Modulation of repopulating microglia in multiple sclerosis models with implications for neuroprotection

Modulation of repopulating microglia in multiple sclerosis models with implications for neuroprotection | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Modulation of repopulating microglia in multiple sclerosis models with implications for neuroprotection View ORCID Profile Neele Heitmann , Sarina Fiene , Lina Rambuscheck , View ORCID Profile Britta Eggers , View ORCID Profile Maria Fernanda Valdes Michel , Anne-Christin Gude , Hasan Hüsein Hendek , Sarah-Marie Oberhagemann , View ORCID Profile Katharina Klöster , Robert Hoffrogge , Anna Karachunskaya , Hannah Märte , Kamilla Adam , Svitlana Rozanova , Verian Bader , Sabrina Reinehr , Carlos Plaza-Sirvent , View ORCID Profile Gayatri Tandon , View ORCID Profile Martin Eisenacher , View ORCID Profile Jason R. Plemel , View ORCID Profile Stephanie C. Joachim , View ORCID Profile Konstanze F. Winklhofer , View ORCID Profile Oliver Gramlich , View ORCID Profile Katrin Marcus-Alic , View ORCID Profile Ingo Schmitz , View ORCID Profile V. Wee Yong , Ralf Gold , View ORCID Profile Simon Faissner doi: https://doi.org/10.1101/2025.08.08.669252 Neele Heitmann 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Neele Heitmann Sarina Fiene 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lina Rambuscheck 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Britta Eggers 2 Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum , Bochum, Germany 3 Medical Proteome Analysis, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Britta Eggers Maria Fernanda Valdes Michel 4 Department of Ophthalmology & Visual Sciences, University of Alabama , Birmingham, AL, USA 5 Department of Ophthalmology and Visual Sciences, University of Iowa , Iowa City, IA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria Fernanda Valdes Michel Anne-Christin Gude 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hasan Hüsein Hendek 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah-Marie Oberhagemann 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katharina Klöster 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katharina Klöster Robert Hoffrogge 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Karachunskaya 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hannah Märte 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kamilla Adam 2 Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Svitlana Rozanova 2 Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum , Bochum, Germany 3 Medical Proteome Analysis, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum , Bochum, Germany 6 Core Unit for Bioinformatics (CUBiMed.RUB), Medical Faculty, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Verian Bader 7 Department of Molecular Cell Biology, Institute of Biochemistry and Pathobiochemistry, Ruhr University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sabrina Reinehr 8 Experimental Eye Research Institute, University Eye Hospital, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Carlos Plaza-Sirvent 9 Department of Molecular Immunology, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gayatri Tandon 9 Department of Molecular Immunology, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gayatri Tandon Martin Eisenacher 2 Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum , Bochum, Germany 3 Medical Proteome Analysis, Center for Protein Diagnostics (PRODI), Ruhr-University Bochum , Bochum, Germany 6 Core Unit for Bioinformatics (CUBiMed.RUB), Medical Faculty, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin Eisenacher Jason R. Plemel 10 Division of Neurology, Department of Medicine and Department of Medical Microbiology and Immunology, University of Alberta , Edmonton, AB, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jason R. Plemel Stephanie C. Joachim 8 Experimental Eye Research Institute, University Eye Hospital, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephanie C. Joachim Konstanze F. Winklhofer 7 Department of Molecular Cell Biology, Institute of Biochemistry and Pathobiochemistry, Ruhr University Bochum , Bochum, Germany 11 Cluster of Excellence RESOLV, Ruhr University Bochum , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Konstanze F. Winklhofer Oliver Gramlich 4 Department of Ophthalmology & Visual Sciences, University of Alabama , Birmingham, AL, USA 5 Department of Ophthalmology and Visual Sciences, University of Iowa , Iowa City, IA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Oliver Gramlich Katrin Marcus-Alic 2 Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katrin Marcus-Alic Ingo Schmitz 9 Department of Molecular Immunology, Ruhr-University Bochum , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ingo Schmitz V. Wee Yong 12 Department of Clinical Neurosciences and the Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary , Calgary, Alberta, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for V. Wee Yong Ralf Gold 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simon Faissner 1 Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital , Bochum, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Simon Faissner For correspondence: simon.faissner{at}rub.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Microglia play a critical role in central nervous system (CNS) pathologies including multiple sclerosis (MS), and their modulation offers therapeutic potential especially during progressive disease courses. Using cell culture and experimental autoimmune encephalomyelitis (EAE) models, we investigated microglial dynamics during depletion and repopulation (MG repo ) and their modulation using siponimod (sipo), an established CNS penetrating MS medication. Repopulating microglia exhibited a transient reactive state (CD86, MHC-II, Il1b, Tnf) . Sipo modulated microglia populations, increasing CD163 + , CD206 + , and CX3CR1 + while reducing CD86 + MHC-II + cells accompanied by a reduction of neuronal damage. Proteomic spinal cord analysis revealed protein expression alterations by MG repo and sipo linked to inflammation, myelination, and neuronal structural organization, supported by RNA sequencing of the spinal cord. The neuroinflammation attenuating role of sipo could be linked to cell maintenance and myelin formation associated processes. These findings highlight the capacity of pharmacological interventions to modulate microglia, offering new insights into therapeutic strategies targeting microglial activity in neuroinflammatory diseases. Introduction Multiple sclerosis (MS) is a neuroinflammatory demyelinating disease of the central nervous system (CNS)[ 28 ], characterized by demyelinating lesions in the brain, spinal cord, and optic nerve mediated by autoreactive T and B lymphocytes and influx of macrophages with consecutive neurodegeneration[ 39 ]. Immune cells within the lesions interact with microglia, promoting a more inflammatory microglia polarization[ 11 , 28 ]. Most patients initially present with a relapsing remitting MS phenotype (RRMS)[ 58 ]. Without treatment, RRMS evolves to a progressive disease course with fewer relapses but continuous disability accumulation, secondary progressive MS (SPMS)[ 58 ]. In a smaller number of people, the disease progresses without relapse activity as primary progressive MS (PPMS). The understanding and treatment of MS have evolved greatly over the past decades, enabling the implementation of different medications, especially for RRMS, and improving the quality of life and the life expectancy of people living with MS (pwMS)[ 58 , 14 ]. While treatment of RRMS has made tremendous headway during the last 20 years, treatment of progressive MS is still unsatisfactory[ 14 ],[ 34 ] with only limited options, including B cell depletion (ocrelizumab, PPMS), siponimod (sipo, SPMS)[ 58 ] and the 2025 expected BTK-inhibitor tolebrutinib[ 16 ]. This emphasizes the present necessity to enhance comprehension of progression in MS, particularly in isolation from relapse activity, with a view to facilitating the development of more efficacious drugs to mitigate progression. Progressive MS is predominantly driven by CNS-based inflammation, which is mediated by microglia[ 25 ][ 58 ]. The participation of microglia in inflammatory responses is characterized by the release of cytokines and oxidative stress. However, these cells also play a crucial role in maintaining normal brain function and facilitating regeneration following damage[ 58 ]. The human analysis of microglia in pwMS is limited to biopsies, sophisticated imaging methods, and post-mortem tissue, showing high microglia prevalence both within lesions and in normal appearing white and grey matter during progression. Microglia at lesion edges can take up iron, driving the enlargement of iron rim lesions associated with faster progression. Moreover, microglia metabolize lipids, leading to the formation of foamy microglia after myelin debris[ 58 , 47 ]. In addition to the restriction of lymphocyte infiltration, microglia modulation is a promising treatment strategy that is currently being explored for the development of new therapeutics, including BTK-inhibitors. Drugs that modulate microglia and other cells in the CNS must be able to cross the blood-brain barrier. Sipo, a medication authorized for the treatment of SPMS, exerts effects on CNS immune cells. Sipo mediates a sphingosine-1 phosphate receptor (S1PR) 1 functional antagonism, preventing immune cell egress from lymphoid organs and limiting CNS infiltration[ 7 ]. Sipo has the ability to cross the blood-brain-barrier and modulates S1PR1 as well as S1PR5, both of which are present on different cell types in the CNS, including microglia[ 19 ]. These properties promise its ability to modify or limit progression[ 58 ]. The potential beneficial properties of sipo have been investigated in previous studies, but have varied in species[ 1 ],[ 38 , 19 ], route of administration[ 18 ], and analysis. The objective of this study was to determine the phenotype of microglia during repopulation in a model of MS, experimental autoimmune encephalomyelitis (EAE), and whether microglia could be regulated by sipo to establish a neuroprotective environment with implications for progression. Given that sipo is already approved for all forms of clinically active MS, the detection of a new mode of action has the potential to provide insights into the long-term effects of the drug within the CNS that may be applicable to other diseases with microglial involvement, including neurodegenerative disorders. Methods Cell culture of human neurons and microglia Fetal brain-derived neurons and microglia were used as previously described[ 12 ],[ 13 ]. The cells were harvested after 12-15 days of culture. Neurons were harvested by trypsinization and seeded at a concentration of 10 5 cells per well in pLorn-coated 96-well flat-bottomed black plates (Falcon 353219) following culture in medium with Ara-C over 72 hours. Thereafter, neurons were pretreated with sipo, and after one hour 2.5*10 4 microglia were added following the addition of FeSO 4 in saline ( Figure 1a ). The co-culture was conducted for 24 hours with live cell imaging by acquiring propidium iodide (PI, ThermoFisher Scientific) and Hoechst 33342 NucBlu Live ReadyProbes reagent (ThermoFisher Scientific) signal every two hours with a Celldiscoverer 7 (Zeiss) or an ImageXpress Micro XLS High-Content Analysis system (Molecular Devices). The PI and Hoechst33342 positive cells were analyzed using ZEN software (Zeiss) or MetaXpress (Molecular Devices). Download figure Open in new tab Figure 1: Iron toxicity studies in cell culture. Primary human and murine neuron and microglia cell cultures (a,h) stimulated with FeSO 4 and treated with siponimod (sipo) were imaged with live-dead-staining every two hours (b,c human neurons; e,f human co-culture; I,j murine neurons; l,m murine microglia; o,p murine co-culture). Statistical analysis of specific time points in the human culture are presented after 24 hours (d) and 20 hours (g) of stimulation of two independent experiments. In murine cultures, the analysis was conducted with the data from time point 22 hours after stimulation (k,n,q) with three independent experiments. Experiments were measured in triplicates and analyzed with mean, SEM and n per experiment with the 2-way ANOVA and Šídák’s multiple comparisons test. Co-culture – neuron and microglia culture, ctrl – control. a and h were created with Biorender. P-values are depicted as * - p≤0.05, ** - p≤0.01, *** - p≤0.001, **** - p≤0.0001. Cell culture of murine neurons and microglia The C57BL/6J pregnant mice, postnatal mice, and embryos were sacrificed by methods that comply with European and German animal welfare guidelines prior to tissue explants. The cerebral hemispheres of embryonic day 15-16 mouse embryos were dissected to obtain neurons as described before[ 12 ]. 