and temporal infiltration during VEEV 415 pathogenesis are of keen interest for follow on studies. 416 In summary, this study provides a comprehensive profiling of transcriptional activity of 417 immune cells in the brain during viral encephalitis. We define the heterogeneity of the immune 418 response in the brain at three timepoints post-VEEV infection, tracing the activation of resident 419 cells, infiltration of cells from the periphery, and the emergence of various subpopulations. We 420 compared the brain gene expression data to that of PBMCs to underscore brain-specific changes. 421 Spatial transcriptomics identified localization of an infiltrating myeloid population to discrete 422 regions of the brain, and furthermore highlighted distinct localization patterns of myeloid cells 423 and lymphocytes. Future studies should utilize the data provided here to further define the 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 20 424 immunopathogenic components of the response to VEEV versus those that are protective to 425 inform the development of vaccine and therapeutic candidates. 426 Materials and Methods 427 Cells and virus 428 Vero E6 cells were obtained from the American Type Culture Collection (ATCC) and 429 maintained in Dulbecco’s modified Eagle’s medium (DMEM, Thermo Fisher) supplemented 430 with 10% fetal bovine serum (FBS, ATCC) supplemented with 100 units/mL penicillin and 100 431 μg/mL streptomycin (Thermo Fisher) at 37 °C in 5% CO2. Venezuelan equine encephalitis virus 432 (VEEV) strain TC-83 (NR-63) was obtained from the NIH Biodefense and Emerging Infections 433 Research Resources Repository, NIAID, NIH. VEEV stocks were propagated in Vero E6 cells 434 and harvested via clarification of cell culture supernatant by centrifugation. Titers of viral stocks 435 were determined by standard plaque assay consisting of a methyl crystalline cellulose overlay 436 and crystal violet staining [42]. 437 Mouse infections 438 All animal work was approved by the Lawrence Livermore National Laboratory Institutional 439 Animal Care and Use Committee under protocol #310. All animals were housed in an 440 Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC)- 441 accredited facility. C3H/HeN mice (strain ID 025) were obtained from Charles River. 5–10- 442 week-old female mice were used in all experiments. Groups of mice were inoculated intranasally 443 with 2x10 7 PFU TC-83 while under anesthesia (4-5% isoflurane in 100% oxygen). Mice were 444 monitored daily for signs of morbidity and animals were humanely euthanized upon signs of 445 severe disease by CO 2 asphyxiation. For tissue harvest, animals were anesthetized under 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 21 446 isoflurane and the whole animal was perfused with 20 mL sterile PBS containing 50,000 U/L 447 sodium heparin via the left ventricle. 448 Brain tissue isolation and preparation 449 Brains for flow cytometric, scRNAseq, cytokine, and viral titer analysis were isolated from 450 infected mice on days 2, 4, or 6 post-infection. Preparation of brain tissue was performed as 451 previously described [19]. Following euthanasia and perfusion, brains were removed and placed 452 in 1 mL digestion buffer (PBS pH 7.4 (Thermo Fisher) + collagenase (Worthington) + DNase I 453 (Roche) to a final concentration of 3 mg/mL and 0.5 mg/mL, respectively) on ice in a 1.5 mL 454 tube. Brains were finely diced into 1-2 mm 3 pieces with scissors and tissue was digested at 37 °C 455 for 30 min in a total of 5 mL digestion buffer. A cell suspension was generated by gentle 456 pipetting followed by passage through a 70 μm cell strainer. The cell strainer was rinsed with 457 PBS supplemented with 5% FBS to a total volume of 20 mL. Aliquots of this suspension were 458 stored at -80 °C for cytokine and viral titer analysis. The remaining suspension was subjected to 459 Percoll gradient centrifugation to purify mononuclear immune cells for flow cytometric analysis 460 or RNA sequencing as previously described [19]. 