Oligodendroglia as functional effectors of Multiple Sclerosis risk variants

Oligodendroglia as functional effectors of Multiple Sclerosis risk variants | 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 Oligodendroglia as functional effectors of Multiple Sclerosis risk variants View ORCID Profile Karl E Carlström , Eneritz Agirre , View ORCID Profile Ting Sun , Özge Dumral , Mukund Kabbe , Neemat Mahmud , Yuk Kit Lor , View ORCID Profile Majid Pahlevan Kakhki , Mohsen Khademi , Maja Jagodic , View ORCID Profile Steve A. Goldman , View ORCID Profile Gonçalo Castelo-Branco doi: https://doi.org/10.1101/2025.11.11.687640 Karl E Carlström 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karl E Carlström For correspondence: karl.carlstrom{at}ki.se goncalo.castelo-branco{at}ki.se Eneritz Agirre 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ting Sun 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ting Sun Özge Dumral 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mukund Kabbe 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet , Stockholm, Sweden 5 Nexus Epigenomics AB , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Neemat Mahmud 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuk Kit Lor 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Majid Pahlevan Kakhki 2 Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Majid Pahlevan Kakhki Mohsen Khademi 2 Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maja Jagodic 2 Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steve A. Goldman 3 Centre for Translational Neuromedicine, University of Copenhagen , Copenhagen, Denmark 4 Centre for Translational Neuromedicine, University of Rochester , Rochester, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Steve A. Goldman Gonçalo Castelo-Branco 1 Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gonçalo Castelo-Branco For correspondence: karl.carlstrom{at}ki.se goncalo.castelo-branco{at}ki.se Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Multiple sclerosis (MS) is a neuroinflammatory disease for which a large number of non-coding single nucleotide polymorphisms (SNPs) have been associated with disease risk/susceptibility. Immune cells have been suggested as the principal functional effector cell types of these common variants. Here, we identify 76 MS-associated SNPs whose loci present accessible chromatin in homeostatic and diseased oligodendroglia (OLG), including both oligodendrocyte precursor cells (OPCs) and mature oligodendrocytes (MOLs). By applying high-throughput functional genomics, we found that a subset of these SNPs led to variant- and cell-specific regulatory effects in human induced pluripotent stem cell-derived oligodendroglia. Phenotypic profiling of these variants indicated that rs483180: PHGDH interfered with human OPC proliferation via long-range chromatin interactions with the S100A6 locus, while variants at rs2248137: CYP24A1 impaired oligodendrocyte differentiation by regulating BCAS1 expression. In addition, variants at rs1415069: DIPK1A enhanced secretion of the cytokine CCL2 by oligodendroglia, suggesting that these variants might be implicated in OLG-driven immune cell recruitment in MS. These findings position oligodendroglia as important drivers of MS pathogenesis through modulation of both their cell-intrinsic oligodendroglial function and intercellular communication by non-coding MS risk variants. INTRODUCTION Multiple Sclerosis (MS) is a chronic neurological disorder with strong environmental and genetic components. Loss of myelin around nerve axons in the central nervous system (CNS) causes progressive impairment of cognition and mobility in patients, among other symptoms ( 1 , 2 ). Given the high-complexity and multifactorial nature of MS-disease, there is an unmet need to identify how different genetic risk loci contributes to disease appearance and course, and in what disease-relevant cell types ( 3 , 4 ). Seminal discoveries through genome-wide association studies (GWAS) have suggested multiple single nucleotide polymorphisms (SNPs) that may be associated with susceptibility to develop MS as well as disease severity in already affected individuals ( 5 , 6 ). However, functional validation of GWAS candidates, allowing the connection of genotype with phenotype has been proven difficult since nearly all identified risk loci fall into non-coding regions. This has been referred to as the variant- to-function (V2F) problem ( 7 – 9 ), which suggests that non-coding variants may influence function in a nuanced and potentially cell-type specific manner, that can strongly bias disease risk and presentation ( 10 ). Addressing the V2F issue in MS is rendered even more difficult by the fact that disease-associated loci typically are co-inherited together with variants in linkage disequilibrium (LD), such that only a fraction may be causal and contribute to regulatory functions ( 11 ). The expanding number of disease-associated SNPs with unknown function further challenges most experimental approaches, and computational approaches alone fall short in clarifying the V2F relationship ( 12 , 13 ). To address this limitation in our ability to identify cell-specific disease-associated SNPs with causally-relevant cis- and trans- regulatory functions, new combinatorial approaches for genomic fine-mapping while defining risk variant cell-specificity, functionally paired with clustered regularly interspaced short palindromic repeats (CRISPR)-based silencing or activation pipelines, have been developed and proven informative in neurodegenerative dementia ( 14 – 18 ). However, analogous studies intended to functionally interrogate MS risk SNPs at a functional level have mainly been focused on peripheral or brain-resident immune cells, whereby SNPs have been linked to exon-skipping ( 19 ), oxidative burst ( 20 ), or expression quantitative trait loci (eQTLs) ( 21 , 22 ). Oligodendrocyte precursor cells (OPCs) play central roles in MS-disease as they give rise to the myelinating and mature oligodendrocytes (MOLs), which are major targets of the immune system in MS ( 23 , 24 ). As such, OPCs necessarily play a major role in MS pathogenesis, disease phenotype and recovery. In addition, astrocytes ( 25 ) and subsets of oligodendroglia also participate in local immune responses, the latter in part via their induced expression of HLA Class I and II-genes enabling antigen presentation ( 26 , 27 ). Nonetheless, MS-associated SNPs primarily localize in genomic proximity to immune-associated genes suggesting the involvement of these genes in disease susceptibility ( 5 ). However, a few MS-susceptibility loci have been linked with specific biological processes in astrocytes ( 28 , 29 ) and oligodendroglia ( 30 ). Moreover, a substantial number of MS-susceptibility genes present open chromatin ( 16 ) or are expressed in oligodendroglia in homeostasis and in disease ( 26 ), raising the question whether these MS-associated SNPs interfere with OLG function during disease initiation and development. Using human oligodendroglial progenitor cells (hGPCs/OPCs) derived from induced pluripotent stem cell (iPS) ( 31 – 33 ), we here introduce a powerful and scalable functional pipeline enabling the assessment of cell-specific variant-to-function relationships of hundreds of suggested MS-associated SNPs. This combinatorial approach targeting MS risk and severity SNPs in hOPCs includes massive parallel reporter assays (MPRA) and CRISPR-based interference/activation screen coupled with single cell RNA-seq. Using this approach, we identified genomic loci with MS risk SNPs presenting enhancer activity and defined how the MS variant interfered with this regulatory activity, allowing us to pinpoint which biological processes were altered by modulating the chromatin landscape of these MS-risk SNPs. We found that activating or repressing the MS-risk SNPs loci lead to functional outcomes in OPCs and their derived oligodendroglia in processes as varied such as proliferation, differentiation and cytokine production. Thus, our results indicate that oligodendroglia, the target populations of the immune attack in MS, have the capacity to actively modulate both cellular vulnerability and disease pathogenesis, through the regulatory actions of MS risk-associated SNPs. RESULTS 76 MS-associated SNPs localized to chromatin accessible regions in oligodendroglia Most GWAS-identified MS-associated risk SNPs fall in genomic proximity with immune genes, including both HLA and non-HLA ( 5 ). However, we have previously shown that numerous immune related genes, including HLA genes are also expressed by the OLG-lineage cells ( 26 ). We hypothesized chromatin acessibility to be a proxy for potential SNP-locus dependent transcriptional regulation and indeed we previously identified GWAS-identified SNPs associated with MS risk/susceptibility ( 5 , 6 , 16 ), as well as outside variants (which interact at the chromatin level with a same gene as MS SNPs, despite presenting a low LD with the latter) ( 30 ), and their genomic overlap with chromatin accessible (CA) regions in OLGs. For this purpose, we characterized chromatin accessibility through assay for transposase-accessible chromatin with sequencing (ATAC-seq) to identify open chromatin in OLGs of several conditions, including in mouse OLGs from experimental autoimmune encephalomyelitis (EAE), primary mouse OLGs stimulated or unstimulated with IFNγ and human homeostatic OLG ( 16 ) ( Fig.1A ). We identified potential OLG regulating SNPs by intersecting mouse OLG specific ATAC-seq peaks, with genomic coordinates LiftOver to GRCh38, and human homeostatic OLG ATAC peaks regions with the candidate SNPs coordinates ( 16 ). Statistical analysis of SNPs in cell type specific chromatin accessible regions showed significant over representation of MS-associated SNPs in microglia, as expected, but interestingly also in OLG populations (particularly MOL5/6 and OPCs) ( 16 ). Our final candidate list included 753 unique SNPs from the list of significant and