neuroimmunology single-cell profiling identifies …...transcriptional profiles and dynamics during...

RESEARCH ARTICLE SUMMARY◥

NEUROIMMUNOLOGY

Single-cell profiling identifies myeloidcell subsets with distinct fatesduring neuroinflammationMarta Joana Costa Jordão, Roman Sankowski*, Stefanie M. Brendecke*, Sagar,Giuseppe Locatelli, Yi-Heng Tai, Tuan Leng Tay, Eva Schramm, Stephan Armbruster,Nora Hagemeyer, Olaf Groß, Dominic Mai, Özgün Çiçek, Thorsten Falk,Martin Kerschensteiner, Dominic Grün, Marco Prinz†

INTRODUCTION:Under homeostasis, the cen-tral nervous system (CNS) hostsmicroglia (MG)and CNS-associated macrophages (CAMs).During experimental autoimmune encepha-lomyelitis (EAE), myeloid complexity drasti-cally increases, with dendritic cells (DCs) andmonocytes seeding the CNS. However, whichdisease-specific populations canbe foundduringneuroinflammation remains largely unknown.

RATIONALE: An important step for the ini-tiation of EAE and multiple sclerosis (MS) isthe infiltration of the CNS by encephalitogenicT cells, which potentially become reactivatedby encountering their self-cognate antigenspresented at the brain interfaces.Myeloid cellshave been shown toplay a critical role in antigenpresentation.Consequently, their transcriptomic

profile and dynamics during neuroinflamma-tion are crucial for understanding neuroin-flammatory pathology.

RESULTS: High-throughput single-cell se-quencing (scRNA-seq) of CD45+ cells isolatedfrom several CNS compartments (includingleptomeninges, perivascular space and paren-chyma, and choroid plexus) allowed us to as-semble a transcriptional atlas comprising 3461immune cells, identified as homeostatic (“h”)or disease-associated (“da”) myeloid subsets.Profiling of all CAMs unraveled a core signa-ture that consists of Mrc1, Pf4, Ms4a7, Stab1,and Cbr2. During disease, onlyMs4a7 remainedstably expressed, and a strong increase ofantigen-presentation molecules (such as Cd74)was observed. Microglia expressed genes that

included P2ry12, Tmem119, Sparc, and Olfml3.Although most of the core genes were down-regulated during disease, Sparc and Olfml3expression remained unaltered and were ac-companied by an up-regulation of Ly86. Sev-eral monocyte populations were observedduring EAE, including monocyte-derived cellsexpressingMertk andMrc1 or expressing Zbtb46and Cd209a. Although DCs were scarce in thehomeostatic CNS, their density highly increasedduring disease, and diverse disease-associated

DCs could be identified.Wenext established the

spatiotemporal relation-ship between infiltratingmonocytes and residentmacrophages using theCx3cr1CreERT2 system.Local

proliferation of resident macrophages oc-curred alongside continuous monocytic infil-tration up to the peak of disease. Monocyteswere transiently integrated into the CNS, andresident macrophages underwent apoptosisduring the chronic phase. An evaluation ofmicroglial expansion by using Cx3cr1CreER:R26Confetti mice revealed their clonal expan-sion during neuroinflammation.We then investigated the capacity of resi-

dent and hematopoietic stem cell–derivedmyeloid cells for antigen presentation. Time-lapse imaging of Cx3cr1CreERT2:R26tdTomato:Cd2GFP and Ccr2RFP: Cd2GFP mice showedprolonged T cell interactions with circulat-ing myeloid cells rather than tissue-residentmacrophages during neuroinflammation. Accord-ingly, MOG35-55 immunization of Cx3cr1CreERT2:H2-Ab1flox mice showed no overt changes in

disease development, indicating that res-ident macrophages are redundant for anti-gen presentation. By contrast, Cd11cCre:H2-Ab1flox mice were highly resistant toEAE, pointing to the potential role of DCsand monocyte-derived cells in EAE onset.

CONCLUSION: In this study, we un-raveled the complexity of the CNS mye-loid landscape and the dynamics ofseveral myeloid populations during neuro-inflammation. Although CNS-residentmacrophages quickly generated context-dependent subsets during disease, theirrole as APCs was irrelevant for the ini-tiation of pathology. DCs and monocyte-derived cells, highly diverse during EAE,remain the major players in antigen pres-entation. The comprehensive characteriza-tion presented here will provide a strongbasis for their future targeting.▪

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Jordão et al., Science 363, 365 (2019) 25 January 2019 1 of 1

The list of author affiliations is available in the fullarticle online.*These authors contributed equally to this work.†Corresponding author. Email: [email protected] this article as M. J. C. Jordão et al., Science 363,eaat7554 (2019). DOI: 10.1126/science.aat7554

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Myeloid cell diversity during neuroinflammation. The homeostatic CNS includes microglia anddifferent CAMs. During disease, microglia clonally expand, and the transcriptomic profile of microgliaand CAMs drastically change. Diverse DC and monocyte subsets simultaneously populate theCNS. The role of resident macrophages for antigen presentation is redundant, whereas DCsand/or monocyte-derived populations show high antigen-presentation capacity, pointing to theircrucial role in experimental autoimmune encephalomyelitis.

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RESEARCH ARTICLE◥

NEUROIMMUNOLOGY

Single-cell profiling identifies myeloidcell subsets with distinct fatesduring neuroinflammationMarta Joana Costa Jordão1,2, Roman Sankowski1,3*, Stefanie M. Brendecke1*†, Sagar4,Giuseppe Locatelli5,6, Yi-Heng Tai5,6, Tuan Leng Tay1,2,7, Eva Schramm1,Stephan Armbruster1, Nora Hagemeyer1, Olaf Groß1,8,9,10, Dominic Mai11,12,Özgün Çiçek11, Thorsten Falk8,11, Martin Kerschensteiner5,6,13,Dominic Grün4, Marco Prinz1,8,10,14‡

The innate immune cell compartment is highly diverse in the healthy central nervoussystem (CNS), including parenchymal and non-parenchymal macrophages. However, thiscomplexity is increased in inflammatory settings by the recruitment of circulatingmyeloid cells. It is unclear which disease-specific myeloid subsets exist and what theirtranscriptional profiles and dynamics during CNS pathology are. Combining deepsingle-cell transcriptome analysis, fate mapping, in vivo imaging, clonal analysis, andtransgenic mouse lines, we comprehensively characterized unappreciated myeloid subsetsin several CNS compartments during neuroinflammation. During inflammation, CNSmacrophage subsets undergo self-renewal, and random proliferation shifts toward clonalexpansion. Last, functional studies demonstrated that endogenous CNS tissuemacrophages are redundant for antigen presentation. Our results highlight myeloid celldiversity and provide insights into the brain’s innate immune system.

Under steady-state conditions, the centralnervous system (CNS) hosts a heteroge-neous population ofmyeloid cells, includingparenchymalmicrogliaandnon–parenchymalperivascular (pvMF), meningeal (mMF),

and choroid plexus macrophages (cpMF), whichare collectively calledCNS-associatedmacrophages(CAMs) (1–3). The various anatomical localizationsof endogenous myeloid cells within the CNS(parenchyma versus brain interfaces) have been

associated with functions such as antigen pre-sentation to encephalitogenic T cells (4) andthe drainage of protein aggregates from theCNS (5) during pathology. However, clear-cutexperimental evidence for myeloid-cell-subtype–specific functions in vivo is scarce. Whether themyeloid milieu differs across the CNS and how itchanges under pathological conditions such asCNS autoimmunity remains largely unexplored.Various immune cells have been described

to initiate the inflammatory processes that un-derlie demyelinating inflammatory diseases, suchas multiple sclerosis (MS) and experimentalautoimmune encephalomyelitis (EAE), a mousemodel of autoimmune demyelination (6, 7). Theimmune subsets found in the inflamed CNSinclude macrophages, several types of monocytes[Ly6Chi and Ly6Clo monocytes and monocyte-derived cells (MCs)], classical dendritic cells(cDCs), plasmacytoid DCs (pDCs), B cells, T cells,and natural killer (NK) cells (8, 9). During MS,immune cells are repeatedly recruited from theperiphery, reinforcing the local inflammatoryreaction within the CNS. These newly engraftedimmune cells engage in a dynamic interplaywith local endogenous macrophages in the CNS,which is still incompletely understood. Untilnow, a clear distinction between tissue-residentmacrophages and invading myeloid cells hasbeen mostly accomplished solely on the basisof their localization and morphology by usingimmunohistochemistry. Only recently, compre-hensive profiling of bulk populations of CNS

macrophages, including of entire transcriptomes(10) and proteomes (11), has helped to uncoverseveral myeloid cell states during homeostasis(12) and disease (13). Although these approachesprovided important insights, they all sufferedfrom some limitations because they were re-stricted to probing a few selected proteins or RNAs,impeding the possibility of studying comprehen-sive landscapes and of discovering previouslyunrecognized cell subsets owing to a bias towardprecharacterized molecules (14). The profiling ofpotentially heterogeneous cell populations led toan average signature, which obscured the puta-tive diversity of CNS macrophages (15–17).Additionally, the antigen presentation required

for T cell activation (18) has been attributed toeither major histocompatibility complex (MHC)class II+ CNS intrinsic cells (19) or circulatingDCs (20). In order to address these contradic-tory results and understand myeloid cell diver-sity during neuroinflammation, we combinedsingle-cell RNA sequencing (scRNA-seq) withfate mapping, transgenic mouse lines, and in vivoimaging. We challenge the prevailing view thatonly a few myeloid cell subsets exist. Further-more, we provide insights into the transcriptionalnetworks, ancestry, and turnover of macrophagesat CNS boundaries and peripheral myeloid cellsduring autoimmune neuroinflammation. We un-covered signature molecules that distinguishmyeloid-cell populations involved in demyelinat-ing neuroinflammatory conditions by highlight-ing context-dependent myelomonocytic subsetsand their distinct signals. These data providepotential therapeutic targets and a complemen-tary resource for the study of disease mecha-nisms in the CNS.

ResultsscRNA-seq identifies new myeloidsubsets in distinct CNS compartmentsduring autoimmune inflammation

In order to analyze the diversity of hematopoieticcells during neuroinflammation on a single-celllevel, we dissected several CNS compartments.Cells from the leptomeninges, choroid plexus,perivascular space, parenchyma, and blood fromnaïve and MOG35-55–immunized mice were iso-lated and subjected to high-throughput scRNA-seq accompanied with unbiased clustering (Fig. 1).We assembled a transcriptional atlas compris-

ing a total of 3461 immune cells recovered fromall CNS compartments and blood (table S1). Werepresented these data using dimensionality re-duction by use of t-distributed stochastic neighborembedding (t-SNE) (Fig. 1A and fig. S1A). RaceID3analysis (21) predicted 26 cell clusters, whichmainly contained innate immune cells such asmyeloid cells but also lymphocytes (fig. S1Aand table S2). In order to define the transcrip-tional changes that allowed us to distinguish celltypes in the inflamed CNS, we generated mapsfor the major myeloid cell populations basedon previously described key signature genes(Fig. 1B) (22–25).Projections of individual cells by using t-SNE

analysis fromnaïve andEAE-diseasedmice across

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Jordão et al., Science 363, eaat7554 (2019) 25 January 2019 1 of 17

1Institute of Neuropathology, Faculty of Medicine, Universityof Freiburg, Freiburg, Germany. 2Faculty of Biology,University of Freiburg, Freiburg, Germany. 3Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg,Germany. 4Max-Planck-Institute of Immunobiology andEpigenetics, Freiburg, Germany. 5Institute of ClinicalNeuroimmunology, University Hospital, Ludwig-MaximiliansUniversity Munich, Munich, Germany. 6Biomedical Center(BMC), Faculty of Medicine, Ludwig-Maximilians UniversityMunich, Munich, Germany. 7Cluster of ExcellenceBrainLinks-BrainTools, University of Freiburg, Freiburg,Germany. 8BIOSS Centre for Biological Signalling Studies,University of Freiburg, Germany. 9Institute of ClinicalChemistry and Pathobiochemistry, University HospitalRechts der Isar, School of Medicine, Technical Universityof Munich, Munich, Germany. 10CIBSS Centre forIntegrative Biological Signalling Studies, University ofFreiburg, Germany. 11Institute of Computer Science,University of Freiburg, Freiburg, Germany. 12Life ImagingCenter, Center for Biological Systems Analysis, Albert-Ludwigs University, Freiburg, Germany. 13Munich Cluster forSystems Neurology (SyNergy), Munich, Germany. 14Centre forNeuroModulation, University of Freiburg, Germany.*These authors contributed equally to this work. †Present address:Department of Pathology, Oslo University Hospital, Oslo, Norway.‡Corresponding author. Email: [email protected]