10 5 neurons were seeded in 100 µL B-27 Plus Neuronal Culture System medium (Gibco A3653401) in a pLorn-coated 96-well plate and cultured for 48 hours, adding 100 µL Neurobasal medium (Gibco 21103049) after 24 hours. Primary murine microglia were prepared from postnatal day 0-2 C57BL/6 mice. Cortices were mechanically dissociated and seeded into Poly-D-lysin hydrobromide-coated (10 µg/mL PDL; Sigma-Aldrich P6407-5MG) cell culture flasks in supplemented DMEM medium (Table S5). Microglia were harvested as they floated over the astrocyte layer. After seeding, the medium was exchanged after 1 hour to Neurobasal medium. To isolate splenocytes, spleens of adult mice were mechanically dissociated and separated via density gradient centrifugation with Ficoll® Paque Plus (Cytiva 17-1440-02) as described before[ 26 ]. The cell pellet was resuspended in 1 mL of complete neurobasal medium (Table S5). For stimulation, splenocytes were incubated in medium supplemented with phorbol 12-myristate 13-acetate (PMA, 1 μg/mL) and ionomycin (iono, 20 ng/mL) for 4 hours. For co-culture with 3*10 4 primary microglia, 5*10 4 splenocytes were added into each well (Figure S2k) in complete neurobasal medium. For assays of neuron-microglia co-cultures, neurons were pretreated for one hour with sipo (Hycultec HY-12355 10 mM in DMSO) in concentrations measured in the CNS after oral administration before[ 38 ] or DMSO as a control. Meanwhile, microglia were harvested. 2.5*10 4 microglia were added per well onto pretreated neurons. After one hour, a full medium change was conducted ( Figure 1h ). The same concentration of sipo was added again with the stimulating agents like FeSO 4 (Carl Roth P015.1 solubilized in 0.9% NaCl), or IFN-γ (BioLegend 575302, 30 ng/mL;i) and LPS (Sigma-Aldrich L4391, 1 µg/mL) (iLPS). To investigate the inhibition of stimulation by sipo, microglia were pretreated with sipo for one hour before adding FeSO 4 , IFN-γ, LPS, or splenocytes. The toxicity of sipo, FeSO 4, and iLPS was assessed by live cell imaging with Hoechst 33342 (Thermo Fisher Scientific H3570) and PI (Invitrogen 00-6990-50) before imaging every two hours in the Cytation5 (Agilent BioTek) over the course of 24 hours. The analysis of positive cells was done automatically with the Gen5 software (BioTek Instruments). For immunocytochemistry (ICC), PFA-fixed 96-well plates were permeabilized with 0.2% Triton X-100 for 10 minutes and blocked with 5% goat and donkey serum each in 0.2% Triton X-100 for 1 hour. Primary and secondary antibodies (see Table S3) were incubated in blocking solution. Eight images per well were acquired using the AxioObserver microscope (Zeiss) and analyzed with the ZEN blue 3.10 software (Zeiss). For flow cytometry, the cells were detached using Accutase (Invitrogen 00-4555-56) and transferred into a 96 well V-bottom plate for staining and measurement. Cell surface markers were labelled with antibodies (see Table S3) for 30 minutes at 4°C, making use of Fc-blocking and True-Stain Monocyte Blocker™ (Biolegend) to increase specificity. Viability was determined using Zombie Aqua™ Fixable Viability Kit (Biolegend) according to the manufacturer’s instructions. Detection of fluorescence signals was achieved with BD FACSCelesta™ Flow Cytometer and analyzed with FlowJo (FlowJo X 10.0.7r2, BD). Experimental autoimmune encephalomyelitis All animal experiments were approved by the animal care committee of North Rhine-Westphalia, Germany (AZ 81-02.04.2019.A425). Mice of both sexes aged 8-12 weeks were included. To induce specific microglia ablation during EAE, we crossed B6.129P2(C)- Cx3cr1 tm2.1(cre/ERT2)Jung /J (The Jackson Laboratory 020940, cx3cr1) and C57BL/6-Gt(ROSA)26Sor tm1(HBEGF)Awai /J (The Jackson Laboratory 007900, iDTR) to generate DTR MG mice. Mice were housed under environmentally controlled conditions and a 12:12 h dark-light cycle under pathogen-free conditions at the central breeding facility of the Medical Faculty of Ruhr-University Bochum. Mice had access to chow and water ad libitum . EAE was induced as described before[ 26 ]. Animals were scored and weighed daily according to a previously defined scoring scheme[ 26 ]. Mice with a score of 7 were euthanized according to animal care guidelines. Before treatment initiation, animals were randomized according to weight or score in the therapeutic experiments. On the day after EAE induction, the animals were divided into 2 groups and given either rapeseed oil (vehicle) or sipo (Hycultec HY-12355) dissolved in rapeseed oil by oral gavage. To specifically ablate microglia, first the DTR MG mice were administered 1 mg of tamoxifen (Sigma-Aldrich T5648-1G; TAM) in corn oil i.p. every other day for five days at weaning age to induce diphtheria toxin receptor (DTR) expression on cx3cr1 expressing cells. 1 µg diphtheria toxin (Sigma-Aldrich D0564-1MG; DT) injection i.p. for three days (MG repo ) was conducted at least four weeks after TAM injection. After microglia ablation by DT administration, the cells begin to repopulate approximately five days after the last DT injection[ 4 ]. The mice were sacrificed as described. Whole blood, spleen, lymph nodes, spinal cord, and brain were explanted and either directly processed for flow cytometry or stored in PBS or PFA. Flow cytometry Cells of blood, spleen and lymph nodes were isolated as previously described[ 26 ]. Single cell suspensions were transferred in 96-well V-bottom plates, and staining (Table S3) and flow cytometry were conducted as described[ 26 ]. The brain and spinal cord were dissociated mechanically. The single cell suspension was separated using percoll gradient (70%, 37%, 30%, PBS). Cells in the interphase of 37% and 70% percoll was stained for flow cytometry (Table S4) or pelleted and frozen for mRNA extraction and subsequent qPCR. Flow cytometry was performed using the BD Celesta (see cell culture) and the Cytoflex LX (Beckman Coulter). Gating (Figures S6-S8) and analysis was done with FlowJo software (BD). tSNE plots were created as described before[ 15 ]. Histological analyses For the following staining protocols, the 5 µm paraffin-embedded sections were melted at 60 °C for two hours before deparaffinizing in Roti-Histol and rehydration. The spinal cord of EAE mice was stained with hematoxylin-eosin (HE) and luxol fast blue (LFB) double staining Pictures were acquired with a Axio.Observer.Z1/7 microscope (Zeiss), using the tile function with a 20x objective. Measuring of white matter and demyelinated or infiltrated areas was conducted in ImageJ. For immunohistochemical staining of the spinal cord of EAE mice, antigen retrieval was achieved by boiling in citric buffer and incubation with ice cold methanol. Primary antibodies were incubated in blocking solution overnight at 4°C (Table S3). The secondary antibodies were incubated for 1-1.5 hours in blocking solution. If necessary, the ready ProbeTM Tissue Autofluorescence Quenching Kit (Thermo Fisher Scientific) was applied for ten minutes before embedding with DAPI Fluoromount (Biozol). Pictures were acquired using the tile function and detailed picture of infiltrates were acquired using z-stacks. Higher resolution images were acquired with apotome. Measuring the intensity of AF647 (GFAP) over the whole cross section of the spinal cord and counting the DAPI + CD3 + cells in the infiltrates was performed with ZEN software (3.10 Zeiss). Three-dimensional reconstruction of Iba-1, NFH, and MBP signals was conducted of local bleaching corrected apotome z-stacks with IMARIS software (10.2.0 Bitplane AG). Construction of surfaces was done by background subtraction and size limitation of the surfaces. The interactions of the surfaces were calculated with the MATLAB extensions with surface functions for surface-surface coloc between the NFH and MBP, Iba-1 and NFH, and Iba-1 and MBP surfaces and for the surface-surface contact area between NFH as primary surface and NFH-MBP coloc surface as secondary coverage surface. The eyes were fixated, dehydrated, embedded and cryosectioned as described previously[ 43 ]. Retinal staining was conducted with antibodies indicated in the supplement (Table S4). Per eye section, the retina was imaged in four areas (two central and two peripheral photos per cross-section) with a 40x objective and apotome. Of each animal six eye slices were acquired. The image analysis was conducted using ZEN. First apotome deconvolution was conducted in batch processing. The ZEN analysis was set up for interactive batch analysis with the same size frame to be analyzed in each picture, positioned on the retina to match the orientation of the ganglion cell layer in the section. The retinal ganglion cells (RBPMS + cells) were semiautomatically counted based on Otsu Threshold (light regions) or manually adjusted using global thresholding and watershed separation. The cell count value was multiplied with 7.6 to equal the cells per mm. The percentage GFAP intensity area was measured after background subtraction (rolling ball radius 20) and lower (7372) and upper (16383) threshold segmentation. The Iba-1 + signal was automatically segmented with Three Sigma Threshold (light regions) and the parameters cell count and percentage intensity area analyzed. DNA isolation The immune brain cells for qPCR were isolated by Percoll-density gradient in the same way as for flow cytometry. Brain immune cells in the interphase were pelleted, liquid removed and stored at -80 °C until mRNA isolation using the RNeasy plus mini kit (Qiagen 74136) according to manufactureŕs instructions. Tissue pieces for RNA isolation were frozen in liquid nitrogen and stored at -80 °C until isolation. For the spinal cord, a one-centimeter piece of lumbar spinal cord was used. The retina was dissected from the eye and one retina and the optic nerve of two animals were pooled with matching EAE scores to retrieve enough RNA. The tissue was disrupted with trizol and the TissueLyser II system (Qiagen) followed by phase separation with chloroform and Phase lock gel tubes (VWR 733-2478). The remaining mRNA isolation was done with the RNeasy plus mini kit. For RNA sequencing, the RNA samples (three of each group) of lumbar spinal cord tissues were pooled to reach the needed concentration. This was necessary due to insufficient RNA yield after isolation; individual samples did not provide enough material for quality control and library preparation. As a result, three samples per group were pooled. Regardless, the