suggestive MHC and non-MHC susceptibility, and severity MS SNPs and outside variants ( 34 – 36 ) (Supplementary Table 15). Some of the SNPs were present in more than one category. Of these, we identified a total of 76 SNPs being located in accessible chromatin structures across the OLG-lineage ( Fig.1A-B , Supplementary Fig.1A and Supplementary Table 1). Download figure Open in new tab Figure 1: Identification of OLG-relevant MS-associated SNPs, where rs2248137: CYP24A1 regulates hOPC differentiation via BCAS1. a , Strategy of analysis pipeline combining MS GWAS data with single-cell OLG-lineage chromatin accessibility (CA) profiling by ATAC-seq to identify OLG-relevant SNPs. b, Example of MS SNP overlap with CA through ATAC-seq and activity-by-contact (ABC) in unstimulated or IFNγ stimulated mouse OLGs. c , Experimental timeline ( top ). mRNA expression 72h following co-transduction with rs2248137-targeting sgRNA and dCas9-KRAB ( left) or dCas9-p300 ( right ). d , Experimental timeline for differentiation over 4 or 7weeks following co-transduction ( top ). Representative images of dCas9 + gRNA-mCherry + BCAS1 + OLGs 4weeks following co-transduction with rs2248137-sgRNA or scramble negative scrambled sgRNA controls in combination with dCas9-KRAB or dCas9-p300. e, Quantification of BCAS1 after 4 weeks. f , Representative imaging of dCas9 + gRNA-mCherry + MBP + OLGs 7weeks following co-transduction with dCas9-KRAB with rs2248137-targeting sgRNA or scramble negative sgRNA controls. g , Quantification of MBP and branching after 7 weeks. Scale-bar 100µm. Data in c - g represents six, four and three independent experiments respectively. P values in c were calculated using one-way ANOVA with correction for multiple comparisons. P values in e and g were calculated using two-tailed Student’s t-test. rs2248137:CYP24A1 SNP locus regulates hOPC differentiation We had previously identified a chromatin accessible region in mouse OLGs to overlap with the MS-susceptibility SNP rs2248137: CYP24A1 on human chromosome 20 ( 16 ). Moreover, chromatin architecture analysis showed that this locus to interconnect with the Breast carcinoma-amplified sequence 1 ( Bcas1 ) gene and does so in an IFNγ-dependent manner in mouse primary OPCs ( Fig.1B ) ( 16 ). BCAS1 is transiently expressed during OLG differentiation, preceding the highly elevated expression of Myelin basic protein (MBP) in myelinating OLG ( 37 ), and has been shown to be necessary for this process, with its knock-out leading to hypomyelination ( 38 ). In addition, eQTL analysis indicated that the MS risk variant at rs2248137: CYP24A1 was associated both with decreased expression from BCAS1 in both OPCs and OLGs (Supplementary Table 2, direction) and was thereby predicted to affect the MS phenotype (Supplementary Table 2, column prop_pos_direction , proportion of all SNPs at the loci that have a positive direction (increase expression = increase disease risk)) ( 39 , 40 ). We thus hypothesized that genetic variations at this locus could interfere with BCAS1- and MBP-associated differentiation of human OPCs. To test this hypothesis, we used CRISPRi/a to target the rs2248137: CYP24A1 locus in human iPS-derived OPCs (clone C27). These have previously been described to express oligodendroglial signatures in vitro and in vivo ( 33 , 41 , 42 ) and to mature into both MBP expressing oligodendroglia ( 32 ), and fibrous astrocytes ( 31 ). Transduction of targeting gRNA in combination with repressive dCas9-KRAB indeed lowered BCAS1 transcription compared to negative scrambled gRNA ( Fig.1C left ). In contrast, transcription was increased using dCas9-p300 activation ( Fig.1C right ). Importantly, no transcriptional alteration was observed for the neighboring genes ( PFDN4 , ZNF217 ) under either CRISPR inhibition or activation at the rs2248137: CYP24A1 locus, for which CYP24A1 mRNA levels were also very low, as previously reported ( 16 ) ( Fig.1C ). Applying CRISPRi/a in combination with rs2248137-targeting gRNA indeed differentially regulated BCAS1 protein level after 4 weeks in differentiation media ( Fig.1D ). Tethering of an activating machinery with dCas9-p300 and thereby activating the rs2248137 SNP locus in human OPCs increased the number of BCAS1+ cells, whereas bringing a repressive machinery to the locus with dCas9-KRAB decreased BCAS1+ cell-numbers compared to scrambled negative gRNA controls ( Fig.1D-E ). Thus, rs2248137: CYP24A1 locus specifically regulates the expression of the BCAS1 a gene that is critically involved in the initiation of myelination, and in the response of OPCs to demyelination, as mentioned above. To assess OLG maturation as associated with MBP expression, we increased the time of exposure to differentiation media from 4 weeks to 7 weeks following initial transduction with dCas9-KRAB and rs2248137-targeting gRNA. Upon this extended cultivation, we observed toxicity to dCas9-p300 independently of co-transfected with targeting or negative scrambled gRNA. The phenomena of CRISPRa-mediated toxicity over time have been reported previously ( 43 ), and thus we only assessed the long-term effects of recruitment of the repressing machinery to the rs2248137 locus. Strikingly, while dCas9-KRAB in combination with negative scrambled sgRNA showed differentiated and branched MBP-expressing cells, this differentiation process was impaired in the rs2248137-targeting gRNA transduced OLGs ( Fig.1F-G ). Thus, our data indicates that the genomic locus harboring the MS-susceptibility SNP rs2248137: CYP24A1 has the capacity to modulate - at a distance - the expression of the neighboring BCAS1 gene in hOPCs, ultimately modulating their differentiation. MPRA unveils MS risk SNPs altering cCRE activity in OLG To investigate whether any other of the 76 identified SNPs (Supplementary Table 1) can serve as cis -regulatory elements (CREs) affecting/controlling expression of distal or nearby genes in OLGs, we performed MPRA including candidate CRE (cCRE) i ( Fig.1A ). MPRA allows for parallel and high-throughput assessment of cCREs associated with multiple barcodes in a minimal promoter vector. Next-generation sequencing is then used to quantify transcribed mRNA barcodes (normalized to integrated DNA barcodes) as a proxy for transcriptional regulation. These barcodes provide multiple replicates within the same experiment ( Fig.2A , Supplementary Fig.2A-B ) ( 44 – 46 ). The GWAS SNPs in CA regions within the OLG-lineage were primarily located in non-coding regions, making MPRA an appropriate choice to assess cis -regulating activities ( Fig.2B ). We applied this approach to assess MS-associated risk variants including; ( i ) all allele variants for SNPs identified by ATAC-seq and GWAS overlap, ( ii ) SNPs immediately up- or downstream of the candidate SNP, ( iii ) all SNP in LD with the lead SNPs located in the same region of accessible chromatin, with a window of 200 bp ( Fig.2A , Supplementary Table 5). In the MPRA1 screen we tested 1405 variants and in MPRA2 we replicated 815 cCREs identified in MPRA1 and tested an additional 582 variants, including 12 recently identified severity variants associated with progressive MS ( 6 ) ( Supplementary Fig.2C , Supplementary Table 1 and 5 and 15). We have previously observed that neuroinflammation can lead to a change in the chromatin landscape in oligodendroglia ( 16 ). Thus, to mimic an inflammatory environment, we also ( i ) supplemented hOPC media with IFN γ (10ng/mL), and ( ii ) conducted MPRA1 was also in an Epstein-Bar virus (EBV) positive human B-cell-line, given the recent reports consolidating the role of EBV as a trigger of MS ( 47 – 49 ). This approach also allowed us to identify cell-specific variant effects between the highly relevant MS-associated immune and glial cell types ( Supplementary Fig.2D ). Download figure Open in new tab Figure 2: Assessment of MS cCRE in hOPC and B-cells a , Strategy of our applied analysis pipeline combining MS GWAS data with single-cell OLG-lineage CA profiling by ATAC-seq to identify OLG-specific cCREs. Schematic illustration of cCRE design including both previously identified lead SNP and LD SNPs falling into chromatin accessible regions in the OLG-lineage. b , Distribution of genomic location of all assessed MS-associated SNPs/cCRE. c , Ranked cCREs ( grey ) by median fold-change Log2 values from MPRA1 and 2, including positive ( red triangles ) and negative ( blue triangles ) controls ( left ). The top 100 ranked cCREs are highlighted and annotated ( right ). d , Schematic illustration of the rs2248137: CYP24A1 locus including surrounding genes and SNPs in LD with rs2248137 in the cCRE. e , Violin-plots of individual BCs for rs42248137:CYP24A1. f , Volcano-plot of rs2248137: CYP24A1 cCREs enriched in hOPCs not supplemented or supplemented with IFNγ. Variant motifs including rs35792925, rs116934994, rs2248137 (lead SNP) and rs147266516 in rs2248137: CYP24A1 cCREs annotated ( purple , yellow , green ). Dotted lines indicate 0.05 (y-axis) and +0.3 - −0.3 (x-axis). g , cCRE ranked on allelic variant-effect depicted by forward ( upward triangle ) and reverse ( downward triangle ) cCRE orientation, SNPs in LD with lead SNP ( diamond ) and controls ( red/black triangle ). adj P values for e were generated through MPRA analysis comparing each paired MS vs non-MS variant and correcting for multiple comparisons. P values in f were calculated using chi-square test. To visualize cCRE dynamics, we ranked them based on median fold-change between replicates. As positive controls we used cell-type specific enhancers including enhancers for Sox10 (OLG) and CCNL1 (pan/B cell), and as negative controls, negative scrambled regions were targeted (Supplementary Table 5). Accordingly, we observed that increased and decreased fold-change for these regions ( Fig.2C , Supplementary Table 13 and 14). Interestingly, we found the cCRE of SNPs: neighboring gene rs2084007: JADE2, rs4392367: CRTAP and rs10951154: HOXA1 showed high MPRA reporter expression in hOPCs (+/− IFNγ) and human B cells ( Fig.2C , Fig.2D-F ). However, for all these loci, and also for rs67476479_CA: HLA-DRB1 (with the lead SNPs in a coding region and multiple LD SNPs withing the tested cCRE) ( Supplementary Fig.2E ), cis -regulatory features were largely independent of variant at the SNP locus or LD SNP loci. Furthermore, despite having cis -regulatory effects in both cell types compared to cell-type specific positive CRE controls, we found that rs67476479_CA: HLA-DRB1- activity was significantly enriched in B cells over hOPCs ( Supplementary Fig.2F ). This is consistent with the presence of enhancers and super enhancers in the non-coding regions of the HLA-locus ( 50 , 51 ) and enhancers in protein-coding regions ( 52 ). In contrast, rs12803321, rs12464096 and rs2246410 in LD with rs34026809: PHLDB1, rs1177228: PUS10 and rs483180: PHGDH respectively were significantly enriched in hOPCs over B-cells ( Supplementary Fig.2F ). Thus, our MPRA approach allows the detection of MS SNPs operating in a cell type-specific manner. The MS-associated C variant at rs2248137: CYP24A1 manifested significantly decreased cCRE reporter expression compared to non-MS variant G, in line with previous findings in Fig1 and MS association with rs2248137 ( 5 , 39 , 40 ), ( Fig.2D-E , Supplementary Table 2, 6). This indicated that our MPRA was effective in detecting functional MS risk SNPs. We had previously showed the rs2248137: CYP24A1 locus to be accessible in a IFN γ- dependent manner in mouse ( 16 ) ( Fig.1B ). We thus assessed cCREs that were significantly enriched in IFNγ-supplemented conditions relative to non-supplemented controls. Indeed, we observed a higher number of rs2248137: CYP24A1 cCREs in the IFN γ conditions ( Fig.2F ). Further, we identified two combinations of motifs involving lead SNP rs2248137s: CYP24A1, in combination with rs35792925, rs116934994, and rs147266516, to be sensitive or insensitive to IFNγ ( Supplementary Fig.2G ).Apart from rs2248137: CYP24A1, we also found additional subset of variants presenting differential cCRE activity, including rs1415069: DIPK1A , rs483180: PHGDH and rs6920979: ARHGAP18 oriented in both forward and reverse orientation ( 45 ) showing the largest effect ( Fig.2G ). In addition, SNP rs839618: PHGDH in LD with the lead SNP rs483180: PHGDH showed variant-dependent reporter expression ( Fig.2G ). In sum, the use of MPRA targeting MS-risk SNPs presenting open chromatin in oligodendroglia allows the identification of a restricted number of variants that can regulate cCRE activity in human OPCs. CRISPRi/a to elucidate cis and trans effects from MS-associated SNPs While assessing cCREs with MPRA may reveal regulatory elements, it will not provide information on which genes might be regulated and targeted in human oligodendroglia. We thus assessed 42 cCRE included in MPRA1 with lentiviral-based pooled single cell (sc)CRISPR-seq screens, with the notion that the recruitment of the activating or repressive epigenetic machinery through dCas9 to the MS-risk SNP loci would mimic features of MS-associated alleles ( Fig.3A ). To achieve this, CRISPRi/a (dCas9-KRAB and dCas9-p300 respectively) was performed, in combination with direct-capture Perturb-sequencing single-guide RNA (sgRNA) with droplet-based single-cell RNA-seq, to achieve high sgRNA capture efficiency and identification of possible transcriptional regulatory mechanisms at a single cell/sgRNA level ( 53 ) ( Fig.3B ). Each SNP locus (within a +/−350bp range) was targeted with at least three sgRNA. None of the SNPs overlapped with known transcriptional start sites (TSS) (Supplementary Table 7) ( 54 ). In addition, previously reported inefficient protospacer seeds were removed during design to further increase sgRNA readout ( 55 ), yielding a library of 140 gRNAs (in addition to the non-targeting gRNA controls). In order to primarily identify direct transcriptional outcomes following CRISPRi/a, and to avoid accumulating off-target effects associated with the use of stably transduced cell lines, we harvested individual hOPCs 72h post combinatorial co-transduction with lentiviral sgRNA and dCas9 ( Fig.3C ), cultures were again either supplemented or not with IFNγ. Lentiviral gRNA vectors were titrated for ∼30% transduction efficiency when co-transduced with dCas9, so as to optimize for sgRNA infection and capture ( Supplementary Fig.3A ). Unique molecular identifier (UMI) difference between the most abundant and the second ranked gRNA within each cell were calculated using z-score normalization ( Supplementary Fig.3B ), where sgRNA-containing hGPCs were identified using previously described criteria( 56 ) ( Supplementary Fig.3B , 3C), where sgRNA where distributed across all cells ( Supplementary Fig.3C ). Cells were acquired in two batches, resulting in a total of captured 30 452 single cells containing sgRNA, after quality control ( Fig.3D ). Uniform Manifold Approximation and Projection (UMAP) dimensional reduction post batch effect correction showed the two batches to overlap and unstimulated and IFNγ stimulated hOPCs to cluster separately ( Fig.3D ). The iPSC-derived cultures showed broad expression of hallmark OLG transcripts ( Fig.3E ). Download figure Open in new tab Figure 3: scCRISPRi/a-seq to identify cis- and trans-regulation in hOPCs a , Illustration of how disease-associated SNPs in cCRE loci may regulate transcription from surrounding genes. scCRISPRi/a-seq was applied to decipher cis- and trans- regulatory features associated with SNPs in cCRE. b , Schematic illustration of lentiviral dCas9-KRAB, dCas9-p300 and gRNA constructs. c , Strategy for scCRISPRi/a-seq screens where naive hOPCs were transduced with dCas9 and sgRNA library (with 10X-compatible capture sequence in the gRNA scaffold) followed by identification of transduced cells using Fluorescence-Activated Cell Sorting (FACS) after 72h. d , UMAP embedding of all identified sgRNA-containing hOPCs transcriptome profiles, colored by batch, interference/activation and supplementary treatment. e , Feature expression of hOLG-lineage marker PDGFRA and CNP on UMAP. f , Heatmap depicting GSEA terms being enriched based on ranked gene list from targeting gRNA vs. negative scramble gRNA combined with dCas9-p300 or dCas9-KRAB, respectively. Black-white heatmap show median forward and reverse cCRE activity (Log2 fold-change), purple-white heatmap show variant-dependent cCRE difference (median forward and reverse) for each locus. chr10:6390285: 8030442B05Rik, rs483180: PHGDH , rs12094392: PHGDH , rs2248137: CYP24A1 and rs67476479: HLA-DRB1 are highlighted with squares. Cellular profiles containing primary sgRNA were compared to non-targeting controls within each condition of individual batch. Gene list of each comparison were ranked based on the differential expression effect size between sgRNA-containing and control cells and subjected through global gene-set enrichment analysis (GSEA). We observed primarily three pathways modules, associated with cell division, immune responses or their mix ( Fig.3F , Supplementary Table 8). MS-risk SNP rs67476479 CA: HLA-DRB1 , which previously manifested potent cis-regulatory effects ( Fig.2C ), revealed relative downregulation of immune associated pathways when targeted with KRAB highlighting potential role of this locus in OLG immune responses ( Fig.3F ). Also, rs4392367: CRTAP showed prominent MPRA regulation in OLGs ( Fig.2C , Supplementary Fig.2E ), and CRISPRi/a targeting its locus suggested reciprocal regulation of cell division upon its modulation ( Fig.3F ). Furthermore, rs2248137: CYP24A1 showed reciprocal regulation of pathways involved in cell and nucleus division, corroborating our previous findings on the role of rs2248137: CYP24A1 in OLG differentiation ( Fig.1 , Fig.3F ). Interestingly, we found overlapping pathways altered for rs483180: PHGDH and rs12094392: PHGDH suggesting potential proliferation/differentiation phenotypes to also be associated with these loci ( Fig.3F ). Thus, our single-cell CRISPR screen suggests that MS-risk SNPs might regulate diverse biological processes in human oligodendroglia. The rs483180:PHGDH locus modulates proliferation in human oligodendroglia We next investigated whether the biological processes identified by the scCRISPR screen indeed were functionally modulated by two of the identified SNPs. The intergenic lead MS-risk SNP rs483180: PHGDH variant C is in LD with multiple SNPs within the CA region at the locus ( 5 ) ( Fig.4A , Supplementary Table 1) including rs3838425 variant C located within the tested cCRE region ( Fig.4A ). Additional cCRE SNPs rs1886736 and rs55899400 are also inherited with rs483180 but are too rare to be predicted in relation to rs483180. Importantly, the MS-associated variant C at rs483180: PHGDH inherited with variant C at rs3838425 showed significantly lower MPRA reporter expression compared to their healthy associated variants G ( Fig.4B , Supplementary Table 6). For rs483180: PHGDH , this reduced activity was present regardless of the variants present in rs1886736 and rs55899400 ( Fig.4B ). However, cCRE with deletion of C (delC) at rs3838425 associated with non-MS, and inherited together with G at rs3838425, instead increased MPRA reporter expression ( Fig.4C , Supplementary Table 6). Additional rs483180 LD SNPs (inherited in the same haplotype ( P <0.0001) but located outside the CRE region also showed significantly different cis -regulatory capacities in variant-dependent manner highlighting the cis -regulatory role of the locus in the context of MS ( Supplementary Fig.4A , Supplementary Table 6). Download figure Open in new tab Figure 4: MS-associated SNP rs483180: PHGDH regulates hOPC proliferation a , Schematic illustration of the rs483180: PHGDH locus on Chromosome 1 (Chr1), with lead SNP annotated in orange and LD SNPs in grey with orange bows indicating LD with rs483180. LD SNPs marked with asterisk are too rare in the population for variant prediction. b-c , Violin-plots of combinatorial individual BCs for rs483180: PHGDH . b , MS-risk variant C at rs483180 decreased