461 Single-cell RNA sequencing of brain samples and data analysis 462 Single-cell suspensions of mononuclear immune cells from uninfected mouse brains and infected 463 brains were prepared as described above. Cells were counted on a Countess II automated cell 464 counter prior to single-cell sequencing preparation using Chromium Single-cell 3ʹ GEM, Library 465 & Gel Bead Kit v3 (10x Genomics Cat # 1000075) on a 10× Genomics Chromium Controller 466 following manufacturers’ protocol. Subsequently, libraries were sequenced on Illumina NextSeq 467 2000. The Cell Ranger Single-Cell Software Suite (10x Genomics) was then used to perform 468 sample demultiplexing, barcode processing, and single-cell gene counting. After demultiplexing 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 22 469 the sequencing files with “cellranger mkfastq”, “cellranger count” was used to perform 470 alignment to mouse reference transcriptome (mm10), barcode processing and gene counting. 471 Further analysis was performed using Seurat [43]. First, cells with fewer than 500 detected genes 472 per cell or mitochondrial content greater than 10% and genes that were expressed by fewer than 473 5 cells were filtered out. Potential doublets and CD45 - cells were also removed. After pre- 474 processing, we performed data normalization, scaling, and identified 2000 most variable 475 features. Then, anchors for data integration were identified using the ‘FindIntegrationAnchors()’ 476 function. Next, these anchors were passed to the ‘IntegrateData()’ function and a new integrated 477 matrix with all four datasets was generated. After data integration, data was scaled, and the 478 dimensionality of the data was reduced by principal component analysis (PCA). Subsequently, 479 cells were grouped into an optimal number of clusters for de novo cell type discovery using 480 Seurat’s ‘FindNeighbors()’ and ‘FindClusters()’ functions. A non-linear dimensional reduction 481 was then performed via uniform manifold approximation and projection (UMAP) and various 482 cell clusters were identified and visualized. Genes differentially expressed between clusters were 483 identified using ‘FindMarkers()’ function implemented in Seurat. Gene ontology (GO) 484 enrichment analysis was performed using ToppGene Suite [44] and heatmaps were generated 485 using custom R scripts. Single-cell pseudo-time trajectories of immune cell subpopulations were 486 constructed with Monocle [45] as described before [46]. 487 Single-cell RNA sequencing of blood samples and data analysis 488 Single-cell suspensions of PBMCs were isolated from uninfected mice and mice at 6-days post- 489 infection. Sequencing libraries were generated using Chromium Single-cell 3ʹ GEM, Library & 490 Gel Bead Kit v3 (10x Genomics Cat # 1000075) on a 10× Genomics Chromium Controller 491 following manufacturers’ protocol. Subsequently, libraries were sequenced on Illumina NextSeq 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 23 492 2000. Sequencing files were demultiplexed using “cellranger mkfastq”. Subsequently, 493 “cellranger count” was used to perform alignment to mouse reference transcriptome (mm10), 494 barcode processing and gene counting. Further analysis was performed using Seurat [43] as 495 described above. 496 Bulk RNAseq data analysis 497 We used publicly available data [20] to identify VEEV infection-induced changes in different 498 regions of the brain. Raw counts were downloaded from Gene Expression Omnibus (GEO, 499 accession ID: GSE213725). Data was then normalized using the Trimmed Mean of M-values 500 (TMM) normalization method implemented in edgeR [47]. For differential expression analysis, 501 the limma package [48] was employed in conjunction with the voom transformation [49]. 502 Heatmaps for genes of interest were generated using the pheatmap package [50], and a boxplot 503 was created using the ggplot2 package [51], both in R (version 4.3.1) [52]. 504 Flow cytometry 505 Cells were incubated for 30 min on ice in 100 μl Hank’s balanced salt solution (Thermo 506 Fisher) + 2% FBS with Fc block (1:100 dilution, clone 2.4G2; BD Biosciences) along with the 507 following antibodies, each diluted 1:500: CD45 APC-Cy7 (clone 30-F11; BD Biosciences), 508 CD11b PE-CF594 (clone M1/70; BD Biosciences), CD3 PerCP-Cy5.5 (clone 17A2; BD 509 Biosciences), CD4 BV650 (clone GK1.5; BD Biosciences), CD8 FITC (53-6.7; BioLegend), 510 CD49b PE (clone DX5; BD Biosciences), and Perforin PE/Dazzle 594 (clone S16009A; 511 BioLegend). Cells were then fixed using BD Cytofix/Cytoperm (BD Biosciences) according to 512 manufacturer’s instructions. Flow cytometry was performed using a FACSAria Fusion and data 513 were analyzed using FlowJo software. Microglia, other myeloid lineage, and lymphocytes were 514 resolved using CD45 and CD11b expression, with microglia identified as CD45 int CD11bint, 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 24 515 other myeloid as CD45 hi CD11bhi, and lymphocytes as CD45hi CD11b– as previously described 516 [19]. 