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Fig. 1. Molecular census of hematopoieticcells during neuroinflammation in differentCNS compartments. (A) t-SNE representationof 3461 individual hematopoietic cells fromall CNS compartments measured withscRNA-seq. Each dot represents an individualcell. Dashed lines indicate different hemato-poietic populations. CAMs, CNS-associatedmacrophages; DCs, dendritic cells. (B) t-SNEplot depicting the expression levels of knowncore signature genes for microglia, CAMs, MCs,and DCs among all hematopoietic cells thatunderwent scRNA-seq. (C) t-SNE representationof 1052 individual meningeal cells, 1324 peri-vascular and parenchymal cells, and 701 choroidplexus cells measured with scRNA-seq andRaceID3 clustering. Yellow dots highlight theanalyzed cells from homeostasis and differentstages of EAE. (D) Identification of the maincell populations in the leptomeninges, peri-vascular space plus parenchyma, and choroidplexus. mMF, meningeal macrophages; pvMF, perivascular macrophages; cpMF, choroid plexus macrophages; MCs, monocyte-derived cells; Granulo,granulocytes; Lympho, lymphocytes. (E) Unbiased cluster analysis of subpopulations of cells found in the leptomeninges, perivascular space,parenchyma, and choroid plexus during EAE measured with scRNA-seq. hmMF, homeostatic meningeal macrophages; damMF, disease-associatedmeningeal macrophages; hpvMF, homeostatic perivascular macrophages; dapvMF, disease-associated perivascular macrophages; hMG, homeostaticmicroglia; daMG, disease-associated microglia; hcpMF, homeostatic choroid plexus macrophages; dacpMF, disease-associated choroid plexusmacrophages; mDCs, meningeal dendritic cells; cpDCs, choroid plexus dendritic cells; mMCs, meningeal monocyte-derived cells; pMCs, perivascularand parenchymal monocyte-derived cells; cpMCs, choroid plexus monocyte-derived cells.

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the CNS revealed the differential involvement ofdistinct CNS immune compartments over time(Fig. 1C). The main hematopoietic populationsfound were distinguishable on the basis of theirtranscriptomic signature (Fig. 1D).We then identifiedmolecularly distinct classes

and subclasses of cells from different CNS com-partments. Across the disease course, we iden-tified 10 hematopoietic cell populations in theleptomeninges, 15 populations in the parenchymaand perivascular space, and 13 subsets in thechoroid plexus (Fig. 1E). Clustering showed thatthe CNS myeloid cell compartments were sep-arated into opposing states during the courseof neuroinflammation: homeostatic (“h”) anddisease-associated (“da”) clusters. Consequently,CNS tissue macrophages were found as homeo-static mMF (hmMF), pvMF (hpvMF), cpMF(hcpMF), or homeostatic parenchymalmicroglia(hMG) subsets in the healthy CNS. EAE-associatedpopulations were designated as disease-associatedmMF (damMF), pvMF (dapvMF), cpMF (dacpMF),and disease-associatedmicroglia (daMG) (Fig. 1E).DC and MC subclasses were enriched during dis-ease stages. Identified myeloid subpopulationsshowed both spatially specific and disease stage–specific kinetics when quantitatively analyzed(fig. S1B). Thus, the innate immune compartmentin the adult CNS comprises transcriptionallydistinct myeloid cell populations, which exhibitdifferent disease stage–related subclasses withvariable distribution across the CNS.

Uncovering tissue-resident macrophagesubsets during neuroinflammation

Profiling of all CAM populations—including in-dividual mMF, pvMF, and cpMF from healthymice—identified the presence of three subsetsof CAMs, designated hCAM1, hCAM2, and hCAM3(fig. S2, A to D), which expressed not only Mrc1but also Ms4a7, Pf4, Stab1, Cbr2, and Cd163(fig. S2E). Fcrls, which was previously describedto be microglia-specific (25, 26), was also detect-able in all hCAM subsets. A comparison betweenhCAM1 and hCAM3 revealed an activated phe-notype for the hCAM3 cluster with higher levelsof the MHC class II–related molecules H2-Aa,H2-Ab1, H2-Eb1, and Cd74 (fig. S2F). hCAM3 alsoshowed high levels of Ccr2 due to the contribu-tion of cpMF, which are known to be partiallyderived from CCR2+ monocytes (24).Disease-associated clusters of CNS endogenous

macrophageswere transcriptionally distinct fromtheir counterparts during homeostasis (Fig. 2).However, CNS myeloid cells are generally char-acterized by their inherent ability to dynamicallychange their transcriptional profile and accom-panying surfacemarker landscapeuponactivation.This makes distinguishing yolk sac (YS)–derivedCNS endogenous tissuemacrophages from hema-topoietic stem cell (HSC)–derived circulatingmyeloid cells during neuroinflammation verychallenging (8, 27, 28). In order to unequivocallycharacterize the lineage and fate of the myeloidsubsets identified with scRNA-seq, we tookadvantage of a tamoxifen-inducible Cx3cr1CreERT2:R26tdTomato line, which distinguishes long-lived

YS-derived CX3CR1+MG,mMF, pvMF, and cpMFfrom HSC-borne short-lived CX3CR1+ myeloidcells, such as monocytes and DCs (24, 29, 30).Profiling of single CAMs in different CNS

immune compartments identified one hmMFpopulation in the leptomeninges that was dis-tinct from the disease-associated damMF1 (Fig. 2A).Both mMF subsets expressed Mrc1, Pf4, Cbr2,Ms4a7, Stab1, Fcrls, Cd163, and Siglec1 (Fig. 2B,and fig. S2G). Individual hmMF expressed higherlevels of Cxcl2, Lyve1, and Nfkbiz, whereasdamMF1 cells exhibited increased levels of theinflammatory chemokine Ccl5 and H2-Ab1, H2-Aa, H2-Eb1, and Cd74, suggesting a functionalantigen presentation role for mMF in the CNS(Fig. 2C). LYVE-1 expression could be confirmedat the protein level in tdTomato+ mMF duringboth the naïve stage and peak of disease (Fig. 2D).In agreement with the transcriptional profileof damMF, we observed a significant down-regulation of the LYVE-1 expression levels intdTomato+ damMF at peak of disease (Fig. 2E).CCL5 immunoreactivity was also up-regulatedon resident damMF1 (Fig. 2E). pvMF, presentduring homeostasis as hpvMF, also showeda previously unidentified subset in the contextof disease (dapvMF1) (Fig. 2F), with similaritiesto mMF (Fig. 2, G and H, and fig. S2H). In gen-eral, dapvMF did not show a strong transcrip-tional differentiation during neuroinflammation.LYVE-1 expression in tdTomato+ resident pvMFwas significantly down-regulated at the peak ofdisease (fig. S2M). However, its down-regulationdid not represent a complete loss of LYVE-1, andthus, the number of LYVE-1–expressing pvMFwas not significantly altered (Fig. 2I). By con-trast, the number of CTSD-expressing pvMFwas down-regulated during disease (Fig. 2I).CD74 and CCL5 were observed to be highly ex-pressed by tdTomato+ pvMF at peak of disease(Fig. 2I). In the choroid plexus, macrophageheterogeneity was higher during homeostasiswith three hcpMF subsets. Two dacpMF sub-sets were also found during disease (Fig. 2J).cpMF shared core genes with mMF and pvMF,such as Mrc1, Ms4a7, Pf4, Stab1, Cbr2, andFcrls (Fig. 2K). dacpMF populations showedan activated phenotype with increased levelsof Il1b and MHC class II–related molecules (Fig.2L). Accordingly, CD74 and interleukin-1b (IL-1b)were found to be highly expressed in the choroidplexus at peak of disease, but specificity forresident macrophages is challenging to addressbecause of the dual ontogeny of cpMF (Fig.2M). dacpMF1 expressed higher levels of S100a9,S100a8, and Lcn2, whereas MHC class II–relatedmolecules and Ctss were highly expressed indacpMF2 (Fig. 2N). Both populations showedan enrichment for the chemotaxis pathway, anddacpMF1 also up-regulated pathways related tocell motility as well as antigen processing andpresentation of exogenous peptide antigen (fig.S5). We also identified a core signature for allCAMs independent of their localization com-prising Mrc1, Pf4,Ms4a7, Stab1, Cbr2, and Fcrls(Fig. 2O and fig. S3, A and B). Fcrls was ex-pressed by all tissue-resident macrophages, in-

cluding MG and CAMs (fig. S3A), whereas Cbr2,Mrc1, Stab1, and Pf4 were highly enriched forCAMs but down-regulated during disease (fig.S3B). By contrast, Ms4a7 was distinctly and con-stantly expressed by individual CAMs throughouthealth and disease (Fig. 2O). Only a few geneswere found to be different when CAMs werecompared among the different CNS localiza-tions during both homeostasis and inflamma-tion (fig. S2K).Both constitutive and disease-inducedmicrog-

lial heterogeneitywas also observed in the paren-chyma, with two populations (hMG1 and hMG2)present during homeostasis and four subsets(daMG1, daMG2, daMG3, and daMG4) duringdisease (Fig. 3A). All MG populations expressedBhlhe41, Gpr34, Hexb, Olfml3, P2ry12, P2ry13,Sall1, Serpine2, Siglech, and Sparc (Fig. 3B andfig. S2J), but daMG clusters showed lower ex-pression of P2ry12, Maf, and Slc2a5 and higherexpression of Ccl2, Cxcl10, Ly86, and Mki67, in-dicating a proliferative capacity of daMG along-side the production of chemokines (Fig. 3C).Additionally, these daMG subpopulations showedreduced levels ofGpr34,Bhlhe41, Serpine2, Siglech,and Sall1 (fig. S3C) during disease. The most in-flammatory disease-associated microglial subsets(daMG2, daMG3, and daMG4) strongly down-regulated the core microglial genes P2ry12,Tmem119, and Selplg and up-regulated Ly86(Fig. 3D). Both P2RY12 and TMEM119 immuno-reactivities were clearly down-regulated withinthe core of spinal cord lesions, whereas CD162(encoded by Selplg) was only weakly reduced(Fig. 3E). By contrast,microglialMD-1 (encodedby Ly86) was strongly up-regulated in the le-sions (Fig. 3E). Because of their transcriptionalprofile and their P2RY12loTMEM119loMD-1hi

phenotype (Fig. 3, F and G, and fig. S2L), wedetermined that only daMG2, daMG3, anddaMG4 localized within the lesion sites. A com-parison of the transcriptomic profile betweenthese three subsets revealed distinct signa-tures, which could be confirmed in situ withdaMG2 as CD74hiCXCL10lo tdTomato+microglia,daMG3 as CXCL10hiCD74lo tdTomato+microglia,and daMG4 as CCL5hiCD74lo tdTomato+microglia(Fig. 3, F and G). Microglial cells were also ob-served to be able to express high levels of CXCL10,CCL5, and CD74, which is an indication of theinterplay between daMG2, daMG3, and daMG4described subsets, likely at different stages ofactivation. Among the core microglial genes,only Sparc and Olfml3 were shown to be stableduring pathology (Fig. 3H). Accordingly, SPARCprotein was expressed by all tdTomato+ mi-croglia during homeostasis and disease (Fig. 3,E and I).Thus, our single-cell profiling identified pre-

viously unknown mMF, pvMF, cpMF, and MGsubclasses associated with neuroinflammation,suggesting that homeostatic subsets of CNSendogenous tissue macrophages are able toquickly change their phenotypes and generatecontext- and time-dependent subsets. Moreover,CAMs strongly up-regulate MHC class II mole-cules during neuroinflammation, suggesting a