reporter expression in combination with MS-risk variant C at rs3838425 but independently of surrounding LD SNPs. c, MS-risk variant C at rs483180 increased reporter expression in combination with non-MS variant delC at rs3838425 but independently of surrounding LD SNPs. MPRA ex d , UMAP highlighting sgRNA-containing hOPCs for rs483180: PHGDH with dCas9-p300 ( red ) and dCas9-KRAB ( blue ) and scrambled-sgRNA ( beige ) (l eft ) without treatment. Chromosome1-located transcripts relative expression differences between rs483180-sgRNA and negative scrambled sgRNA controls ( right ). e , Micro-C from hOPCs showing Chr1 and its genomic interactions where the locus for rs483180: PHGDH ( orange ) and S100A3/4/6 genes ( green ) and differentially regulated transcripts from d are annotated ( circles ). f , Pseudo-4C tracks showing outbound rs483180-interactions ( orange ) and outbound S100A3/4/6-interactions (green). g , mRNA expression 4days following co-transduction with dCas9-KRAB and S100A6 TSS-gRNA. h , mRNA expression following 4days following co-transduction with dCas9-KRAB and rs483180-gRNA or PHGDH TSS-gRNA. i , Illustration of experimental timeline where hOPC were cultured with EdU for 6h, 4 days following initial co-transduction ( top ). Histogram of %EdU+ of all dCas9-KRAB and gRNA double-positive cells ( bottom ). j , %EdU+ of all dCas9-KRAB and gRNA double-positive cells. k , %EdU+ of all dCas9-p300 and gRNA double-positive cells. Data in g - k represents four independent experiments. adj P values for b were generated through MPRA analysis comparing each paired MS vs non-MS variant and correcting for multiple comparisons. P values in g - k were calculated using one-way ANOVA with correction for multiple comparisons. To explore potential cis - and trans -regulatory interactions between the rs483180 locus and genes located on chromosome 1 (Chr1), we ranked all Chr1 transcripts that were differentially expressed relative to the scrambled sgRNA control using default FindMarkers parameter implemented in Seurat analysis toolbox. We identified different degrees of regulation from the S100 calcium-binding protein family following dCas9-p300 or KRAB ( Fig.4D , Supplementary Fig.4B , Supplementary Table 9). Subsequently, we overlapped all ranked Chr1-genes with our previously published Micro-C datasets ( 57 ), a proximity ligation-based chromatin architecture assay ideal to identify genome interaction at high-resolution ( 58 , 59 ) ( Fig.4E ). Following this analysis, we could identify genomic interactions spanning the Chr1 centromere connecting rs483180 locus with a genomic region containing S100A6 and surrounding members of the S100-family (as S100A3 and S100A4). This protein family has been reported to be involved in proliferation and cell-cycle regulation, but to also act as secreted proteins to regulate neuronal maturation ( 60 – 62 ) ( Fig.4E-F ). Long-distance intra-chromosomal and even inter-chromosomal genomic interactions are well-established phenomena also described in mammalian context ( 63 , 64 ). The rs483180/ S100A6 loci interactions span a ∼30Mb distance, corresponding to previous reported interactions across the centromere ( 65 ). Pseudo-4C tracks based on the Micro-C dataset further revealed rs483180: PHGDH locus interconnectivity with TSS for both S100A6 and PHGDH ( Fig.4F ). The functional genomic linkage between S100A6 and the rs483180 locus was further validated by CRISPRi ( Fig.4G ,H, Supplementary Table 2). dCas9-KRAB in combination with S100A6 TSS-targeting sgRNA lowered expression from both S100A6 and PHGDH compared to scrambled negative sgRNA controls ( Fig.4G , Supplementary Fig.4C , Supplementary Table 4). Similarly, targeting either the rs483180 locus or PHGDH TSS also lowered expression from both S100A6 and PHGDH relative to scrambled sgRNA controls ( Fig.4H , Supplementary Fig.4D ). These results were concordant with the observed reciprocal interconnection between rs483180, S100A6 and PHGDH from the Micro-C dataset ( Fig.4E-F ). S100A6 has been described to be involved in cell-cycle regulation ( 60 ). We and others have previously shown by eQTL analysis that lower risk of MS disease is associated with increased PHGDH transcription specifically in OPCs ( 22 , 39 ). Given this and that cell division pathways were reciprocally modified in PHGDH intergenic SNPs in our single-cell CRISPR screen analysis ( Fig.3F ), we asked if targeting either the rs483180 locus, PHGDH or S100A6 with dCas9-KRAB would affect hOPC proliferation, by performing EdU (5-ethynyl-2’-deoxyuridine) incorporation assays. Strikingly, specifically targeting both the rs483180 locus and PHGDH TSS with the repressive dCas9-KRAB complex did lower incorporation of EdU in hOPCs, whereas no change on Edu incorporation were observed targeting the S100A6 TSS alone ( Fig.4I-J , Supplementary Fig.4E ). Thus, we investigated whether PHGDH transcription could be elevated via rs483180 locus-targeting in OPCs, by applying activating CRISPRa, where dCas9-p300 installs functional acetyl groups at histones facilitating increase chromatin accessibility. Applying this in combination with sgRNA targeting the rs483180 locus but not PHGDH TSS rendered elevation of S100A6 transcription, suggesting interconnection between S100A6 and PHGDH to occur, likely in a p300 acetylation-independent manner ( Supplementary Fig.4C-D ). Furthermore, dCas9-p300-targeting of rs483180 locus but not PHGDH nor S100A6 increased hOPC proliferation ( Fig.4K ). Thus, our data suggests that the MS-associated rs483180: PHGDH locus is associated with MS susceptibility by regulating OPC proliferation, that can be reverted by epigenetic activation of the locus. The rs1415069:DIPK1A locus regulates human PBMC recruitment via hOPC-secreted CCL2 The intergenic SNP rs1415069: DIPK1A on chromosome 1 ( Fig.5A ) showed cis -regulatory capacity in a variant-dependent manner, as the MS-associated G variant increased MPRA reporter expression compared to the C variant ( Fig.5B ). The variants for LD SNP rs12747275, rs6681345 and rs6681240 also showed cis -regulatory properties compared to the reference variant ( Supplementary Fig.5A ). Targeting rs1415069: DIPK1A with single-cell CRISPRi/a screen in combination with IFN γ stimulation showed expression level alterations of hallmark cytokine C-C Motif Chemokine Ligand 2 (CCL2) ( Fig.5C , Supplementary Fig.5B , Supplementary Table 4). CCL2 is highly relevant to MS-disease, being involved for instance in recruitment of peripheral blood cells for disease propagation ( 1 ). In addition, CCL5 and C-X-C motif chemokine 9 (CXCL9) CXCL10 and CXCL11 were also changed in expression relatively to negative scrambled sgRNA controls ( Fig.5C , Supplementary Table 4). Further validation indicated that lentivirus with scrambled gRNA elevated CCL2 mRNA expression from hOPCs compared to non-lentiviral exposed controls, whereas rs1415069-targeting gRNA further elevated both CCL2 and CCL5 expression from hOPCs, with the cognate DIPKA1 transcription being unaffected ( Supplementary Fig.5C ). Since CCL2 / 5 and CXCL9 / 10 / 11 are located on Chr.17 and Chr.4 respectively, the observed alternation might be secondary to primary genomic interactions from the rs1415069: DIPK1A locus. A possible candidate is RING finger protein 220 (RNF220), an E3 ligase critical to the ubiquitination of STAT1, a hallmark mediator of IFN signaling. RNF220 is located ∼25kB downstream of the rs1415069: DIPK1A locus ( 66 ), and was indeed shown to be upregulated in the CRISPRi/a screen comparing to non-targeting control cells when co-stimulated with IFN γ ( Fig.5C ). Download figure Open in new tab Figure 5: rs1415069: DIPK1A regulates CCL2 cytokine production to attract healthy human PBMCs a , Schematic illustration of the rs1415069: DIPK1A locus, with lead SNP annotated in orange and LD SNPs in grey with orange bows. b , Violin-plots of individual BCs for rs1415069: DIPK1A . c , UMAP for IFNγ-stimulated sgRNA-containing cells for rs1415069 with dCas9-p300 ( red ) and dCas9-KRAB ( blue ) and scrambled-sgRNA ( beige, left ). Transcripts expression differences between rs1415069-sgRNA or negative scrambled sgRNA controls ( right ). d , hCCL2 ELISA on hOPC media 4 days following co-transduction with dCas9-p300 and rs1415069-sgRNA or scrambled negative sgRNA or non-transduced or IFNγ (10ng/uL). e , Schematic illustration PMBC migration following engineering of rs1415069 locus with dCas9-p300. f , Quantification and representative images of PBMC migration to conditioned media from dCas9-p300 and rs1415069-sgRNA or scrambled negative sgRNA engineered hOLG, scale-bar 200µm. adj P values for b were generated through MPRA analysis comparing each paired MS vs non-MS variant and correcting for multiple comparisons. Data in d and f represent three independent replicates. P values in d were calculated using one-way ANOVA with correction for multiple comparisons. P values in f were calculated using two-tailed Student’s t-test. We then asked if the expression of the CCL2 protein from human OPCs was affected by modulating the SNP rs1415069 locus. ELISA analysis indeed showed that CCL2 protein levels were elevated in hOPC media following co-transduction with dCas9-p300 and rs1415069-targeting gRNA relative to scrambled gRNA controls ( Fig.5D ). To then assess if hOPCs were able to recruit immune cells through chromatin engineering at the rs1415069 locus, we targeted human OPCs with dCas9-p300 and rs1415069-targeting gRNA and place them in wells in contact with trans-wells containing peripheral blood mononuclear cells (PBMCs) from healthy human donors ( Fig.5E ). We found that the presence of these engineered hOPCs induced increased migration of healthy CD45+ PBMCs compared to hOPCs transduced with negative scrambled gRNA controls ( Fig.5E-F ). Further, the levels of migrating PBMCs were recapitulated by supplementing with conditioned media from the engineered hOPCs or recombinant hCCL2 (100ng/mL) ( Supplementary Fig.5D ). Importantly, this rs1415069 locus activation induced migration