Mulenge et al. Journal of Neuroinflammation
https://doi.org/10.1186/s12974-024-03197-2(2024) 21:203

Journal of Neuroinflammation

Open Access

RESEARCH

Transcriptomic analysis unveils bonafide molecular signatures of microglia
under conditions of homeostasis and viral
encephalitis
Felix Mulenge1, Olivia Luise Gern1, Lena Mareike Busker1,2, Angela Aringo1, Luca Ghita1,3, Inken Waltl1,
Andreas Pavlou1 and Ulrich Kalinke1,4*Abstract
Microglia serve as a front-line defense against neuroinvasive viral infection, however, determination of their actual
transcriptional profiles under conditions of health and disease is challenging. Here, we used various experimental
approaches to delineate the transcriptional landscape of microglia during viral infection. Intriguingly, multiple
activation genes were found to be artificially induced in sorted microglia and we demonstrated that shear stressencountered during cell sorting was one of the key inducers. Post-hoc analysis revealed that publicly available
large-scale single-cell RNA sequencing datasets were significantly tainted by aberrant signatures that are associatedwith cell sorting. By exploiting the ribosomal tagging approach, we developed a strategy to enrich microgliaspecific transcripts by comparing immunoprecipitated RNA with total RNA. Such enriched transcripts were
instrumental in defining bona fide signatures of microglia under conditions of health and virus infection. These
unified microglial signatures may serve as a benchmark to retrospectively assess ex vivo artefacts from available
atlases. Leveraging the microglial translatome, we found enrichment of genes implicated in T-cell activation andcytokine production during the course of VSV infection. These data linked microglia with T-cell re-stimulationand further underscored that microglia are involved in shaping antiviral T-cell responses in the brain. Collectively,
our study defines the transcriptional landscape of microglia under steady state and during viral encephalitis andhighlights cellular interactions between microglia and T cells that contribute to the control of virus dissemination.

*Correspondence:
Ulrich Kalinke
ulrich.kalinke@twincore.de1
Institute for Experimental Infection Research, Centre for Experimental
and Clinical Infection Research, a joint venture between The HelmholtzCentre for Infection Research, Hannover Medical School, TWINCORE,
Feodor-Lynen-Str. 7, 30625 Hannover, Germany
2
Department of Pathology, University of Veterinary Medicine Hannover,30559 Foundation, Hannover, Germany
3
Genentech, South San Francisco, CA 94080, USA
4
Cluster of Excellence RESIST (EXC 2155), Hannover Medical School,
30625 Hannover, Germany© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Mulenge et al. Journal of Neuroinflammation(2024) 21:203

Introduction
Microglia are resident immune cells of the central nervous system (CNS) that arise from erythromyeloid
progenitors, which seed the brain parenchyma during embryonic development [1]. They are highly plastic
in nature allowing them to continually survey the CNS
milieu and to establish brain homeostasis by regulating
synaptic wiring, myelination, vasculogenesis, and remodeling neuronal circuits [2]. Moreover, microglia are central mediators that respond subtly and sometimes grossly
to various neuropathologies such as injury, inflammation,
cancer and neurodegeneration [3–6], by instrumentalization of signaling mechanisms that are not fully resolved,
yet. To better understand the diverse multifunctionalroles of microglia, it is critical to precisely illuminatetranscriptomic signatures of certain microglial phenotypes. With the recent advent of novel sequencing technologies, cell isolation approaches, and computationalframeworks, a significant expansion of high-throughput
profiling of different cell types was promoted, which provided new insights into the biology of cells within theirtissue context [7, 8]. Furthermore, these large-scale transcriptomic datasets serve as a reference atlas providing ahigh-level overview of expression patterns of cell subsetsthat would otherwise be too costly or time-consuming
for individual laboratories to generate. These data are,however, not without caveats.
A critical step in transcriptomic studies is the isolation and preparation of pure suspensions of the relevant
cell subset. This is particularly challenging for microglia, since their low abundance in the brain often resultsin little cell yields. Microglia can be enriched from brain
cell preparations by fluorescence-activated cell sorting(FACS) [9]. Combined immunolabeling of core markers
such as CD11b+ and CD45low allows discrimination of
microglia from recirculating monocytes [4]. However,microglial CD45 levels may change after brain injury
and during brain disease [10], thus making discrimination of microglia from monocytes difficult [11]. Substantial efforts have been undertaken to generate inducible
mouse lines expressing fluorescent markers under thecontrol of microglia-specific promoters such as CX3CR1,
HEXB, TMEM119, and P2RY12 [12–15]. These models
allow sorting of microglia without the need to immunolabel the cells by antibodies. The ribosomal tagging
approach (RiboTag) is an alternative way to studying
microglia-specific gene expression profiles from complex tissue. This method relies on Cre recombinase mediated expression of a haemagglutinin (HA) tag fused tothe core ribosomal protein 22 (Rpl22) [16], allowing thepull-down of mRNA bound to HA-tagged ribosomes anddetermining the translational profile of brain resident
myeloid cells [17]. In addition, various microglia in vitro
models, including primary cultivated cell cultures, stem

Page 2 of 22cell-derived microglia cultures, organoid, and immortalized microglia cell lines have been developed [18–21].
Such in vitro systems are assumed to model microglia
physiology and function in an experimentally amenable
manner.
A key pursuit in molecular neuroscience is to gain amechanistic understanding of orchestrated microglial
responses in health and disease. However, the heterogeneity of microglial signatures, as detected in various studies, the discrepancies between transcriptomic datasetsand the lack of harmonized microglia isolation protocols
have made it difficult to achieve a consensus on the mainfeatures, pathways and effects of microglia responses topathological insults. Here, we performed a side-to-side
comparative analysis of the transcriptomes obtained
from in vitro cultivated microglia, sorted microglia, andthe microglial translatome determined by the RiboTag
approach, to discern specific molecular triggers and signatures that are defining microglia under homeostaticand inflammatory conditions. Our data illustrate thatconventional bulk RNA transcriptomes of sorted microglia contain ex vivo aberrant activation signatures. Such
“spurious” signatures were also detected in post-hoc
analysis of publicly available large-scale single-cell RNA
sequencing (scRNA-seq) datasets or atlases, underscoring the need to develop approaches that identify, compensate and/or avoid such biases. In brief, we incorporated
the concept of enrichment of relevant gene signaturesby comparing immunoprecipitated RNA with total RNA
in the RiboTag approach and assessing the specificity ofgiven transcripts known to be abundantly present in thetissue. Transcripts that are enriched are more likely specifically expressed within microglia, whereas transcriptsthat are depleted are more likely to be expressed in othercell types (i.e., non-myeloid) contained within the tissue. This approach allowed generation of accurate in situ
transcriptional profiles of microglia under homeostatic
conditions and after vesicular stomatitis virus (VSV)
infection that will be especially useful in retrospective
removal of ex vivo artefacts present in existing dataset.
Leveraging the microglial translatome, we demonstrated
that upon VSV infection, microglia adopt unique expression profiles implicated in cytokine production and antigen presentation. We further reported dramatic influxof monocytes, neutrophils, B cells, natural killer (NK)cells, and T cells in the CNS of VSV-infected animals
compared to controls, demonstrating substantial shift inbrain immune cell composition. Indeed, we found that
infiltrated T cells surrounded the dense conglomerates of
microglia, suggesting that T cells intimately interact withactivated microglia. Together, this study investigates protective mechanisms orchestrated by microglia in severe
neuroinflammation and CNS infection.

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Fig. 1 Comparative analysis of transcriptomes and translatomes reveals diverse cellular profiles of microglia. A, Schematic depiction of the experimental
models that were used to study the transcriptome and translatome of microglia. In vitro: Mixed glia cultures were prepared from P3 pups and microglia
harvested after 10 days. IP: The brain of tamoxifen treated CX3CR1-CreERT2+/−Rpl22wt/HA RiboTag mice was extracted, microglia-specific HA-tagged ribosomes were pulled down, and the attached RNA was isolated. Input: RNA from whole brain lysate was isolated. Sorted: Immune cells from the brain oftamoxifen-treated CX3CR1-CreERT2+/−TdTomatowt/ST mice were isolated and TdTomato-positive microglia were sorted. B, Heatmap representing k-meansclustering of highly enriched and de-enriched genes in RNA-seq analysis of input, IP, in vitro, and sorted microglia. Red represents enriched and blue
de-enriched gene expression levels. Boxplots with overlaid dotplots of normalized gene counts for (C) astrocyte, (D) oligodendrocyte, (E) neuron, and(F) microglia lineage markers. In each group three individual mice or triplicates of microglia cultures were analyzed (n = 3). Each boxplot represents interquartile range, while dotplots illustrate individual data points. Normalized reads represent read counts that have been normalized by DESeq2 (for details
see Materials and Methods)

Results
Adoption of different experimental approaches variably
impacts microglia gene expression profiles