samples provided good quality, and quantity reads to continue with downstream analysis of the data. Reverse transcriptase qPCR cDNA transcription, qPCR and analysis were done as described before[ 26 ]. The primers used are listed in Table S4. The CT values were normalized to the housekeeping genes, and the fold change expression was normalized to the control group (naïve vehicle for retina and optic nerve, vehicle for brain immune cells and spinal cord). RNA sequencing of lumbar spinal cord tissue Gene expression profiling using RNA-Seq was performed by the University of Iowa Genomics Division using manufacturer-recommended protocols. Briefly, 125 ng of total RNA was used to prepare sequencing libraries using the Illumina stranded total RNA prep, ligation with ribo-zero plus library kit (Cat. #20040529, Illumina, Inc., San Diego, CA). The molar concentrations of the resulting indexed libraries were measured using the 2100 Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA) and combined equally into a pool for sequencing. The concentration of the library pool was measured using the Illumina Library Quantification Kit (KAPA Biosystems, Wilmington, MA) and sequenced on the Illumina NovaSeq 6000 genome sequencer using 100 bp paired-end SBS chemistry. Data analysis The data generated were analyzed using the Nextflow pipeline nf-core/rnaseq v3.19.0 [ 42 ], run on the Argon High-Performance Computing (HPC) cluster at the University of Iowa. Briefly, fastq data were merged and strandedness was automatically detected using Salmon Quant. The tool Trim Galore! was used to perform quality and adapter trimming of the fastQ files, then STAR was used to map the raw reads to the reference genome GRCm39 and Salmon to quantify the data. The remainder of the bioinformatic analysis was performed using Rstudio 2025.05.1 (Posit Software PBC, Boston,MA) [ 44 ]. The RNA-seq gene-count level data were imported into Rstudio where differentially expressed genes (DEGs) were identified using the statistical thresholds of p-value <.05 and fold change of absolute 2. DEGs gene expression values were extracted and normalized using row-wise z-score adjustment to facilitate the comparison among genes. The heatmap was generated using the pheatmap package [ 32 ], using hierarchical clustering to both samples and genes. Additionally, a curated list of microglia-related genes was used to filter the data set. Gene expression values for four of the experimental conditions (EAE_vehicle, EAE_sipo, EAE_MG repo vehicle, EAE_MG repo sipo) were extracted, normalized and visualized as described above. For Principal Component Analysis (PCA) gene-level count data were normalized and transformed using a variance-stabilizing transformation. The DESeq2 package was used to conduct the PCA [ 37 ]. The first two principal components were visualized to assess sample clustering and variance. The plot was generated using ggplot2 [ 56 ]. The NOISeq R package was used as a non-parametric approach for differential expression analysis [ 54 , 53 ]. The NOISeq method was chosen since it was developed to deal with datasets with no replicates. The data was normalized to TMM. The data was filtered with the thresholds of log2FC ≥2 and a probability of ≥0.95, considering any gene that passes such a threshold as differentially expressed and significant. Ensemble gene identifiers were converted to EntreZ IDs and MGI symbols with the biomaRT package [ 59 ]. Gene ontology (GO) and pathway enrichment analyses were performed and visualized using the clusterProfiler package [ 2 , 3 ]. The enrichGO() function was utilized to identify GO terms that are significantly enriched. The genes were annotated with the Bioconductor – org.Mm.eg.db [ 5 ], the mouse genome annotation database, focusing on the Biological Process (BP) ontology. An adjusted p-value of < 0.05 (Benjamini-Hochberg correction) and q-value < 0.2 were used as a significance threshold. The top 20 enriched GO terms were visualized using the dotplot package [ 45 ]. Protein isolation and proteome analysis Sample lysis and digestion 50µL of urea buffer (30 mM TrisBase, 2 M Thiourea, 7 M Urea) was added to isolated spinal cord samples. Lysis was achieved by homogenization utilizing a pistil and a subsequent sonication cycle, consisting of 30 seconds sonication and 30 seconds rest on ice, in a sonication bath. Protein concentration was determined by Bradford assay and protein digestion was performed utilizing the single-pot solid-phase-enhanced sample preparation (SP3) method[ 50 ] to remove interfering lipids. Resulting peptides were dried in vacuum and resuspended in 0.1% TFA. Mass spectrometric measurements 200 ng of each sample was injected and concentrated on a precolumn (PepMap Neo C18 Trap Cartridge 300 μm × 0.5 cm, particle size 5 μm) using the Vanquish Neo UHPLC system (Thermofisher Scientific, Bremen, Germany) and then separated on an analytical column (DNV PepMapTM Neo, 75 μm x 150 mm, C18, particle size 2 μm, pore size 100 Å). The peptides were separated at a flow rate of 400 nl/min and a solvent gradient of 1 % B to 21 % B (B: 84 % acetonitrile, 0.1 % formic acid) for 70 minutes followed by an increase in the gradient up to 40%B for 15 min. The column was rinsed with 95 % B for 5 min. The chromatography was online coupled to an Orbitrap Fusion Lumos mass spectrometer (ThermoFisher Scientific, Bremen Germany). Before entering the mass spectrometer, the peptides were ionized by ESI. The capillary temperature was set at 275 °C and the spray voltage at 1800 V. MS1 spectra were recorded in the range of 350 to 1400 m/z with a resolution of 120,000 at 200 m/z (normalized AGC target 100%, maximum injection time 54 ms). For data-independent acquisition, 20 equally sized isolation windows between 400 and 900 m/z were scanned at the MS2 level. The resolution of the Orbitrap was set to 30,000 and fragment ions were generated by HCD at a normalized collision energy of 32. The AGC target was set to 1000% and injection time to 54 ms. Data analysis The generated DIA data (raw format) were analyzed in the SpectronautPulsar program (version 19.5.241126.62635, Biognosys, Schlieren, Switzerland) utilizing the direct DIA option. The analysis settings were selected according to the manufacturer’s instructions, with the exception that trypsin was added as digestion enzyme and carbamidomethylation on cysteine was set as fixed and oxidation on methionine as variable modification. The mouse reference proteome from Uniprot (accessed 03.2025, 54,739 entries) was selected as database. The abundances (MaxLFQs) of protein groups were LOESS-normalized and analyzed using R (version 4.5.0, R Core Team, 2021, https://www.R-project.org/ ) with the ‘ProtStatsWF’ package (version 1.0.0, [ https://github.com/mpc-bioinformatics/ProtStatsWF ]). Label free quantified (LFQ) values were further utilized for statistical analysis in Perseus[ 55 ]. Stringent filtering criteria were applied, whereby only proteins present in at least 3 replicates per group were chosen for further statistical evaluation. Remaining missing values were imputed from normal distribution utilizing a width of 0.3 and a down shift of 1.8. Welch’s t test was conducted to identify significantly differentially expressed proteins. A protein with a p-value <0.05 was considered statistically significant. To calculate the direction of regulation of a protein between two sample groups averaged log2 LFQ values per protein were subtracted from each other, resulting in a fold change (FC). Gene ontology enrichment analysis was performed as described before[ 57 ]. In detail, proteins found to be of higher abundance within one comparison were functionally annotated towards biological processes (BP), as background the whole mouse proteome was chosen. A Benjamini-Hochberg adjusted p value < 0.05 was used as a significance threshold and fold enrichment scores were taken to assess the enrichment of resulting GO-terms. Unsupervised hierarchical clustering analysis was applied to proteins related to microglia with the aim of grouping the proteins with similar abundance profiles across different conditions. To this end, the mean MaxLFQs for the replicates within conditions were calculated and utilized to calculate the z-scores for each protein. The results were then visualized using a heat map. The distance metric was based on the Pearson correlation coefficient. Statistics The cell culture experiments were measured in at least triplicates per experimental group and a single experiment. All statistical analyses were performed with Prism 10 (GraphPad Software). In the experiments with replicates per experiment or animal, the mean and standard error of the mean (SEM) were calculated and used together with the N (number of replicates) for each experiment or animal used for grouped analysis 2-way ANOVA with Šídák’s multiple comparison test. Data with one measurement were assumed to be non-Gaussian and analyzed using the Kruskal-Wallis test with Dunn’s post hoc multiple comparisons. The EAE incidences were analyzed with survival curve comparison Log-rank (Mantel-Cox) test. Correlations were analyzed with Spearman correlation. The graphs depict the means ± SEM. Statistical significance was reached with p≤0.05 and displayed as follows: *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. Results The impact of sipo on CNS cells in pwMS remains unconfirmed. While sipo is an effective treatment for pwMS by reducing the leukocyte infiltration into the CNS, previous data suggest additional protective properties when reaching the CNS in a suitable concentration to agonize the S1PRs on CNS resident cells[ 18 , 38 ]. We examined sipo’s protective potential against stressors in vitro while exerting no adverse effects. Sipo prevents iron toxicity in human and murine cell cultures Sipo was tested in human and murine cells to investigate its efficacy and transferability to humans. The treatment and stimulation were done similarly for both species ( Figure 1a,h ). In live-dead cell imaging, sipo did not increase the percentage of dead cells on neurons and in neuron-microglia co-cultures (Figure S1a,c,e,h). The fold change of dead cells treated with sipo did not exceed the control group, both in the human and the murine system over time and at a late time point (Figure S1b,d,f,g,I,j). As iron deposition activates microglia in iron rim lesions[ 60 ], iron was used as a stressor to simulate the MS-diseased brain. Neurons were pretreated with sipo before adding microglia and subsequently iron. In human neuron cultures, iron induced 16.6% more dead cells after 24 hours, which was reduced by 10.5% with 1 µM sipo pretreatment ( Figure 1b-d ). In human neuron-microglia co-cultures, cell death by iron was increased from 16 hours after stimulation started ( Figure 1e,f ). After 20 hours, iron induced roughly a two-fold increase in dead cells ( Figure 1g , p<0.0001). Pretreatment with sipo protected against iron toxicity (p<0.0001; Figure 1g ). In the murine system, neuron, microglia, and neuron-microglia co-cultures were tested for iron toxicity susceptibility. Iron induced cell death in all cultures ( Figure 1i,l,o ). As in human cultures, cell death was most pronounced in microglia and microglia-neuronal co-cultures with no effect of 0.1 µM sipo ( Figure 1j,k,l,n,p,q ). Both 1 and 3 µM sipo reduced cell death significantly after 22 hours of iron stimulation ( Figure 1n,q ). The neuroprotective effects of sipo in the context of iron toxicity in the co-culture were evaluated by neuron morphology analysis (Figure S2a). One µM and 3 µM sipo treatment ameliorated the reduced fiber length by 6-6.5% but failed to reach statistical significance (Figure