was abolished by addition of antibodies targeting the CCL2 protein ( Fig.5F ). Thus, our data indicates that the MS-risk SNP rs1415069 can modulate the capacity of human OPCs to attract immune cells. DISCUSSION Understanding how non-coding genetic variants contribute to complex genetic diseases remains an ultimate challenge of modern genetics ( 3 , 4 , 67 ). While EBV virus infection has been recently shown to play a key role in disease etiology, a large proportion of the human population harbors latent EBV following childhood infection, yet only a small minority will ever develop MS. Thus, the influence of environmental factors on gene expression is both critical yet poorly understood. Indeed, while the effect of non-coding variants on gene expression has been extensively studied in immune cells, there have been few studies on how they affect neural cells in the central nervous system, particularly oligodendroglia. Our study addresses the V2F gap by integrating single-cell chromatin accessibility profiling with CRISPR-based interference/activation screens, previously proven to be highly robust and quantifiable ( 14 , 15 , 18 ). However, as CRISPR screens target entire enhancer regions, we performed high throughput cCRE MPRA screens in parallel, allowing us to evaluate single nucleotide variation ( 44 , 68 ). In addition to elucidating cell-type specific genomic traits, we performed these screens in human OPCs derived from iPS-cells and verified the V2F mechanisms for the top identified variants. Our data indicates that MS-associated SNPs exert regulatory effects in oligodendroglial lineage cells in a cell-type and variant-dependent manner, influencing key oligodendroglial processes such as OPC proliferation, oligodendrocytic differentiation, and immune signaling. To identify MS-risk SNPs with potential regulatory influence on transcription and cell function we took a novel approach integrating genomic risk-loci with single-cell CA along the OLG-lineage, both during homeostatic and inflammatory states. Given the notion that most risk-SNPs reside in non-coding regions and not necessarily influence the transcription of the closest gene, we consider this approach to be more robust compared to integration between genetic risk traits and transcription profiles. Moreover, it allows narrowing down the association to a large number of SNPs (due to LD) to one or a smaller set of SNPs that are more likely to be causative, in a specific cell type and under given conditions. Furthermore, this approach should be highly applicable to other diseases influenced by complex genomic traits. Out of 753 GWAS SNPs associated with MS risk/susceptibility and severity and outside variants, we identified 76 MS-associated SNPs within chromatin accessible regions in OLGs, suggesting they possess potential regulatory roles. cCRE screening revealed variant-specific cis-regulatory activity for several loci, including rs483180: PHGDH , rs2248137: CYP24A1 , and rs1415069: DIPK1A , where the MS-associated variant both increased or decreased expression. Since our parallel cCRE-screens in B cells failed to identify overlapping cCRE, our data highlights the importance of performing these screens in disease and context-relevant cell types, as previously suggested ( 3 , 67 ). It is also worth noting that MS-associated SNPs like rs2286974: CLC16a, previously shown to have function in astrocytes was not identified by our experimental pipeline ( 28 ). Indeed, the majority of cCREs with high cis -enhancing effects did not show cell type specificity or influence from single nucleotide variants (e.g. rs67476479_CA: HLADRB1-H2EB1 and rs10951154: HOXA1 ). In contrast, we found three loci (rs1415069: DIPK1A , rs2248137: CYP24A1 and rs1483180: PHGDH ) that showed both OLG- and nucleotide-specific responses. The absence of any B cell-specific responses validates our OLG-specific chromatin accessibility filtering of MS SNPs, highlighting this as a robust approach for future studies on cell-specific effects of disease SNPs. Recent eQTL analysis have underlined the association between MS-associated SNPs and expression in CNS-resident cells, further underlining the role of cell type-specific SNP influence ( 22 , 39 ). Interestingly, these studies find PHGDH but also NOTCH2 eQTLs for rs1483180 to be specific to oligodendrocytes, further highlighting the notion that non-coding variants may exercise cell-specific genome regulatory features ( 10 ). In our study, targeting rs483180: PHGDH locus not only modulated PHGDH expression but also affected S100A6 , located ∼30 Mb away. Previous studies have reported such long-range regulation to occur. Here, based on our CRISPRi/a as well as chromatin conformation data, we further provide an example where these trans effects mediated by noncoding variants ( 65 ). This also mirrors findings from STING-seq where pooled CRISPR screens revealed both cis and trans regulatory networks for blood trait loci ( 15 ). Environmental factors such as EBV have been implicated in the etiology of MS ( 47 , 69 ). Since around 90% of the human population is infected with this virus, although only a fraction of the population progress with MS, additional factors such as the underlying genetic susceptibility are most likely been involved. Here we identified three MS-risk variants that regulate different cellular processes in the oligodendroglia, implicating these processes in disease initiation and/or progression. rs483180: PHGDH and rs2248137: CYP24A1 regulate processes which could be involved in remyelination processes and are thereby most likely downstream of the MS disease induction. Interestingly, PHGDH loss of function mutations are associated with microcephaly and hypomyelination ( 70 , 71 ). Our identification of the rs2248137: CYP24A1 locus where CYP24A1 participate in Vitamin D metabolism is well in line with previous publications both pinpointing the SNP to be associated with MS and Vitamin D deficiency and also how vitamin D promotes differentiation ( 40 , 69 , 72 ). In contrast, rs1415069: DIPK1A regulates the recruitment of peripheral immune cells by OPCs and thereby could be involved in the initial disease initiation. Our data, together with recent findings ( 47 , 69 ), points to a putative double hit scenario, in which EBV infection of immune cells as B cells, in individuals presenting the rs1415069: DIPK1A variant, would lead to increased migration of EBV-activated immune cells, driven by the higher secretion of cytokines as CCL2 by the target cells, oligodendroglia. This would be similar to what has been observed with astrocyte mediated CCL2 release ( 73 , 74 ). It is also possible that the rs1415069: DIPK1A variant leads to OPC-driven recruitment of CXCR3+ autoproliferating T and B cells to CNS via CXCL9/10/11. Another possibility is that CCL5, also increased by the rs1415069: DIPK1A variant, modulates neuronal senescence in MS, since this cytokine has been shown to negatively regulates neuronal autophagy and hence accelerates senescence in the context of neurodegeneration ( 75 ). Since several SNPs have been associated with MS risk, this might be a component of complex network of interactions between functional SNPs operating in tandem across diverse CNS and immune cell types and environmental factors, such as in the setting of altered vitamin D availability, serine availability, or antecedent EBV infection, as might be predicted from our loci of interest. The MS-risk SNPs that we have identified in our study to be operational in oligodendroglia are common variants, whose effect size is small. The use of MPRAs allowed us to dissect the effects of these individual variants, while the recruitment of activating or repressing chromatin machinery to the loci of the SNPs allowed us to infer and investigate the biological processes they regulate. While we found that prime editing was inefficient in human OPCs (data not shown), the application of high-throughput PRIME-editing screen strategies such as those applied to breast cancer ( 76 ) might provide further insights into the role of MS risk-associated SNPs in MS pathogenesis. It will be also of interest to investigate if the downstream effects of a specific risk SNP involve differential binding of specific transcription factors or other epigenetic mechanisms. In summary, our study functionally links MS-associated non-coding variants to gene regulation and cellular phenotypes in OLGs, providing novel insights into the biological basis for known risk factors of MS, while providing a broader, scalable framework for better understanding - and ultimately predicting - the contributions of non-coding variants to complex traits. Download figure Open in new tab Supplementary Figure 1 a, Example of MS SNP overlap with CA through ATAC-seq and activity-by-contact (ABC) in unstimulated or IFNγ stimulated mouse OLGs. b , mRNA expression of BCAS1 and MBP following 4 weeks of human IPS derived OPCs, cultured in indicated media. Data in b shows two independent experiments. Download figure Open in new tab Supplementary Figure 2 a , Representative image of hOPCs 48h following transduction with a 9kB lentiviral-GFP plasmid. b , Schematic illustration of MPRA-pipeline as every cCRS is associated with a unique barcode able to be detected both in DNA and mRNA fraction with next generation sequencing. c , Forward ( + ) and reverse ( − ) oriented cCREs from MPRA2 associated with severity ranked by average fold-change Log2, including positive control ( red triangle ). d , Correlation of fold-change between all replicates for MPRA1 and MPRA2 in hOPCs and B-cells. e , Top 100 ranked cCREs from MPRA1 and 2 ranked by median fold-change expression between replicates including positive controls ( red triangles ). f , Volcano-plot of all cCREs in forward and reverse significantly enriched in hOPCs or B-cells with absolute numbers. Dotted lines indicate 0.05 (y-axis) and +0.3 - −0.3 (x-axis). g , Violin-plots from Fig.2D and F illustrating cCRE motifs for rs2248137: CYP24A1 being enriched or depleted if