Microglia are brain resident myeloid cells that can adopt
a wide range of different functions in health and disease.
Here, we aimed at determining the bona fide molecular signatures of microglia under homeostatic conditions and following VSV infection. To address this, westudied the transcriptomes of microglia isolated fromdifferent experimental models and performed a combinatorial analysis of the retrieved data. First, we cultivated mixed glial cultures from brain cortices of P3mouse pups and harvested in vitro microglia after 10days (hereafter denoted “in vitro”) (Fig. 1A). Secondly,
we exploited the RiboTag approach, i.e., we isolated thebrain from CX3CR1-CreERT2+/−Rpl22wt/HA mice 8 weeks
after tamoxifen treatment, prepared tissue lysates, anddirectly immunoprecipitated ribosome-bound RNA frombrain-resident myeloid cells (hereafter denoted “immunoprecipitated [IP]”), or we isolated the total RNA frombrain homogenates (hereafter denoted “input”). Lastly,
we included RNA-seq data from our lab [4], which wereretrieved from isolated brain resident immune cells oftamoxifen treated CX3CR1-CreERT2+/−TdTomatowt/ST
mice and sorted TdTomato positive myeloid cells (hereafter denoted “sorted”). After RNA extraction from material obtained by the different approaches, RNA-seq was
performed. Initially, we analyzed microglia from mocktreated samples to decipher baseline transcriptional profiles of homeostatic microglia. As sample processing andsequencing was performed at different times, we firstused ComBat-seq [22] to model batch effects and otherlatent sources of noise. After batch effect adjustment, we
performed differential analysis (log2-fold change >|1|,
padj < 0.05) by employing the likelihood ratio test (LRT)
and the resulting genes were subjected into k-meansclustering. This analysis revealed the presence of 7 distinct expression patterns amongst input, IP, in vitro, andsorted samples (Fig. 1B). Cluster I comprised genes thatwere enriched in the transcriptome of sorted microglia, whereas cluster II consisted of 1,668 genes that wereenriched in IP and sorted microglia and therefore can be
annotated as CX3CR1-dependent. Cluster III comprised
1,906 genes that were highly abundant in IP, in vitro, andsorted samples, but that were less abundant in input samples, and therefore these genes were identified as being
microglia-associated. Cluster IV consisted of genes thatwere prominently enriched in the input, in vitro, andsorted samples. In cluster V input, IP, and in vitro samples displayed 1,480 prominent genes that were absent insorted microglia. Cluster VI comprised 3,484 genes thatwere abundantly expressed in input samples and corresponded to genes that were typically expressed in nonmyeloid cells of the CNS. In cluster VII, 3245 genes wereenriched in input and IP samples, and de-enriched in in
vitro and sorted microglia, respectively.
To assess the cell type-specificity of the obtained translatomes, we evaluated the expression of well-known
neuronal and glia-specific marker genes. Quantificationof normalized gene counts revealed that the astrocytic
marker genes Gfap, S100b, and Sox9, the oligodendrocytic marker genes Plp1, Mog, and Olig2, and the neuronal marker genes Syn1, Tubb3, and Map2 were highly
expressed in input samples (Fig. 1C-E). In contrast, thecore microglia marker genes Cx3cr1, Hexb, P2ry12,
and Csf1r were abundantly expressed in IP, in vitro, andsorted microglia (Fig. 1F), illustrating that all three methods were suitable to enrich RNA from microglia and thatthey are amendable to investigate microglia RNA expression profiles.
Transcriptomic and translatomic analyses of microglia
reveal confounding gene signatures that are associatedwith cell sorting

To address whether the similarities and differences ingene signatures observed in the applied experimental
models reflected true biological effects, we performed
unbiased k-means clustering of differentially regulated
genes in IP, in vitro, and sorted microglia samples. Threeclusters were detected (Fig. 2A), while cluster I is definedby genes that were highly expressed in IP microglia relative to in vitro and sorted microglia, cluster II comprised
genes that were enriched in sorted samples including
genes encoding for chemokines (Ccr7, Ccl4) and interleukins (Il1a, Il1b). Cluster III comprised genes thatwere similarly enriched in in vitro and sorted samples.
Microglial cluster-specific genes in cluster I and cluster III highlight differences between transcriptomes andMulenge et al. Journal of Neuroinflammation(2024) 21:203

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Fig. 2 Sorted microglia exhibit artificially induced transcriptional alterations. A, Heatmap representing k-means re-clustering of IP, in vitro, and sorted
microglia, showing cluster-specific gene enrichment and de-enrichment. B, Gene ontology analysis displaying top five pathways associated with genes
retrieved from clusters in A. C, Boxplots with dotplot overlays representing normalized gene counts of selected genes from cluster II, revealing highexpression of activation-related genes in sorted samples (for additional details see caption of Fig. 1). D, UMAP plot of scRNA-seq data [23] from 16,506
cells that were analyzed from whole brain, depicted are myeloid cell subsets. E, UCell score distribution for cell activation signatures retrieved from bulk
RNA-seq in cluster II shown in UMAP space. F, UMAP representation of scRNA-seq analysis of 11,850 cells from bone marrow highlighting major cell
types. G, UCell score distribution in UMAP space for cell activation signatures in bone marrow cells. H, UMAP embedding of scRNA-seq data from 14,146
cells analyzed from spleen, showing key immune cell types. I, UMAP plot showing UCell score distribution of cell activation signatures in splenocytes.
DC, dendritic cells; HSC, hematopoietic stem cells; MSC, mesenchymal stem cells; NMP, neutrophil-myeloid progenitor cells; BAMs, border associatedmacrophages; IFN, interferontranslatomes. Using Gene Ontology (GO) terms, we
identified cluster I and III-specific genes that are implicated in DNA repair, ncRNA processing, mRNA processing, and RNA splicing functions (Fig. 2B). Importantly,
genes involved in leukocyte migration, cell chemotaxis
and myeloid leukocyte migration were the most highly
overrepresented in cluster II. When we probed for specific genes associated with the aforementioned pathways,
we detected abundant expression of stress-induced genes(Zfp36, Dusp1, Jun) as well as immune-related genes(Socs3, Cd74, Ccl4, Nfkbiz, Ccl3) in sorted microglia andto a lesser extent in IP and in vitro samples (Fig. 2C and
Fig. S1A), suggesting a transcriptionally activated state of
the sorted microglia.
To analyze whether microglial activation signatures
were also present in large-scale transcriptomics studies,

we first performed a re-analysis of scRNA-seq data ofbrain resident immune cells published by Van Hove etal. [23]. Unsupervised clustering identified 14 transcriptionally distinct clusters representing all immune cell
types known to be present in the CNS (Fig. S1B). Subclustering of microglia, border associated macrophages(BAMs) and monocytes revealed 10 discrete myeloid
cell subpopulations (Fig. 2D), including three microglia
clusters that correspond to homeostatic microglia (Hexb,
P2ry12, Tmem119), chemokine microglia (Il1a, Tnf, Ccl3)
and IFN-responsive microglia (Ifit3, Isg15, Ccl12) (Fig.
S1C). The latter represent surveying cells that can rapidly
respond to any brain dysfunction, infection or damage[24]. To visualize the cell types in the dataset that showenrichment of cell activation signatures, we performed
gene module scoring by using UCell [25]. We generated

Mulenge et al. Journal of Neuroinflammation(2024) 21:203“activation-induced signatures” by applying the consensus of genes that was retrieved from cluster II in Fig. 2A
as well as previously published data [17, 26–28] (Supplemental Table 1). The enrichment analysis projected onto
UMAP demonstrated that activation-induced signatures
were mostly enriched in myeloid cell subpopulations,albeit to different extents (Fig. 2E). Moreover, marker
expression analysis revealed that the identified cell activation genes Jun, Socs3, Nfkbia, Ccrl2, Dusp1, and Zfp36
were highly expressed in microglia and BAMs and low inmonocytes (Fig. S1D).
We next asked whether these spurious signals wereunique to CNS-resident myeloid cells, or whether
they were also detected in peripheral immune cells. To
this end, we analyzed a recently published scRNA-seq
immune cell map that included T and B cells, NK cells,
and neutrophils from murine bone marrow [29] andspleen samples [30]. Clustering of these datasets reproduced cell type labels as described in the original publications (Fig. 2F and H). Unbiased gene module scoring ofactivation-induced signatures on all cell types indicatedthat nearly all hematopoietic cells in the bone marrow(Fig. 2G and Fig. S2D) as well as splenocytes (Fig. 2I and
Fig. S2E) were enriched for cells carrying activation signatures. Thus, aberrant transcriptional signatures areprevalent in immune cells from the CNS and the periphery. Moreover, these spurious signals appeared to be systemically present not only in study-specific datasets but
also in reference atlases that are intended to be used as
reference-based methods for cell type annotation.
Microglia show induction of transcriptional activation
following flow cytometric sortingTo pinpoint the causative sources of artefacts in microglia
transcriptomic profiles, we next leveraged publicly available scRNA-seq datasets of microglia isolated by different protocols with single variations in key experimental
steps and performed in-silico integration. These datasets encompassed samples from (i) Van Hove et al. [23]who applied standard enzymatic tissue dissociation andflow cytometric sorting, (ii) Tepe et al. [26] who applied
standard enzymatic tissue dissociation, (iii) Hammond etal. [31] who applied mechanical tissue dissociation andflow cytometric sorting under ice-cold conditions, and(iv) Marsh et al. [27] who used standard enzymatic tissue dissociation, flow cytometric sorting, and included
transcriptional inhibitors. Datasets from Van Hove et al.,Hammond et al., and Marsh et al. are based on CD45+
cells that were sorted from the mouse brain (Fig. S2A
and S2B), while Tepe et al. included all CNS resident
cells in their analysis (Fig. S2C). The integration pipeline
started by separately processing each dataset and annotating CNS cell types in line with the source study, andPage 6 of 22

subsequently selecting the immune cell clusters that represent microglia.
After quality control, data integration, and regressingout the technical noise, single-cell transcriptomic profiles for 25,406 putative microglia cells were retained foranalysis. Visual representation of integrated data projected onto UMAP revealed that microglia consolidated
into one single cluster across all the analyzed experimental setups (Fig. 3A). As expected, using unsupervised
clustering, the integrated data revealed three microglial
clusters (Fig. S3A), verifying the earlier observation that
homeostatic microglia are transcriptionally heterogeneous [27, 32, 33]. Depending on the experimental protocols applied by the authors, we observed qualitative
differences in the percentages of microglia identified
amongst the analyzed cells (Fig. 3B). Of note, we foundthat microglia isolated by enzymatic digestion without
enrichment by flow cytometric sorting yielded lower
abundance of microglia amongst the analyzed cells thanother experimental approaches that involved cell enrichment steps. Nevertheless, the composition of the microglia subsets remained relatively similar in all the analyzed
datasets (Fig. 3C). These observations indicated thatthe distribution of microglia subsets is not significantly
affected by the different experimental approaches.
To examine the presence of activation signatures in the
integrated datasets, we performed gene module scoring using activation-induced signatures. By projecting
this score onto UMAP, we found that microglia isolated
by standard enzymatic tissue dissociation coupled withflow cytometric sorting were enriched for cells that werepositive for activation signatures (Fig. 3D and Fig. S3B).
Although to a lesser extent, microglia isolated by sorting
without enzymatic dissociation also displayed cell activation signatures. In contrast and consistent with previous data [27, 31], we found that microglia isolated under
ice-cold conditions or in the presence of transcriptional
inhibitors exhibited de-enriched cell activation scores(Fig. 3D and Fig. S3B).
We further examined expression levels of these activation-induced signatures at the gene level. In concordancewith gene module scoring, microglia isolated by enzymatic digestion and sorting showed significant upregulation of immediate early genes (Jun, Egr1), stress-induced
genes (Hspa1a, Dusp1) as well as immune-related genes(Ccl4, Nfkbiz) (Fig. 3E). Of note, these genes were abrogated in microglia isolated under ice-cold conditions ortreated with transcriptional inhibitors during the tissue
dissociation step (Fig. 3E). Furthermore, we observed
negligible differences in the expression levels of these
activation signatures between the different microglia
subsets (Fig. S3C).
A key aspect of nearly all scRNA-seq experiments
involves dissociation of solid tissues, enzymatically orMulenge et al. Journal of Neuroinflammation