S2b). Immunostaining of microglia revealed that iron reduced the proportion, increased the percentage of iNOS + microglia, and reduced ramification (Figure S2c-d). With 1 and 3 µM sipo pretreatment, these iron impacts on microglia were reversed. Upon stimulation of microglia with iLPS, an increase of microglia in the culture expressing significantly more iNOS was observed while covering a higher surface with no adverse effects on neuron or microglia health and minor reversing effects by sipo (Figure S2f-I, Figure S1k-p). To assess effects of sipo on Interferon-gamma stimulation, we performed flow cytometry, documenting a reduction of CD86 expression by sipo (Figure S2j). Given that sipo exerts greater effects on microglia than neurons, we tested if sipo can also prevent microglia stimulation in microglia-splenocyte co-cultures, mimicking infiltrating peripheral immune cells into the CNS, activating microglia (Figure S2k). Although there were more antigen-presenting lymphocytes upon PMA/Iono stimulation, microglia populations were not differentially affected (Figure S2l,m). Sipo pretreatment reduced the frequencies of CD86 + microglia and elevated the homeostatic marker CX3CR1 significantly (Figure S2n). In summary, sipo demonstrated protective effects against iron-induced cell death in both human and murine neuronal and microglial cultures and against inflammatory stimuli like iLPS and splenocytes, reducing cell death and modulating microglial activation, morphology, and function. Low-dose sipo reduces disease activity of EAE but does not modulate brain microglia The objective of microglia depletion during EAE was to deplete the disease-mediating microglia, thereby creating conditions conducive to the subsequent microglia repopulation. The end of the experiment with tissue explant was conducted during microglia repopulation (MG repo ). It was hypothesized that repopulating microglia would not become disease-promoting and would be susceptible to modulation by sipo. A subclinical dosage of sipo was implemented in a prophylactic treatment paradigm to investigate whether MG repo would modulate EAE ( Figure 2a ) while still reaching sufficient concentrations in the CNS. To evaluate the characteristics of microglia depletion during EAE and to exclude adverse effects of tamoxifen or DTR expression[ 46 ], several control experiments were performed (Figure S3a). After confirming no adverse effects of tamoxifen or DTR expression in EAE and no apparent behavioral phenotype in mice by MG repo without EAE induction, the timing of MG repo was confirmed to be most tolerable after remission of the initial signs of EAE (Figure S3b-f). MG repo was confirmed by flow cytometry of the brain, documenting repopulation of CD86 + microglia (Figure S3g,h). 0.45 µg sipo reduced EAE incidence and severity, but did not prevent EAE-induced weight loss ( Figure 2b,c,d ). Contrarily, in MG repo vehicle and MG repo sipo, microglia, macrophages, and lymphocytes displayed higher CD86 + cell frequencies in the brain ( Figure 2e ). Although the cell culture data suggested a microglia modulation by sipo, this could not be shown with the sipo dosage of 0.45 µg per day during EAE. Furthermore, the effectiveness of sipo in preventing lymphocyte egress from the lymphoid organs was only present as a tendency of altered lymphocyte subsets ( Figure 2f ). In summary, low-dose sipo had moderate effects on EAE disease severity and peripheral immune cells and failed to prevent CD86 + microglia in the brain. Download figure Open in new tab Figure 2: Effect of low dosage preventive siponimod administration in MG repo -EAE. Experimental autoimmune encephalomyelitis (EAE) experimental setup (a), incidence (b), weight (c) and clinical score (d) with flow cytometry immune cell analysis of brain (e), blood, spleen and lymph nodes (f) in mice with conditional microglia depletion on three days indicated in red. Dpi – days post immunization (EAE induction), MG repo – microglia depletion with diphtheria toxin administration in cx3cr1-iDTR mice, sipo – siponimod, TAM – tamoxifen administration for diphtheria toxin receptor induction (iDTR), Tcons – conventional T cells (living CD3 + CD4 + FoxP3 - cells), Tregs – T regulatory cells (living CD3 + CD4 + FoxP3 + cells), Th1 – T helper 1 cells (living CD3 + CD4 + FoxP3 - Interferon-gamma + cells). Kruskal-Wallis with Dunn’s multiple comparisons tests. (c,d) vehicle n=3, MG repo vehicle n=5, sipo/MG repo sipo n=6. P-values are depicted as * - p≤0.05, ** - p≤0.01, *** - p≤0.001, **** - p≤0.0001. Sipo ameliorates EAE disease course and spinal cord pathology by neuroprotection A higher dosage of 1 µg sipo was implemented in EAE to allow increasing concentrations to reach the CNS to enable the exertion of direct effects on CNS resident cells like microglia ( Figure 3a ). Here, sipo reduced the EAE incidence ( Figure 3b ) and ameliorated overall EAE severity by preventing weight loss, ameliorating EAE scores, and reducing the peak of disease ( Figure 3c-e ). MG repo led to cerebrospinal ataxia three days after the last DT injection, remaining unaffected by sipo ( Figure 3f ). The spinal cord pathology was assessed initially by combining H&E with LFB staining (Figure S4a-c). The area of infiltrating leukocytes (H&E) was reduced significantly by sipo, and MG repo reflected by CD3 + T cells and GFAP intensity-based astrogliosis (Figure S4d, Figure 3g,h ). Flow cytometry of lymphocytes and macrophages confirmed the sipo-induced reduction in the spinal cord by the prevented egression from lymph nodes and spleen (Figure S5). There was still a significant influence of sipo treatment on lymphocyte and macrophage subsets evident despite the 35 day long preventive treatment period with a low dose of sipo and the analysis during the chronic phase of EAE. Download figure Open in new tab Figure 3: EAE and spinal cord pathophysiology alterations by siponimod and microglia repopulation. (a) experimental setting. Experimental autoimmune encephalomyelitis (EAE) with a preventive siponimod (sipo) treatment paradigm presenting incidence (b), weight loss (c) and EAE scores (d,e). Conditional microglia depletion is indicated with red lines followed by microglia repopulation (MG repo ), leading to an atypical cerebrospinal ataxia (f) (n=8). In the (immuno-) histological stainings, the EAE pathophysiology was reflected by infiltration of CD3 + T cells (g), and astrogliosis (GFAP, h). Imaris-assisted analysis of spinal cord infiltrates (i,j) was used to determine axonal and myelin properties and microglia/macrophage phagocytic properties with axonal count (k), myelin area (l), myelin-axonal contact (m), myelin ellipsoid axis (n), phagocytes (o), Iba-1 axon coloc surface (p), and Iba1 myelin coloc surface (q). Dpi – days post immunization (EAE induction), TAM – tamoxifen administration for diphtheria toxin receptor induction, NFH – neurofilament heavy (axon marker), MBP – myelin basic protein. Data are shown as mean ±SEM. Clinical data were assessed by the Kruskal-Wallis test with Dunn’s multiple comparisons tests. The cerebrospinal ataxia differences per day were analyzed by 2-way ANOVA and Tukey’s multiple comparisons test. Spinal cord analyses were tested with 2-way ANOVA with Šídák’s multiple comparisons test with mean, SEM and n per animal. Scale bars are 50 µm. P-values are depicted as * - p≤0.05, ** - p≤0.01, *** - p≤0.001, **** - p≤0.0001. To better understand spinal cord pathology, we utilized three-dimensional reconstruction techniques, following staining for axons (NFH), myelin (MBP), and phagocytes (Iba-1) ( Figure 3i,j ). Consistent with the ameliorated EAE course, there was greater axon density with sipo treatment compared to vehicle, suggestive of neuroprotection ( Figure 3k ). MG repo reduced the number of axons compared to vehicle control, while sipo prevented this axonal reduction irrespective of MG repo . As the integrity of axons is supported by intact myelin sheaths, the results for the surface contact area between the axons and the myelin showed the same treatment effects ( Figure 3m ). The demyelinated area in the white matter was significantly reduced by sipo, irrespective of MG repo (Figure S4b) leading to further analysis of the myelin structure. The myelin area was altered by the MG repo vehicle and MG repo sipo in a similar way to the myelinated axon surface ( Figure 3l ) with a smaller myelin diameter in the vehicle and the MG repo sipo group compared to sipo and MG repo groups ( Figure 3n ). Iba-1 + cell counts were evaluated within the infiltrates, which represents both infiltrating macrophages and microglia reacting to tissue damage. The highest number of Iba-1 + cells was observed in areas of inflammation of vehicle-treated animals, which was significantly higher than both in MG repo and sipo ( Figure 3o ). We found fewer Iba-1 + cells in MG repo sipo compared to MG repo vehicle. By calculating the colocalization surface of Iba-1 + reconstructed cells with either axon or myelin structures, it was possible to allocate myelin and axonal structures within the Iba-1 + cells indicating phagocytes. Sipo reduced the number of phagocytic structures within Iba-1 cells ( Figure 3p,q ). Both MG repo and sipo reduced Iba-1-axon colocalization, suggesting that both reduce phagocytosis of axons. In MG repo sipo, there were more Iba-1-axon colocalization objects compared to sipo. The Iba-1-myelin colocalization surface counts were similarly decreased after MG repo and sipo. In summary, sipo treatment reduced spinal cord inflammation, astrogliosis and demyelination in EAE, preserved axonal integrity and myelination during MG repo , and decreased phagocytic activity of Iba-1 + cells in infiltrates, highlighting its neuroprotective effects. Sipo modulates microglial state during microglia repopulation but not infiltrating macrophages 35 days after immunization and 5 days after microglia depletion, mice were sacrificed, and brain microglia characterized by flow cytometry to better understand the effects on microglial state ( Figure 4a ). In MG repo mice, microglia were starting to repopulate but had not completely repopulated yet ( Figure 4b ). Repopulating microglia were characterized by lower expression of homeostasis markers CX3CR1 and P2RY12 and upregulation of inflammation-associated markers CD86 and I-A/I-B ( Figure 4b,c,f ). Sipo reduced the frequency of CD86 + MHC-II + microglia during repopulation ( Figure 4b ) and, additionally, elicited upregulation of CX3CR1 + , CD163 + , CD206 + and CD68 + microglia frequencies ( Figure 4c ). Download figure Open in new tab Figure 4: Microglia and macrophages are altered by microglia repopulation under siponimod treatment in chronic EAE. (a) experimental design of experimental autoimmune encephalomyelitis (EAE) with siponimod (sipo) treatment and timing of microglia depletion by diphtheria toxin injection and repopulation (MG repo ) and analysis of central nervous system tissues. Flow cytometry and qPCR analyses of brain (b-h) of microglia (b,c) and macrophages (d,e). Changes in microglia populations in the brain are visualized by unsupervised clustering of the living cells with the samples per group in one concatenated file based on the gating presented in the gating strategy (f). qPCR of brain immune cells isolated by density gradient centrifugation (g,h). Data are shown as mean ±SEM. Kruskal-Wallis tests with Dunn’s multiple comparisons tests. (a) was in part created with Biorender. P-values are depicted as * - p≤0.05, ** - p≤0.01, *** - p≤0.001, **** - p≤0.0001. Furthermore, the role of infiltrating macrophages in the context of treatment with sipo was analyzed in detail. Macrophages infiltrated the brain to fill