517 Cytokine analysis 518 Cytokines were quantified using LEGENDplex TM multiplex bead-based assay (BioLegend) using 519 the mouse anti-virus response panel according to manufacturer’s instructions. Flow cytometry of 520 the beads was performed using a FACSAria Fusion and data were analyzed using BioLegend’s 521 cloud-based analysis software available at https://legendplex.qognit.com. 522 Immunohistochemical staining of mouse brains 523 Brains for immunohistochemical staining were collected from infected mice at 6 dpi. Upon 524 euthanasia and perfusion as described above, mice were perfused with 20 mL 10% neutral 525 buffered formalin (NBF). Isolated brains were subsequently immersed in 10% NBF at 4° C for 3 526 days with gentle agitation. Following fixation, brains were paraffin-embedded and cut into 5 μm 527 thick sections. All sections were dewaxed with xylene and hydrated with alcohol. Citrate or Tris- 528 EDTA were used for antigen retrieval, and hydrogen peroxide (ab64218, Abcam) was used to 529 block endogenous peroxidase. After blocking non-specific sites with CAS-block (008120, 530 Thermo Fisher Scientific), sections were incubated with Ly6c primary antibody (ab314120, 531 Abcam) and secondary antibody (ab6720, Abcam). 3,3′-diaminobenzidine (DAB) kit (ab64238, 532 Abcam) was used for visualization, and hematoxylin was used to stain the nuclei. All sections 533 were rinsed with distilled water and sealed with permount (sp15-100, Fisher Scientific). 534 Spatial transcriptomics 535 Spatial transcriptomic analysis of a VEEV-infected brain collected 6 days post-infection was 536 conducted using 10x Visium technology (10x Genomics). 5 µm brain sections were mounted 537 onto charged slides, deparaffinized, and stained with hematoxylin and eosin. The brain sections 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 25 538 were imaged at 4x magnification using an ECHO Revolve microscope and the tiles were stitched 539 together using Affinity Photo (Serif). The brain sections were then destained and decrosslinked 540 to release RNA sequestered by formalin fixation. Next, the sections were incubated with mouse- 541 specific probes targeting the whole transcriptome (PN-1000365, 10x Genomics), allowing for the 542 hybridization and ligation of each probe pair. Gene expression probes were released from the 543 tissue and captured by spatially barcoded oligonucleotides on the Visium slide surface using the 544 Visium CytAssist instrument. Gene expression libraries were then prepared from each tissue 545 section and sequenced using the Illumina NextSeq 2000. 546 After sequencing, the data and histology images were processed with the Space Ranger software 547 (10x Genomics) and Seurat [43]. After demultiplexing the sequencing files with “spaceranger 548 mkfastq”, the fastq files and the brightfield image of the tissue were provided to “spaceranger 549 count” and read alignment, tissue detection, fiducial detection, and barcode/UMI counting were 550 performed. Seurat was used then used for filtering (nFeature_Spatial > 500 & nCount_Spatial > 551 500 & percent.mt < 30), data normalization, dimensionality reduction, clustering, identification 552 of spatially variable genes, and visualization. For each sample, after filtering, the data was 553 normalized using “SCTransform()” function implemented in Seurat. Dimensionality reduction 554 and clustering analysis was performed using “RunPCA()”, “FindNeighbors()”, “FindClusters ()” 555 and RunUMAP() functions. We then applied the “FindAllMarkers()” function to identify genes 556 enriched in each cluster. We also performed integrative analysis of uninfected and infected brain 557 samples using the “SelectIntegrationFeatures()”, “PrepSCTIntegration()”, 558 “FindIntegrationAnchors()”, and “IntegrateData ()” functions, followed by dimensionality 559 reduction using PCA and UMAP. Clusters were visualized in UMAP space using DimPlot() and 560 overlaid on the tissue image using “SpatialDimPlot()” function. 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 26 561 Statistical analyses 562 Statistical significance was determined using Prism Version 10.2.3 (347) (GraphPad, La Jolla, 563 CA). Specific tests and p values are indicated in the figure legends. 564 Data Availability 565 The single cell RNA-sequencing and spatial data have been deposited at the NCBI Gene 566 Expression Omnibus (GEO), accession numbers GSE274566 and GSE275201, respectively. 