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Fig. 3 Meta-analysis of microglia identifies profound effects of cell sorting on microglial transcriptional profiles. A, UMAP plot showing clustering of microglia from Von Hove et al. [23], Tepe et al. [26], Hammond et al. [31], and Marsh et al. [27] after QC and data filtering using Harmony integration. The cells
are colour-coded according to the legend shown on the right. B, Bar plot depicting microglia yield in percentages across the integrated dataset. C, Distribution of microglia subsets per dataset displayed on bar plot. D, UMAP plot displaying UCell score distribution for the cell activation signatures within the
microglia-integrated dataset stratified by experimental setup. E, Heatmap showing differential expression of cell activation genes across the four datasets.
F, Volcano plot displaying differentially regulated genes in microglia isolated by enzymatic dissociation and cell sorting (blue) versus cell sorting without
enzymatic dissociation (magenta). Genes with coefficient of <|0.25| and FDR > 0.05 are colour coded in grey. G, Volcano plot highlighting differentiallyregulated genes in microglia isolated by cell sorting without enzymatic dissociation (green) relative to cell sorting with enzymatic dissociation (umber). H,
Violin plot generated from the integrated dataset displaying characteristic microglial marker genes across the experimental setup. I, Plot of percentagesof microglia from CD45+CD11b+ cells in unsorted and sorted fractions. J, Boxplot with dotplot overlays displaying percentages of microglia expressing
CD74, MHC I and MHC II in unsorted and sorted cells. K, Barplot depicting percentages of CD45+ cells after sorting and resorting (n = 4 mice per group,
two-tailed paired t test; * p < 0.05, ** p < 0.01, *** p < 0.001)

mechanically, to release individual cells and the useof sorting to enrich microglia in a final step. We wondered whether these two entwined cell isolation steps
could contribute to cell activation. To address this, we
performed differential state analysis on the integrated
microglial datasets using the MAST package [34]. To
determine whether the sorting procedures induced aberrant microglial signatures, we performed differential
analysis on microglia isolated by enzymatic dissociation with or without cell sorting (Van Hove et al. versus
Tepe et al.). Interestingly, we observed that cell activation
genes (Fos, Jun, Dusp1, Ccl4, Zfp36, Socs3) were significantly higher expressed (Coeff > 0.25 and FDR < 0.05) inmicroglia isolated by enzymatic tissue dissociation andcell sorting than in microglia isolated by enzymatic dissociation without cell sorting (Fig. 3F and Fig. S3D).
To further assess the impact of enzymatic digestion,
we carried out differential analysis of microglia isolated
by cell sorting without enzymatic dissociation versus
cell sorting with enzymatic dissociation (Hammond et
al. versus Marsh et al.). Surprisingly, we found complete
absence of activation-induced gene signatures in microglia that were isolated by either cell sorting without enzymatic digestion or cell sorting with enzymatic digestion(Fig. 3G and Fig. S3D). Arguably, this implies that the useof cell sorting rather than enzymatic dissociation is the
key factor inducing aberrant cell activation signatures in
microglia. Furthermore, post-hoc analysis revealed thatirrespective of the experimental differences that wereadopted by the authors, expression levels of the canonicalmicroglia markers Cx3cr1, Hexb, P2ry12, C1qa, Siglech,
Csf1r, and Trem2 were rather similar, highlighting thatthese markers are overall stably expressed in microglia(Fig. 3H and Fig. S3E).
To corroborate the impact of cell sorting on microglia
expression profiles on the proteomic level, we isolated
brain-resident immune cells by standard enzymatic tissue dissociation and analyzed the expression of cell surface markers by flow cytometry on unsorted and CD45+
sorted cells. Quantitative analysis revealed that the percentage of microglia from the CD45+CD11b+ population
was similar in unsorted and sorted cells (Fig. 3I; gating

strategy in S3G). Nevertheless, we observed a significant
increase in the percentage of microglia expressing CD74,
MHC I and MHC II in the sorted fraction relative to theunsorted cells (Fig. 3J and Fig. S3H). Interestingly, after asecond sorting of CD45+ cells, we detected a significant
decrease in the absolute number of CD45+ cells (Fig. 3K),
which can be explained by cell loss due to cell death thatwas induced by the cell sorting procedure. This control
experiment highlighted that shear stress or traumatic
injury is induced during the cell sorting procedure, whichmight be a potential inducer of transcriptional and proteomal alterations of microglia.
Differential state analysis reveals that bona fide microglial
signatures are confounded by ex vivo induced artefacts

Beyond post-hoc analysis of large-scale datasets, wenext assessed whether differential analysis would reveal
aberrant activation signatures on VSV infected samples
that were processed concurrently by same methods. To
address this, we performed transcriptomic and translatomic analyses of microglia derived from IP, in vitro, andsorting after either PBS treatment or VSV infection. By
controlling several batches as covariates, principal component analysis (PCA) showed a clear separation of IP,
in vitro and sorted microglia (Fig. 4A). Notably, VSVinfected samples clearly segregated from uninfected
controls as shown by PC1 accounting for 59% variance,suggesting the induction of substantial transcriptionalchanges of microglia upon virus infection. Among the
VSV-infected groups, in vitro samples largely segregatedfrom IP and sorted samples, whereas this effect wasmuch less pronounced when IP and sorted samples werecompared. This highlighted that microglia from different experimental setups display distinct expression profiles upon virus infection. On the other hand, uninfected
samples from IP, in vitro and sorted microglia clusteredseparately (Fig. 4A). It could seem counterintuitive that
IP, in vitro or sorted microglia from uninfected samples
were so dissimilar, considering that all microglia wereanalyzed at homeostatic state. However, this observation
indicated that adoption of different experimental setups

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Fig. 4 Microglia bona fide signatures are masked by microglial activation-induced signatures. A, PCA displaying clustering of samples derived from IP,
in vitro, and sorted microglia after VSV treatment. Each dot represents a single mouse. B, Triwise plot displaying expression strength (log-fold change) of
overlapping genes in IP, in vitro, and sorted microglia. The internal hexagon corresponds to genes with the same expression in all three samples. Genes
lying on hexagonal gridline have the same maximal fold change between any two pairwise comparisons. C, Heatmap clustered by k-means clustering,comparing VSV-treated with mock controls from IP, in vitro, and sorted microglia samples, revealing five clusters. D, Top five gene ontology terms associated with differentially regulated genes from heatmap clusters in Fig. 4C. E, Boxplots with dotplot overlays showing normalized gene counts of selected
genes related to microglial activation regulated upon VSV challenge in IP, in vitro, and sorted samples (for additional details see caption of Fig. 1)

for transcriptional profiling of microglia have different
confounding effects.
We performed differential analysis on VSV-infected
samples and their respective controls. By applying selection criterion of log2-fold change >|1|, padj < 0.05, we
identified 2332, 3063 and 3110 genes that were differentially expressed upon VSV challenge in IP, in vitro, andsorted microglia, respectively, and 492 differentiallyexpressed genes being shared by all three setups (Fig.
S4A). Of the 492 consensus genes, 20% were induced atsimilar levels as indicated by the log-fold changes of theexpression in IP and sorted samples (Fig. S4B). These
genes included interleukins such as Il2rg, Il2ra (Fig. 4B).
Correspondingly, we identified 15% of the consensus
genes that had a maximal fold change in in vitro microglia (Fig. S4B). These genes comprised Mx2, Ifnb1, andCxcl9 amongst others (Fig. 4B). Interestingly, interferonstimulated genes (ISGs) such as Isg15, Cgas, and Stat2
were expressed at similar levels in all three microglia

samples (Fig. 4B). Unbiased k-means clustering of differentially regulated genes revealed the presence of fivedifferent clusters (Fig. 4C). Cluster I and II comprised
genes that were upregulated upon VSV infection in in
vitro and sorted microglia. Cluster III was represented bygenes that were upregulated in IP and sorted microglia(Fig. 4C). Notably, cluster IV consisted largely of genesthat were upregulated under all three experimental conditions, while cluster V comprised genes that were abundantly expressed in in vitro and sorted samples, and thatwere expressed to a lesser extent in IP samples (Fig. 4C).
To corroborate the functional phenotype associatedwith the identified cluster-specific genes, we performed
pathway analysis (P value cutoff < 0.05) using GO genesets. Genes in cluster I did not attain the cutoff criteria to infer associated pathways. Nevertheless, cluster
II exclusively expressed genes associated with positive
regulation of cell adhesion, leukocyte migration, andcell chemotaxis (Fig. 4D). Regulated genes in cluster

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Fig. 5 VSV-induced inflammation causes significant changes in the microglial transcriptome. A, GSEA displaying top two enriched GO terms associatedwith differentially expressed genes in the microglia translatome. B, Boxplots with dotplot overlays representing normalized gene counts of selected
genes associated with above enriched pathways in microglia translatome (additional details in caption of Fig. 1). C, UMAP representation of scRNA-seq
analysis of 4,010 cells from brain highlighting major myeloid and T-cell subsets. D, Barplot representing the observed proportion of microglia clusters
in mock and WNV-infected groups. E, Barplot highlighting the in-silico predicted proportion of microglia subtypes in PBS and VSV-infected samples. F,
UMAP plot showing segregation of immune cell subtypes within the brains of uninfected and VSV-infected mice at 6 dpi (n = 3 mice per group). G, Barplot
representing percentages of immune cell composition within the brains of PBS and VSV-infected mice. H, Representative images of OB immunolabeled
for IBA1 and CD3. VSV-infected OB show influx of CD3+ cells on day 6 after infection. The white arrows denote IBA1+ cells engaged with CD3+ cells (n = 3
mice per group, N = 2; combined data; Scale bar 200 μm)