the gap left by depleted microglia ( Figure 4j ). Furthermore, the retention of macrophages in secondary lymphoid organs by sipo did not appear to be a significant phenomenon, as no substantial differences were observed between MG repo vehicle and MG repo sipo ( Figure 4d ). The infiltrating macrophages in MG repo were proinflammatory despite sipo treatment with no significant changes in the expression of markers CX3CR1 + , CD206, CD163, and CD68 ( Figure 4d,e ). Phagocytes expressing CD206 were lower in the MG repo groups, especially under sipo treatment ( Figure 4e ). These macrophage-specific analyses do not suggest clear evidence for macrophage modulation in the brain by sipo. In the brain, lymphocytes and macrophages were elevated after microglia depletion and filled the gap left behind by absent microglia (Figure S5j,k,l). Of note, the infiltration of antigen presenting lymphocytes was significantly reduced following sipo treatment (Figure S5i). We aimed to confirm the brain immune cell alterations by gene expression analysis. We documented the same proinflammatory trend induced by MG repo by elevated expression of Il1b and Tnf , and reduced expression of Mrc1 ( Figure 4g ). We analyzed genes associated with the regulated phenotype and mechanisms important for brain regeneration which could not be included in the flow cytometry analysis. Arg1 was significantly upregulated in the MG repo sipo brain compared to sipo ( Figure 4g ). Trem2 expression was reduced after MG repo , Ym1 expression was elevated in all groups compared to vehicle, but only the MG repo group showed statistical significance, and Tlr4 was not differentially expressed ( Figure 4h ). The microglia depletion or repopulation status in the spinal cord could not be verified by flow cytometry, despite having clear evidence when conducting flow cytometry analysis of the brain with the same cell isolation methods ( Figure 4i ). Although there were no differences in microglial and macrophage frequencies, the animals with sipo treatment or MG repo lacked microglia with high cytokine levels (Figure S5m,n). In conclusion, MG repo during EAE resulted in inflammation-associated microglia with reduced homeostasis markers and elevated CD86 and MHC-II, whereas sipo treatment attenuated microglial activation, promoted regeneration-associated markers (CD163, CD206, CD68), and showed limited effects on infiltrating macrophages, highlighting its potential to modulate CNS immune responses. Sipo and microglia repopulation are neuroprotective and reduce inflammation in the visual system The retina and optic nerve of the mice were used to investigate if the MG repo with or without sipo treatment had different effects compared to the brain and spinal cord compared to naïve mice. The retina was stained for retinal ganglion cells (RBPMS), astrocytes (GFAP) and microglia/macrophages (Iba-1, Figure 5a ). The retinal ganglion cell loss in EAE vehicle was prevented by sipo and MG repo ( Figure 5b ). When evaluating the gene expression for retinal ganglion cells, the same tendencies as in cell counts were present. The Pou4f1 gene expression was significantly lower in the EAE vehicle sample compared to naïve vehicle-treated animals, and the MG repo and under sipo treatment lacking significance compared to vehicle ( Figure 5d ). Astrogliosis was evaluated by measurement of the GFAP + area in the retina. The proportion of GFAP + area was significantly elevated in MG repo with additive elevation by MG repo sipo ( Figure 5c ). The cytokines Tnf and Tgfb1 were increased in MG repo with no influence of sipo treatment ( Figure 5d ). The number of Iba-1 + cells and immunoreactivity in the retina was higher when treating with sipo during EAE ( Figure 5c,e ). In MG repo sipo, the counted cells were significantly lower compared to sipo. During microglia repopulation, the microglia genes Tmem119 , Cx3cr1 , the phagocyte gene Cd68, and the destructive glia gene Nos2 were elevated by MG repo ( Figure 5d,f ). In the mRNA analysis of the optic nerve, the inflammation-associated genes Tnf and Il6 were expressed at higher levels in EAE compared to naïve ( Figure 5g ). The expression of these genes was lower after MG repo under sipo treatment in the EAE compared to vehicle. The anti-inflammatory associated cytokine gene Il10 was expressed higher during microglia repopulation with sipo ( Figure 5g ). The Iba1 expression was higher after MG repo in the optic nerve ( Figure 5h ). The genes associated with inflammation or disease in microglia Cd86 and Arg1 were expressed higher in the untreated EAE, with a reduction lacking significance after MG repo with sipo ( Figure 5h ). In summary, sipo treatment in MG repo modulated microglia/macrophage activation, and inflammatory gene expression in the retina and optic nerve during EAE, highlighting compartment-specific neuroprotective and immunomodulatory effects. Download figure Open in new tab Figure 5: Alterations in the visual system during chronic EAE by siponimod treatment and microglia repopulation. a) example images with RBPMS (retinal ganglion cells) in green, GFAP (astrocytes) in red and DAPI in blue. Immunostaining (b,c,e) and qPCR (d,f,g,h) analysis of retina and optic nerve. For qPCR, the retina or optic nerves of two animals were pooled for enough starting material for mRNA isolation, leading to n=3-4 samples per group which were analyzed using Kruskal-Wallis with Dunn’s multiple comparisons tests. The replicate images of every animal per immunohistochemical staining were compared with mean SEM and n in a 2-way ANOVA with Šídák’s multiple comparisons test. GCL – ganglion cell layer, IPL – inner plexiform layer, INL – inner nuclear layer, OPL – outer plexiform layer, MG repo – microglia depletion with diphtheria toxin administration in cx3cr1-iDTR mice inducing microglia repopulation, sipo – siponimod. Data are shown as mean ±SEM. Scale bars are 50 µm. P-values are depicted as * - p≤0.05, ** - p≤0.01, *** - p≤0.001, **** - p≤0.0001. MG repo and sipo alter myelin and microglia-associated proteins in the spinal cord Global proteomic characterization of spinal cords of EAE mice treated with sipo or vehicle and MG repo resulted in the quantification of over 5000 proteins, with a higher replicate variability in the sipo groups than the vehicle groups (Table S1). In general, high biological variances within the experimental groups could be observed limiting statistical analysis as well as interpretation of resulting data. Thus, we need to emphasize that proteomic-derived results should be interpreted carefully. To determine proteomics alterations, changes in protein abundance were determined by relative quantification and subsequent statistical analysis to identify significantly differentially expressed proteins (unpaired Welch’s t-test p-value < 0.05, see Table S1, Figure 6b ). MG repo resulted in the strongest effect on the proteome, representing the inflammatory response by different cells induced by the microglia depletion and the repopulation process shown by the atypical ataxia scoring, cytokine elevations in the CNS by qPCR and flow cytometry. In detail, the comparison of vehicle and MG repo vehicle identified 472 differentially expressed proteins. Treatment with sipo led to a drastic reduction in altered proteins, between sipo and MG repo sipo. This indicates that sipo ameliorated the EAE regardless of whether reactive microglia were present. It was likewise observed that sipo treatment had a greater effect without MG repo , whereby 283 proteins were found to be differentially regulated between vehicle and sipo and solely 69 proteins between the MG repo groups, potentially revealing that sipo effectively decreases the high inflammatory status in EAE mice. Lastly quantitative comparison between vehicle and MG repo resulted in the identification of 201 differentially expressed proteins. Download figure Open in new tab Figure 6: Proteomic changes in the spinal cord induced by microglia depletion and repopulation and siponimod treatment. Protein quantification by mass spectrometry (n=5) of the lumbar spinal cord tissue. The differentially quantified proteins in group comparisons are shown with matching groups between comparisons in (a). Gene ontology (GO) Term analysis on the basis of biological processes (b). The top regulated terms (p-value <0.05 and lowest Benjamini Hochberg, are displayed sorted after fold enrichment values, whereby enriched terms for proteins in vehicle mice compared to MG repo vehicle mice are shown in blue with the higher abundances indicated above. Enriched terms for proteins in the comparison of vehicle mice to sipo mice are shown in red. The green bars show the enriched terms for proteins being of higher abundance in MG repo vehicle mice compared to MG repo sipo mice. In the yellow bar, the GO Term enriched in sipo mice compared to MG repo sipo mice is presented and the brown bars show the terms enriched in vehicle mice compared to MG repo sipo mice. The differentially expressed proteins in group comparisons are presented in (c) in volcano plots based on the Welch’s T test. A heatmap was created in (d) to present microglia-related protein expression differences between the experimental groups. MG repo – microglia depletion with diphtheria toxin administration in cx3cr1-iDTR mice and repopulation, sipo – siponimod. To further evaluate the model of microglial repopulation described in this study, proteomic differences between vehicle and MG repo vehicle were analyzed. Of 474 differentially expressed proteins, 177 proteins were higher abundant in vehicle and 295 proteins in MG repo mice. Gene ontology (GO) term analysis on the basis of BP revealed that proteins upregulated in vehicle were associated with energy metabolism (TCA cycle, mitochondrial functions, synapse organization and transmission, Table S1, Figure 6b ). In contrast, MG repo enriched terms were involved in structural cytoskeleton organization, cell-matrix adhesion and neuronal projections development. This may be due to microglia repopulation, causing structural spinal cord changes. To further study the effect of MG repo , the Top30 proteins increased in MG repo vehicle vs vehicle were manually curated. Of those, various proteins were involved in immunomodulatory functions or were directly linked to oligodendrocytes and microglia (Table S1). In concordance, conducting GO term enrichment studies of all proteins found to be increased in MG repo vehicle, a 20-fold-enrichment of the GO term “Schwann cell development” and a 6.6-fold enrichment of the term “myelination” could be identified, supporting the increased myelin diameter in histology (Table S1, Figure 3r ). Additionally, several terms associated with cell structure and maintenance were identified to be enriched as well. Since the direct comparison of MG repo sipo and sipo resulted in a low number of differential proteins, leading to the assumption that sipo itself attenuates the inflammation, we cross-compared proteins being differentially expressed between vehicle vs MG repo vehicle and the sipo groups (Table S1). Interestingly, only 8 proteins were found to be differentially expressed in both comparisons ( Figure 6a ). In a next step, it was aimed to reveal the role of sipo and MG repo sipo (Table S1). Within the direct comparison, it was apparent that proteins displaying the highest abundance in MG repo sipo were mainly associated with chromatin remodeling, mRNA transcription and splicing, but also proteins involved in myelination and immune cells. were identified. In contrast, proteins found to be of highest expression in sipo mice were associated with the actin cytoskeleton, as well as protein transport. Interestingly, some