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Fig. 2. Identificationof disease-inducedpopulations ofCNS-endogenoustissue macrophages.(A) mMF, (F) pvMF,and (J) cpMFsubsets during dis-ease identified bymeans of unbiasedclustering. Thearrow indicates thedirection of cellcluster differentiationduring disease.(B, G, and K) Log-transformed ofexpression levelsof differentiallyexpressed genes,given as significantlyincreased basedon the negative bino-mial distributionsin (B) mMF, (G)pvMF, and (K) cpMFsubsets. Data areshown as whiskerplots with means ±SEM of expressionvalue. Data arerepresentative fromthree independentexperiments (n =6 mice per experimentfor naïve stage,n = 5 mice from onset,and n = 5 mice frompeak phase). (C, H,and L) Top differen-tially regulated genesin (C) hmMF com-pared with damMF1,(H) hpvMF comparedwith dapvMF1, and(L) hcpMF (hcpMF1,hcpMF2, andhcpMF3) comparedwith dacpMF(dacpMF1 and dacpMF2). Data are presented as log2-fold changes.Arrows highlight genes that were verified on the protein level. (D, E andI) Representative immunofluorescence picture of Cx3cr1CreERT2:R26tdTomato mice depicting (D) LYVE-1, CD74, and CCL5 expression onresident mMF (tdTomato+) and (I) expression of LYVE-1, CTSD,CD74, and CCL5 on resident pvMF (tdTomato+) at naïve stage and peakof EAE. The dashed line indicates the barrier between the meninges(Men) and parenchyma (PC), or the vessel lumen and perivascularspace (PV). Asterisks or arrows indicate resident macrophageseither expressing or not expressing the specified proteins, respectively.Scale bars, 50 mm (overview) and 10 mm (inset). Representativepictures of four mice from two independent experiments are depicted.Quantification of (E) resident mMF expressing LYVE-1 and CCL5 and(I) resident pvMF expressing LYVE-1, CTSD, and CCL5 during the naïvestage and the peak of EAE. Data are from four mice from two

independent experiments and are presented as mean ± SEM. Anunpaired two-tailed Mann-Whitney U test revealed significant differ-ences between the groups. (M) Representative immunofluorescence forCD74 and IL-1b in EAE-diseased Cx3cr1CreERT2:R26tdTomato mice at naïvestage and peak of disease. Asterisks show CD74 and IL-1b expression byresident cpMF (tdTomato+). Scale bars, 50 mm (overview) and 10 mm(inset). A representative picture of three mice from two independentexperiments is displayed. (N) Top highly expressed genes in dacpMF1 anddacpMF2 subsets. Specified subsets are highlighted in the t-SNE plots.(O) t-SNE plots showing the mRNA expression of Ms4a7 duringhomeostasis and neuroinflammation in all CNS hematopoietic cells.The t-SNE plot reflects the map shown in Fig. 1A. The dotted line limits thecells belonging to CAM subsets, and arrows indicate the dynamics ofcell populations during disease. hCAM, homeostatic CNS-associatedmacrophages; daCAM, disease-associated CNS-associated macrophages.

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Fig. 3. Identification of microglia subsetsduring health and disease. (A) Microglia (MG)subsets found during homeostasis and EAE.The arrow indicates the direction of cell clusterdifferentiation during disease. (B) Log-transformedexpression of enriched genes in MG subsets,given as significantly increased based on thenegative binomial distributions, related to othermyeloid cell populations. Data are presentedas whisker plots with means ± SEM of expres-sion value. Data are representative of 16 micepooled from three independent experiments.(C) Most differentially regulated genes in hMGsubsets (hMG1 and hMG2) in comparison withboth extreme daMG populations (daMG3 anddaMG4). Data are presented as log2-foldchanges. The arrow indicates the gene that wasverified on protein level. (D) t-SNE plot dis-playing the expression of genes down- or up-regulated in daMG populations (daMG2,daMG3, and daMG4). t-SNE at the left illus-trates daMG (daMG2, daMG3, and daMG4)with red dots, whereas the same population ishighlighted by a red dotted line in the rightt-SNE plots. (E) Representative immuno-fluorescence pictures of Cx3cr1CreERT2:R26tdTomato

mice during peak of EAE showing the expres-sion of CD162, P2RY12, TMEM119, MD-1, andSPARC on resident (tdTomato+) microgliawithin the lesion compared with outside of thelesion. Scale bars, 50 mm (overview) and 10 mm(inset). Asterisks indicate resident tdTomato+

microglia. Representative pictures of five micefrom three independent experiments aredepicted. (F) Transcriptomic profile of daMG2,daMG3, and daMG4 subsets. Specified subsetsare highlighted in the t-SNE plots, and arrowsindicate the up- or down-regulation of specificgenes. (G) (Left) Representative immuno-fluorescence pictures of Cx3cr1CreERT2:R26tdTomato

mice during peak of EAE showing the down-regulation of P2RY12 and up-regulation of MD-1 inthe lesion and the expression of P2RY12 and lowexpression of MD-1 in the nonlesion site of thespinal cord. Asterisks indicate resident tdTomato+

microglia. Scale bars, 500 mm (overview)and 10 mm (inset). (Right) Representativeimmunofluorescence pictures depicting daMG2(CXCL10loCD74hi), daMG3 (CXCL10hiCD74lo),and daMG4 (CCL5+CD74lo) subsets within thecore of lesions of the spinal cord. Scale bars,10 mm. Pictures are representative of four micefrom two independent experiments. (H) t-SNEplots showing the expression of Olfml3 andSparc during homeostasis and neuro-inflammation in all CNS hematopoietic cells.The dotted line limits the cells belongingto MG subsets. Arrows indicate the dynamicsof cell populations during disease. hMG,homeostatic microglia; daMG, disease-associatedmicroglia. (I) Quantification of resident microgliaexpressing SPARC at naïve stage and peakof disease. Data are from five mice from twoindependent experiments and are presentedas mean ± SEM. An unpaired two-tailedMann-Whitney U test revealed no significantdifference between the groups.

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*




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prominent role for antigen presentation aspreviously postulated (31, 32).

Heterogeneity of HSC-derived myeloidcells during CNS autoimmunity revealedwith single-cell analysis

We then used a combination of high-throughputscRNA-seq profiling and Cx3cr1CreERT2-basedfate mapping to identify disease-induced sub-sets of MCs and DCs that could contribute toEAE pathogenesis (20, 33–35). We identified sev-eral neuroinflammation-associated MC subsetsin the leptomeninges (Fig. 4A). One subset,termedmeningeal monocyte-derived cell subset 1(mMC1), was reminiscent of Ly6Chi monocytes(fig. S4, A, B, and C) and showed high levels ofLy6c2, Ccr2, Fn1, and Cd44. This combinationof genes was previously described (12) and con-firmed by us as being highly expressed by pe-ripheral immune cells, including monocytes andMCs (Fig. 4B and figs. S4C and S6). We furtherrecognized MCs subsets that, in addition to theirmonocytic signature, also expressed macrophage-or DC-associated genes. In the leptomeninges,mMC1, mMC2, and mMC3 expressed the macro-phage markerMertk, whereas high levels ofMrc1were observed in mMC4. Moreover, mMC2 andmMC3 both expressed high levels of Arg1 andSpp1 mRNA (Fig. 4B and fig. S4D). Given theproximity of mMC4 to the resident damMF1subset, we then compared their transcriptomicprofiles (Fig. S4F). We could observe high ex-pression of monocyte-associated genes, such asCcr2 and Vim in mMC4. However, we were un-able to exclude the possibility of resident mac-rophages up-regulating these markers duringneuroinflammation. CD206 (Mrc1) expressionwas confirmed on tdTomato− mMCs alongsidewith resident tdTomato+CD206+ mMF (Fig. 4C).By contrast,mMC5 appear to resemblemonocyte-derived DCs (moDCs) as described by others(36, 37), which not only expressed the mono-cytic markers Ly6c2 and Ccr2 but additionallyexpressed Cd209a, Kmo, Zbtb46, and Clec9a(Fig. 4B). DC-SIGN (CD209a) was confirmedon CD11c+ cells in the leptomeninges (Fig. 4D).All individual monocytes and MCs expressedFcgr1, which encodes CD64 and was previouslydescribed to be present in all macrophages andmonocytes (22). Thus, we contend that Ly6c2+

Ccr2+CD44+Fcgr1+ cells are likely cells of mono-cytic origin that may acquire macrophage or DCmarkers.Within the perivascular space and paren-

chyma, we identified five monocyte-derivedsubsets (pMCs). The pMC1 population was ob-served at an earlier inflammatory stage, whereaspMC2, pMC3, pMC4, and pMC5 subsets werepresent only during fully developed CNS inflam-mation (Fig. 4E). All five populations werecharacterized by a Ly6c2, Ccr2, Fn1, and Cd44signature, whereasMertkwas present only inthe pMC2, pMC3, and pMC4 subsets (Fig. 4F).Reporter CCR2-RFP mice allowed us to con-firm the presence of CCR2+CD206+ MCs besideCCR2−CD206+ resident mMF and pvMF in theleptomeninges and perivascular space, respec-

tively (Fig. 4G). Thus, these cells have the capacityto gain phenotypic markers often associated withtheir resident counterparts. Similar to the lepto-meninges, pMC5 in the perivascular space andparenchyma expressed Kmo and Zbtb46. How-ever, CD209a was absent in this subset (Fig. 4F).We were further able to identify three disease-associated monocyte-derived subsets in the cho-roid plexus (cpMCs), with one subset presentingan undifferentiated monocytic profile (cpMC1)and the others presenting a transcriptional sig-nature, including DC (cpMC2) or macrophage(cpMC3) markers (Fig. 4, H and I).The presence of MerTK+ and CD209+ MCs

may represent an ongoing differentiation intomonocyte-derived macrophages or monocyte-derived DCs, but their function remains to beinvestigated.DC subsets in the leptomeninges were barely

detectable in the healthy CNS. However, duringdisease, one subset (mDC1) was clearly distin-guishable (Fig. 5A). This population expressedLy75, P2ry10, Ccr7, and Flt3 (Fig. 5B). No DCscould be detected in the perivascular space. Bycontrast, DCs in the choroid plexus fell into fourdisease-associated populations (cpDC1, cpDC2,cpDC3, and cpDC4) (Fig. 5C), with differentiallevels of Ly75, Ccr7, Ccl22, Cadm1, Cd81, and Ccl5expression (Fig. 5D). Ccr7 expression by mDC1and cpDC2 was indicative of their migratory ca-pacity (Fig. 5D). Immunoreactivity for Ly75, whichis enriched in specific DCs subsets (fig. S4H),was found in tdTomato− cells in all different CNScompartments (fig. S4I).Extensive work on DCs has been performed

by others (37), where cDC1 are defined asCD11b−CD103+CD24hiIrf8hiIrf4lo, cDC2 asCD11b+CD24loCD11chiCD172hiIrf8loIrf4hi, andpDCs as CD11b−CD11cintB220+Ly6C+Siglec-H+

(37, 38). Our single-cell analysis failed to iden-tify any pDCs within the populations examined.Furthermore, cDCs were only present as rarepopulations. By comparing the transcriptionalprofile of CNS DC subsets and CD209+ MCs,we observed shared gene signatures betweensome of the subsets across CNS compartments(fig. S4G). CD209+ MCs could be identified inall compartments as expressing Irf8, Itgam, Cd24,and Sirpa. The cDCs found in the leptomeningesand choroid plexus showed differential expressionof these markers (fig. S4G), and their relationshipto cDC1 and cDC2 requires further assessment.We next performed flow cytometric analysis

to evaluate DC population kinetics in differentCNS compartments during the course of disease(Fig. 5E). We confirmed that homeostatic CNSpossessed few DCs, mostly cDCs (Fig. 5F). Al-though DCs are scarce in the homeostatic CNS,their density increases dramatically during dis-ease. Although pDCs and CD209+ MCs showedsubstantial increase in the leptomeninges, peri-vascular space, and parenchyma during the peakof disease, theywere reduced in the choroid plexus.The distinction between cDC2, CD209+ MCs,

and activated macrophages remains challengingbecause of overlapping phenotypic markers. Al-though CD64/Fcgr1 is often solely expressed by

macrophages and monocytes (22), cDC2 can alsoexpress CD64 in certain tissues (38) or underspecific inflammatory conditions (13). We con-firmed by means of flow cytometry that typicalcDC2 can express Ly6C, CD44, and CD209a(Fig. 5E). Consequently, it is possible thatCD209+ MCs may resemble a subset of cDC2that are more closely related to the remainingMCs in the scRNA-seq analysis because of theexpression of some monocytic markers. Usinga fate-mapping approach, CD209+ MCs wereverified to differ from CX3CR1+ long-lived resi-dent macrophages because no tdTomato expres-sion was observed within the former population(fig. S4J). Instead, MerTK+ MCs showed a sur-face marker profile similar to that of the CD209+

MCs (fig. S4K). Thus, these results suggest theemergence of disease-related monocyte and DCpopulations in distinct CNS compartments.