567 Acknowledgements and Funding. 568 Funding for this research was provided by internal Lawrence Livermore National Laboratory 569 Directed Research and Development funds (22-ERD-038 to D.R.W.). The funders had no role in 570 study design, data collection and analysis, decision to publish, or preparation of the manuscript. 571 This work was performed under the auspices of the U.S. Department of Energy by Lawrence 572 Livermore National Security, LLC, Lawrence Livermore National Laboratory under Contract 573 DE-AC52-07NA27344. 574 Author Contributions. 575 M.V.R., A.S., N.R.H, and D.R.W. conceived the project and designed experiments; M.V.R., 576 N.F.L., A.M.P, N.R.H, and D.R.W. performed experiments; A.S. and B.M.G. analyzed 577 sequencing data; D.R.W. supervised the project. M.V.R. and D.R.W. wrote the original draft, 578 and all authors were involved in manuscript review and editing. 579 Competing Interests Statement 580 The authors declare no competing interests. 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. 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PubMed PMID: 25605792; PubMed 725 Central PMCID: PMCPMC4402510. 726 49. Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model 727 analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. Epub 20140203. doi: 728 10.1186/gb-2014-15-2-r29. PubMed PMID: 24485249; PubMed Central PMCID: 729 PMCPMC4053721. 730 50. Kolde R. Pheatmap: pretty heatmaps. R package version 1, 726. 2019. 731 51. Wickam H. ggplot2: Elegant Graphics for Data Analysis: Springer Cham; 2016. 732 52. Team RC. R: A Language and Environment for Statistical Computing. R Foundation for 733 Statistical Computing, Vienna. 2023. 734 735 Figure Legends 736 Fig. 1: VEEV TC-83 Infection in C3H mice. 5–8-week-old C3H mice were infected 737 intranasally with 2e7 PFU of VEEV TC-83 and symptoms (a) and weight loss (b) were 738 monitored. Data are shown as mean  standard error of mean (SEM) (n=4-12). Experiments were 739 repeated at least twice. Two-tailed p values were calculated using 2-way ANOVA with 740 Bonferroni’s correction for multiple comparisons. Brain viral titers were evaluated (c). Two- 741 tailed p values were calculated using Kruskal-Wallis and Dunn’s multiple comparisons test. 742 Signs of pathology in the brain were observed by H&E histological staining (d, e); yellow arrows 743 denote vascular cuffing. (f) Schematic representation of experimental workflow for scRNAseq 744 and spatial transcriptomic analysis of VEEV-infected brains. Following VEEV infection as 745 described above, the cerebral cortex of 3 uninfected mice or 3 infected mice harvested at 2, 4, 746 and 6 dpi were processed to yield a single cell suspension of mononuclear immune cells that 747 were analyzed by scRNAseq. Alternatively, uninfected and infected cerebral cortices harvested 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 31 748 at 6 dpi were fixed and processed for analysis by spatial transcriptomics. Created with 749 BioRender.com. 750 Fig. 2: Temporal profiling of immune cells in the brain by scRNAseq in VEEV infection. 751 (a) Uniform Manifold Approximation and Projection (UMAP) visualization of cell clusters 752 identified in each experimental group. Cell clusters are color-coded and numbered. (b) Dot plot 753 showing the expression of select cell-type markers. Dot size represents the fraction of cells of the 754 indicated cluster expressing the markers and the intensity of color represents the average marker 755 expression level in that cluster. (c) The relative proportion of cell types within brains of each 756 experimental group. (d) Line graph representation of the change in proportion of prominent cell 757 types over time in uninfected and infected brains. (e) Flow cytometric analysis of cell types in 758 uninfected and infected brains. Representative CD11b vs CD45 plots are shown with gates 759 identifying lymphocytes (CD11b -, CD45hi), myeloid cells (CD11bhi, CD45hi), and microglia 760 (CD11b hi, CD45low) (f) Percentages of lymphocytes and myeloid cells. Data are shown as mean 761  SEM (n= 4-7). Experiments were repeated at least twice. Two-tailed p values were calculated 762 using one-way ANOVA with Tukey’s correction for multiple comparisons. 763 Fig. 3: Analysis of myeloid populations in the brain in by scRNAseq in VEEV infection. 