III are involved in T-cell differentiation, regulation ofT-cell activation, and activation of immune responses(Fig. 4D). Notably, cluster IV and V contained genes thatare implicated in responses to virus, positive regulationof cytokine production, and pro-inflammatory responses
such as interleukin-1 (IL) beta, IL-8 production, andNF-kB signaling (Fig. 4D). Importantly, the homogenous expression of genes associated with these pathways
reinforces the idea that microglia are key meditators ofinflammatory responses during VSV infection. Intriguingly, we observed that the microglial responsivenessto VSV infection was not affected by the experimental
approaches applied (Fig. 4D).
The identification of GO terms corresponding to (i)leukocyte migration, (ii) the ERK1 and ERK2 cascade,and (iii) cell chemotaxis in cluster II (Fig. 4D) prompted
us to ask whether the identified aberrant signatures insorted microglia could be misinterpreted as being differentially expressed upon exposure to VSV. To address
this, we sorted and filtered genes based on the aforementioned GO terms. Remarkably, genes associatedwith pro-inflammatory signaling (Ccl3, Tnf, Ccl4, Il1r2),
antigen processing and presentation (Tap2, Cd74), oxidative stress (Hspa1a), and IFN-induced response (Socs3)
were highly elevated in sorted microglia relative to in
vitro or IP microglia under basal conditions, and were
further upregulated in sorted microglia after VSV infection, albeit to different extents in in vitro and IP microglia (Fig. 4E). Collectively, these data highlighted that
activation-induced signatures are masked as being “differentially expressed” in samples with disease status, thus
jeopardizing attempts to delineate bona fide microglia
disease signatures.
Microglia develop transcriptional heterogeneity inresponse to VSV-induced encephalitis

Thus far, our data demonstrated that the RiboTag
approach not only allows for the analysis of microgliaassociated mRNA expression profiles, but also precludes
non-specific microglia activation and concurrent upregulation of early response genes [35]. We therefore focusedon IP samples for in-depth profiling of microglia to assess
the nature of innate response to viral infection. We identified 2037 genes (1322 up- and 715 downregulated)that were differentially expressed in response to VSV

infection (Fig. S4C). Correlation of functional relevanceof the identified genes using gene set enrichment analysis (GSEA) demonstrated significant enrichment of genesthat are mostly associated with T-cell activation and cytokine production (Fig. 5A). Among these, genes encoding for the MHC complex (Tap1, B2m, H2-Q6, H2-K1,
H2-D1), co-stimulation (Cd86), Fc receptor (Fcgr4), andphagocytosis (Lgals3) showed enhanced expression in
microglia derived from VSV-infected animals (Fig. 5B).
Given that MHC I expression usually is very low or even
undetectable on cells in the CNS under homeostatic conditions, the pronounced expression of MHC I on microglia from VSV-infected mice indicated an inflammatory
status of the cells that is presumably associated withenhanced antigen cross-presentation. To explore thisobservation beyond the translatomic level, we performed
re-analysis of scRNA-seq data of CD45+ cells isolated
from the brain of mice that recovered from West Nilevirus (WNV) infection and the respective mock controls[33]. Notably, infection with an attenuated WNV strain
induces self-limiting brain inflammation accompanied
by microglia activation that is similarly observed during VSV-induced encephalitis. In accordance with Rosenet al. [33], we identified six distinct clusters comprisingfour microglia subsets (homeostatic, immediate early
gene, chemokine, and IFN responsive clusters) and two
T cell subsets (CD4+ and CD8+ T cells) (Fig. 5C and Fig.
S4D). Closer examination of the microglia clusters for the
expression of genes that are associated with MHC complexes and phagocytosis revealed pronounced expressionof the genes Tap1, B2m, H2-Q6, H2-Q7, H2-K1, H2-D1,
Fcgr4, and Lgals3 in IFN-responsive microglia, whereas
these genes were induced to a lesser extent in chemokine
microglia, emphasizing that microglia exhibit transcriptional heterogeneity in response to viral infection (Fig.
S4E). In transcriptomic analyses of whole tissues, RNAs
from all cells are analyzed en masse and therefore cellular heterogeneity can be missed. To circumvent thisand to analyze distinct microglial subtypes, we implemented an in-silico deconvolution method that leverages
scRNA-seq data to infer cell subtype proportions in bulk
samples [36]. Since deconvolution methods often underperform when used to compare the proportions between
different cell types [37, 38], we selected microglia clusters from the above scRNA-seq data [33] as a reference.

Mulenge et al. Journal of Neuroinflammation(2024) 21:203

Within the computed proportions of scRNA-seq microglial sub-clusters in the mock control or in WNV-infected
samples (Fig. 5D and Fig. S4D), we predicted significant
abundance of homeostatic, immediate early gene (IEG),
chemokine and IFN responsive microglia subtypes in ourbulk microglial translatome (Fig. 5E). Intriguingly, 19.4%
and 17.2% of the microglia translatome from the PBS
control were enriched for IFN-responsive and chemokine
microglia subtypes, respectively, which under conditionsof VSV infection increased to 38.0% and 24.0%, respectively (Fig. 5E). Unexpectedly, the translatome of PBS
microglia was highly enriched for homeostatic microglia(59.1%), whereas this subset was decreased to 33.8% in
VSV infected microglia, suggesting elimination or phenotypic transformation of activated microglia during viral
encephalitis. As expected, the microglia subtype expressing immediate early genes was present at low abundance
in PBS and VSV-infected microglia translatome (4.2%)(Fig. 5E). Taken together, the expression deconvolution
analysis revealed the presence of microglia subtypes atdifferent abundancies in mock vs. VSV infection at thebulk RNA level.
VSV-induced encephalitis is associated with massive T-cell
infiltration and close interaction of T cells with microglia

Recent studies demonstrated that VSV titres in the OB
are controlled by day 6 post-infection, which coincides
with activation of microglia and influx of monocytes [4].
Although the immune cell dynamics are very complex
in the infected brain and can vary significantly during
the course of infection, we previously reported robust
recruitment of CD45+ cells as early as day 4 after infection, and that infiltration further exacerbated by day 6
after infection [39]. To provide a snapshot of the diversityof infiltrating immune cells in the brain, we performed
flow cytometric analysis of immune cell populations in
the brain of C57BL/6 mice 6 days after VSV infection.
For unbiased analysis of the samples, we gated on CD45+
cells and performed clustering and dimensionality reduction by using UMAP (Fig. S3G). This approach revealed
the presence of 7 distinct clusters including lymphocyte
populations comprising CD4+ and CD8+ T cells, B cells,
NK cells, granulocytes (neutrophils), and myeloid subsets
such as monocytes and microglia (Fig. S4F). However, aminor population of an undefined cluster was detectedthat corresponded with cells for which the analyzed
marker combination did not allow further specification(Fig. S4F). Nevertheless, segregation of samples basedon the treatment revealed clear stratification of immune
cell populations (Fig. 5F). The proportions of cell types
varied considerably across the treatment groups withhigh percentages of microglia detected in the control
group (75.8%) that declined to 24.2% after VSV infection(Fig. 5G). In VSV-infected samples we detected dramatic

Page 12 of 22

influx of monocytes, neutrophils, B cells, NK cells, and T
cells when compared with PBS treated controls. Specifically, in the mock-treated group, the percentage of CD8+
and CD4+ T cells amongst CD45 + cells was low with
3.4% and 8.9%, respectively, whereas under conditions of
VSV-infection a profound increase of both CD8+ (96.7%)
and CD4+ (91.1%) T cells was detected (Fig. 5G). These
data demonstrated that the uninflamed CNS contained
very few adaptive immune cells, whereas following VSV
infection, T cells were massively recruited into the brain.
Our translatomic analysis revealed that, unlike homeostatic microglia, VSV-experienced microglia express
MHC and co-stimulatory molecules, which would allow
the microglia to closely interact with infiltrating T cells.
To further analyze the microglia-T cell interaction on
the protein level, we inoculated C57BL/6 mice intranasally with either PBS or VSV and performed histologicalanalysis of the OB at 6 dpi. As similarly detected by flow
cytometry, histological analysis of OB samples labeled
for IBA1 and CD3 revealed a dramatic influx of CD3+
cells into the OB of infected mice when compared with
PBS treated controls (Fig. 5H). Interestingly, CD3+ and
IBA1 + cells were located in close proximity, suggesting that T cells interacted with the myeloid cells. Dense
IBA1+ conglomerates being surrounded by CD3+ cells
further supported the hypothesis that microglia present antigen to the T cells. In summary, our histological
data showed that CNS infiltrating T cells enter into closeinteraction with microglia in the virus infected CNS.