CNS-resident macrophages accumulateand expand clonally duringneuroinflammation

The accumulation of myeloid cells during in-flammation can occur either through localproliferation of tissue macrophages or the re-cruitment of peripheral monocytes from theblood. Although engrafted Ly6Chi monocytesrapidly die during autoimmune inflammation,the microglial pool quickly expands owing tolocal self-renewal (39). However, macrophagekinetics at CNS interfaces during inflammationare only poorly understood.To establish the spatiotemporal relationship

between microgliosis and the expansion of in-filtrating myeloid cells, pvMF, and mMF, weused Cx3cr1CreERT2:R26tdTomato mice in whichtissue-resident macrophages are efficiently la-beled in contrast tomonocytes (fig. S7, A and B).Although leakiness of tdTomato expression wasobserved in CAMs and microglia in Cre+ micewithout tamoxifen treatment, blood monocytesshowed no tdTomato expression, validating the useof these mice to discriminate between residentmacrophages and peripheral immune cells (fig. S7C).We immunized Cx3cr1CreERT2:R26tdTomato miceand analyzed spinal cord sections at differentphases of disease (fig. S7D). The recruitment ofperipheral myelomonocytic cells (IBA-1+tdTomato−)was first observed when animals reached the on-set phase (Fig. 6A) and continuously increased upto the peak of disease. Their numbers then de-clined during the chronic stage. This suggeststhat circulating blood cells are not permanentlyintegrated into the CNS, as previously hypothe-sized (39). IBA-1+tdTomato− cells were initiallyfound in proximity to the leptomeninges andperivascular spaces, suggesting that the entryof circulating myeloid cells into the CNS occursthrough these compartments. The contributionof IBA-1+tdTomato−MCs was shown to be moreprominent when compared with the residentmacrophage pool during full blown inflamma-tion. In addition to infiltrating IBA-1+tdTomato−

cells, the number of IBA-1+tdTomato+ microgliadramatically increased during the peak of dis-ease, whereas pvMF and mMF expanded more



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modestly (Fig. 6A). Ki67+ proliferating mMFwere already evident during the onset phase,suggesting that the expansion of mMF is anearly event in EAE pathogenesis (Fig. 6B). Atpeak of the disease, all IBA-1+tdTomato+ resi-

dent macrophages significantly expanded. Arobust decrease in the frequency of prolifer-atingMG andmMFwas observed in the chronicphase, whereas pvMF proliferation remainedstable. This drop in cell proliferation was ac-

companied by the presence of TUNEL+ ap-optotic CNS macrophages in the chronic phase(Fig. 6B and fig. S6E). Resident macrophagesand infiltrating monocytes at the leptomeningeswere indistinguishable by morphology, whereas


Fig. 4. Single-cell pro-filing of monocyte-derived populationsduring EAE. (A, E, andH) Visualization of(A) meningealmonocyte-derived cellsubsets (mMCs),(E) perivascularand parenchymalmonocyte-derived cellsubsets (pMCs), and(H) choroid plexusmonocyte-derived cellsubsets (cpMCs)on a t-SNE 2D map.Arrows indicate thedirection of cluster dif-ferentiation duringdisease. (B, F, andI) Expression levels ofenriched genes in dif-ferent monocyte-derived populationsin (B) the lepto-meninges, (F) peri-vascular space andparenchyma,and (I) choroidplexus. Data aredepicted as whiskerplots, where mean ±SEM of expressionvalue is presented on alogarithmic scale.Data are representa-tive of 16 mice fromthree independentexperiments. (C) Rep-resentative immuno-fluorescence picturefrom Cx3cr1CreERT2:R26tdTomato miceshowing infiltratingtdTomato− MCs(arrows) and residenttdTomato+ mMF(asterisks) expressingCD206 in the lepto-meninges. The dashedline delimits the barrierbetween the meninges(Men) and parenchyma(PC). Scale bars, 10 mm. A representative picture of three mice from twoindependent experiments is displayed. (D) Representative immuno-fluorescence image of DC-SIGN (encoded by Cd209a) onCD11c+ cells in the leptomeninges. Scale bars, 10 mm. The dashed linedelimits the barrier between the meninges (Men) and parenchyma(PC). Representative picture from five investigated mice fromtwo independent experiments is shown. (G) Representative immuno-

fluorescence picture from Ccr2RFP mice showing CD206+CCR2+ MCs(arrows) and CD206+CCR2− resident macrophages (asterisks) inthe leptomeninges and perivascular space. The dashed line delimits thebarrier between the meninges (Men) and parenchyma (PC) or theperivascular space (PV). Scale bars, 50 mm (overview) and 10 mm(inset). A representative picture of four mice from two independentexperiments is displayed.

BA mMC1 mMC2 mMC3 mMC4 mMC5

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infiltrating monocytes in the perivascular spacewere significantly smaller compared with resi-dent pvMF (fig. S7, F and G). The discriminationof different microglial subsets within the spinalcord allowed us to recognize the robust prolifer-ation of daMG (CD45lotdTomato+MD-1+) in com-parison with hMG (CD45lotdTomato+MD-1−) atthe peak of disease (Fig. 6C). daMG from EAEmice showed a significantly higher rate of pro-liferation when compared with hMG from naïvemice, demonstrating the differential impact ofneuroinflammation on microglia. An evaluation

of individual lesion-associated daMG subsets re-vealed higher proliferative capacity for daMG3and daMG4 (Fig. 6D).Microglial self-renewal under steady-state con-

ditions constitutes a stochastic process thatduring facial nerve axotomy shifts from randomto selected clonal expansion (40). To investigatewhether the EAE-driven expansion of microgliaand CAMs was random or clonal, we immunizedCx3cr1CreERT2:R26Confetti mice. These animals fea-ture the stochastic recombination and expres-sion of nuclear green fluorescent protein (nGFP),

cytoplasmic yellow fluorescent protein (YFP),cytoplasmic red fluorescent protein (RFP), andmembrane-tagged cyan fluorescent protein (mCFP)in resident macrophages (fig. S7H). At the peakof disease, Confetti-labeled cells could be ob-served in the CNS interfaces and parenchyma(Fig. 6E). Because the increase of Confetti+IBA-1+

cell density was only significant within the pa-renchyma (Fig. 6F), we focused on these mac-rophages for clonal analysis. Computationalanalysis of confocal images from the spinalcords of Cx3cr1CreERT2:R26Confetti mice allowed


cDC2cDC1 pDCs CD209+ MCs

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Fig. 5. Profiling of DC subsets at different CNS compartments duringneuroinflammation. (A and C) DC subsets in the (A) leptomeninges(mDCs) and (C) choroid plexus (cpDCs) on a t-SNE 2D map. (B andD) mRNA expression levels of enriched genes in DCs from (B) theleptomeninges and (D) choroid plexus compared with other myeloid cells.Whisker plots are displayed as mean ± SEM of expression value.Logarithmic scales are used. Data are representative of 16 mice pooledfrom three independent experiments. (E) Representative fluorescence-activated cell sorting (FACS) gating strategy for cDC1 (MerTK−CD64−

CD11c+MHC-II+CD11bloCD24hi), cDC2 (MerTK−CD64−CD11c+MHC-II+

CD11bhiCD24lo), pDCs (MerTK−CD64−Ly6C+B220+Siglec-H+), and CD209+

MCs (MerTK−CD64+Ly6C+CD44+CD209a+) in the CNS. Gating strategy isrepresentative of five mice from two independent experiments. (F) Quantifi-cation of the absolute cell number (per milliliter) of cDC1, cDC2, pDCs,and CD209+ MCs present at naïve stage and during the course of EAE indifferent CNS compartments. Data are representative of five to seven miceper time point from two independent experiments for onset and threeindependent experiments for naïve, peak, and chronic phases and arepresented as mean ± SEM. Two-way ANOVA followed by Tukey’s multiplecomparisons test revealed significant differences between the groups.


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Fig. 6. Fate mapping of resident tissuemacrophages in the CNS and infiltratingmonocytes during EAE. (A) Resident macro-phages (asterisks) and infiltrating MCs (arrows)in diseased Cx3cr1CreERT2:R26tdTomato mice(representative pictures of at least four miceare shown) and quantification thereof. Datarepresent mean ± SEM. of four to six mice pergroup from two independent experiments fornaïve and onset phases and three independentexperiments for peak and chronic phases.Dotted lines indicate the leptomeningeal-parenchymal barrier or the perivascular space(PV). (B) Quantification of proliferating(percent Ki67+) and apoptotic (percent TUNEL+)mMF, pvMF, and MG in Cx3cr1CreERT2:R26tdTomato mice. Data show mean ± SEM offour mice per group from two independentexperiments. n.d., nondetectable. (C) (Left)Representative flow cytometric gatingstrategy for the proliferation analysis of hMG(tdTomato+MD-1−) and daMG (tdTomato+MD-1+)at both the naïve stage and the peak ofdisease. (Bottom right) Proliferation presentedas mean ± SEM. Data are representativeof six mice from two independent experiments.(D) Quantification from immunohisto-chemistry of the proliferation of daMG2,daMG3, and daMG4 at peak of the disease.Data show mean ± SEM of four miceper group from two independent experiments.(E) Immunofluorescence images of naïveand diseased Cx3cr1CreERT2:R26Confetti miceshowing Confetti+ MF (asterisks). Pictures arerepresentative of four mice from twoindependent experiments. Scale bars, 50 mm(overview) and 10 mm (inset). (F) Density ofConfetti+ MF. Bars represent mean ± SEM ofthree mice per group from two independentexperiments. (G) (Left) Representative con-focal image from Cx3cr1CreERT2:R26Confetti miceat peak of EAE. Scale bars, 30 mm (overview)and 50 mm (inset). Nine mice were investigatedfrom three independent experiments. (Right)Representation of the confocal image shown inthe left demonstrating the analysis of Confetti+

microglial density. Microglia labeled by YFP,RFP, and CFP are represented by yellow, red,and cyan spheres, respectively. Magentaspheres show IBA-1+Confetti− microglia. Thedensity of same-colored cells is quantifiedwithin rings (gray) of increasing radius (arrows)from the reference cell (RC). (H) Densities ofConfetti+ microglia according to (G). Microglialclonal expansion is assumed if the EAEdistribution (blue line) lies outside of the randomdistribution area (gray, 98th percentile ofMonte Carlo simulation). Data were obtainedfrom nine mice at the peak of EAE from threeindependent experiments. (I) Analysis ofConfetti+ microglia clone sizes for naïve (n = 3)and diseased (n = 9) mice for up to a 200-mmradius from each RC. Data are representativefrom two (naïve) or three (EAE) independentexperiments. In (A), (C), and (D), Kruskal-Wallis test followed by Dunn’s multiple comparisons; in (B), two-way ANOVA followed by Tukey’s multiplecomparisons test; and in (F), two-way ANOVA followed by Sidak’s multiple comparisons test were used to calculate significant differences between groups.

A

E

Res

. MΦ

MC

s(t

dTom

ato−

IBA

-1+)(t

dTom

ato+

IBA

-1+)

pvMΦmMΦ MG

G

I

F

H Naive Peak

10%

90%

Single cell2

1%5%

21% 32%

42%

2-56-1516-3031-40

Single cell

500100 200 300 400

50010001500200025003000

Con

fetti

+ M

G (

mm

3 )

Radius from RC (µm)

Random distributionEAE distribution

Clonal expansion0

*

COLL-IV CFP RFP YFP

RC

IBA-1 CFP RFP YFP

RC

MG cell density analysis

IBA-1 YFP/CFP/GFP RFP

Leptomeninges

*

**

* *

*

* ** *

Naive PeakParenchymaPerivascular space

Naive Peak Naive Peak

* *

*

Men

PC

* *

* *

* *

*PV *

* *

*

* *

# C

onfe

tti+ c

ells

(m

m3 )

0

2000

4000

6000

mmΦ pvmΦ MG

Naive Peak

*P=0.039

n.s. n.s.

8000

B

0

10

15

20

% K

i67/

td+ c

ells Onset

PeakChronic

5

*P= 0.0004

n.s.

Naive*P< 0.0001

*P= 0.0004

*P= 0.016*P= 0.004

n.s.

n.s.

*P< 0.000125

% T

UN

EL/

td+ c

ells

05

101520

n.d. n.d. n.d.

D

pvMΦmMΦ MG

OnsetPeakChronic

Naive*P< 0.0001

*P= 0.004

0500

10003000

Cel

ls/m

m2

NaiveOnse

tPeak

ChronicNaive

OnsetPeak

Chronic

27000

Cel

ls/m

m2

0

2000300040005000

Cel

ls/m

m2

2000

4000

6000

0

*P= 0.0056

n.s.n.s.

n.s.

1000 n.s.*P= 0.039

*P= 0.041n.s.

*P= 0.001n.s.

15000

n.s.

*P= 0.021n.s.

n.s.

*P= 0.027n.s.

daM

G2

daM

G3

daM

G4

% K

i67+

cel

ls

10

20

30

40 *P= 0.049*P= 0.022

0

n.s.