supplemented with IFNγ. P values in f were calculated using chi-square test. P values in g were calculated using two-tailed Student’s t-test. * P <0.05, ** P <0.01, *** P <0.001 Download figure Open in new tab Supplementary Figure 3 a , Gating strategy of dCas9-BFP and gRNA-mCherry using FACS. b , z-score of gRNA UMI in all sequenced cells to identify sgRNA-containing cells. Where each row in heatmap represents an individual cell, and columns are z-score-ranked gRNA expression, from left to right are highest to the lowest gRNA expression. c , Heatmap describing sgRNA-containing cells guide expression distributions, where each line is a unique gRNA and each column represents a hOPC identified to contain a dominant sgRNA as indicated by z-score normalized UMI expression. Download figure Open in new tab Supplementary Figure 4 a, Violin-plots of individual BCs for SNPs in LD with rs483180 being significantly different in non-MS vs. MS-associated variants. b , Chromosome1-located differentially regulated transcripts between rs483180-sgRNA or negative scrambled sgRNA controls ranked by expression during IFNγ stimulation. c , mRNA levels of S100A6 and PHGDH 4days following transduction with dCas9-p300 in combination with either S100A6 TSS-targeting gRNA or negative scramble gRNA controls. d , mRNA levels at the same time-point after targeting rs483180 or PHGDH TSS with gRNA or scrambled negative gRNA controls. e , Representative images showing gating strategy following transduction with dCas9-BFP and gRNA-mCherry followed by EdU-FITC labeling. adj P values for a were generated through MPRAnalysis comparing each paired MS vs non-MS variant and correcting for multiple comparisons. P values in c and d were calculated using one-way ANOVA with correction for multiple comparisons. Download figure Open in new tab Supplementary Figure 5 a , Violin-plots of individual BCs for SNPs in LD with rs1415069 being significantly different non-MS vs. MS-associated variants. b , Transcript expression differences between rs1415069-sgRNA or negative scrambled sgRNA controls ranked by expression not supplemented with IFNγ. c , mRNA expression 4days following co-transduction with dCas9-p300 and rs1415069-sgRNA or scrambled negative sgRNA or non-transduced. d , Representative DAPI-staining overlapped phase-contrast images of hOPCs and migrating PBMCs stained for CD45 following co-transduction of hOPCs with dCas9-p300 in combination with rs1415069-targeting sgRNA or scrambled negative sgRNA controls, scale-bar 50µm. adj P values for a were generated through MPRAnalysis comparing each paired MS vs non-MS variant and correcting for multiple comparisons. Data in c and d represents three independent experiments. P values in c were calculated using one-way ANOVA with correction for multiple comparisons. P values in c were calculated using two-tailed Student’s t-test. METHODS Cultivation and lentiviral transduction of iPS-derived hOPCs iPS-derived hOPCs (clone C27) were provided by Steve Goldman’s lab as previously described by Wang. Et.al. ( 32 ). All work in Sweden was performed under the ethical permit 2020-00398, with amendment 2023-04598-02, granted by the Swedish Ethical Review Authority (EPN). 6-well cell culture plates were pre-coated with Poly-L-Ornithine (PLO, Sigma #P4957-50ML) and incubated for >1h at 37°C followed by wash using 1X DPBS -/- (Thermo Fisher, #14190144) followed by incubation with 5ug/mL Laminin (Corning, #354232) in HBSS +/+ (Thermo Fisher, #24020117) for >1h at 37°C with. Cells were cultured in proliferation media (DMEM/F12 (Invitrogen #11330-057) supplemented with 1X B27 (Invitrogen #12587-010), 1X N1 (Sigma, #N6530), 1x NEAA (Invitrogen #11140-050), 60ng/mL T3 (Sigma, #T5516-1MG), 1uM dcAMP (Sigma, #D0260), 100ng/mL Biotin (Sigma, #B4639), 10ng/mL PDGF-AA (R&D #221-AA-50), 10ng/mL IGF-1 (R&D #291-G1-050), 10ng/mL NT3 (R&D #267-N3-025)), and refreshed every 2-3 days, prepared media where kept at +4 °C for <7days. For differentiation, we optimized the concentration of growth factors described in previous protocols ( 32 ) (Supplementary Table 3). We could observe a prominent elevation in BCAS1 and MBP transcription following complete removal of PDGF-AA and thus this was used throughout to enhance OLG differentiation following viral transduction ( Supplementary Fig.1A-B , Supplementary Table 4). All lentiviral transduction where always done acutely in naive iPS hOLG (or Raji Bcell), no cell lines were generated. For lentiviral transduction (excl MPRA and single-cell CRISPRi/a), hOPCs were seeded in 96-, 48- or 12-well plates where 6-12µL100X lentiviral dCas9 particles and 4-6µL20X lentiviral sgRNA particles were added after <24h and kept for an additional 24h. For MPRA lentiviral MPRA particles were added at an MOI of 10 to balance library complexity with required sequencing depth. For single-cell CRISPRi/a, lentiviral sgRNA particles were added at an MOI of 0.5 to secure optimal single sgRNA per cell ratio. For co-stimulations with IFNγ, IFNγ (10ng/mL) were supplemented from the timepoint of lentiviral removal. For EdU-incorporation experiments, EdU (10uM) was supplemented to the media 6h prior to analysis and 4 days following co-transduction using Click-iT™ EdU Alexa Fluor™ 488 Flow Cytometry Assay Kit (Thermo Fisher, #C10420) following the manufacturers exact instructions. Cultivation and lentiviral transduction of B-cells and PBMC Raji B-cells and PBMCs were cultured in RPMI-1640 media (Thermo Fisher, #11875093) supplemented with 10 % FBS (Thermo Fisher, #10500064) where PBMC media also was supplemented with 1X Pen/Strep (Thermo Fisher, #10378016). PBMCs isolated from healthy donors and kept in liquid nitrogen were thawed and recovered in media for 5 days prior to experiments. Raji cells where split two times per week and transduced with 40µL20X lentiviral MPRA particles and Polybrene 5ug/mL in 6-well plates followed by spinfection for 1 h at 800 x g at 33 °C and replenished of fresh media after <24 h. For co-stimulations with IFNγ (R&D, #285-IF-100), IFNγ (10 ng/mL) where supplemented from the timepoint of lentiviral removal. Considerations, design and cloning of MPRAs Candidate cis -regulatory elements (cCRE) to be tested were selected as described in Fig.2A . For MPRA1 and MPRA2, 1402 and 1397 CRE plus 25 negative and positive controls were ordered respectively from Twist Bioscience, CA. Cloning and acquisition was done as previously described by Gordon et al ( 44 ). Throughout, each biological replicate was performed with a unique amplified CRS pool and cloning into the MPRA vector in contrast of using the same CRE pool for each replicate. This was done in order to avoid any skewness or bias in the generated CRE pools. The backbone of MPRA-acceptor plasmid (Addgene, #137725) was changed into a different lentiviral backbone (Addgene, #135956). The obtained vector was linearized using 5ug plasmid with 10µL10X CutSmart buffer, 2.5µL AgeI - HF SPRI (20U/uL) (NEB, #R3552), 2.5µL SbfI-HF (20U/uL) (NEB, # R3642) and ddH2O up to 100µLfollowed by overnight incubation at 37 °C followed by repeated addition of 5µLrestriction enzyme and additional overnight incubation. Oligonucleotide pools (Supplementary Table 5) (1pmol synthesis scale) were PCR amplified adding 2µLpooled oligos (to a final con of 1nM) with 100µL NEBNext HF x2 PCR Matser Mix (NEB, # E2621), 1µL100 μM 5BC-AG-f01, 1µL100 5BC-AG-r01 with 96µLddH20, split into 5 PCR tubes and run 98°C:2min, 5X(98°C:15sec, 60°C:20sec,72°C:30sec), 72°C:5min. Samples were combined and purified using 1.0X SPRI magnetic beads (Beckman Coulter, #B23317) and eluted in 40uL. Barcoded CRS inserts (250 ng) were assembled with linearized pLS-SceI vector (1 µg) using NEBuilder HiFi DNA Assembly Master Mix (NEB, #E2621) in a 200 µL reaction, incubated at 50 °C for 1 h. The product was purified using 0.5× SPRIselect beads and eluted in EB buffer. Recombinant DNA was digested with I-SceI (1 µL, 20 U/µL) in CutSmart buffer at 37 °C for 1 h, followed by purification with 1.2× SPRIselect beads (Beckman Coulter, #B23317). DNA concentration was confirmed to be >25 ng/µL prior to electroporation. Electroporation was performed with 8 µL DNA into 50 µL competent cells, followed by incubation and plating. Colony counts indicated ∼257,000 colonies, sufficient for 180 BCs across all CRS elements. Plasmid DNA was extracted via miniprep. To associate CRS with random BCs, CRS vector libraries (40 ng) were amplified using NEBNext High-Fidelity 2× PCR Master Mix and specific primers (P5-pLSmP-ass-i741/i742 and P7-pLSmP-ass-gfp) in a 200 µL reaction. The PCR protocol included 15 cycles (98 °C 15 s, 60 °C 20 s, 72 °C 3 min; final extension 72 °C 5 min), yielding a 470 bp product. Samples were purified using 0.75× SPRIselect beads and eluted in EB buffer. Sequencing was performed using paired-end 146 bp reads for CRS regions, with 15 cycles for index read 1 (barcode) and 10 cycles for index read 2 (sample index). cCRE assessment, MPRA library sequencing Human OPC and Raji B-cells were cultured in 6-well plates and an amount of 1×10 6 and 3×10 6 respectively was transduced with MPRA lentivirus <24h after seeding in media supplement with or without 10ng/mL human recombinant IFNγ (R&D, #285-IF-100). After 24h media was renewed but kept with its supplement. After 72h, cells were checked for heterogenous GFP expression, washed 3x in DPBS, lysed and RNA/DNA was isolated using RNA/DNA using the AllPrep DNA/RNA Mini Kit (#80204, Qiagen) according to the manufacturers’ exact instructions. Reverse transcription was carried out by first treating obtained RNA with TURBO DNA-free Kit (Invitrogen, #AM1907). RNA was measured using Qubit RNA HS Assay Kit (Invitrogen, #Q32852). Using SuperScript II Reverse Transcriptase Kit (Invitrogen, #18064014), 60ug total RNA was combined with 0.25µL100uM P7-pLSmP-ass16UMI-gfp, 5µLdNTP mix and ddH20, to make up 65µLfollowed by incubation at 65 °C for 5min, placed on ice and followed by addition of 20µL5X First Strand buffer, 10 µL 0.1 M DTT followed by incubation at 42°C for 2 