Discussion
Microglia are resident myeloid cells of the CNS thatare implicated in numerous physiological and pathological processes [9, 40, 41]. Here, we exploited several
approaches, including in vitro generated microglia, themyeloid cell-selective RiboTag approach, and cell sortingof brain resident myeloid cells, to decipher bona fide signatures of microglia under conditions of health and viral
encephalitis. All three models revealed a common set ofcore microglial transcripts, highlighting the suitability forthe applied approaches to investigate microglia biology.
However, we discovered that sorted microglia showedaberrant transcriptional alterations that were associatedwith the cell sorting procedure. Interestingly, such signatures we detected also in large-scale transcriptomic
atlases that are commonly used for reference-based cell
type annotations. Analysis of the translatome of virusexperienced microglia revealed pronounced expressionof genes encoding for components of the MHC complex
and co-stimulatory receptors, implying that microglia
exhibit an enhanced potency to present antigen. Cytometric analysis revealed the massive influx of peripheral
immune cells into the infected brain, including lymphocytes and myeloid cells, by day 6 after infection. At theMulenge et al. Journal of Neuroinflammation(2024) 21:203

protein level, we uncovered that infiltrating T cells surrounded the dense conglomerates of microglia, suggesting that T cells intimately interact with activatedmicroglia.
A major challenge in transcriptomic analysis of microglia is contamination of sorted cell preparations with other
CNS-resident cells that might occur during conventional
whole-cell preparation procedures [42, 43]. Our results
demonstrated efficient enrichment of myeloid cells in
IP and sorted microglia when compared with the input
cells (Fig. 1C-E), as indicated by the absence of astrocytic,
neuronal, and oligodendrocytic markers in the RNA-seq
data. Although these data emphasize the specificity of the
CX3CR1-CreERT2+/− model in inducing efficient recombination in myeloid cells, we cannot rule out targeting ofother long-lived non-parenchymal brain macrophages.
Although there are other mouse lines showing inducible Cre expression in microglia such as HEXB-CreERT2[14], TMEM119-CreERT2 [15] and P2RY12-CreERT [213]
as well as binary Cre models [44], they are less efficient
in recombining all microglia [45] and their utility in thecontext of infectious and non-infectious encephalitis
models remains to be proven. The absence of core genesignatures of other brain-resident cell types in in vitro
cultivated microglia indicated that also this approach wasappropriate and effective in generating highly pure preparations of microglia that are suitable for transcriptomic
studies.
Comparison of the gene expression profiles derived
from IP, in vitro, and sorted microglia revealed commonalities and differences in gene expression. Consistent
with prior reports [17], we detected exclusive expressionof several bona fide stress-regulators (Zfp36, Dusp1, Jun,Ccl4) in sorted microglia (Fig. 2C and S1A). Notably,
Zinc finger protein 36 (Zfp36) is involved in regulating
immune responses through mRNA destabilization andalternative splicing [46], whereas dual-specificity phosphatase-1 (Dusp1) is a key component in regulating antiinflammatory responses that is expressed in response tostressors, such as heat shock or oxidative damage [47].
Kinetics evaluation of chemokine ligand-4 (Ccl4) previously revealed this gene to be expeditiously expressedfollowing stimulation [48], and as such, it is likely that
detection of this gene in sorted microglia is an ex vivo
activation artifact. Noteworthy, given the prevalentnature of Dusp1, Zfp36, and other genes identified here
(Supplemental Table 1), these are useful proxy markers
for evaluating aberrantly activated microglia. A longstanding concern is whether such ‘spurious’ signatures
are unique to microglia, or whether they are also prevalent in peripheral immune cells. Our post-hoc analysis
on published scRNA-seq datasets revealed a widespreadexistence of aberrant transcriptional signatures in CNSresident myeloid cells and non-CNS cell types, calling

Page 13 of 22

into question whether findings reported in those studies fully reflect the in vivo status of the analyzed cells[23, 29, 30]. In an era when biologists increasingly relyon scRNA-seq to discern cellular functions, ex vivo transcriptional alterations constitute a major technical challenge for many studies. Moreover, these spurious signals
appeared to be present not only in study-specific datasets but also in reference atlases, underscoring the needfor benchmarking to assess the quality and accuracy of
single-cell reference atlases. Furthermore, with the emergence of large-scale consortia such as Human Cell Atlas
project [49] and murine cellular atlases [50] that are aiming at establishing comprehensive transcriptional maps
for multicellular tissues, the development of approaches
that compensate or avoid such transcriptional biases is
crucial.
A critical step in transcriptomic studies is the isolationof intact single cells from tissue with the highest possible
purity. The isolation of microglia represents a major challenge. Due to their elaborate and long processes entwinedwith other cell types within the CNS, separation of
microglia from neighboring cells requires extensive tissuedegradation, which increases the likelihood of rupturing
the microglial cell membrane. Notwithstanding, enzymatic digestion or mechanical dissociation of the brain
tissue combined with cell sorting is a standard method
for obtaining sufficiently high cell numbers of microglia that are required for omics applications [23, 51]. We
found that microglia yields were lower using enzymatic
digestion protocols without cell sorting (Fig. 3B), reemphasizing the importance of cell enrichment in transcriptomic analyses of low abundant cell types of the
CNS such as microglia. However, we uncovered thatcell sorting is a harsh procedure that elicits fundamental
alterations of the microglial transcriptome or proteome.
Specifically, we found that hydrodynamic stress and/or
traumatic injury encountered during cell sorting are key
inducers of ex vivo microglial activation. Such aberrant
signatures not only distort baseline transcriptional profiles of microglia, but also confound expression patterns
that are truly induced by pathological insults. Indeed, we
detected elevated expression of genes associated with
pro-inflammatory signaling (Ccl3, Tnf, Ccl4, Il1r2), antigen presentation (Tap2, Cd74), oxidative stress (Hspa1a)and IFN-induced response (Socs3) which are concealed
as being ‘differential’ in sorted microglia upon VSV infection (Fig. 4E). Induction of Cd74, Ccl3, Tnf, and Ccl4
genes that are well documented in neuroinflammationand disease states [52–54], indicates microglia switchingfrom a homeostatic to a more inflammatory state. This
is in contrast to the view that microglia which are polarized towards an activation state (i.e., response to disease)are less responsive to new stimuli (i.e., cell isolation) [27].
Therefore, it appears that ex vivo activation signatures

Mulenge et al. Journal of Neuroinflammation(2024) 21:203

associated with cell sorting are confounded by biologicaltreatment. In conclusion, our results underscore the needfor recognizing cell sorting-related signatures, whichhave substantial implications in obscuring the identification of true biological states of cells in health and disease.
Moreover, we re-emphasize the importance of validating
RNA-seq results from cell sorted tissues to disentangle
these aberrant signatures from bona fide in vivo derived
signatures of tissue resident cells such as microglia thatare particularly sensitive to removal from their in situ
microenvironment.
Efforts to circumvent cell-specific transcriptionalbiases are indispensable not only for enhancing thecomparability of the findings, but also to avoid data
misinterpretation and drawing false conclusions. Cold
isolation protocols and transcriptional inhibitors haveboth emerged as potential strategies for preserving invivo cellular signatures [27, 31]. In line with previous
studies [27, 31], we found that scRNA-seq of microglia
generated with inclusion of transcriptional inhibitors,
or processing tissue under ice-cold conditions, invariably diminishes aberrant microglial activation signatures.
Although transcriptional blockers are effective in preventing de novo gene expression, it must be highlighted
that they do not prevent degradation of pre-existing
RNAs [55]. Thus, the expression of genes such as Ubc,
Rac1, and Tgfb1 under conditions of Actinomycin D
treatment most probably reflects the activation status of
microglia (Fig. S3F). Unfortunately, the above-mentioned
strategies can have unintended consequences on cellularfunctions beyond gene expression. By blocking protein
synthesis, transcriptional inhibitors can disturb normal
cellular processes that potentially impact cell viability,
proliferation, and metabolism. Moreover, transcriptional
inhibitors were reported to interfere with the ability ofcells to respond to stimuli, impairing their capacity tomount appropriate immune or stress responses [56].
Several studies have demonstrated that exposing mammalian cells to sub-physiological temperatures invokes acoordinated cellular response involving the induction ofcold-shock proteins and the modulation of translation,
metabolism, cell cycle, and the cytoskeleton [57]. Therefore, we strongly urge the selection of appropriate cellisolation methods to favor accurate biological profiling instudies involving stress-responsive immune cells.
Considering the immense effort put into the generation of transcriptomic atlases, it is essential to retrospectively attenuate or completely remove ex vivo activation
signatures from existing datasets. Indeed, unprecedented
numbers of computational strategies that entail complex
machine-learning algorithms to detect and remove exvivo activation genes have been developed [58]. However,this comes with major caveats: Firstly, the expression
levels and dynamics of different activation genes vary

Page 14 of 22

substantially between cell types and brain regions [59].
Thus, pre-selecting one or few genes without a systematicsurvey may limit the detection of activated cell populations. Secondly, computational removal of ex vivo activation genes can eliminate ‘real’ signals thus jeopardizing
studies that intend to examine biologically relevant acutegene expression changes. Lastly, ex vivo transcriptionalchanges may not always be restricted to early response
genes and determining which genes to exclude from theanalysis could be difficult or impractical.
Using the RiboTag approach, we discerned cell-type
specific transcripts that were actively translated by
microglia, herein referred to as ‘translatome’. Since the
RiboTag approach inherently relies on the enrichment ofribosomes from selected cell types rather than the purification of cell types from whole tissues [16, 60], we incorporated the concept of a log2-fold change as enrichment
factor between the IP and input to assess the cell-type
specificity of a given transcript. As previously describedby Sanz et al. [16], we adopted the stringent cut-off of
1-fold higher or lower for transcript enrichment or depletion, respectively. With this approach, transcripts that areenriched are more likely specifically expressed within thecell type of interest, whereas transcripts that are depleted
are more likely to be expressed in other cell types contained within the tissue. Of note, by direct comparison of
input, IP, in vitro, and sorted microglia, our study highlighted unique signatures of homeostatic microglia as
being represented in cluster II and III (Fig. 1B). Specifically, unlike scRNA-seq or conventional bulk RNA-seq,
RiboTag does not require cell dissociation and/or cellsorting [16]. Therefore, this unified set of genes accurately reflects in situ microglia profiles. Indeed, determining the degree of ex vivo activation artefacts has beenchallenging because the field lacked a resting cell typespecific reference, i.e., absence of any ex vivo activation as
a comparator in building an accurate and complete CNS
atlas. It is conceivable that the microglial translatome
identified here provides a comparator to deconvolute exvivo artefacts in publicly available scRNA-seq datasets.
More broadly, the RiboTag approach represents a uniquemethodology to discover transcripts expressed by rareand difficult-to-isolate cell populations such as tissue
macrophages and other resident leukocyte-derived cells.
In the context of viral infections, microglia are often
the first responders that are critically needed to protect
the host against lethal encephalitis [61, 62]. We exploited
the RiboTag approach to illuminate microglia expression profiles in a more complex inflammatory scenario
than neuroinflammation induced by intraperitoneal
LPS administration [17]. It is worth pointing out thatthe choice of an infection model such as intranasal VSV
infection relies mainly on the fact that VSV infection
causes a high infiltration of lymphoid and myeloid cells