Mod

e

Ki67101 103 105

C

Peak

Nai

ve

Isotype

SS

C-A

10-3

103

105

tdTomato101 103 105

CD

45

103

105

CD11b101 103 105

CD

45

10-3

103

105

MD-1101 103 105

101

Gated on living single cells; Lin-

3.daMG

2.hMG

Gated on living single cells; Lin-

SS

C-A

10-3

103

105

tdTomato101 103 105

CD

45

103

105

CD11b101 103 105

CD

45

10-3

103

105

MD-1101 103 105

101

1.hMG

hMG (MD-1−)hMG (MD-1−)daMG (MD-1+)

% K

i67+

Mic

rogl

ia

010203040

1.hM

G

2.hM

G

3.da

MG

n.s.

*P= 0.003

0.0%

0.07%3.7%32.1%

n.s.

Naive Onset Peak Chronic

IBA-1 tdTomato

Leptomeninges

*

*

** *

*

*

**

* *

*

*

* ***

*

* *

*

Men

PC

PV

Perivascular space

Parenchyma

Naive Onset Peak Chronic

Naive Onset Peak ChronicPC


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us to evaluate the distribution of all Confetti+

microglia within the tissue (Fig. 6G), reveal-ing clonal expansion of microglia during EAE(Fig. 6H). Microglia only developed clones ofsame-colored cells under disease conditions(Fig. 6I). Thus, during autoimmune inflamma-tion, macrophages appear to expand throughlocal self-renewal alongside the recruitment ofperipheral myeloid cells.

Prolonged T cell interactions occurwith circulating myeloid cellsduring neuroinflammation

In the context of EAE, CD4+ T cells primed in theperiphery migrate to the CNS and potentiallybecome reactivated by encountering their self-cognate antigens at the site of brain interfaces(41). Antigen presentation to T cells has beenshown in vivo for leptomeningeal myeloid cells(4, 19) and in vitro for several myelomonocyticpopulations (42). However, the precise nature ofthe myeloid cell type involved in T cell activationduring neuroinflammation remains enigmaticwith tissue-resident and circulatingmyeloid cellsas potential candidates.With scRNA-seq, we first determined themye-

loid subsets with the most prominent antigen-presenting cell (APC)–related genetic profileacross the different CNS immune compartments.In the leptomeninges, damMF1, and Cd209+

(mMC5) and Mertk+ (mMC2 and mMC4), MCpopulations showed the highest expression ofthe core antigen presentation signature (Fig. 7A).Increased levels of CD74 during disease could beconfirmed in situ on MCs (IBA-1+tdTomato−)compared with lower levels on tdTomato+ resi-dent mMF (Fig. 7B). Within the perivascularspace andparenchyma,daMG3, residentpvmMF1,and pMCs induced the highest levels of APCgenes (Fig. 7C). These cells could be detected inspinal cord sections, where the highest CD74levels were observed in monocytes from bothcompartments (Fig. 7, D and E). In the choroidplexus, inflammation-induced dacpMF, andCd209+ (cpMC2) and Mertk+ (cpMC3), MCsshowed the highest induction of APC-relatedmolecules (Fig. 7F). Because of the dual originof cpMF from pre- and postnatal hematopoi-etic sources (24), we were unable to apply ourCx3cr1CreERT2-based fatemapping to discriminatetissue-resident cells from monocyte-derived sub-sets. However, high levels of CD74 onmyeloid cellsin the choroid plexuswere confirmed by using thepan-macrophage marker IBA-1 (Fig. 7G).To analyze which of these APC-competent

myeloid subsets physically interacted with in-filtrating T cells, we tracked the contact dy-namics of fluorescently labeled CD2+ T cellswith CNS-resident myeloid cells in spinal EAElesions of Cx3cr1CreERT2:R26tdTomato:Cd2GFP miceby use of intravital microscopy (Fig. 7H). Time-lapse imaging revealed that mMF, pvMF, andmicroglia had similar T cell contacts (Fig. 7K).Likewise, the average duration of these contactsdid not significantly differ between CNS-residentmacrophages and microglia, with most lastingonly a few minutes (Fig. 7L). Because CCR2

controls EAE susceptibility (34, 43), we nextdetermined Ccr2 RNA expression levels on allimmune-cell subsets during disease. In all CNSimmune compartments, Ccr2 expression washighest in blood-derived populations such asCd209+ and Mertk+ mMCs. By contrast, it wasabsent in pvMF and mMF populations (fig.S8D). To directly monitor interactions betweenHSC-derived myelomonocytic cells and T cells,we next crossed Ccr2RFP/WT mice to Cd2GFP

T cell reporter mice (Fig. 7I). Although pDCsexpressed almost no RFP, ~30% of cDC1 andcDC2 were targeted in this line, which is inagreement with recent findings (13). Similarlabeling was observed in CD209+ MCs. Amongall subsets within the CNS, MerTK+ MCs ex-pressed the highest levels of RFP. As expected,circulating monocytes were also efficientlylabeled in this model (Fig. 7J). In vivo imagingof acute spinal EAE lesions revealed that CCR2+

myelomonocytic cells were equally likely astheir CNS-resident counterparts to be in contactwith T cells (Fig. 7K). However, the averageduration of such contacts was substantiallylonger (Fig. 7L), and the proportion of long-lasting contacts (>20 min) was significantly in-creased (Fig. 7M). Evaluation of the interactionbetween T cells and hMG (tdTomato+P2RY12hi)or daMG (tdTomato+P2RY12lo/−) showed pref-erential contact with the daMG subset (Fig. 7N).Discrimination between the different daMGsubsets revealed that daMG2 were more likelyto interact with T cells as compared with daMG3/daMG4 (Fig. 7O).Antigen recognition by encephalitogenic T cells

is associatedwith long-lasting T cell–APC contacts(19). Our results suggest that preferentially HSC-derived myeloid cells such as cDCs, CD209+, orMerTK+ MCs play a crucial role in antigen pre-sentation during CNS autoimmune disease.

MHC II on circulating myeloid cells playsa pivotal role in neuroinflammation

The prevailing concept thatmacrophages at CNSborders present antigens and subsequently reac-tivate T cells to induce full encephalitogenicityduring neuroinflammation is largely based onprevious work with irradiated bone-marrow chi-meras (31, 32). However, irradiation of the hostprimes the tissue and induces the artificial en-graftment of donor-derived cells into the CNS(44, 45).To test whether antigen presentation on

CAMs contributes to the pathogenesis of neuro-inflammation, we again used the Cx3cr1CreERT2

system to conditionally ablate MHC class II onpvMF, mMF, cpMF, andMG (Fig. 8A). This ledto the robust deletion of this molecule in all res-ident CNS macrophages and microglia (Fig. 8A).In agreement with a recent study (46),Cx3cr1CreERT2:H2-Ab1flox mice surprisingly showed no overtchanges in disease development (Fig. 8B). Thisindicates that MHC class II on CX3CR1+ macro-phages, including resident macrophages at braininterfaces, is redundant for disease pathogenesis.Accordingly, accompanying histological analysisof Cx3cr1CreERT2:H2-Ab1flox mice revealed no

changes in demyelination, myeloid-cell infil-tration, T cell or B cell density, or amyloidprecursor protein (APP) deposits (Fig. 8C).To determine whether the expression ofMHC

class II on circulatingmyeloid cells was essential,we examined the development of EAE in micelacking MHC class II on CD11c+ cells (Fig. 8, Dto I). Because resident macrophages also up-regulate CD11c during neuroinflammation, theCd11cCre transgene–mediated excision of MHCclass II was detectable in both tissue-resident cellsand bloodmyelomonocytic cells that engrafted inthe diseased CNS (Fig. 8D). Cd11cCre:H2-Ab1flox

mice were greatly resistant to MOG35-55 immuni-zation (Fig. 8E), and neuropathological changeswere not observed. Cd11cCre:H2-Ab1flox mice didnot exhibit CNS-infiltrating immune cells (such asB and T lymphocytes), and theirmyelin remainedundamaged. By contrast, Cre− controls showedtypical EAE inflammatory responses (Fig. 8F). Theadoptive transfer of encephalitogenic T cells intoCd11cCre:H2-Ab1flox mice confirmed the essentialrole of MHC class II on CD11c+ APCs (Fig. 8I).Thus, CD11c-expressing peripheral immune

cells show a more critical role for T cell primingand initiation of the pathology.

Discussion

This study provides an unbiased view of thetranscriptional landscapes of CNS-resident andcirculating myeloid cells during homeostasisand CNS autoimmunity. We found that homeo-static cell-specific profiles are rather uniformthroughout the various analyzed CNS regions,whereas a considerable compartment- and dis-ease stage–specific myeloid subtype specifica-tion with high plasticity emerges during thedevelopment and maintenance of neuroinflam-matory pathology. This phenomenon is medi-ated by the specific occurrence of distinct ratiosof tissue-macrophage populations.Unlike most peripheral tissue macrophages,

MG and most CAMs such as pvMF and mMFarise from Lin−c-Kit+ erythromyeloid progenitorsin the YS during embryonic development andare maintained throughout life by self-renewal(40, 47) independent of bone-marrow precursorcells (45, 48). By using the Cx3cr1CreER:R26tdTomato

fate-mapping system, which tracks long-livedYS cells, we showed that CAMs have consider-able longevity and remain stable even duringCNS autoimmunity. By contrast, blood-derivedIBA-1+tdTomato−myeloid cells, presumablymono-cytes, infiltrated the spinal cord during diseasebut were only transiently present at the lesionsite. This confirms previous studies, which lackedthe proper fate-mapping tools (39, 49). Fur-thermore, we used a recently established tissuemacrophage–focusedmulticolor-reportermousemodel (40), which revealed the organization ofspinal cord MG and CAM networks duringhealth and disease. We discovered selectivemicroglial clonal expansion in response to in-flammatory damage in the CNS, which maysupport the self-renewal and longevity of thesetissue macrophages. Within the different CAMpopulations, different numbers of TUNEL+



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Fig. 7. In vivo imaging of T cellsand CNS-resident and circu-lating myeloid cells. (A, C, andF) t-SNE plot showing genesassociated with antigen-presentationcapacity (APC) across cells foundat the (A) meninges, (C) peri-vascular space and parenchyma,and (F) choroid plexus. Popula-tions with highest levels of theAPC-associated genes areshown. (B) Images of CD74+

mMF and MCs (asterisks) in themeninges of diseasedCx3cr1CreERT2:R26tdTomato mice(representative of four mice fromtwo independent experiments)and quantification thereof (dataare presented as mean ± SEM ofat least four mice per grouppooled from three independentexperiments). Scale bars, 10 mm.Dotted lines reveal theleptomeningeal-parenchymalbarrier. (D) Images of CD74+ pvMF,MG, and MCs (asterisks) at peak ofthe disease (representative offive mice from three independentexperiments). Scale bars, 10 mm.Dotted lines indicate thevessels. (E) Quantification from(D). Data are presented asmean ± SEM of five mice pergroup from three independentexperiments. (G) (Left) CD74+IBA-1+

cells (including both cpMF andMCs) in the choroid plexus.Pictures are representative offour mice from two independentexperiments. Scale bars, 10 mm.(Right) Quantification thereof.Data are presented as mean ±SEM of four mice per group fromthree independent experiments.(H and I) Confocal picture from(H) Cx3cr1CreERT2:R26tdTomato and(I) Ccr2RFP mice crossed withCd2GFP mice at onset of EAE.Scale bar, 50 mm. Representativepicture of five to seven mice fromthree independent experiments.(J) Percentage of cells labeled inCcr2RFP mice as determined bymeans of flow cytometric analy-sis at the onset of EAE. Data arerepresentative of five to sevenmice from two independentexperiments. (K) Contacts between macrophages/microglia or CCR2+

cells with Tcells. Data are presented as mean ± SEM and are representativeof five to seven mice from three independent experiments. (L) Quantifica-tion thereof. Each dot indicates a single cell, and the dotted lineindicates the threshold limit past which a contact is considered long-lasting. Data are representative of four to seven mice from threeindependent experiments. (M) Quantification. Data are presented asthe mean percentage ± SEM and are representative of four to sevenmice from three independent experiments. (N) 3D reconstruction fromCx3cr1CreERT2:R26tdTomato mice (representative of four mice from two

independent experiments). Asterisks indicate the contact points. Scalebars, 10 mm (overview) and 2 mm (inset), and quantification [representa-tive of four mice (26 hMG and 34 daMG cells) from two independentexperiments]. (O) Contacts of daMG2 subsets with T cells at onset of thedisease. Data are representative of four mice (19 daMG2 and 15 daMG3/daMG4 cells) from two independent experiments. In (B), (E), (N), and(O), a two-tailed Mann–Whitney U test; in (J), Kruskal-Wallis test followedby Dunn’s multiple comparisons test; and in (K) and (M), one-way ANOVAfollowed by Bonferroni’s multiple comparison tests revealed significantdifferences between the groups. n.s., not significant.