min. 5 µL of Superscript II were added following 50min at 42°C and 15min at 70°C. For library preparation, gDNA and cDNA was diluted to 120ng/µLor undiluted respectively in 100µLand mixed with 200µL NEBNext High-Fidelity 2× PCR Master Mix (NEB, #M0541), 2µL100uM P7-pLSmP-ass16UMI-gf, 2µL100uM P5-pLSmP-5bc-I, 96µLddH2O to a total volume of 400µLand split into 8 reactions and run on the first-PCR program 98°C:1min, 3x(98°C:10sec, 60°C:30sec, 72°C:1min), 72°C:5min. Samples were combined and purified using 1.8X SPRI magnetic beads (Beckman Coulter, #B23317) and eluted in 60uL. For qPCR, 5µLFirst-round PCR product was combined with 10µLNEBNext High-Fidelity 2x PCR Master Mix (NEB, #M0541), 0.1µL100uM P5 primer, 0.1µL100uM P7 primer, 0.1 100X SYBR Green I nucleic acid gel stain and 4.7µLddH2O and run using 98°C:1min, 30x(98°C:10sec, 60°C:30sec, 72°C:1min) where the number of cycles was determined just prior to raw amplification curve plateau. For second-PCR, 50µLFirst-round PCR product was combined with 100µL NEBNext High-Fidelity 2x PCR Master Mix (NEB, #M0541), 1µL100uM P5 primer, 1µL100uM P7 primer and 48µLddH2O to a total of 200µLand split into 5 reactions and amplified as determined by the qPCR. Samples were combined and purified using 1.8X SPRI magnetic beads (Beckman Coulter, #B23317) and eluted in 20µLand measured using Bioanalyzer. To prepare the library for sequencing, RNA/DNA samples were pooled at a ratio of 3:1 and sequenced with paired-end sequencing using custom primers at SciLifeLab-NGI Stockholm using MiSeq nano 2×250 or NextSeq P2-100 for sCRE/barcode association or barcode sequencing respectively. All primers are listed in Supplementary Table 10. MPRA analysis DNA and RNA libraries were sequenced to obtain barcode-level counts, representing construct abundance and transcriptional output, respectively. Base calls were demultiplexed with bcl2fastq software Illumina v2.20.0.422 with parameters -- minimum-trimmed-read-length 0 --mask-short-adapter-reads 0 and “Y151,Y15,I10,Y151“ read setup for the sequenced plasmids. RNA and DNA libraries for all conditions were demultiplexed with same parameters and ’Y15,Y16,I10,Y15’ read setup. Barcode association to the candidate CRE for each plasmid and RNA/DNA counts tables were calculated with MPRAflow v2.3.5 software ( 44 ) NextFlow v20.01.0.5264 version. Association was run with parameters; nextflow run association.nf adding --mapq 1 option. Within our candidate CREs there are identical sequences except for one nucleotide to allow alignment of all the sequences we decreased the mapq value. DNA and RNA counts were calculated with MPRAflow count.nf function with –mpranalyze option to additionally generate outputs compatible with MPRAnalyze v1.26.0 Bioconductor package ( 77 )for downstream analyses. RNA and DNA count tables were loaded in R v4.5.0. To account for technical variability, MPRAnalyze estimateDepthFactors was used with upper quartile normalization to compute size factors for both DNA and RNA datasets, library factors replicate and condition (hOPC vs. Bcell and Ctrl vs. IFNg). This approach enabled robust pairwise comparisons of CREs across experimental conditions, accounting for barcode multiplicity and technical variability. Allele pairs comparisons; we selected 96 pairs of CRE, nonMS and MS allele. Since different CRE do not need to share barcodes, we formatted the RNA and DNA counts tables for the 96 pairs including allele and barcode information. To measure the activity changes between paired sequences, nonMS vs. MS allele, we run MPRAnalyze estimateDepthFactors with library factor replicate and analyzeComparative with design; dnaDesign = ∼ barcode + allele, rnaDesign = ∼ allele. Barcode counts in RNA and DNA per CRE in selected variants, shown as violin plots, were calculated from the association output, filtered_coords_to_barcodes.pickle, first identifying the barcode sequences associated with each CRE and then counting the occurrences in the filtered count tables from MPRAnalyze work output. While the assessment of cCREs with MPRA is independent of the genotype of the host cell C27 used, this line presented the non-MS-associated variants at the rs2248137, rs483180 and rs1415069 loci. gRNA design and cloning All 42 loci from MPRA1 were prioritized to be validated with pooled single-cell CRISPRi/a. Three protospacers per loci within a +/−350bp range but not spanning the SNP were designed using CRISPick (Broad Institute), for arrayed validation four to five sgRNA were design in the same way but in addition also filtered to remove poor seed sequences ( 55 ). All protospacers are listed in Supplementary Table 4 and 7. Pooled sgRNA-libraries were provided by CRISPR Functional Genomics, Karolinska Institute, Stockholm, Sweden whereas sgRNA for arrayed CRISPRi/a were produced in-house. All sgRNA were produced following the same protocol. In brief, oligos for golden gate assembly were purchased and diluted to a final volume of 25 µL annealing buffer. The buffer was prepared by mixing 500 µL of 1 M Tris-HCl (pH 8.0) with 500 µL of 5 M NaCl and adjusting the final volume to 50 mL using ddH₂O. The annealing reaction was incubated at 95 °C for 3 minutes, then cooled to 22 °C at a rate of 0.1 °C/sec, and subsequently diluted in 75 μL ddH₂O. The lentiviral U6-GG-acceptor plasmid (Supplementary Table 12) was pre-linearized using BsmBI (20 U/µL, NEB #R0580) and BsiWI-HF (20 U/µL, NEB #R3553), gel-purified, and aliquoted at a concentration of 30 ng/µL. T4 DNA ligation was performed by combining 1 µL of 1 µM pre-annealed oligos, 30 ng of linearized GG-acceptor plasmid, 0.2 µL each of BsmBI and BsiWI-HF (both at 20 U/µL), 0.5 µL of T4 DNA ligase (40 U/µL, NEB #M0202), 1 µL of 10X T4 DNA ligase buffer, and 6.1 µL ddH₂O. The ligation mix was incubated for 10 minutes at 20 °C, followed by 5 minutes at 37 °C and 5 minutes at 80 °C. Next, 1 µL of the ligation reaction was added to 10 µL of competent cells, incubated on ice for 10 minutes, heat-shocked at 42 °C for 30 seconds, and returned to ice for 5 minutes. Then, 100 µL of SOC medium was added, and the cells were incubated at 37 °C with shaking at 250 RPM for 60 minutes. The transformed cells were plated on agar plates containing ampicillin and incubated overnight. Colonies were picked and grown in liquid LB medium, and plasmids were isolated using the ZymoPURE Plasmid Miniprep Kit (Zymo Research, #D4210) according to the manufacturer’s instructions. The presence of the insert was verified by Sanger sequencing using the primer 5′-cgatacaaggctgttagagag-3′. Lentiviral packaging dCas9-KRAB-BFP, dCas9-p300-BFP and U6-GG-acceptor lentiviral plasmids were provided from CRISPR Functional Genomics, Karolinska Institute, Stockholm, Sweden (Supplementary Table 12). Lentivirus was packaged in HEK293 cells cultured in DMEM (Gibco, #31966021) supplemented with 10%FBS (Gibco, #10500064) on poly-L-lysine (Sigma, #P4707) pre-coated culture ware, seeded to be 70-80% confluent after >24h. A total of 16ug of transfer vector, pCMV-VSV-G, pRSV-REV and pCgpV (Takara, #631278) was mixed at a ratio of 3:1:1:1 with 32µLlipofectamine-2000 (ThermoFisher, #11668030) and Opti-MEM (Gibco, #31985070) due to the manufacture’s protocol. Viral particles were harvested from the media after 48h, centrifuged at 500 x g for 10min and filtered through 0.45um and incubated with Lenti-X concentrator (TaKaRa #631231) at a ratio of 3:1 for >30min at 4°C, before centrifugation at 1500 x g for 45min at 4°C and resuspended in DMEM at a 20X or 100X concentration and kept at −80°C. scCRISPRi/a-seq transduction and library preparation Concentrated lentivirus was added <20h after seeding and media supplements were adjusted accordingly. dCas9-BFP was added at virus:media ratio of 1:4-1:3 transducing 25-20% of the hOPC, whereas sgRNA-mCherry was added 1:100 transducing 30% of cells for scCRISPRi/a and at 1:20 for arrayed CRISPRi/a transducing >80%. Media was replaced after 24h and kept for 3days. Cells were harvested using TrypLE (Gibco, #12605010) to obtain single cell suspension and kept on ice prior to sorting on MA900 Multi-Application Cell Sorter (Sony Biotechnology). Following sorting single cell droplet and sequent mRNA and gRNA library were prepared using Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 (CG000316 Rev D) or Chromium Next GEM Single Cell 3’ HT Reagent Kits v3.1 (CG000418 Rev D) (10X Genomics) both with dual indexes and with feature barcode technology for CRISPR screening. Prepared library quality, size and concentrations were verified with bioanalyzer and pooled and sequenced according to the manufacturer’s exact instructions. Libraries were sequenced at SciLifeLab-NGI Stockhom using NovaSeq X 10b-300 or Novaseq 25b-300. scCRISPRi/a-seq data preprocessing Raw data were returned in fastq Format. Each sample contains two sequencing files, for single-cell RNA profile, paired with sgRNA count profile. Subsequently, RNA sequencing data were aligned against GRCh38, where paired sgRNA are aligned to feature matrix, containing sgRNA information especially the matched protospacers, according to the sgRNA library design of the current study. Aligned results were stored in RNA assay and gRNA assay, respectively. Aligned RNA data from each sample then underwent normalization using both log normalization, as well as SCTransform methods, independently. For guide effect analysis, log normalization was used for the following analysis of linear dimensionality reduction using principal components analysis (PCA), highly variable gene detection, and scaling with a scaling factor of 10,000 using R toolbox Seurat ( 78 ). Data integration and cell type label transfer To observe data diversity, integration analysis was performed on all batches of sequencing results. Both RNA and SCTransform normalized assays were used, eventual integration used SCTransform for better accommodation of potential batch effects. Merged objects were assessed for integration using R package Seurat implemented methods, including CCA, MNN, and Harmony ( 79 – 81 ). To understand