of these proteins play a crucial role in cell protection against oxidative stress. To determine the influence of sipo on inflammation, the next step was to analyze proteins that differed significantly between vehicle and sipo. Of the total 283, 111 were of higher abundance in the vehicle group. GO Term enrichment analysis of said proteins resulted in the identification of 46 enriched terms, mainly associated with transcription and translation, as well as antigen processing, autophagic processes and complement activation ( Figure 6c ). Confirmatively, among the Top10 proteins being of highest abundance in the vehicle group, Immunity-related GTPase family M protein 3 (FC 5.6), CD68 antigen (FC 4.6), Immunity-related GTPase family M protein 1 (FC 4.4), and Interferon-inducible GTPase 1 (FC 3.4) were identified. Conversely, proteins increased in sipo mice were connected to cell structure and maintenance, such as membrane and ECM organization and tissue development, potentially hinting towards protective processes induced by sipo treatment (Table S1, Figure 6b ). In particular, GO terms associated with neuronal cell types were found to be enriched, among them astrocyte development, synapse assembly, synaptic vesicle transport, axon guidance, dendrite development and Schwann cell development. The comparison between MG repo vehicle and MG repo sipo led to a comparable low number of differential proteins ( Figure 6a ), concluding that the depletion of microglia overcomes the effects of sipo treatment. Interestingly among the proteins displaying the highest fold changes in the MG repo vehicle group Ubiquitin-like protein NEDD8 and Metallothionein-1 and 3, being induced by inflammatory and stress stimuli in microglia were found to be enriched (Table S1)[ 20 ]. In contrast, MG repo sipo mice exhibited an increased abundance of several proteasomal subunits. Lastly, differences between vehicle and MG repo sipo mice were assessed. GO Term enrichment analysis of proteins being of higher abundance in vehicle mice resulted in the terms associated with autophagy, the autophagosome, and the immune response ( Figure 6b ) confirmed by proteins of highest abundance were associated with these processes (Table S1). MG repo sipo mice instead displayed an increased abundance of proteins associated with the cytoskeleton, protein folding, and transport. To our surprise, Periostin, a protein thought to be specifically upregulated in EAE, was found to be of higher abundance in MG repo sipo mice[ 6 ]. However, Ptgds was found to be upregulated in MG repo sipo mice as well. The quantified proteins were searched for microglia-related proteins and sorted in a heatmap presenting the differential expressions. The clusters one and two included proteins enriched in the MG repo vehicle group. The clusters comprised proteins associated with homeostatic microglia (Tmem119, Mrc2), cell adhesion upregulated in MS models (CD9), phagolysosome (Lyz2, Lamp1, Lamp2, CD206), lipid droplet accumulation (Plin3, Fabp5, Cat), iron storage (Fth1), oligodendrogenesis (Chil3)[ 31 , 10 , 41 , 23 , 9 , 21 , 24 , 17 , 52 ]. The cluster 3 included inflammation or EAE-associated microglia proteins elevated in the vehicle groups and low differential expressions in the sipo-treated groups (CD74, Hexb, CD68, B2m, Cybb, ApoE, Ctss, C1qa, Lgals3) [ 41 , 24 , 40 , 51 , 10 ]. Cluster four comprises markers for microglia (CD81), immunosuppression (CD200), antigen presentation (CD74), lipid droplet accumulation (Acly) and responsive microglia proteins associated with diseases (Ctsd, Hspd1, Nos1)[ 30 , 49 , 10 , 29 , 31 , 41 ]. RNA sequencing of the lumbar spinal cord supports structural reorganization of immune cell populations Given the results obtained with the global proteomics analysis, we were interested in the gene expression changes that induced the observed alterations in myelin and microglia-associated proteins. First, we assessed the overall variance in gene expression profiles across the six different samples by performing PCA. The first two principal components (PC1 and PC2) accounted for 78% and 11% of the total variance, respectively. Samples clustered according to the condition, naïve, EAE, or EAE MG repo , indicating distinct transcriptional profiles between groups and that the condition is giving the most variance between their gene expression profiles ( Figure 7a ). Notably, the treatment, sipo, was responsible for inducing changes in gene expression, specifically for PC2. The analysis also showed how MG repo resulted in a gene expression profile more comparable to the naïve condition ( Figure 7a ). Download figure Open in new tab Figure 7: RNA sequencing of lumbar spinal cord tissue reveals effects of siponimod treatment and microglia depletion. RNA sequencing of pooled samples per group of the lumbar spinal cord tissue. DEGs in group comparisons are shown as principal component analysis (a) and heatmap (b). Gene ontology (GO) Term analysis on the basis of biological processes (c). A heatmap was created in (d) to present microglia-related DEGs between the experimental groups with EAE. PCA was conducted with normalized and transformed gene-level count data by variance-stabilizing transformation with the DESeq2 package and plotted with ggplot2. DEGs were identified in Rstudio by a p-value threshold of <0.05 with a fold change of 2 and normalized using the row-wise z-score adjustment. The heatmaps were created with the pheatmaps package. GO term analysis of BP was conducted with the clusterProfiler package with a threshold based on an adjusted p-value of <0.05 (Benjamini-Hochberg correction) and q-value <0.2, and visualized with the dotplot package. MG repo – microglia depletion with diphtheria toxin administration in cx3cr1-iDTR mice and repopulation, sipo – siponimod, naïve – no EAE induction. DEGs were identified by calculating the log2 fold change (FC) across all samples. A total of 3895 genes were differentially expressed (Table S2). To visualize the DEGs, we constructed a heatmap to more easily observe differences in gene expression between all samples ( Figure 7b ). Notably, EAE sipo showed an overall upregulation, while the microglia repopulation in the absence of sipo resulted in an extensive downregulation of the DEGs observed ( Figure 7b ). Some interesting patterns were observed in the MG repo , presenting with clusters with similar expression regulation as the naïve groups ( Figure 7b ). To identify genes differentially expressed between our samples, the NOISeq package was used due to its ability to simulate technical replicates to estimate the differential expression probability [ 54 , 53 ]. We further analyzed the gene lists of four comparisons of the EAE samples: MG repo vehicle vs vehicle, sipo vs vehicle, MG repo sipo vs vehicle, and MG repo sipo vs MG repo vehicle. NOISeq identified 456 DEGs, which were filtered to a probability threshold of q ≥ 0.95 and a log2 fold change (M value) of ≥ 2. There were 134 DEGs that passed the set thresholds and were utilized in downstream analysis. We performed GO enrichment analysis to identify BPs that are overrepresented based on the DEGs that pass the set thresholds. When compared to vehicle, the microglia repopulation (MG repo vehicle) exhibited overrepresentation of BPs that affected the general cellular homeostasis within the spinal cord. We observed shifts across various immune cell populations, including B-cells and T-cells ( Figure 7c ), suggesting a strong humoral immune response. There was an overall upregulation of genes associated with chemokine signaling, including Ccl8, Cxcl10, Ccr7, Ccl22, and Ccr12 (Table S2). Additionally, upregulation of Ccr7, Ccr2, Ccl2, and Trgc1 increased T cell proliferation and migration ( Figure 7c , Table S2). Microglia depletion also resulted in differential expression of genes related to inflammation and granulocyte activation ( Figure 7c and Table S2). KEGG pathway analysis further supported the deregulated BPs, with cytokine-cytokine receptor interaction (p.adjst 0.0033) and chemokine signaling (p.adjst 0.0055) pathways were significantly enriched (Figure S9). Next, we looked at the gene expression changes in EAE treated with sipo when compared to vehicle. This resulted in 136 DEGs that passed the previously set thresholds (Table S2). GO analysis of the gene list revealed that sipo resulted in upregulation of genes related to chemokine signaling and immune cell migration ( Figure 7c ). The multiple GO hits observed in the dot plot have a very similar list of DEGs, suggesting that there are affected downstream signaling cascades that could result in multiple BPs being overrepresented ( Figure 7c ). More specifically, we observed significant enrichment of regulation of dendritic cell chemotaxis (p. adjst=9.96E-09), positive regulation of leukocyte (p.adjust=1.01E-06) and mononuclear cell migration (p.adjust =1.22E-06) and pseudopodium assembly (p-adjst = 1.25E-06). The KEGG pathway analysis strengthens the results observed with five significantly enriched pathways related to cytokines and chemokine signaling, the complement and coagulation cascade (p.adjst=0.036789949), the hematopoietic cell lineage (p.adjst=0.036789949), and NFkappa-Beta signaling (p. adjst=0.044124239) (Figure S9). Lastly, we compared the microglia repopulation in the presence of sipo (MG repo sipo) to the vehicle control. NOISeq identified 134 significant DEGs (Table S2). GO analysis revealed changes in gene expression associated with similar BPs as observed in the previous comparisons, an overall upregulation of genes related to humoral immune response and immune cell migration ( Figure 7c ). Interestingly, we also observed upregulation of genes related to complement activation, kynurenine metabolic process (Kmo and Aldh8a1), and retinoic acid and diterpenoid biosynthetic processes (Rdh9) ( Figure 7c ). These data suggest that when the microglia are repopulated in the presence of sipo there are significant metabolic shifts that are relevant to neuroinflammation and neuroprotection [ 48 ]. With the set threshold to establish significance, the KEGG analysis resulted in no significant pathways. The comparison of MG repo sipo with MG repo vehicle resulted in 175 DEGs that were related mainly to cytoskeleton structure and regulation, and energy and metabolism regulation (Figure S9c,d). To supplement the results of the microglia-specific protein expression in the spinal cord, a heatmap of microglia-related genes was analyzed. Comparable to the unspecific clustering, most microglia-related genes were higher expressed in the sipo group in the clusters labelled 1-3, with an exception for cluster 4, exhibiting mostly neutral to lower expressed genes ( Figure 7d ). The microglia depletion and repopulation induced mostly a lower expression of the microglia-related genes, irrespective of sipo treatment. That this effect was more profound in the MG repo sipo group might be induced by reduced infiltration of peripheral immune cells, as shown in flow cytometry of lymphocytes and macrophages of the spinal cord. Cluster 4 seems to represent a sipo-induced lowered gene expression level. Cluster 3 exhibits the largest difference induced by the microglia depletion, comprising the microglia-specific genes P2ry12 , Tmem119 and Sall1 . Discussion Despite great advances over the past years to improve treatment outcomes for pwMS, the treatment of underlying progression is still unsatisfactory[ 28 ]. In this study, we focused on the modulation of microglia in models of pMS with the aim of establishing a neuroprotective environment following depletion of disease-associated microglia. As iron rim lesions and iron-loaded microglia are abundant in slowly expanding lesions, especially common for pMS[ 8 ], we performed iron toxicity studies[ 60 ]. 