A BLeptomeninges

D

E

H

G

MCsmMΦ

tdTomato CD74

FChoroid plexus

tdTomato CD74 IBA-1

Perivascular space & Parenchyma

*

**

*

*

*

Men

PC

pvMΦ MCsMG

*

*

PV

*

*

*

**

*

K L M

MGmMΦ pvMΦ CCR2+ Long

-last

ing

cont

act (

%)

0

204060

80

100

*P= 0.0016*P= 0.004

*P= 0.0003

CD2 CX3CR1 Dextran

VL

CD2 CCR2 Dextran

Cx3cr1CreERT2:R26tdTomato x Cd2GFP Ccr2RFP x Cd2GFPI J

N

APC signature Meninges (Cd74, H2-Aa, H2-Ab1, H2-Eb1, H2-Ab1,

Cd80, Cd86, Cd40)

0

200

mMC5

damMΦ1

mMC4mMC2

0

250

daMG3 dapvMΦ1

APC signature PV & PC(Cd74, H2-Aa, H2-Ab1, H2-Eb1, H2-Ab1,

Cd80, Cd86, Cd40)

pMC5

pMC2

cpMC2

0

250

APC signature CP(Cd74, H2-Aa, H2-Ab1, H2-Eb1, H2-Ab1,

Cd80, Cd86, Cd40)

dacpMΦ

cpMC3

0

0.5

1.0

1.5

# C

onta

cts

*P= 0.029

hMG

daM

G

hMG daMG

P2RY12 CX3CR1 CD4 Coloc.

hMG (P2RY12hi)

daMG (P2RY12lo/−)

**

*

*T cell*

O

tdTomato CD74 tdTomato CD74 tdTomato CD74 IBA-1

# C

onta

cts

0

8

10

MGmMΦ pvMΦ CCR2+

n.s.

642

020406080

100*P= 0.032

% C

D74

+

mMΦ MCs

020406080

100

% C

D74

+

PV Parenchyma*P= 0.008 *P= 0.008

pvMΦ MCs MCsMG0

20406080

100

% C

D74

+/IB

A-1

+ c

ells

cpMΦ & MCs

020406080

100

% R

FP

+ c

ells

pDCs

CD209+ MCscDC1

Ly6Chi

MoncDC2

MerTK+ MCs

*P= 0.002*P< 0.0001

*P= 0.001*P= 0.001

daM

G2

daM

G3/

daM

G4

0

0.5

1.0

1.5

# C

onta

cts

*P= 0.0079

cpMΦ & MCs

IBA-1 CD74

*

*

C

60

Con

tact

tim

e (m

in)

0

20

40

MGmMΦ pvMΦ CCR2+


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Fig. 8. Absence of MHC II on circulatingCD11c+ myeloid cells preventsautoimmune inflammation in theCNS. (A) Quantification of MHC IIexpression in different immune cellsfrom Cx3cr1CreERT2:H2-Ab1flox mice asdetermined with flow cytometric analysis.(Left) MHC II expression in residentmmF, pvMF, and cpMF at naïve stage,8 weeks after tamoxifen application.(Right) MHC class II expression inmicroglia (MG), cDC1, cDC2, pDCs,CD209+, and MerTK+ MCs, and B cellsat the peak phase of EAE. Bars representmeans ± SEM of 9 mice in total (fourCre− and five Cre+) from two independentexperiments. A two-tailed Mann-WhitneyU test revealed a significant differencebetween the groups. n.s., not significant.(B) Course of EAE in Cx3cr1CreERT2:H2-Ab1flox mice. Each data point representsthe mean ± SEM of 33 mice in total(14 Cre− and 19 Cre+) from four independentexperiments. A two-tailed Mann-WhitneyU statistical test revealed no significantdifference between the groups. (C) Histologyof spinal cord sections from Cx3cr1CreERT2:H2-Ab1flox mice by using Luxol fast blue (LFB)for demyelination (blue), MAC-3 for macro-phages (brown), CD3 for T lymphocytes(brown), B220 for B cells (brown), andamyloid precursor protein for APP deposits(brown). Scale bars, 10 mm. In total, eightmice (four Cre− and four Cre+) from threeindependent experiments were used. Graphsshow quantification of infiltrates. A two-tailed Mann-Whitney U statistical testrevealed a significant difference betweenthe groups. (D) Quantification of MHC IIexpression in CAMs/MerTK+ MCs, microglia(MG), cDC1, cDC2, pDCs, CD209+ MCs,and B cells from Cd11cCre:H2-Ab1flox miceat peak phase of EAE as determined withflow cytometric analysis. Bars representmeans ± SEM of 11 mice in total (four Cre−

and seven Cre+) from two independentexperiments. A two-tailed Mann-WhitneyU test revealed a significant differencebetween the groups. (E) Clinical EAEcourse in Cd11cCre:H2-Ab1flox mice. Eachdata point represents the mean ± SEM of27 mice in total (11 Cre− and 16 Cre+)pooled from four independent experiments.Significant differences as obtained fromtwo-tailed Mann-Whitney statistical testare depicted in the graph. (F) Spinal cordhistopathology from Cd11cCre:H2-Ab1flox

mice by using LFB (blue), MAC-3 (brown),CD3 (brown), B220 (brown), and APP

0

2

4

6

% D

emye

linat

ion

Cre− Cre+ # C

D3+

cel

ls (

mm

2 )

0

20

60

80

Cre− Cre+# M

AC

-3+ c

ells

(m

m2 )

0

20

40

80

60

Cre−Cre+ # B

220+

cel

ls (

mm

2 ) 15

0

5

10

Cre−Cre+

G

1

1.5

2

0

0.5

(d.p.i)10 15 20 255

Ave

rage

EA

E s

core

# A

PP

+ d

epos

its (

mm

2 )

0

20

60

40

*P=0.029

Cre− Cre+

Cre−

Cre+

8 *P=0.029*P=0.029

40

*P=0.029*P=0.029

*P=0.026

A4

0

1

2

3

(d.p.i)10 15 20 255

Ave

rage

EA

E s

core

B

Cx3

cr1C

reE

RT

2 :H

2-A

b1flo

x

% M

HC

-II+

cel

ls fr

om

who

le p

opul

atio

n

0

10

20

30

40

pDCscDC1 cDC2 CD209+

MCsmMΦ pvMΦ cpMΦ

Naive

0204060

80

Peak

100

MG

n.s. n.s.

n.s.n.s. n.s.

B cells

n.s.

Cre− Cre+Cre− Cre+

MerTK+

MCs

n.s.*P=0.022

*P=0.022*P=0.022 *P=0.022

0

5

15

10

% D

emye

linat

ion

Cre− Cre+

n.s.

# M

AC

-3+ c

ells

(m

m2 )

100

300

200

0

n.s.

Cre− Cre+ # C

D3+

cel

ls (

mm

2 )

50100

250

200150

n.s.

Cre− Cre+0

n.s.

# B

220+

cel

ls (

mm

2 )

5

1510

0Cre− Cre+

2025 n.s.

# A

PP

+ d

epos

its (

mm

2 )

020

40

8060

n.s.

Cre− Cre+

100

D E

Cd1

1cC

re:H

2-A

b1flo

x

Cre− Cre+

2

3

4

(d.p.i)10 15 20 2550

1

Ave

rage

EA

E s

core

*P<0.001

Cre− Cre+

0

2040

80

60

CAMs/MerTK+ MCs

cDC1MG% M

HC

-II+

cel

ls fr

om

who

le p

opul

atio

n

Peak

cDC2 pDCs B cells

100

CD209+

MCs

*P=0.002

*P=0.009

*P=0.002*P=0.002

*P=0.002

*P=0.002

*P=0.002

C

F

Cre+

Cre−

LFB MAC-3 CD3 B220 APP

Cre+

Cre−

LFB MAC-3 CD3 B220 APP

(brown). Scale bar, 10 mm. Graphs show quantification of infiltrates. Data are presented as mean ± SEMand were collected from eight mice (four Cre− and four Cre+) pooled from three independentexperiments. A two-tailed Mann-Whitney statistical test revealed significant differences between thegroups. (G) The course of passive EAE in Cd11cCre:H2-Ab1flox mice until day 25 after adoptive T celltransfer. Data are shown as the mean ± SEM of 28 mice in total (13 Cre− and 15 Cre+ mice) fromthree independent experiments. Significant differences as obtained from two-tailed Mann-Whitneystatistical test revealed significant differences between the groups.


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apoptotic CNS endogenous macrophages couldalso be detected. This indicates the differentialmacrophage response to inflammation, which issuggestive of the functional diversity of thesecells during disease.We provide in vivo evidence of single-cell MG

and CAM heterogeneity in the normal mouseCNS. Cellular diversity among CAMs in thehealthy CNS was especially apparent in cpMFin comparison withmMF or pvMF. This findingmay reflect the dual origin of cpMF from pre-natal (YS/fetal liver) andpostnatal (bonemarrow)sources compared with the purely prenatal originof mMF and pvMF (24).MG and CAMpopulations were detectable in

the steady-state CNS as distinct clusters. Indeed,duringEAE, the ability of CNS-tissuemacrophagesto swiftly adapt to environmental changes wasobserved with several unappreciated cell subsetsappearing within the MG population as well asfor each CAM subtype. MGwithin the inflamma-tory lesions of the spinal cord down-regulatedseveralmarkers designated to their core signaturerepertoire (25), such as P2ry12, Tmem119, andSelplg. By contrast,MD-1 (encoded by Ly86 and amember of the Toll-like receptor family) wasup-regulated during CNS autoimmune disease.Moreover, the expression of different chemo-kines was increased in disease-linked microg-lial subsets. Three distinct microglial subsetswere identified that were linked with the le-sion sites during neuroinflammation: daMG2,daMG3, and daMG4. Although they all shared theP2RY12loTMEM119loMD-1hi profile, differenceswere found in the expression of specific chemo-kines, cytokines, and cysteine proteases. More-over, differential cellular dynamics could beallocated to specific microglial subsets. daMG2expressed high levels of Cd74, Ctsb, and Apoe.This population also proliferated less but mademore contacts with encephalitogenic T cells com-pared with other daMG subsets. By contrast,daMG3 expressed high levels of Cxcl10, Tnf, andCcl4, whereas daMG4 expressed high levels ofCcl5, Ctss, and Itm2b. Both populations showedhigh proliferative capacity and rather low fre-quency and duration of T cell contacts. Despitethe distinct daMGs profiles, Olfml3 and Sparcremained unaltered in the daMG cluster, indi-cating that these genes may serve as robustmicroglial markers in health and disease.This thorough microdissection of the several

CNS compartments together with scRNA-seqenabled us to acquire an independent and un-biased overview of the inflammation-drivenresponses across the CNS. The individual tran-scriptional profiling of CAMs highlighted theirclose relationships during both homeostasis andinflammation irrespective of their anatomicallocalization within the CNS. A core gene signa-ture comprisingMrc1, Pf4,Ms4a7, and Cbr2wasfound to distinctly characterize CNS-associatedmacrophages and distinguish them from othermyeloid populations. Furthermore, we identifiedseveral daCAM subsets during inflammatory dis-ease. These daCAM subsets showed surprisingtranscriptional similarity among the various