764 UMAP visualization of myeloid sub-clusters colored by identification (a) or experimental group 765 (b). (c) Heat map showing expression level of selected cell type markers in each cluster. (d ) Heat 766 map of IFN ⍺/β response genes. (e ) Violin plots showing the expression of select ISGs across 767 experimental groups. (f) Dot plot showing expression level of select inflammatory signaling 768 genes across experimental groups. Dot size represents the fraction of cells in the indicated group 769 expressing the indicated gene and the intensity of color represents the average marker expression 770 level in that group. (g) Heat map showing expression level of select inflammatory signaling 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 32 771 genes across key myeloid populations. (h) Protein levels of select immune mediators measured 772 using BioLegend LEGENDplex multiplex immunoassay. Data are shown as mean  SEM (n=4). 773 Two-tailed p values were calculated using unpaired Student’s t test. 774 Fig. 4: Spatial transcriptomic profiling of myeloid populations in the brain during VEEV 775 infection. 5-m coronal sections through the hippocampus were derived from uninfected mice or 776 infected mice at 6 dpi. Sections were used for either ST (a-h) or immunostaining (i). Distribution 777 of transcriptionally unique populations detected in uninfected (a) or infected (b) brains. 778 Populations are numbered and color-coded. (c) Annotation of major brain regions. MB= 779 midbrain, HPF= hippocampal formation, CTX= cortex. (d) Dot plot showing expression levels 780 of identifying markers of cell types across the brain. Dot size represents the fraction of cells in 781 the indicated cluster expressing the markers and the intensity of color represents the average 782 marker expression level in that cluster. Distribution of cluster 2 is shown in uninfected (e) or 783 infected (f) brains. Distribution of select myeloid gene and cytokine expression (g) and 784 microglial gene expression (h ). (i) Representative images of Ly6C staining in the hippocampus 785 (magnification = 20). 786 Fig. 5: Analysis of lymphocyte populations in the brain by scRNAseq and spatial 787 transcriptomics. UMAP visualization of lymphocyte clusters colored by identity (a) or 788 experimental group (b). (c) Proportions of lymphocyte populations. (d ) Violin plots showing 789 expression of cytotoxic genes across experimental groups. (e) Violin plots showing expression of 790 select NK and T cells genes within the indicated subpopulations. Colors indicate timepoint post 791 infection. (f) Spatial distribution of select NK and T cell genes in uninfected or infected brains at 792 6 dpi. (g) Representative flow cytometry plots showing perforin expression in NK and T cell 793 populations in the brain at 6 dpi. Quantification of NK and T cells in the brain is shown in (h) 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 33 794 and perforin in the brain versus spleen in (i). (j) Protein analysis of IFN- in uninfected or 795 infected brains at 6 dpi. Data in h-j are shown as mean  standard error of mean (SEM) (n=4-7). 796 Two-tailed p values were calculated using 2-way ANOVA with Bonferroni’s correction for 797 multiple comparisons (h, i) or unpaired Student’s t test (j). 798 Fig. 6: Comparative scRNAseq analysis immune cells in the brain and PBMCs during 799 VEEV infection. UMAP visualization cell clusters in the blood at 6 dpi colored by identity (a) 800 or treatment (b). c Expression of identifying markers across clusters. d Relative proportions of 801 populations. e Heat map showing expression of select antiviral response genes across cell 802 populations in uninfected versus infected blood. Comparison of the expression of select genes in 803 mono/mac (f), NK cell (g) and CD8 + T cell (h) populations in the brain versus blood. 804 805 806 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 34 807 Figures 808 Fig. 1 809 810 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 35 811 Fig. 2 812 813 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 36 814 815 Fig. 3 816 817 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 37 818 819 Fig. 4 820 821 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 38 822 Fig. 5 823 824 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint 39 825 Fig. 6 826 827 105 and is also made available for use under a CC0 license. (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 The copyright holder for this preprintthis version posted September 14, 2024. ; https://doi.org/10.1101/2024.09.12.612602doi: bioRxiv preprint