Mulenge et al. Journal of Neuroinflammation(2024) 21:203

into the brain. Furthermore, by fate mapping experiments
we have previously observed that several microglia canshift into the CD45high gate of monocytes [4]. By analysisof the translatome of microglia, we uncovered the induction of gene signatures implicated in T-cell activationand cytokine responses upon VSV infection (Fig. 5A).
The very abundant expression of genes encoding elements from MHC I and II complexes, cell activation, andFc receptors (Fig. 5B) further supported the concept thatmicroglia are involved in the regulation of T-cell mediated antiviral responses within the CNS. Previous studies
reported synergistic roles of T cells and microglia in CNS
viral infection [63, 64]. Depletion of CD4+ and/or CD8+
T cells was shown to impede VSV containment within
the OB and to promote development of lethal encephalitis in infected mice [39, 63], suggested that T cells exert
antiviral functions during CNS infection. Furthermore,
microglia depletion was reported to accelerate encephalitis, and alter T cells dynamics as well as cytokine
responses during viral CNS infection [64, 65]. Cytokine
signaling has different effects depending on the cell typethat receives the signal. The IFN-γ released by infiltrating
T cells was shown to induce microglia activation, as evidenced by upregulation of MHC II on these cells, which
is normally expressed at lower levels [66]. However, Klein
et al. [67] and Garber et al. [66] demonstrated that in thecontext of WNV and Zika virus encephalitis prolonged
IFN-γ secretion that persists beyond viral clearance leads
to microglia-mediated synaptic stripping and memory
impairment. Since CNS entry of immune cells is tightly
regulated [68], the influx of monocytes, neutrophils, B
cells, and NK cells affirms their importance in controllingviral infection. However, the interplay between these cells
and microglia remains to be in depth investigated.
Following VSV infection, IBA1+ cells were surrounded
by, and some were in direct contact with, CD3+ cells(Fig. 5H) indicating close T cell-microglia interaction.
Given that microglia are antigen presenting cells, it isconceivable that infiltrating T cells recognize cognate
viral peptides displayed on the surface of microglia andthat this way T cell-mediated immunity is orchestrated in
the brain. Indeed, Moseman and colleagues [63] observed
an overall decline in the calcium flux of cytotoxic T lymphocytes in mice deficient of MHC I in the CNS resident
compartment, supporting the notion that antigen presentation to VSV-specific CD8+ T cells primarily relies onradiation resistant brain cell populations such as microglia. Considering the dramatic influx of CD4+ T cells in
the inflamed brain (Fig. 5G) it is likely that MHC II-positive microglia take up viral antigens and present antigen
fragments to infiltrating CD4+ T cells. Recent studies
reported a critical role of CD4+ T cells in viral infectionof the CNS. For instance, Wheeler et al. [64] revealed
diminished virus-specific CD4+ T cell responses in the

Page 15 of 22

absence of microglia and decreased survival upon mouse
hepatitis virus (MHV) infection of mice. Nevertheless, in
CNS auto-immune disorders, microglial MHC II antigen
presentation appears obsolete for the establishment of
EAE [69, 70].
In conclusion, the RiboTag approach reveals accurate bona fide signatures of homeostatic as well as viral
encephalitis microglia. Such signatures serve as a comparator that can be used to re-evaluate the presenceof aberrant microglial signatures that are associatedwith cell isolation procedures in other bulk RNA-seq orscRNA-seq datasets. We further illuminated transcriptional changes associated with virus-induced inflammation in microglia and deciphered microglia-T cellinteraction in shaping distinct antiviral response of thebrain. Taken together, this study provides comprehensive
molecular insights in the biology of microglia and theirinterplay with peripheral innate and adaptive immune
cells, which makes it possible to identify novel genes,
pathways, and regulatory factors that are critical for
microglia functions in health and disease.

Methods
Mouse lines

Experiments to investigate functional traits of microglia were conducted with C57BL/6J that were referred
to as wild type (WT), CX3CR1-CreERT2+/− [71] andRpl22HA/HA [16] mice on the C57BL/6 background.
To selectively label ribosomes of microglia in vivo,
CX3CR1-CreERT2+/−, which express a Cre-ERT2 fusion
protein under the CX3CR1 promoter, were crossed with
Rpl22HA/HA, in which exon 4 of the Rpl22 gene is floxed
and followed by a modified exon 4 to which a HA tag is
fused. Upon tamoxifen injection of CX3CR1-CreERT2+/−
Rpl22wt/HA offspring, the floxed exon 4 is deleted and thetagged exon 4 is expressed selectively in myeloid cells. 8
weeks after tamoxifen injection, only long-lived myeloid
cells including microglia carry HA-tagged ribosomes. All
experiments randomly included both male and female
mice aged between 8 and 10 weeks. Mice were housedin ventilated cages on 12-hour light: dark cycles under
specific pathogen-free condition. All animal experiments
were approved by local administration in Germany
and were performed in strict conformity to respective
national, federal and institutional regulations and theguidelines of the Federation of European Laboratory Animal Science Association (Identification number: 22/0055
and 18/2899).
Preparation of microglial cultures

Microglia were generated from cortices of day 3 postnatum newborn pups as previously described [72].
Briefly, cortices were extracted in ice-cold Hanks Balanced Salt Solution (Gibco, 14065-056) containingMulenge et al. Journal of Neuroinflammation(2024) 21:203

4-(2-hydroxyethyl)-1-piperazineethanesulfonicacid(HEPES; Gibco, 15630-056) and the meninges were
removed under the stereomicroscope. The cortical tissue
was finely minced and digested with 0.1% trypsin (Capricorn, TRY-1B10) for 10 min at 37 °C on a shaker and
0.25% DNase (Roche, 11284932001) was added. Mixed
glial culture progenitor (MGP+; Capricorn, DMEMHXA) medium supplemented with 10% fetal bovine
serum (Capricorn, FBS-11 A) and 1% penicillin/streptomycin (Capricorn, PS-B) were added. The homogenate
was centrifuged, and the supernatant was removed. The
pellet was re-suspended in triturating solution, triturated several times with 100 µm strainer and seeded into
poly-L-lysine pre-coated flasks with MGP + medium at
37 °C and 5% CO2. The medium was changed at day 1
and 5. After 10 days of mixed glial culture, microglia were
harvested by shaking the flasks (125 rpm) for 40 min
at 37 °C. Microglial cells were re-plated at a density of
2.0 × 105 cells per well in 24 well plates and incubated at
37 °C and 5% CO2. Microglia were used the following day
for experiments.
Tamoxifen administration

To induce expression of HA epitopes in microglial ribosomes, CX3CR1-CreERT2+/− Rpl22HA/wt mice were subcutaneously injected with 4 mg of tamoxifen (Sigma,
T5648) suspended in 200 µL of corn oil (Sigma, C8267)
for two consecutive days. All animals were tamoxifen-treated first at age of 6 weeks. Mice were ready forexperiment 8 weeks after initial tamoxifen injection, thus
allowing complete turnover of CX3CR1+ circulating cells.
VSV infection

For in vivo VSV infection, mice aged 14 weeks wereintranasally instilled with 10 µL of 103 PFU VSV-Indiana (Mudd-Summers isolate). Control animals received
the same volume of vehicle solution (PBS). Six days post
infection, the animals were sacrificed by cervical dislocation prior to brain harvesting.
For in vitro VSV treatment, culture microglia were
inoculated at MOI of 0.5 with VSV diluted in DMEM
medium for 1 h at 37 °C in 5% CO2. At 8 h post infection,
supernatant was removed, and the adherent cells werelysed by TRIzol (ThermoFischer, 15596018), harvested
and stored at -80 °C until further use.
Tissue immunoprecipitation

Immunoprecipitation of ribosomes in CX3CR1-CreERT2+/−
Rpl22HA/wt mice was performed as previously
described, though with minor modifications [17]. In brief,
the brain tissue was placed in a prechilled 2 mL Dounce
homogenizer and 500 µL lysing buffer was added containing 50 mM Tris-HCl (pH 7.4), 12 mM MgCl2 (ThermoFischer, AM9530G), 100 mM KCl (ThermoFischer,Page 16 of 22

AM9640G), 1 mM DTT (Sigma, D9779), 200 U/mL RNasin (Promoga, N2515), 1% NP-40 (Sigma, CA-630), 1×
protease inhibitor cocktail (Merck, 4693159001), 100 µg/
mL cycloheximide (Sigma, C7698) and 1 mg/mL heparin (Sigma, H3149). Samples were centrifuged at 4 °C at
10,000xg for 10 min, and supernatant was collected. 50µL of the lysate was aliquoted and was kept as “input
fraction”. 10% v/v of re-suspended HA-specific antibody
beads (ThermoFischer, 88836) was added to the remaining supernatant, and RiboTag-IP fractions were rotated at
4 °C overnight. Afterwards, samples were washed 3 times
with high salt-buffer (50 mM Tris-HCl (pH 7.4), 300 mM
KCl, 12 mM MgCl2, 100 µg/mL Cycloheximide, 1 mM
DTT and 1% NP-40), 5 min per wash in the cold room on
end-to-end rotator. After washes, ribosome-RNA boundcomplexes were magnetized and excess buffer removed.
The beads were re-suspended in 350 µL RLT buffer supplemented with 1% β-mercaptoethanol (Sigma, M6250).
RNA isolation

RNA from “input” and RiboTag-IP fractions was
extracted using Qiagen RNeasy Mini Kit following manufactures instruction. For in vitro cultured microglia,
RNA was isolated using Direct-zol RNA Miniprep Plus
Kit (Zymo Research). RNA quality and concentrations
were accessed using Bioanalyzer RNA kit (Agilent) andsamples with RIN value > 8.0 were selected for further
processing.
Bulk RNA sequencing and analysis