CNS immune compartments, including the sharedreduced expression of key core genes such asLyve1. By contrast, immune-related moleculeswere especially strongly induced. CD74, for ex-ample,wasmost highly expressed inCAMsduringdisease. CD74 associates with MHC class II andis an important chaperone regulating antigenpresentation during immune responses. It alsoserves as a cell-surface receptor for the cytokinemacrophage migration inhibitory factor, whichinitiates survival pathways and myeloid-cell pro-liferation (50). Moreover, CAMs did not com-pletely lose their homeostatic core signature andmaintained levels of molecules such as Ms4a7unchanged. The functions of Ms4a7 are not com-pletely understood but are thought to be associ-ated with mature cellular function within themonocytic lineage. It may also be a component ofa receptor complex involved in signal transduc-tion (51). Recent studies showedMs4a7 expres-sion by embryonic microglia by embryonic day14.5 (52) and suggested that they are ontologicallydiverse from the YS-derived macrophages (53)and potential progenitors of CAMs (52).Strategically positioned at the CNS barriers,

mMF, pvMF, and cpMF are thought to modu-late immune-cell entry and phenotype possibly bypresenting antigens to circulating lymphocytes(54–56). In order to unravel mMF, pvMF, andcpMF APC function, we used the Cx3cr1CreERT2:H2-Ab1floxmice, which show specificMHC classII deficiency on long-living tissue macrophages.Surprisingly, we found no changes, either in theclinical course or in the histopathology of miceafflicted with EAE. Similarly, we observed thatinfiltrating T cells preferentially show long-lasting interactions with CCR2+ myeloid cellsof peripheral origin, whereas most in vivo in-teractions with MG and CAMs were transient.Because T cells arrest in response to antigenrecognition (19), these observations support animportant role for infiltrating myeloid cells inthe activation of T cells during EAE. When wesubsequently deleted MHC class II expressionon both peripheral and CNS-resident myeloidcells in Cd11cCre:H2-Ab1flox mice, EAE inductionwas effectively prevented.The precise nature of the CD11c+ myeloid cells

that drive CNS pathology by amplifying T cellresponses via MHC class II in EAE remains un-clear. One possible candidate subset comprisesDCs that represent the intersection of the innateand adaptive immune systems (57). This idea issupportedby the fact that CD11c-driven transgenicoverexpression of MHC class II facilitates EAEpathogenesis (20).However, this previous approachdid not exclude the potential contribution ofMHCclass II onCD11c+MGorCAMsduring disease.Weobserved the presence of disease-specific CD209+

and MerTK+ MCs and disease-associated DCsmostly in the choroid plexus, leptomeninges, and,to a lesser extent, the perivascular space. Thus,we identified these locations as putative entrysites for MHC class II–dependent T cells.The role of microglia during MS/EAE remains

controversial. For several decades, the activationof microglia has been described during CNS

inflammation and considered to be an initialevent in MS pathology (58). Even in early stagesof MS, activated microglial clusters (so-calledmicroglial nodules) are found in preactive le-sions in the white matter of MS patients (59).To gain further insights into the involvement ofactivated microglia, several transgenic mousemodels have been developed. These suggestedmajor roles of microglia, including the expres-sion of proinflammatory mediators and effectormolecules at the peak of disease (30, 60) and theremoval of debris, which allows proper remyeli-nation during the recovery phase (49, 61). Fur-thermore, microglia are thought to contributeto antigen-dependent T cell activation (58). How-ever, selective gene-targeting experiments nowdemonstrate that MHC class II–mediated T cellpriming by microglia is not critical for the in-duction or progression of EAE.Thus, the identification of disease- and CNS-

compartment–specific myeloid subsets duringEAE should provide the basis for implementationof therapeutic approaches, specific to previouslyunidentified subsets, for neuroinflammatorydisorders. These should entail reduced riskscompared with the somewhat global immunesuppressive therapies currently administered toa large number of MS patients.

Material and methodsMice

C57BL/6N mice were used as WT mice and alltransgenic lines (Cx3cr1CreERT2, Cd11cCre, Cd2gfp,Ccr2RFP, andH2-Ab1flox) were on a C57BL/6 back-ground.Micewere bred in-house under pathogen-free conditions. Cx3cr1CreERT2 were crossed toeitherR26tdTomato orR26Confettimice. All mice arecommercially available in The Jackson Labora-tory. Littermate controls were used for the dif-ferent experiments. All animal experimentswereapproved by the local administration and wereperformed in accordance to the respective na-tional, federal and institutional regulations.

Tamoxifen treatment

For induction of the Cre recombinase inCx3cr1CreERT2:R26tdTomatomice, 6-week-old animalswere treated twice with 4mg of tamoxifen (TAM,Sigma-Aldrich, Taufkirchen, Germany) dissolvedin 200 ml of corn oil (Sigma-Aldrich, Taufkirchen,Germany), injected subcutaneously at twotime points, 48 hours apart. For Cx3cr1CreERT2:R26Confetti mice, 6-week-old animals were treatedoncewith 8mg of tamoxifen dissolved in 200 ml ofcorn oil (subcutaneous injection).

Induction of experimentalautoimmune encephalitis

For the induction of EAE, mice were immu-nized subcutaneously with 200 mg of MOG35-55

peptide emulsified in CFA containing 0.1 mg ofMycobacterium tuberculosis (H37RA;Difco Labo-ratories, Detroit, Michigan, USA). The mice re-ceived intraperitoneal injections with 250 ngof pertussis toxin (Sigma-Aldrich, Deisenhofen,Germany) at the timeof immunizationand48hourslater. For experiments involving C57BL/6 mice,



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immunizations were performed in 6- to 8-week-old mice. For experiments using Cx3cr1CreERT2:R26tdTomato and Cx3cr1CreERT2:R26Confetti mice,immunizations were performed 8 weeks afterTAM induction. Mice were scored daily accord-ing to their clinical symptoms (score 1: completelimp tail; score 1.5: limp tail and hindlimbweakness; score 2: hind limbs paresis; score2.5: unilateral hind limb paralysis; score 3:bilateral hind limb paralysis). For the EAE ex-periments presented in this study, mice wereanalyzed either at the preclinical phase (day 8after immunization), onset phase (score 1, typi-cally observed between day 11-13), peak phase(score 3, typically observed between days 15and 20), or at the chronic phase (day 30 post-immunization) of EAE.

Adoptive transfer EAE

Passive EAE was induced by intravenous in-jection of MOG-reactive lymphocytes (3 × 106/mouse) into recipient mice. Mice also received200 ng of pertussis toxin intraperitoneally (i.p.)on the day of immunization and 2 days later.

Intravital imaging and image processing

Mice were imaged at the onset of EAE symp-toms (only animals with an EAE score ≥ 1 wereincluded). For this purpose, mice were anesthe-tized with medetomidin (0.5 mg/kg), midazolam(5.0 mg/kg), and fentanyl (0.05 mg/kg), placedon a heating pad, and then tracheotomized andintubated. The dorsal spinal cord was surgicallyexposed as previously described (62) and theopening constantly superfused with artificialcerebrospinal fluid (aCSF). For the imaging ses-sion, the vertebral column was fixed using aspinal clamping device (Narishige STS-a) and thespinal opening surrounded by a 4% agarose well.Mice were injected i.p. with 200 mg of dextran-AF647 (Life Technologies) to reveal the vascu-lature. In vivo imaging was performed usingconfocal or two-photon laser excitation on anOlympus FV1200 MPE setup equipped with a25×/1.25 water immersion objective (Olympus)at a 1024 by 1024 pixel resolution. For confocalimaging, GFP, tdTomato, and dextran-AF647were sequentially excited using 488 nm, 568 nm,and 647 nm lasers, respectively. For two-photontime-lapse imaging of interaction dynamics, theIR laser was tuned to 820 nm and fluorescencewas collected using a standard green/red filterset (BA575-630). For image representation,maxi-mum intensity projections of image stacks weregamma-adjusted and processed with a despeck-ling filter using Photoshop software (Adobe).The time and frequency of CD2+ T-cell contactswith different populations of CNS-resident andCCR2+ infiltrating myeloid cells were assessedmanually in 60-min-long movies using Fiji soft-ware (63). Cellular interactions that began duringthe final 20 min of imaging were excluded fromour analysis. To calculate the contact probabilityof different phagocyte subsets, the number ofT-cell contacts on a given phagocyte per 60minwas adjusted for the T-cell infiltration densityin the imaging area.

HistologyMicewere transcardially perfusedwith phosphate-buffered saline (PBS). Spinal cords were thendissected and fixed in 4% paraformaldehyde(PFA) overnight. Tissue was then embedded inparaffin and stained with Luxol fast blue to as-sess the degree of demyelination, rat anti-mouseMAC-3 (2.5 mg/ml, cloneM3/84, BDPharmingen)for macrophages and microglia, rat anti-humanCD3 (3.5 mg/ml, cloneCD3-12, Serotec, Düsseldorf,Germany) forT cells, rat anti-mouseB220 (2.5mg/ml,clone RA3-6B2, BD Pharmingen) for B cells, andmouse anti-mouse APP (3 mg/ml, clone 22C11,Millipore) for indication of axonal damage. ForMAC-3, CD3, B220, and APP immunohistochem-istry, the primary antibodies were incubatedovernight (4°C) followed by incubation withbiotin-labeled goat anti-rat, goat anti-mouse, orgoat anti-rabbit secondary antibodies (2.5 mg/ml,SouthernBiotech) for 45min at RT. Streptavidin(Southern Biotech) was then added for 45 minat RT. 3′ -Diaminobenzidine (DAB) brown chro-mogen (Dako) was used to resolve the aforemen-tioned antibodies.

Immunofluorescence

After transcardial perfusion with phosphate-buffered saline (PBS), brains and spinal cordswere fixed in 4% PFA for 6 hours, dehydratedin 30% sucrose and embedded in Tissue-TekO.C.T.TM compound (Sakura Finetek EuropB.V., Netherlands). Cryosectionswere thenblockedandpermeabilizedwithPBS containing 5%normaldonkey serum (NDS, Abcam) and 0.5% Triton-X100 for 1 hour at RT. Primary antibodies wereincubated overnight at a dilution of 1 mg/ml forrabbit anti-mouse IBA-1 (WACO, Japan), 1 mg/mlfor goat anti-GFP (Rockland ImmunochemicalsInc., Gilbertsville, USA), 1 mg/ml for rabbit anti-laminin (Sigma-Aldrich), 1 mg/ml for goat anti-collagen-IV (Millipore), 1 mg/ml for rabbit anti-Ki67(Abcam), 3 mg/ml for rat anti-mouseCD206 (cloneMR5D3, Bio Rad), 2 mg/ml for rat anti-mouseCD74 (clone In1/CD74, Biolegend), 1.5 mg/mlfor rabbit anti- LYVE-1 (Abcam), 2 mg/ml forhamsteranti-mouseCD11c (cloneN418,eBioscience),2 mg/ml for mouse anti-mouse CD209a (MMD3,Biolegend), 1 mg/ml for rabbit anti-mouse P2RY12(AnaSpec), 1mg/ml for rabbit anti-mouseTMEM119(clone 106-6, Abcam), 2.5 mg/ml for rat anti-mouseMD-1 (clone MD113, Abcam), 3 mg/ml for mouseanti-SPARC(FITC-labeled,R&DSystems), 2.5mg/mlfor rat anti-mouse CD162 (clone 4RA10, BDPharmingen), 1.5 mg/ml for rabbit anti-CCL5/RANTES (Novus Biological), and 2 mg/ml for goatanti-mouse CXCL10 (clone BAF466, R&D Sys-tems) at 4°C. The following secondary antibodieswere used: Alexa Flour 405-labeled donkey anti-goat 2 mg/ml, Alexa Flour 488-labeled donkeyanti-rabbit 1 mg/ml, Alexa Flour 488-labeled don-key anti-rat 1 mg/ml, Alexa Flour 568-labeled don-key anti-rabbit 1 mg/ml, Alexa Flour 568-labeleddonkey anti-rat 1 mg/ml, Alexa Fluor 647-labeleddonkey anti-rabbit 1 mg/ml, and Alexa Fluor 647-labeled chicken anti-rat 1 mg/ml for 2 hours at RT(ThermoFisher Scientific). Nuclei were counter-stained with DAPI (0.1 mg/ml) when necessary.

Images were taken using conventional fluores-cence microscopes (Olympus BX-61 with anOlympusXC10 camera andKeyencewith a 2/3 inch,1.5 million pixel monochrome CCD (colorised withLC filter) camera), and the confocal pictures weretakenwith Fluoview FV 1000 (Olympus). Imageswere processed with Photoshop (Adobe) or Fiji(63) software.

TUNEL assay

TUNEL assays were carried out using the In SituCell Death Detection Kit, TMR fluorescein(12156, Roche) according to the manufacturer’sinstructions. Briefly, specimens were permea-bilized with 5% NDS + 0.5% Triton-X 100 andsubsequently incubated with the TUNEL reac-tion solutionmixture in a humidified 37°C cham-ber for 1 hour. Cell nuclei were labeledwithDAPI(0.1 mg/ml). Images were taken using Keyencefluorescence microscope. Images were processedwith Photoshop (Adobe) and the percentage ofTUNEL-labeled cells versus all tdTomato+ cellswas calculated on Excel (Microsoft) and plottedwith GraphPad Prism 6.

Three-dimensional (3D) reconstructionof macrophages

Free-floating 50-mm cryosections from spinalcords were blocked and permeabilized with 5%NDS + 0.5% Triton for 4 hours followed by in-cubation overnight with anti-IBA-1 and anti-collagen IV at 4°C. Secondary antibodies wereincubated for 4 hours at RT. Imaging was per-formed on an Olympus Fluoview 1000 confocallaser scanning microscope using a 20×/0.95 NAobjective with a 3× zoom. Z stacks assembledfrom 1.15-mm steps in the z plane, 1024 by 1024pixel resolutions were recorded and analyzedusing IMARIS software (Bitplane).