major cell populations from all samples, integrated object cell transcriptome profiles were queried upon cell type annotations from the in vitro experiment of GSE285158 using Map query function implemented in Seurat. Gene Set Enrichment Analysis (GSEA) and visualization Singlets (cells assigned with dominant sgRNA) were subset under each batch and were separated based on perturbation and treatment condition (KRAB control, KRAB IFN γ , P300 control, P300 IFN γ ). Within each condition, differential expression analysis of singlets from individual sgRNAs were performed against non-targeting controls, the output gene list was ranked entirely based on fold changes. Ranked gene lists were then used for GSEA analysis ( 82 , 83 ) using gene ontology biological process (GO:BP) database, significant terms (adjP< 0.1) were kept for further analysis. Results of individual guide from each condition were merged across batches, by preserving all unique terms and their corresponding normalized enrichment score (NES) and keeping higher NES value when overlapping terms are detected among replicates. Representative ontology terms were visualized using ComplexHeatmap v2.18.0 ( 84 , 85 ) using NES values. Singlet identification and perturbation effect analysis Singlet identification and perturbation effect analysis To investigate sgRNA-related perturbation effects, sgRNAs were ranked based on UMI count from each cell to detect cells that are mostly dominated by one single sgRNA, termed as singlet. Specifically, sgRNA UMI count were ranked and z-score scaled, singlets are called under the condition when the top expressed guide is >2 z-score compared to the second expressed guide ( 56 ). Singlets within each sample were selected for further differential expression analysis against singlets with non-targeting guides using FindMarker function from Seurat package. qPCR Cells were harvested using miRNeasy Micro Kit (Qiagen, #217084) according to manufacturer’s protocol. cDNA libraries were prepared using High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher, #4368814). RT-qPCR was performed on a StepOnePlus System (Applied Biosystems) in triplicate and with reverse transcriptase negative re-actions to control for genomic DNA. Fast SYBR Green Master Mix (Applied Biosystems, #4385616) was used according to the manufacturer’s protocol, each PCR reaction had a final volume of 10µLand 2µLof diluted cDNA and RT- and incubated at 20s at 95°C, 40x cycles of 3s of 95°C and 30s of 60°C, followed by 15s at 95°C 60s at 60°C and 15s at 95°C. Melting curve was obtained for each PCR product after each run, to control for primer dimers and gene-specific peaks. Expression levels were generated by dividing the quantity by the value of the geometric mean of the housekeeping genes. All primers are listed in Supplementary Table 11. Micro-C Micro-C was generated for a previous study ( 57 ), in brief, genome-interaction data was generated using the Dovetail Micro-C Kit (#21006) with 100,000–200,000 iPSC-derived hOPCs. Cells were crosslinked, digested with MNase for nucleosome-resolution fragmentation, and ligated with biotinylated adapters. After reverse crosslinking and amplification, libraries were prepared in three biological replicates. Initial sequencing was performed on an Illumina NovaSeq6000 S Prime (2×150 bp, 151-19-10-151 setup). Following quality control and library complexity assessment (preseq), two hOPC and all three B-cell replicates were re-sequenced on a NovaSeq S4 flow cell to a depth of 6 billion reads. Micro-C derived pseudo 4C tracks were calculated with HiCExplorer ( 86 ), with parameters hicPlotViewpoint --region “chr1:119724782-119724982” and –rp “chr1:153534599-153535991“ and vice versa to retrieve the interactions in the S100A6 and rs483180 locus. The output bedgraph was reformatted to bedpe keeping only positive correlations for visualization purposes. Immunocytochemistry For immunocytochemistry hOLGs were washed with 1X PBS and fixed in 100 μL 4 % PFA at RT for 10 min, washed with PBS and blocked with 3 % NDS with 0.1 % TritonX-100 in PBS for 1 h at RT. Cells were stained with (1:500) anti-MBP (Abcam, #Ab7349), (1:500) anti-BCAS1 (Thermo Fisher, #PA5-20904) or (1:500) anti-Cas9-AF647 (Thermo Fisher, #51-6499-82) in 3 % NDS with 0.1 % TritonX-100 in PBS over night at +4 °C. Washed with 1X PBS and stained with (1:1000) anti-Rb-488 and/or anti-Rt-555 (Thermo Fisher, # A-21206, #A48270) for 1 h at RT. Images were acquired with Zeiss LSM800-Airy. CCL2 ELISA and Migration assay The sue of PBMC was covered by ethical permit number 2009/2107-31/2 inc. amendment 2022-03650-02. To assess secreted CCL2 levels from OPCs following co-transduction, media was centrifuged at 800 x g for 10min at 4 °C and stored at –80 °C until analysis. Anit hCCL2 was performed due to the manufacturer’s exact instructions (R&D, #DY279). To assess chemotactic responses, a trans-well migration assay was performed using PBMCs from healthy human donors and conditioned media from hOPC cultures (co-transduced with negative scrambled gRNA controls or rs1415069-targeting gRNA in combination with dCas9-p300 or no virus. The assay was based on a previously published protocol by Oner et al 2022 ( 87 ). In brief, PBMCs were washed twice in PBS to minimize serum carryover, which can otherwise confound chemotactic gradients. PBMCs were resuspended in migration medium (RPMI + 1% FBS) at 14 × 10⁶ cells/mL and seeded into the upper chamber of a trans-well insert. The lower chamber was filled with conditioned or control media. Care was taken to avoid bubble formation under the insert, as trapped air can disrupt the chemotactic gradient and impair migration accuracy. After 4 hours of incubation at 37 °C, inserts and wells were fixed and counterstained with Hoechst for imaging. Images were acquired with Olympus IX73. Data and code availability Single-cell CRISPR screen raw data will be deposited in the European Genome-phenome Archive (EGA) repository. MPRA raw data will be deposited in European Nucleotide Archive (ENA). Analysis code is available in https://github.com/Castelo-Branco-lab/Carlstrom_et_al_MS_SNPs_in_OLGs . Processed data files are available at https://doi.org/10.5281/zenodo.17415989 . Authors contributions K.C. and G.C.-B. conceptualized the project and designed the experiments. E.A. performed SNP identification, MPRA data processing and analysis, and pseudo 4C. T.S. performed CRISPR data analysis. Mu.K. performed Micro-C data analysis. N.M. supported with CRISPR design and experiments. Ö.D. performed qPCR experiments. Y.K.L. performed cloning for MPRA2. M.J. and M.P.K. assisted with MS GWAS and B cell analysis. S.A.G. provided iPSC-derived hOPCs. Mo.K. provided patient PMBCs. K.C. and G.C.B. wrote the manuscript. All co-authors read and approved the manuscript. Conflicts of interest G.C.-B. and Mu.K. are shareholders of Nexus Epigenomics. Acknowledgments We thank Tony Jimenez-Beristain for writing human ethical permits and Fredrik Piehl for providing patient PBMC material and Bernhard Schmierer, KI at CRISPR Functional Genomics core facility for production of gRNA library. We acknowledge support from the National Genomics Infrastructure in Stockholm funded by Science for Life Laboratory, the Knut and Alice Wallenberg Foundation, and the Swedish Research Council. Part of the computations/data handling were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725. Part of the computing was also performed in the Linnarsson group Monod Linux cluster at MBB-KI, and we thank Peter Lönnerberg for maintenance and support. Work in M.J research group was supported by the European Research Council Consolidator Grant (Epi4MS, grant agreement number 818170), Knut and Alice Wallenberg Foundation (2019–0089), the Swedish Association for Persons with Neurological Disabilities, the Swedish Brain Foundation, the Swedish MS Foundation, the Stockholm County Council - ALF project. M.P.K. was supported by funds from StratNeuro and the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement number 875510). T.S. was supported by a Marie Skłodowska-Curie Actions post-doctoral fellowship. Work in G.C.-B.’s research group was supported by the Swedish Research Council (grant 2019-01360 and Distinguished Professor grant 2023-00324), the European Union (Horizon 2020 Research and Innovation Programme/European Research Council Advanced Grant SingleMS, grant agreement number 101096064), the Swedish Brain Foundation (FO2023-0032), the Swedish Cancer Society (Cancerfonden grant 23 2945 Pj 01 H), Knut and Alice Wallenberg Foundation (grant 2019-0089 and Wallenberg Scholar grant 2023-0280), the Göran Gustafsson Foundation for Research in Natural Sciences and Medicine, the Swedish Society for Medical Research (SSMF, grant JUB2019 and post-doctoral fellowships to K.C. and N.M), Olav Thon Foundation (GC.-B and S.A.G.), Ming Wai Lau Centre for Reparative Medicine, and Strategic Research Area Stem Cells and Regenerative Medicine (Karolinska Institutet). Funder Information Declared Knut and Alice Wallenberg Foundation, https://ror.org/004hzzk67 , 2019-0089 , 2023-0280 Swedish Research Council, https://ror.org/03zttf063 , 2019-01360 , 2023-00324 European Research Council Consolidator Grant , 818170 , 101096064 Swedish Association for Persons with Neurological Disabilities Swedish Brain Foundation , FO2023-0032 Swedish MS Foundation, the Stockholm County Council StratNeuro , 875510 Marie Skłodowska-Curie Actions post-doctoral fellowship Swedish Cancer Society , 23 2945 Pj 01 H Swedish Society for Medical Research , JUB2019 , post-doc grants Olav Thon (Norway), https://ror.org/05ve4qs86 Ming Wai Lau Centre for Reparative Medicine Strategic Research Area Stem Cells and Regenerative Medicine References 1. ↵ C. A. Dendrou , L. Fugger , M. A. Friese , Immunopathology of multiple sclerosis . Nature reviews. Immunology 15 , 545 – 558 ( 2015 ). OpenUrl CrossRef PubMed 2. ↵ R. J. M. Franklin , Why does remyelination fail in multiple sclerosis? 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