Library preparation of selected samples was performed
with NEBNext Low input RNA Library Prep Kit for Illumina (NEB, E6420) following manufacturer’s instructions. Constructed cDNA libraries were sequenced on an
Illumina NovaSeq-6000 platform with a 50 bp paired-end
read configuration. Quality of raw fastq-files was assessedusing FastQC software (version 0.11.9) and mapped toreference genome assembly of Mus musculus (GRCm38)
from Ensembl using STAR v2.5.4b software [73]. Only
reads with unique mapping were considered for downstream analysis. Gene-level read counts were obtainedwith FeatureCounts [74]. Prior to differential analysis,batch effect adjustment was performed using ComBatseq [22]. Differential expression analysis was performed
using DESeq2 package [75] in R environment (version
1.26.0). We used both Wald test of the negative binomial
model coefficients (DESeq2-Wald) as well as likelihood
ratio test compared with reduced model (DESeq2-LRT)
to test significance of gene expression differences asa function of samples at an absolute log2-fold change
threshold of 1. To control false discovery rate (FDR), test
p-values were adjusted to multiple comparisons using
the Benjamini-Hochberg procedure. To display expression levels of selected gene signatures between samples,Mulenge et al. Journal of Neuroinflammation(2024) 21:203

raw counts were normalized based on median of ratios
method in DESeq2. In brief, DESeq2 generates normalized reads by dividing counts with sample-specific size
factors that were determined by the median ratio of genecounts relative to the geometric mean per gene [76]. We
exploited this approach to enable accurate comparisonsof gene expression levels between different samples,
ensuring that the differences observed reflect true biological variability rather than technical artifacts. Visualizations of differentially expressed genes represented asheatmaps were generated using standardized functionsof normalized counts from DESeq2 analysis. Optimal
k-means clusters were obtained using elbow method.
Exploration of the underlying biological characteristics

Functional annotation of differentially expressed genesignatures was performed in Gene Ontology (GO), subcategory “Biological process” using clusterProfiler Bioconductor package [77]. Specifically, over representation
analysis (ORA) implemented in the package was used todetermine enrichment of GO terms with FDR of q < 0.05.
The p values were calculated using hypergeometric test
and adjusted for multiple comparison with the Benjamini–Hochberg method. Gene set enrichment analysis(GSEA) [78] was performed to determine whether a predefined set of DEGs that associated with GO terms shows
statistical significance by ranking normalized enrichmentscore (NES). GO term was considered enriched with a
NES > 1 and was predicted de-enriched with NES <-1.
Processing and analysis of archived datasets

We assembled a compendium of seven published scRNAseq studies and one-bulk RNA-seq study. For scRNAseq, raw count matrices or loom files containing countmatrices were obtained from Gene Expression Omnibus(GEO) database or laboratory websites. Datasets thatcomposed of different genotypes, treatments and gender,
only samples from WT, controls, or mixed gender mice
were selected for downstream analysis.
Von Hove et al. [23], full aggregated raw gene-cellcount matrix consisting of dura, choroid plexus, enrichedsubdural meninges, macrophages and whole brain of
WT mice were obtained from GSE128855. The study
used 10X genomics sample preparation protocol, antibody-based cell sorting to cells and sequenced with 10X
genomics platform. Metadata, including cell type annotations, were retrieved from Brain Immune Atlas.
Zhong et al. [29], processed digital gene expression
matrix from the whole bone marrow of healthy WT
mice were downloaded from GEO (GSE182986). Femur
and tibia were dissociated with enzymatic digestion andlibraries of resulting cell suspensions were constructedwith the Singleron protocol. Sample and cell-level

Page 17 of 22

metadata, including cell type annotations, were retrieved
from the authors.
Kimmel et al. [30], sparse matrices from the spleen of 4
adult WT mice (7–8 months) were obtained from GEO(GSE132901). Spleen was mechanically dissociated andeach tissue cell suspension from each animal was processed in two technical replicates using the 10X protocol.
Metadata and cell type annotations were retrieved fromthe original publication.
Tepe et al. [26], raw digital gene expression matrix fromthe olfactory bulb of adult WT mice were downloaded
from GSE121891. Tissues were dissociated according to
10X genomics sample preparation protocol. Metadata
was obtained from the same source.
Hammond et al. [31], processed digital gene expression
matrices from whole brain of 2 male and 2 female adult
WT mice (P100) were retrieved from GEO (GSE121654).
Tissues were mechanically dissociated under ice cold
conditions, cells enriched via FACS and sequenced using
10X genomics. Metadata, including cell type annotations,were obtained from the supporting information files
accompanying the publication.
Marsh et al. [27], raw count matrices from wholebrain of 4 adult mice (P89-P90) were downloaded from
GSE152183. The study used modified 10X genomic sample preparation protocol with inclusion of transcriptional
inhibitors. Metadata, including biological replicates,
experimental conditions and cell type annotations, wereobtained from the supporting information files accompanying the published manuscript.
Rosen et al. [33], raw sparse count matrix from murine
cortical and hippocampal tissues were downloaded from
GSE212199. Tissues were dissociated using Miltenyi dissociation protocol and the resulting cell suspensions were
processed with Chromium Single Cell 3′ Library Kit.
Meta files including experimental conditions and biological replicates were obtained from the same source.
For bulk RNA-seq from Chhatbar et al. [4], raw pairedend fastq-files from CX3CR1-CreERT2+/−TdTomatowt/ST
adult mice (P100) challenged with VSV and matched controls were obtained from GEO (GSE110188). Braintissues were processed using Miltenyi dissociation protocol and pre-sorted td-tomato+ cells via FACS. Metadata including biological replicates and experimental
conditions were aggregated from the publication. Data
processing and differential expression analysis was performed with aforementioned bulk RNA-seq analysis
pipeline.
Seurat version 4 was used for the single-cell analysis[79]. For scRNA-seq datasets, an initial round of cleaning and clustering was performed using dataset-specificparameters described in the original publications. Cluster identities were determined by calculating enrichedmarkers using the FindAllMarkers () function implement

Mulenge et al. Journal of Neuroinflammation(2024) 21:203

Page 18 of 22

in Seurat. Cell types were assigned by identifying genesthat were unique to each cluster and through cross-referencing to known markers of each cell type and existing
published datasets.
To study microglia, we applied a uniform processing pipeline to process each dataset. Microglia clusters
were identified in each dataset with canonical markers
and selected for downstream analysis. A second round of
microglial clustering was performed on each dataset separately using defined set parameters. In brief, SCTranform () function was used to normalize and scale data,then UMIs and mitochondrial gene percentages per cellwere regressed out. Principal component analysis (PCA)
was performed with the first 30 PCs, and a by sharednearest neighbor (SNN) graph was build using 10 dimensions. Clusters were identified using the Louvian-clustering algorithm at a resolution of 0.8, and a graph of all cell
populations was generated through uniform manifold
approximation and projection (UMAP) using 10 PCs.

‘ngeneson’ factor to adjust for the cellular detection rate.
Likelihood ratio test (LRT) was performed to test thedifferences across the datasets. Only genes that attained
cutoff criteria of FDR < 0.05 and log2-fold change >|0.25|
were selected for downstream analysis.

Data correction and integration

For deconvolution analysis, we utilized scRNA-seq datasets of microglia from Rosen et al. [33] as reference. We
evaluated cell-type proportions in IP microglia using Single-cell RNA Quantity Informed Deconvolution (SQUID)[36], which combines bulk RNA-seq transformationand dampened weighted least squares deconvolution
approaches to predict the composition of cell mixtures.

Prior to integrative analysis, we first performed normalization of pre-selected microglia cell clusters from Van
Hove et al. [23], Tepe et al. [26], Hammond et al. [31],Marsh et al. [27] datasets with Seurat. All datasets weremerged, and low-quality cells were filtered out basedon threshold cut-off for minimum numbers of genes
detected at 200 genes per cell and maximum mitochondrial percentage of 10. Gene expression of split datasets
was normalized and transformed using SCTransform ()function to identify highly variable genes (HVG). The
top 3000 HVG were used as input to identify crossdataset pairs of cells that are in matched biological state(anchors) to integrate the datasets together. Due to differences in cell isolation and handling protocols, library
preparation technology, and sequencing platforms acrossthe datasets, we then performed iterative linear correction based on soft clustering implemented in Harmony[80] to integrate technical covariates in a low-dimensional space. The output was used for linear dimensionality reduction using 11 PCs, followed by application of
the non-linear UMAP dimensionality reduction withresolution of 0.8. UMAP plots and gene expression plots
were generated using built-in Seurat/ggplot2 plotting
functions.
Differential gene expression analysis

The significance of inter-dataset gene expression of
microglia was determined using the MAST package[34]. In brief, the integrated Seurat object was log transformed and converted into SCA object. Genes expressedin less than 10% of cells were filtered out and identity ofeach dataset (batch) was assigned as predictor. We fitted a hurdle model, modelling the batch and centered

Gene signature scoring

To define cell type enrichment of cell activation signature
identified from bulk RNA-seq analysis, the UCell package
was used [25]. UCell is a gene signature scoring method
based on Mann-Whitney U statistics. In brief, the algorithm calculates the relative gene expression in individual
cells and ranks genes for each cell for each gene set togenerate UCell scores. In the end, UCell scores depend
only on the relative expression of genes in each cell andare not dependent on dataset composition. UCell enrichment scores were projected on UMAP using FeaturePlot
function implemented in Seurat.
In silico deconvolution

Immunohistochemistry

Mice were anesthetized with a ketamine/xylazine andtranscardially perfused with ice cold 1x PBS followed byice cold 4% PFA. Brains were harvested, post-fixed in 4%
PFA for 4 h and dehydrated in 30% sucrose overnight at
4 °C. Brains were then embedded in Tissue-Tek optimal
cutting temperature compound (Sakura, 4583) and cut
into 7 μm sagittal sections. Prior to immunolabeling,slices were rehydrated with 0.5% Triton-X100 in PBS for
15 min. Tissue slices were blocked in tissue blocking solution (PBS containing 5% donkey serum [Sigma, S30M]
and permeabilized with 0.5% Triton-X 100 [BioRad,
1610407]) for 1 h at room temperature (RT). Primary
antibodies: CD3 (1:100; Biolegend, 100202) and IBA1
(1:500; Abcam, ab5076) were added and incubated overnight at 4 °C. Excess antibodies were removed through3 washing steps with 0.5% Triton-X100 in PBS for 5 min
followed by secondary antibodies labeling with donkey
anti-rat AF568 (1:500; Abcam, 175475) and donkey antigoat AF647 (1:500; Invitrogen, A21447) for 1 h at RT. Following secondary staining, sections were washed 3 times
with 0.5% Triton-X100 in PBS. Nuclei were stained withDAPI (1:1000; Sigma D9542) for 2 min, dried and coverslips were mounted using Dako fluorescence mounting
medium. Fluorescent images were acquired with Olympus FV3000 and analyzed with ImageJ.