1 µM sipo, resembling the amount of drug administered in humans best[ 26 , 1 ], was effective in both human and murine cell culture systems. The dose-dependent reduced incidence of cell death is indicative of the protective properties of sipo against iron toxicity, exerting a beneficial effect on neurons and microglia. This has the potential to prevent neurodegeneration in iron rim lesions in patients with multiple sclerosis (PwMS) treated with sipo. The increased iron toxicity in the presence of microglia in vitro indicated a destructive microglia polarization by iron[ 27 ]. Together with reduced levels of highly reactive microglia polarization by cytokines and splenocytes, we present a broad beneficial effect of sipo on CNS cells in the setting of neuroinflammation. In EAE, we investigated treatment paradigms that should resemble the currently emphasized early treatment start to maximize the potential protective effect of the drug[ 39 ]. The approach was to induce a low dose to still induce disease, rather than prevent it completely, but to potentially detect additive effects of long-term administration and microglia depletion, and how that affects immune cells. Despite a disease amelioration with 0.45 µg sipo daily, as already shown in C57BL/6[ 26 ], there was only a minor effect on the peripheral and brain immune cells detectable. It is plausible that the initial prevention of lymphocyte egress from the lymphoid organs that was presumably present at treatment initiation, reducing EAE initiation, was compensated over the 27 days of treatment[ 19 ]. Another possibility is that the prevention of lymphocyte egress was initially not significant, suggesting that sipo reduced the development of EAE by other mechanisms, such as preventing microglia and astrocytes from becoming destructive and preventing cell-cell interactions, as shown in the cell culture experiments. We intended to reset the microglia population during chronification of EAE to simulate ablation of disease-associated microglia[ 22 ]. The hypothesis under consideration posits that the presence of a reduced number of peripheral immune cells within the CNS during this specific phase would facilitate the repopulation of microglia that are not primed by leukocytes. Together with sipo treatment, the inflammation-associated induction of repopulating microglia should further be reduced by preventing CNS infiltration and direct modulatory effects of sipo on microglia, as shown in cell culture[ 11 , 18 ]. Nevertheless, the low dose of 0.45 µg sipo did not alter the microglia population. Based on these results, we decided to implement the higher dosage of 1 µg sipo to have a sufficient dose reaching the CNS[ 1 ], which effectively ameliorated EAE. In addition to the expected reduction in spinal cord pathology with sipo treatment, pathology was also reduced by microglial repopulation during the chronic phase of EAE[ 22 ]. Microglial depletion and repopulation elevated both lymphocyte and glial cell populations in the CNS, but sipo treatment improved the abundance and activation of glial cells but not lymphocytes, in addition to mild changes in spleen and lymph node lymphocytes[ 1 ]. The reduced frequency of EAE-associated cells was most pronounced in sipo treatment with microglial repopulation. Microglia are abundant in EAE spinal cord lesions, so their depletion is thought to have resulted in the ablation of disease-promoting microglia within the lesions. This reduced the overall size of the infiltration by repressing cell numbers, reduced recruitment of T cells to the spinal cord due to the absence of microglia[ 11 ], and limited interaction with astrocytes and therefore lowered activation[ 17 ]. Astrogliosis and microgliosis were significantly reduced by sipo treatment, supporting the hypothesis that the concentration of sipo reaching the spinal cord may be high enough to have a direct regulatory effect on the cells[ 7 , 33 ]. Microglia were not completely repopulated at the time point of analysis, particularly in combination with sipo treatment in the spinal cord, brain, and optic nerve[ 4 ]. The infiltration of reactive peripheral immune cells into the microglia-ablated CNS is a process in consequence of the replenishment of the redundant microglia niche, as well as a reaction to the accumulation of cell debris. Sipo presumably reduced this infiltration, explaining the lowered microglia/macrophage-related genes and proteins detected upon MG repo . The microglia repopulation under sipo treatment led to altered protein expression, reducing antigen-presenting properties and inflammation-associated proteins while enhancing protein expression associated with regenerative properties[ 11 ]. Myelinoprotective properties of sipo have also been described in a model of toxic demyelination using Cuprizone[ 35 ]. Sipo treatment in vitro and in vivo showed neuroprotective properties against iron toxicity and prevention of neuronal cell loss in the spinal cord and optic nerve, possibly through both direct effects on neurons and by indirect effects by providing myelin integrity and modulating reactive cell types such as microglia. In particular, microglial resetting in the chronic phase of EAE and subsequent microglial repopulation under sipo treatment showed beneficial effects on spinal cord pathology and microglial function. Our study is subject to several limitations that must be addressed. First, the use of EAE as a model of progression is not without its own set of limitations[ 36 ]. To overcome this limitation, we implemented MG depletion to allow robust repopulation of phagocytes under divergent conditions. Further research should focus on single-cell analysis of microglia and other cell types to verify the results obtained from pooled samples. However, we believe that a major strength of this work lies in the conclusive analysis of different aspects of the model, focusing on the visual system, the brain, and the spinal cord. This study underlines the possible beneficial effects of microglia modulation in chronic neuroinflammation with potential for multiple sclerosis progression and neurodegenerative disorders. Declarations Ethics approval All animal experiments were approved by the animal care committee in Düsseldorf Nordrhein-Westfalen, Germany (LANUV, no. 82-02.04.2019-A425). Primary human cells were isolated in accordance with the ethics approval of the University of Calgary ethics committee[ 13 ]. Author contributions NH designed, performed and analyzed experiments, designed figures and wrote the first draft of the manuscript. SaF, LR, BE, A-CG, HHH, S-MO, KK, RH, AK, HM, SR, CPS, GT, MVM performed experiments, acquired and analyzed data and critically revised the manuscript. SR, ME, SCJ, OG, KFW, KMA, IS, VWY, RG analyzed data and critically revised the manuscript. SF designed and supervised the study, analyzed data, drafted figures and wrote the manuscript. RG and SF acquired funding. All authors read and approved the final version of the manuscript. Competing interests The authors declare the following potential conflicts of interest, all not related to the content of this manuscript: NH, SaF, LR, ACG, HHH, SO, KK, RH, AK, HM, CPS, SR, KFW, SCJ, JRP, BE, MVM, SR, ME,VB, GT, OG, KMA, IS have nothing to disclose. VWY is funded by research grants from MS Canada, the Canadian Institutes of Health Research, USA Department of Defense Multiple Sclerosis Research Program, Genentech and Novartis. He has received speaker honoraria from Biogen, EMD Serono, Novartis, Roche, Sanofi-Genzyme and Teva Canada. He is the recipient of unrestricted educational grants from Biogen, EMD Serono, Novartis, Roche, Sanofi-Genzyme and Teva Canada to support educational activities of the Alberta MS Network, which he directs. RG serves on scientific advisory boards for Teva Pharmaceutical Industries Ltd., Biogen, Bayer Schering Pharma, and Novartis; has received speaker honoraria from Biogen, Teva Pharmaceutical Industries Ltd., Bayer Schering Pharma, Merck and Novartis; serves as editor for Therapeutic Advances in Neurological Diseases and on the editorial boards of Experimental Neurology and the Journal of Neuroimmunology; and receives research support from Teva Pharmaceutical Industries Ltd., Biogen Idec, Bayer Schering Pharma, Genzyme, Merck Serono, and Novartis. SF has received speaker’s and/or scientific board honoraria from Academy2, Biogen, BMS, Celgene, Janssen, Merck, Novartis, Roche, Sanofi and grant support from Ruhr-University Bochum, DMSG, DFG, Stiftung für therapeutische Forschung, Lead Discovery Center GmbH, Neuraxpharm and Novartis. Acknowledgements The authors thank Xiomara Pedreiturria for technical support and organization in the laboratory in Bochum and Claudia Silva, Dorsa Moezzi, Cenxiao Li, and Marlene Thorsen Mørch for technical support in Calgary. We thank the Hotchkiss Brain Institute Advanced Microscopy Platform Facility at the University of Calgary for their help. RNA-Seq data presented herein were obtained at the Genomics Division of the Iowa Institute of Human Genetics (RRID: SCR_023422), which is supported, in part, by the University of Iowa Carver College of Medicine. KFW is supported by the German Research Foundation (RTG 2862, project number 492434978 and Germany’s Excellence Strategy - EXC 2033 - 390677874 – RESOLV). This research was funded by funds of the Medical faculty Ruhr-University Bochum to RG and SF, and in part by Novartis to RG and SF. Funder Information Declared Medical faculty Ruhr-University Bochum , N/A Novartis (Germany), https://ror.org/0013shd50 , N/A Abbreviations BP biological processes CNS central nervous system DEGs differentially expressed genes DT diphtheria toxin DTR diphtheria toxin receptor EAE experimental autoimmune encephalomyelitis FC fold change GO Term gene ontology term of biological processes H&E hematoxylin and eosin ICC immunocytochemistry iLPS interferon-gamma and lipopolysaccharide LFB luxol fast blue MGrepo microglia depletion and repopulation MS Multiple sclerosis pMS progressive forms of MS PPMS Primary progressive multiple sclerosis PCA principal component analysis PI propidium iodide PMA/Iono phorbol 12-myristate 13-acetate/ionomycin PNS peripheral nervous system pwMS people living with MS qRT.PCR quantitative reverse transcriptase polymerase chain reaction RRMS Relapsing-remitting multiple sclerosis SEM Standard error of the mean Sipo siponimod SPMS Secondary progressive multiple sclerosis S1PR sphingosine 1 phosphate receptor TAM tamoxifen Tc cells Cytotoxic T cells Tcons conventional T cells Th cells T helper cells Treg cells Regulatory T cells References 1. ↵ Bigaud M , Rudolph B , Briard E et al. ( 2021 ) Siponimod (BAF312) penetrates, distributes, and acts in the central nervous system: Preclinical insights . Multiple Sclerosis Journal— Experimental, Translational and Clinical : 1 – 13 . doi: 10.1177/20552173211049168 OpenUrl CrossRef 2. ↵ Bioconductor biomaRt package . https://bioconductor.org/packages/release/bioc/html/biomaRt.html 3. ↵ Bioconductor clusterProfiler package . https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html 4. ↵ Bruttger J , Karram K , Wörtge S et al. ( 2015 ) Genetic Cell Ablation Reveals Clusters of Local Self-Renewing Microglia in the Mammalian Central Nervous System . Immunity 43 : 92 – 106 . doi: 10.1016/j.immuni.2015.06.012 OpenUrl CrossRef PubMed 5. ↵ Carlson M ( 2023 ) org.Mm.eg.db: Genome wide annotation for Mouse. Bioconductor 6. ↵ Choi Y , Kim J , Ahn M et al. ( 2020 ) Upregulation of periostin in MOG-induced experimental autoimmune encephalomyelitis in mice . Neurosci