Analysis of T cell–microglial interactions

Free-floating 20-mm cryosections from spinalcords were blocked and permeabilized with 5%NDS + 0.5% Triton for 4 hours followed by incu-bation overnight with rabbit anti-mouse P2RY12(AnaSpec), 1.5 mg/ml for rabbit anti-CCL5/RANTES(Novus Biological), 2 mg/ml for rat anti-mouseCD74 (clone In1/CD74, Biolegend), 2 mg/ml forgoat anti-mouse CXCL10 (clone BAF466, R&DSystems), and 2 mg/ml for rat anti-mouse CD4(clone GK1.5, eBioscience) at 4°C. Secondaryantibodies were incubated for 4 hours at RT.Imagingwas performed on anOlympus Fluoview1000 confocal laser scanning microscope usinga 60×/0.95 NA objective with a 2× zoom. Con-focal pictures were evaluated on Imaris software(Bitplane). The colocalization plugin was used toevaluate the interaction points between myeloidcells and T lymphocytes.

Dissection of CNS compartments andflow cytometry

To separate different CNS compartments, bothbrain and spinal cord were dissected frommiceand placed in ice-cold PBS. Under binoculars, theleptomeningesweredissected from the spinal cordto obtain separate samples of the leptomeninges



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and of the parenchyma and perivascular space.All choroid plexuses were removed from the ven-tricles of the brain. Tissue samples were placedin HBSS containing 1 mg/ml of collagenase D(Sigma-Aldrich) and digested for 30min at 37°C.After digestion, myeloid cells from the paren-chyma and perivascular space were isolated usinga 37% Percoll gradient (Sigma-Aldrich) from thehomogenized tissue. Tissue from the choroidplexuses and leptomeninges was treated inde-pendently by mechanical dissociation through a70-mm cell strainer to obtain cell suspensions.Monocytes were isolated from blood. Cells wereacquired on FACSCanto II and LSRFortessa sys-tems (BD Bioscience, Heidelberg, Germany)and analyzed with FlowJo software (TreeStar).Cell sorting was performed on a MoFlo Astrios(Beckman Coulter, Krefeld, Germany). The fol-lowing antibodies were used for staining cells:anti-CD45 (BV786, 0.5 mg/ml, clone 30-F11,eBioscience, San Diego, USA), anti-CD11b (BV605,0.5 mg/ml, clone M1/70, eBioscience, San Diego,USA), anti-CD3 (PE-Cy7, 0.8mg/ml, clone eBio500A2,eBioscience, San Diego, USA), anti-CD19 (PE-Cy7,0.8 mg/ml, clone eBio1D3, eBioscience, San Diego,USA), anti-NK1.1 (PE-Cy7, 0.8 mg/ml, clone PK136,eBioscience, SanDiego, USA), and anti-MD-1 (Alexa488, 1mg/ml, Abcam). Before surface staining, deadcells were stained using the Fixable Viability DyeeFluor 780 or eFluor 506 (eBioscience, San Diego,USA) followed by incubation with Fc receptorblocking antibody CD16/CD32 (1 mg/ml, clone2.4G2, BD Bioscience, Heidelberg, Germany).

scRNA amplification andlibrary preparation

Myeloid cells from the different CNS compart-ments were obtained as detailed above. Samplescontaining cells from the different compartments(leptomeninges, parenchyma, and perivascularspace, and choroid plexus) were subjected tosingle-cell sorting in the MoFlow Astrios ma-chine. After lineage exclusion (Lin−: CD3ɛ, CD19,NK1.1, and Ly6G), all CD45+ cells were sortedinto 382-well plates. To enrich for rare myeloidpopulations, we separated the sorted cells usingthree strategies (CD45loCD11b+, CD45hiCD11b+,and CD45hiCD11blo/−). The same number of cellswas sorted for each of these populations. Circu-lating Ly6Chi and Ly6Clomonocytes were sortedfrom blood. Single-cell RNA sequencing was per-formed using the mCEL-Seq2 protocol, an auto-mated and miniaturized version of CEL-Seq2on a mosquito nanoliter-scale liquid-handlingrobot (21, 64). Eight libraries with 192 cellseachwere sequenced per lane on IlluminaHiSeq2500 or 3000 sequencing system (pair-endmulti-plexing run) at a depth of ~130,000-200,000 readsper cell.

Quantification of transcript abundance

Paired-end reads were aligned to the transcrip-tome using bwa (version 0.6.2-r126) with defaultparameters (65). The transcriptome contained allgene models based on the mouse ENCODE VM9release downloaded from the UCSC genomebrowser comprising 57,207 isoforms with 57,114

isoforms mapping to fully annotated chromo-somes (1 to 19, X, Y, M). All isoforms of thesame gene were merged to a single gene locus.Furthermore, gene loci overlapping by > 75%were merged to larger gene groups. This pro-cedure resulted in 34,111 gene groups. The rightmate of each read pair was mapped to the en-semble of all gene loci and to the set of 92 ERCCspike-ins in sense direction (66). Reads mappingto multiple loci were discarded. The left readcontained barcode information: the first sixbases corresponded by six bases representingthe unique molecular identifier (UMI) followedby the cell-specific barcode. The remainder ofthe left read contained a polyT stretch. The leftread was not used for quantification. For eachcell barcode, the number of UMIs per transcriptwas counted and aggregated across all tran-scripts derived from the same gene locus. Basedon binomial statistics, the number of observedUMIs was converted into transcript counts (67).

scRNA sequencing data analysis

Fifty-four libraries were sequenced and afterquality control 3461 cells (PV: 1324, Men: 1052,CP: 701, Blood: 384) were analyzed. Data anal-ysis and visualization were performed usingthe RaceID3 algorithm (21). Cells with a totalnumber of transcripts < 1,500 were discardedand count data of the remaining cells werenormalized by downscaling. Cells expressing> 2% of Kcnq1ot1, a potential marker for low-quality cells, were not considered for analy-sis. Additionally, transcript correlating toKcnq1ot1 with a Pearson’s correlation coefficient> 0.65 were removed. The following parameterswere used for RaceID3 analysis: mintotal = 1500,minexpr = 5, outminc = 5, FSelect = TRUE,probthr = 10-8. The top 20 principal componentsof the datasets were considered for clusteringusing the CCcorrect() function with the param-eters nComp = 20 and mode = “pca”. We foundno batch-associated variability in the dataset.Furthermore, to assess the batch effects associ-ated with tissue dissociation-induced genes (68),RaceID3 was re-run where CGenes argumentwas initialized with the following genes: Dusp1,Jun, Fos,Hspa1a,Atf3 andMalat1. There was novisible dissociation-induced variability in thedataset. Differentially expressed genes betweentwo subgroups of cells were identified similar toa previously published method (69). First, nega-tive binomial distributions reflecting the geneexpression variability within each subgroupwereinferred based on the background model for theexpected transcript count variability computedby RaceID3 (21). Using these distributions, aP value for the observed difference in tran-script counts between the two subgroups wascalculated and multiple testing corrected by theBenjamini-Hochberg method.

Confocal microscopy and imageprocessing for microgliaclonal expansion

Six-channel images were acquired using a gatedSP8 STED-WS confocalmicroscope (LeicaMicro-

systems) with aHCX PLHCL PL APO C 20×/0.75NA glycerine objective lens and the LAS X soft-ware. Fluorophores, including Alexa Fluor 405,membrane-localized CFP, nuclear GFP, cyto-plasmic YFP, cytoplasmic RFP, and Alexa Fluor647, were detected using sequential and simulta-neous acquisition mode with the HyD detectorsin the gating mode. The excitation wavelengthsused were: UV Diode Laser 405 nm, Argon Laser458nm,WL480nm, 525nm, 565nm, and647nm.The pinhole was set to 1 AU. The 30-mm stackscomprising 2048 pixel by 1500 pixel tiles weresampled at a 284-nmpixel size and 1-mm z-steps.Overlapping tiles were acquired and automati-cally stitched using XuvTools (70). An average ofnine tiles per animal was imaged.

Computational analysis forclonal expansion

IBA-1+ and Confetti+IBA-1+microglial cells weredetected in three dimensions by using ourcustomMatlab-based program and assessmentofmicroglial clonality as detailed previously (40).IBA-1+ meningeal and perivascular macrophageswere excluded based on cell morphology andlocation (also indicated by collagen+ vessels).Briefly, we performed 10,000Monte Carlo simu-lations to derive the baseline random distribu-tion of Confetti+IBA-1+ microglial cells detectedin each animal. The averages of measuredConfetti+IBA-1+ microglial distributions werecompared to the 98th percentile of simulatedresults to determine the probability of EAE-associated microglial clonal expansion. Clonesizes were estimated on the assumption thatneighboring Confetti+IBA-1+ microglial cellsof the same color belonged to the same clonegiven the extremely low number of Confetti+

cells in the homeostatic spinal cord.

Statistical analysis

Statistical analysiswas performedusingGraphPadPrism (GraphPad Software, Version 6.0, La Jolla,USA). Data were tested for normality applyingthe Shapiro–Wilk normality test. If normalitywas given, an unpaired t-test or one-way analysisof variance (ANOVA) was applied. If the data didnot meet the criteria of normality, a Kruskall–Wallis test followed byDunn’smultiple compar-isons test was applied to datasets of more thantwo groups; otherwise, theMann–WhitneyU testwas applied for datawith two groups. Differenceswere considered significant when the P valuewas <0.05.

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ACKNOWLEDGMENTSThe authors thank M. Oberle for excellent technical support. Funding:This work was supported by the the Sobek Foundation (M.P.); theErnst-Jung Foundation (M.P.); the Deutsche Forschungsgemeinschaft(DFG) (SFB 992, SFB1160, SFB/TRR167, Reinhart-Koselleck-Grant) (M.P.); the Ministry of Science, Research and Arts,Baden-Wuerttemberg (Sonderlinie “Neuroinflammation”)(M.P.); the European Union’s Seventh Framework ProgramFP7 under grant agreement 607962 (nEUROinflammation)

(M.P. and M.J.C.J.); the Bundesministerium für Bildung,Wissenschaft, Forschung und Technologie (BMBF)–fundedcompetence network of multiple sclerosis (KKNMS) (M.P. andM.K.); DFG grant GR4980 (S.); and the Behrens-Weise-Foundation(S.), the Max Planck Society (S. and D.G.), the DFG grant KIDGEM(SFB 1140) (D.M.), the Centre for Biological Signalling Studies(BIOSS) (EXC294) (D.M.), and the European Research Council(ERC) Starting Grant (337689) (O.G). Author contributions:M.J.C.J., S.M.B., E.S., S.A., N.H., T.L.T., Ö.Ç., D.M., O.G., and T.F.conducted experiments, analyzed the data, or provided essentialtools for the study. G.L., Y.-H.T., and M.K. contributed to thein vivo imaging studies. R.S. and S. analyzed the scRNA-seq dataunder the supervision of D.G.; M.J.C.J. and M.P. supervised theproject and wrote the manuscript. Competing interests: The

authors declare no competing interests. Data and materialsavailability: The scRNA-seq data are deposited in the GeneExpression Omnibus under the accession no. GSE118948.All other data needed to evaluate the conclusions in thispaper are present either in the main text or the supplementarymaterials.

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neuroinflammationSingle-cell profiling identifies myeloid cell subsets with distinct fates during

Kerschensteiner, Dominic Grün and Marco PrinzTay, Eva Schramm, Stephan Armbruster, Nora Hagemeyer, Olaf Groß, Dominic Mai, Özgün Çiçek, Thorsten Falk, Martin Marta Joana Costa Jordão, Roman Sankowski, Stefanie M. Brendecke, Sagar, Giuseppe Locatelli, Yi-Heng Tai, Tuan Leng

DOI: 10.1126/science.aat7554 (6425), eaat7554.363Science

, this issue p. eaat7554Sciencecharacterization may inform future therapeutic targeting strategies in MS.cells, but not resident macrophages, played a critical role by presenting antigen to pathogenic T cells. This exhaustivetransformed into various context-dependent subtypes during EAE. Furthermore, dendritic cells and monocyte-derived encephalomyelitis (EAE), a mouse model of MS. Microglia and other CNS-associated macrophages expanded andsequencing and intravital microscopy to compile a transcriptional atlas of myeloid subsets in experimental autoimmune

combined high-throughput single-cell RNAet al.the initiation and exacerbation of multiple sclerosis (MS). Jordão Myeloid cells, such as dendritic cells and macrophages, in the central nervous system (CNS) play critical roles in

A myeloid cell atlas of neuroinflammation

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