Mulenge et al. Journal of Neuroinflammation(2024) 21:203

Cell sorting and flow cytometry

Mice were euthanized and perfused with ice cold 1x PBS.
Brains were isolated and dissociated by enzymatic digestion in a 2 mL RPMI (Capricorn, RMPI-XA) containing
20% collagenase (Sigma, C9263), 40% DNase and 5% FBS
in gentleMACS C tubes 3 times for 8 min at 37°C. The
homogenates were loaded on a three layered Percoll gradient (30, 37 and 70%; Sigma, P1644) and centrifuged at
500 g for 30 min. Immune cells were collected at 37% and
70% Percoll interphase, washed in PBS and immediately
processed for downstream analysis. For sorting experiments, freshly isolated cells were incubated with antiFcγIII/II receptor antibody (CD16; Biolegend, 156603)
for 10 min and thereafter immunolabeled for CD45.2
PacBlue (Biolegend, 109820). An aliquot of 250 µl was
kept aside as “unsorted” fraction. After cell sorting, boththe “sorted” and “unsorted” fractions were immunolabeled with the following flourochrome-conjugated antibodies: CD11b APC-Cy7 (Biolegend, 101226), P2RY12APC (Biolegend, 848006), CD74 AF647 (Biolegend,
151004), LY6C AF700 (Biolegend, 128024), CX3CR1
BV510 (Biolegend, 149025), MHCI FITC (Biolegend,
125508), LY6G PE-Cy7 (Biolegend, 127618) and MHCIIPE-Cy5 (Biolegend, 107611). Samples were incubatedfor 20 min at 4°C, washed and re-suspended in 300 µL of
FACS buffer.
For flow cytometric analysis of brain immune cells
during VSV infection, the freshly isolated cells wereincubated with anti-FcγIII/II receptor antibody (CD16;
Biolegend, 156603) to prevent unspecific binding of
the antibodies. Thereafter, cells were further labeledwith the following flourochrome-conjugated antibodies against cell surface markers in FACS buffer: CD45.2
PacBlue (Biolegend, 109820), CD11b APC-Cy7 (Biolegend, 101226), LY6C AF700 (Biolegend, 128024), LY6G
PE-Cy7 (Biolegend, 127618), CX3CR1 BV510 (Biolegend,
149025), MHCI BUV661 (BD, 749702), MHCII BV785(Biolegend, 107645), CD4 BV570 (Biolegend, 100541),
CD8b BV711 (Biolegend, 126633), CD11c BV605 (BD,563057), TCR-β PE-dazzle (Biolegend, 109240), B220
PE-Cy5 (Invitrogen, RM2618) and NK1.1 BV421 (Biolegend, 108732). Samples were incubated for 20 min at 4 °C,
washed and re-suspended in 300 µL of FACS buffer. All
samples were acquired on a Sony ID7000 flow cytometerequipped with 405, 488, 561 and 633 lasers. Obtained
data were analyzed with FlowJo software (Tree Star).
Dimensional reduction and clustering analysis of flow
cytometry data

Unsupervised clustering of flow cytometry data was performed with FlowSOM in R environment. First, nonlymphocyte cells and doublets were excluded based on
FSC/SSC parameters. Then, fcs files containing both
CD45+CD11b+ and CD45+CD11b− cells from each

Page 19 of 22

sample were exported for further analysis. Marker intensities were transformed with biexponentialTransform ()function, scaled and reduced into low-dimensional space
using BuildSOM () function in FlowSOM algorithm.
FlowSOM clusters cells with similar phenotypic appearance into subpopulation or subsets, which are depictedin special proximity on the UMAP plot. Optimal clusters
were iteratively estimated based on delta area generated
by ConsensusClusterPlus.
Statistics and reproducibility

Statistical analysis and data visualization in the present study was performed by using the R software (version 4.1.1, http://www.r-project.org). Unless specificallystated, p or FDR values < 0.05 were considered statistically significant.

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12974-024-03197-2.
Supplemental Fig. 1: Expression of cell activation signatures in bulk RNA
and scRNA-seq. A, Box plot with overlaid dotplot of normalized counts
revealing prominent expression of aberrant activation signatures in sorted
microglia (for additional details see caption of Fig. 1). B, Violin plots of basic
scRNA-seq quality metrics and UMAP embedding of 21,463 cells from Van
Hove at al. [23], highlighting major cell types. C, Heatmap depicting thescaled and log-normalized expression values of the top three most highly
enriched genes in microglia clusters. A maximum of 100 cells per cluster
are displayed. D, Cell-specific expression of selected cell activation genes
overlaid on the UMAP coordinates. Color scale represents gene-weighted
kernel density estimates.
Supplemental Fig. 2: Integrative analysis of publicly archived large-scaletranscriptomic datasets. Basic quality metrics and UMAP embeddingof scRNA-seq datasets of brain immune cells from (A) Hammond et al.
[31], (B) Marsh et al. [27], and (C) Tepe et al. [26]. Violin plot displaying
distribution of cell activation enrichment score in cell types from (D) bonemarrow and (E) splenocytes. OPC, oligodendrocyte progenitor cells; OEC,olfactory epithelial cells; RBC, red blood cells; DC, dendritic cells; HSC,
hematopoietic stem cells; MSC, mesenchymal stem cells; NMP, neutrophilmyeloid progenitors.
Supplemental Fig. 3: Expression of cell activation signatures in integrated
scRNA-seq with key experimental variations. A, UMAP plot of 25,406
integrated microglia cells depicting three microglial clusters. Violin plot
displaying distribution of cell activation enrichment score in integrated
microglia grouped by (B) dataset and (C) microglia subtypes. D, Abundance of selected key cell activation genes in the integrated microglia
datasets. E, Average expression of canonical microglial markers across theintegrated datasets. F, Violin plot showing expression of key cell activation
genes in microglia treated with transcriptional inhibitors. G, Contour plots
representing the gating strategy for myeloid cells. Gates are indicated inblue. Doublets and dead cells were excluded before. Both CD45+CD11b+
and CD45+CD11b− populations were exported for dimensionality reduction and clustering, whereas CD45+CD11b+ were further gated for the
microglia population. H, Contour plots representing CD74+, MHCI+ or
MHCII+ expressing microglia.
Supplemental Fig. 4: Viral infection of the brain induces transcriptional
shift of microglia. A, Venn diagram showing intersection of differentiallyregulated genes between VSV versus PBS in IP, in vitro and sorted samples.
B, Roseplot showing directional distributions of overlapping genes
regulated in IP, in vitro, and sorted microglia based on expression changes.
C, Volcano plot highlighting fold change of differentially regulated genes
between VSV versus PBS from IP microglia. Genes with up- or downregulated expression are highlighted in amber and pink, respectively. D, UMAP

Mulenge et al. Journal of Neuroinflammation(2024) 21:203

plot of scRNA-seq datasets from Rosen et al. [33] depicting segregationof immune cells based on treatment. Microglia isolated from controls
segregated from microglia isolated from WNV-infected mice. E, UMAP
plot displaying density expression levels of genes encoding for MHC
complexes and phagocytosis in Cx3cr1 expressing microglia. F, UMAP
depicting immune composition in the brain following VSV infection (left)
and surface maker expression associated with identified clusters (right).
Supplemental table 1: Cell activation gene signatures of microglia andconsensus gene list for the gene scoring module.
Acknowledgements
We would like to thank Marco Galardini for his technical assistance in data
analysis and Elisabeth Janecek-Erfurth for help with preparation of themanuscript. We thank Jennifer Skerra and Kira Baumann for their assistancewith animal breeding and screening. We acknowledge the assistance of the
Cell Sorting Core Facility of the Hannover Medical School. BioRender software(https://biorender.com/) was used for preparation of Fig. 1A. This work was
supported by funding of the Deutsche Forschungsgemeinschaft (DFG;
German Research Foundation), project ID 398066876 - GRK 2485/1 (VIPER) andproject ID 158989968 – SFB 900-B2 to U.K., and by the Helmholtz Association[Zukunftsthema “Immunology & Inflammation” (ZT-0027)] to U.K.. F.M. was
supported by the Hannover Biomedical Research School (HBRS) and theCentre for Infection Biology (ZIB).
Author contributions
F.M. designed and performed experiments, analyzed Bulk RNA and scRNAseq data, prepared the figures, and wrote the manuscript. O.L .G performedin vitro generation of microglia, in vitro infection, RNA extraction and editing
manuscript. A.P. and L.G. performed RiboTag experiments, contributed
to RNA extraction and editing of manuscript. L.M.B. contributed toimmunofluorescence microscopy experiments and data analysis. I.W. and
A.A. contributed to flow cytometry experiments, data analysis and editing
manuscript. U.K. planned and supervised the project, conceptualized thestudy and wrote the manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Data availability
The RNA-seq data generated in this study have been deposited in the NCBI
Gene Expression Omnibus and are accessible through GEO Series accession
number GSE271293. No original codes were generated in this study. Further
information and requests for resources and reagents should be directed to thelead contact.

Declarations
Ethical declaration
All animal experiments were approved by local administration in Germany
and were performed in strict conformity to respective national, federal andinstitutional regulations and the guidelines of the Federation of European
Laboratory Animal Science Association.
Competing of interests
The authors declare no competing interests.

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Received: 24 April 2024 / Accepted: 6 August 2024
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