Lett 715 : 134558 . doi: 10.1016/j.neulet.2019.134558 OpenUrl CrossRef PubMed 7. ↵ Chun J , Giovannoni G , Hunter SF ( 2021 ) Sphingosine 1-phosphate Receptor Modulator Therapy for Multiple Sclerosis: Differential Downstream Receptor Signalling and Clinical Profile Effects . Drugs 81 : 207 – 231 . doi: 10.1007/s40265-020-01431-8 OpenUrl CrossRef PubMed 8. ↵ Dal-Bianco A , Grabner G , Kronnerwetter C et al. ( 2017 ) Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging . Acta Neuropathol 133 : 25 – 42 . doi: 10.1007/s00401-016-1636-z OpenUrl CrossRef PubMed 9. ↵ DePaula-Silva AB , Gorbea C , Doty DJ et al. ( 2019 ) Differential transcriptional profiles identify microglial- and macrophage-specific gene markers expressed during virus-induced neuroinflammation . J Neuroinflammation 16 : 152 . doi: 10.1186/s12974-019-1545-x OpenUrl CrossRef PubMed 10. ↵ Distéfano-Gagné F , Bitarafan S , Lacroix S et al. ( 2023 ) Roles and regulation of microglia activity in multiple sclerosis: insights from animal models . Nat Rev Neurosci 24 : 397 – 415 . doi: 10.1038/s41583-023-00709-6 OpenUrl CrossRef 11. ↵ Dong Y , Yong VW ( 2019 ) When encephalitogenic T cells collaborate with microglia in multiple sclerosis . Nat Rev Neurol 15 : 704 – 717 . doi: 10.1038/s41582-019-0253-6 OpenUrl CrossRef PubMed 12. ↵ Dong Y , D’Mello C , Pinsky W et al. ( 2021 ) Oxidized phosphatidylcholines found in multiple sclerosis lesions mediate neurodegeneration and are neutralized by microglia . Nat Neurosci 24 : 489 – 503 . doi: 10.1038/s41593-021-00801-z OpenUrl CrossRef PubMed 13. ↵ Faissner S , Mishra M , Kaushik DK et al. ( 2017 ) Systematic screening of generic drugs for progressive multiple sclerosis identifies clomipramine as a promising therapeutic . Nat Commun 8 : 1990 . doi: 10.1038/s41467-017-02119-6 OpenUrl CrossRef PubMed 14. ↵ Faissner S , Plemel JR , Gold R et al. ( 2019 ) Progressive multiple sclerosis: from pathophysiology to therapeutic strategies . Nat Rev Drug Discov 18 : 905 – 922 . doi: 10.1038/s41573-019-0035-2 OpenUrl CrossRef PubMed 15. ↵ Faissner S , Heitmann N , Plaza-Sirvent C et al. ( 2022 ) Immune response in ofatumumab treated multiple sclerosis patients after SARS-CoV-2 vaccination . Frontiers in Immunology 13 : 980526 . doi: 10.3389/fimmu.2022.980526 OpenUrl CrossRef PubMed 16. ↵ Fox RJ , Bar-Or A , Traboulsee A et al. ( 2025 ) Tolebrutinib in Nonrelapsing Secondary Progressive Multiple Sclerosis . N Engl J Med . doi: 10.1056/NEJMoa2415988 OpenUrl CrossRef 17. ↵ Garton T , Gadani SP , Gill AJ et al. ( 2024 ) Neurodegeneration and demyelination in multiple sclerosis . Neuron 112 : 3231 – 3251 . doi: 10.1016/j.neuron.2024.05.025 OpenUrl CrossRef PubMed 18. ↵ Gentile A , Musella A , Bullitta S et al. ( 2016 ) Siponimod (BAF312) prevents synaptic neurodegeneration in experimental multiple sclerosis . J Neuroinflammation 13 : 207 . doi: 10.1186/s12974-016-0686-4 OpenUrl CrossRef PubMed 19. ↵ Gergely P , Nuesslein-Hildesheim B , Guerini D et al. ( 2012 ) The selective sphingosine 1-phosphate receptor modulator BAF312 redirects lymphocyte distribution and has species-specific effects on heart rate . Br J Pharmacol 167 : 1035 – 1047 . doi: 10.1111/j.1476-5381.2012.02061.x OpenUrl CrossRef PubMed 20. ↵ Giralt M , Penkowa M , Lago N et al. ( 2002 ) Metallothionein-1+2 protect the CNS after a focal brain injury . Exp Neurol 173 : 114 – 128 . doi: 10.1006/exnr.2001.7772 OpenUrl CrossRef PubMed Web of Science 21. ↵ Goldmann T , Zeller N , Raasch J et al. ( 2015 ) USP18 lack in microglia causes destructive interferonopathy of the mouse brain . EMBO J 34 : 1612 – 1629 . doi: 10.15252/embj.201490791 OpenUrl Abstract / FREE Full Text 22. ↵ Green KN , Crapser JD , Hohsfield LA ( 2020 ) To Kill a Microglia: A Case for CSF1R Inhibitors . Trends Immunol 41 : 771 – 784 . doi: 10.1016/j.it.2020.07.001 OpenUrl CrossRef PubMed 23. ↵ Gross PS , Durán-Laforet V , Ho LT et al. ( 2025 ) Senescent-like microglia limit remyelination through the senescence associated secretory phenotype . Nat Commun 16 : 2283 . doi: 10.1038/s41467-025-57632-w OpenUrl CrossRef PubMed 24. ↵ Guerrero BL , Sicotte NL ( 2020 ) Microglia in Multiple Sclerosis: Friend or Foe? Frontiers in Immunology 11 : 374 . doi: 10.3389/fimmu.2020.00374 OpenUrl CrossRef PubMed 25. ↵ Hammond BP , Panda SP , Kaushik DK et al. ( 2024 ) Microglia and Multiple Sclerosis . Adv Neurobiol 37 : 445 – 456 . doi: 10.1007/978-3-031-55529-9_25 OpenUrl CrossRef PubMed 26. ↵ Hendek HH , Blusch A , Heitmann N et al. ( 2024 ) Siponimod treatment response shows partial BDNF dependency in multiple sclerosis models . Sci Rep 14 : 17823 . doi: 10.1038/s41598-024-68715-x OpenUrl CrossRef PubMed 27. ↵ Hill KE , Zollinger LV , Watt HE et al. ( 2004 ) Inducible nitric oxide synthase in chronic active multiple sclerosis plaques: distribution, cellular expression and association with myelin damage . J Neuroimmunol 151 : 171 – 179 . doi: 10.1016/j.jneuroim.2004.02.005 OpenUrl CrossRef PubMed Web of Science 28. ↵ Jakimovski D , Bittner S , Zivadinov R et al. ( 2024 ) Multiple sclerosis . Lancet 403 : 183 – 202 . doi: 10.1016/S0140-6736(23)01473-3 OpenUrl CrossRef PubMed 29. ↵ Jurga AM , Paleczna M , Kuter KZ ( 2020 ) Overview of General and Discriminating Markers of Differential Microglia Phenotypes . Front Cell Neurosci 14 : 198 . doi: 10.3389/fncel.2020.00198 OpenUrl CrossRef PubMed 30. ↵ Kamma E , Lasisi W , Libner C et al. ( 2022 ) Central nervous system macrophages in progressive multiple sclerosis: relationship to neurodegeneration and therapeutics . J Neuroinflammation 19 : 45 . doi: 10.1186/s12974-022-02408-y OpenUrl CrossRef 31. ↵ Kent SA , Miron VE ( 2024 ) Microglia regulation of central nervous system myelin health and regeneration . Nat Rev Immunol 24 : 49 – 63 . doi: 10.1038/s41577-023-00907-4 OpenUrl CrossRef 32. ↵ Kolde R ( 2019 ) pheatmap: Pretty Heatmaps. R package version 1.0.12 . https://CRAN.R-project.org/package=pheatmap 33. ↵ Komiya H , Takeuchi H , Ogasawara A et al. ( 2025 ) Siponimod inhibits microglial inflammasome activation . Neurosci Res . doi: 10.1016/j.neures.2025.02.002 OpenUrl CrossRef 34. ↵ Krämer J , Bar-Or A , Turner TJ et al. ( 2023 ) Bruton tyrosine kinase inhibitors for multiple sclerosis . Nat Rev Neurol 19 : 289 – 304 . doi: 10.1038/s41582-023-00800-7 OpenUrl CrossRef PubMed 35. ↵ Krüger J , Behrangi N , Schliep D et al. ( 2025 ) Siponimod supports remyelination in the non-supportive environment . Sci Rep 15 : 4216 . doi: 10.1038/s41598-025-87825-8 OpenUrl CrossRef PubMed 36. ↵ Lassmann H , Bradl M ( 2017 ) Multiple sclerosis: experimental models and reality . Acta Neuropathol 133 : 223 – 244 . doi: 10.1007/s00401-016-1631-4 OpenUrl CrossRef PubMed 37. ↵ Love MI , Huber W , Anders S ( 2014 ) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol 15 : 550 . doi: 10.1186/s13059-014-0550-8 OpenUrl CrossRef PubMed 38. ↵ Mannioui A , Vauzanges Q , Fini JB et al. ( 2018 ) The Xenopus tadpole: An in vivo model to screen drugs favoring remyelination . Mult Scler 24 : 1421 – 1432 . doi: 10.1177/1352458517721355 OpenUrl CrossRef 39. ↵ Marrie RA , Allegretta M , Barcellos LF et al. ( 2022 ) From the prodromal stage of multiple sclerosis to disease prevention . Nat Rev Neurol 18 : 559 – 572 . doi: 10.1038/s41582-022-00686-x OpenUrl CrossRef PubMed 40. ↵ Masuda T , Sankowski R , Staszewski O et al. ( 2020 ) Microglia Heterogeneity in the Single-Cell Era . Cell Rep 30 : 1271 – 1281 . doi: 10.1016/j.celrep.2020.01.010 OpenUrl CrossRef PubMed 41. ↵ Paolicelli RC , Sierra A , Stevens B et al. ( 2022 ) Microglia states and nomenclature: A field at its crossroads . Neuron 110 : 3458 – 3483 . doi: 10.1016/j.neuron.2022.10.020 OpenUrl CrossRef PubMed 42. ↵ Patel H , Manning J , Ewels P et al. ( 2025 ) nf-core/rnaseq: nf-core/rnaseq v3.19.0 - Tungsten Turtle . Zenodo 43. ↵ Petrikowski L , Reinehr S , Haupeltshofer S et al. ( 2021 ) Progressive Retinal and Optic Nerve Damage in a Mouse Model of Spontaneous Opticospinal Encephalomyelitis . Frontiers in Immunology 12 : 759389 . doi: 10.3389/fimmu.2021.759389 OpenUrl CrossRef PubMed 44. ↵ Posit team ( 2025 ) RStudio: Integrated Development Environment for R . http://www.posit.co/ 45. ↵ R Core Team ( 2019 ) graphics: The R Graphics Package: Version 3.6.2 . https://www.rdocumentation.org/packages/graphics/versions/3.6.2/topics/dotchart 46. ↵ Rubino SJ , Mayo L , Wimmer I et al. ( 2018 ) Acute microglia ablation induces neurodegeneration in the somatosensory system . Nat Commun 9 : 4578 . doi: 10.1038/s41467-018-05929-4 OpenUrl CrossRef PubMed 47. ↵ Safaiyan S , Besson-Girard S , Kaya T et al. ( 2021 ) White matter aging drives microglial diversity . Neuron 109 : 1100 – 1117 .e10. doi: 10.1016/j.neuron.2021.01.027 OpenUrl CrossRef PubMed 48. ↵ Savonije K , Weaver DF ( 2023 ) The Role of Tryptophan Metabolism in Alzheimer’s Disease . Brain Sci 13 . doi: 10.3390/brainsci13020292 OpenUrl CrossRef 49. ↵ Schwabenland M , Brück W , Priller J et al. ( 2021 ) Analyzing microglial phenotypes across neuropathologies: a practical guide . Acta Neuropathol 142 : 923 – 936 . doi: 10.1007/s00401-021-02370-8 OpenUrl CrossRef PubMed 50. ↵ Sielaff M , Kuharev J , Bohn T et al. ( 2017 ) Evaluation of FASP, SP3, and iST Protocols for Proteomic Sample Preparation in the Low Microgram Range . J Proteome Res 16 : 4060 – 4072 . doi: 10.1021/acs.jproteome.7b00433 OpenUrl CrossRef PubMed 51. ↵ Sobue A , Komine O , Hara Y et al. ( 2021 ) Microglial gene signature reveals loss of homeostatic microglia associated with neurodegeneration of Alzheimer’s disease . Acta Neuropathol Commun 9 : 1 . doi: 10.1186/s40478-020-01099-x OpenUrl CrossRef PubMed 52. ↵ Starossom SC , Campo Garcia J , Woelfle T et al. ( 2019 ) Chi3l3 induces oligodendrogenesis in an experimental model of autoimmune neuroinflammation . Nat Commun 10 : 217 . doi: 10.1038/s41467-018-08140-7 OpenUrl CrossRef PubMed 53. ↵ Tarazona S , García-Alcalde F , Dopazo J et al. ( 2011 ) Differential expression in RNA-seq: a matter of depth . Genome Res 21 : 2213 – 2223 . doi: 10.1101/gr.124321.111 OpenUrl Abstract / FREE Full Text 54. ↵ Tarazona S , Furió-Tarí P , Turrà D et al. ( 2015 ) Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package . Nucleic Acids Res 43 : e140 . doi: 10.1093/nar/gkv711 OpenUrl CrossRef PubMed 55. ↵ Tyanova S , Temu T , Sinitcyn P et al. ( 2016 ) The Perseus computational platform for comprehensive analysis of (prote)omics data . Nat Methods 13 : 731 – 740 . doi: 10.1038/nmeth.3901 OpenUrl CrossRef PubMed 56. ↵ Wickham H (ed) ( 2016 ) ggplot2: Elegant Graphics for Data Analysis. Use R! Springer International Publishing , New York 57. ↵ Wulf M , Barkovits K , Schork K et al. ( 2022 ) Neuromelanin granules of the substantia nigra: proteomic profile provides links to tyrosine hydroxylase, stress granules and lysosomes . J Neural Transm (Vienna) 129 : 1257 – 1270 . doi: 10.1007/s00702-022-02530-4 OpenUrl CrossRef PubMed 58. ↵ Yong HYF , Yong VW ( 2022 ) Mechanism-based criteria to improve therapeutic outcomes in progressive multiple sclerosis . Nat Rev Neurol 18 : 40 – 55 . doi: 10.1038/s41582-021-00581-x OpenUrl CrossRef PubMed 59. ↵ Yu G , Wang L-G , Han Y et al. ( 2012 ) clusterProfiler: an R package for comparing biological themes among gene clusters . OMICS 16 : 284 – 287 . doi: 10.1089/omi.2011.0118 OpenUrl CrossRef PubMed Web of Science 60. ↵ Zierfuss B , Wang Z , Jackson AN et al. ( 2023 ) Iron in multiple sclerosis - Neuropathology, immunology, and real-world considerations . Mult Scler Relat Disord 78 : 104934 . doi: 10.1016/j.msard.2023.104934 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 11, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. 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