{"paper_id":"0b14bd4b-27df-40f7-b4e0-85d751fe81e5","body_text":"Myeloid-Driven Inflammation in Prodromal Parkinson’s Disease | 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 Myeloid-Driven Inflammation in Prodromal Parkinson’s Disease View ORCID Profile Yoshiaki Yasumizu , View ORCID Profile M. Elizabeth Deerhake , View ORCID Profile Jeonghyeon Moon , View ORCID Profile Nicholas Buitrago-Pocasangre , View ORCID Profile Anthony Russo , Haowei Wang , View ORCID Profile Biqing Zhu , View ORCID Profile John P. Seibyl , Vijaya Reddy , Qianchang Wang , Karen J Neish , Maria Grazia Spillantini , View ORCID Profile David A. Posner , View ORCID Profile Menna Clatworthy , View ORCID Profile Tomokazu S. Sumida , View ORCID Profile Erin E. Longbrake , View ORCID Profile Jesse M. Cedarbaum , View ORCID Profile Le Zhang , View ORCID Profile David A. Hafler doi: https://doi.org/10.1101/2025.05.16.654530 Yoshiaki Yasumizu 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA 2 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yoshiaki Yasumizu For correspondence: david.hafler{at}yale.edu M. Elizabeth Deerhake 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA 2 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for M. Elizabeth Deerhake Jeonghyeon Moon 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jeonghyeon Moon Nicholas Buitrago-Pocasangre 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicholas Buitrago-Pocasangre Anthony Russo 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anthony Russo Haowei Wang 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Biqing Zhu 3 Department of Biostatistics, Yale School of Public Health , New Haven, CT, 06510, USA 4 Program of Computational Biology and Bioinformatics, Yale University , New Haven, CT 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Biqing Zhu John P. Seibyl 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John P. Seibyl Vijaya Reddy 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qianchang Wang 5 Molecular Immunity Unit, University of Cambridge Department of Medicine , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karen J Neish 5 Molecular Immunity Unit, University of Cambridge Department of Medicine , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Grazia Spillantini 2 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA 6 Department of Clinical Neurosciences, University of Cambridge Department of Medicine , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site David A. Posner 5 Molecular Immunity Unit, University of Cambridge Department of Medicine , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David A. Posner Menna Clatworthy 5 Molecular Immunity Unit, University of Cambridge Department of Medicine , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Menna Clatworthy Tomokazu S. Sumida 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA 7 Department of Immunobiology, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tomokazu S. Sumida Erin E. Longbrake 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Erin E. Longbrake Jesse M. Cedarbaum 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA 2 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jesse M. Cedarbaum Le Zhang 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA 2 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA 8 Department of Neuroscience, Yale School of Medicine , New Haven, CT, 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Le Zhang For correspondence: david.hafler{at}yale.edu David A. Hafler 1 Department of Neurology, Yale School of Medicine , New Haven, CT, 06510, USA 2 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA 7 Department of Immunobiology, Yale School of Medicine , New Haven, CT, 06510, USA 9 Broad Institute of MIT and Harvard , Cambridge, MA, 02142, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David A. Hafler For correspondence: david.hafler{at}yale.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract We hypothesized that prodromal Parkinson’s Disease (PD), characterized by REM Sleep Behavior Disorder and hyposmia, is an inflammatory disorder initiated in the gut that evolves into a neurodegenerative process manifest as classical PD. To test this hypothesis, we performed single-cell RNAseq analysis of cerebrospinal fluid (CSF) and blood from 118 individuals, comparing healthy subjects with prodromal PD and manifest PD to patients with the central nervous system (CNS) autoimmune disease, multiple sclerosis (MS). Surprisingly, we identified increased numbers of immune cells in the CSF of patients with prodromal PD. Single-cell RNA sequencing revealed increases in CSF-specific microglia-like macrophages with enhanced JAK-STAT and TNFα signaling signatures in prodromal PD. CSF macrophages exhibited similar transcriptional profiles to dural macrophages and gut muscularis macrophages from α-synuclein-expressing PD model mice. Finally, shared memory T cell clones were found in humans between gut and CSF T cells. These findings uncover a myeloid-mediated TNFα inflammatory process in the CNS of patients with prodromal PD, with an immunological linkage between the gut and the CNS. Introduction Inflammation involving both innate and adaptive immune responses is a common feature across a wide range of central nervous system (CNS) diseases. This most often arises as a secondary response to neurodegeneration or tissue damage, although in certain adaptive T cell-mediated conditions, such as multiple sclerosis (MS), immune activation itself can play a more primary pathogenic role. Parkinson’s Disease (PD) is a neurodegenerative disorder, predominantly in older populations, characterized by the progressive loss of dopaminergic and other neurons and the accumulation of α-synuclein aggregates in the brain. 1 Both genetic and pathologic evidence support the notion that CNS inflammation contributes to PD pathogenesis. 2 – 6 Specifically, the presence of TNFα in PD microglia 7 and the recent discovery of autoreactive α-synuclein T cells in patients with early PD 8 raises the hypothesis that autoreactive T cells drive early CNS inflammation in prodromal PD, as is seen in autoimmune, relapsing-remitting MS, with later, secondary neurodegeneration manifesting as classic PD. 9 – 14 However, pathological studies reveal only a minimal presence of CD8 + T cells in the substantia nigra of PD patients early in disease pathogenesis We previously identified myeloid cells in the cerebrospinal fluid (CSF) that express microglia-associated cell surface proteins, including APOE, C1Q, and TREM2, that are presumably derived from a monocyte lineage. While first observed in higher numbers in the CSF of patients with HIV, 15 they are also observed in the CSF of healthy subjects. These CSF microglia-like macrophages share gene expression patterns with border-associated macrophages located along the brain vasculature at the interface of the brain parenchyma. 16 , 17 Surprisingly, the frequency of this population of microglia-like macrophages was markedly decreased in the CSF of patients with autoimmune, relapsing-remitting MS as compared to age-matched healthy controls. 18 With highly effective B cell depletion therapy, this population re-emerged in the CSF of MS patients, with associated increases in myeloid TNFα expression in blood, suggesting an important role in autoimmune disease pathogenesis. 18 One early clinical manifestation of α-synucleinopathy is Rapid Eye Movement (REM) Sleep Behavior Disorder (RBD), a parasomnia that can appear years or even decades before the onset of typical symptoms of PD or Dementia with Lewy Bodies (DLB). 19 Thus, RBD serves as an early indicator of α-synuclein-related neurodegeneration. There is strong epidemiologic data indicating a link between autoimmune disorders, such as inflammatory bowel disease and PD, adding further support for a prominent role for the immune system in mediating the inception and progression PD pathology. 20 , 21 In addition, mounting evidence implicates the gut as an early site of PD-related pathology, including α-synuclein accumulation and intestinal inflammation, raising the possibility of a gut–brain axis in prodromal synucleinopathy. 22 Thus, examining whether there is CNS inflammation in RBD as a manifestation of prodromal synucleinopathy and determining the pathways mediating inflammation can provide a rationale for clinical experiments to prevent the disease and to provide more convincing evidence that an early autoimmune-like process precedes a secondary neurodegenerative form of disease. Here, we performed single-cell RNAseq analysis of immune cells from CSF and blood from 118 individuals, including 36 with RBD, 15 with PD without RBD, 18 with PD and RBD, 15 age-matched healthy controls for PD, and 27 patients with recent onset MS that had been compared to 7 healthy individuals. We unexpectedly identified an increased number of immune cells in the CSF of patients with RBD, which was not present in patients with longer-standing PD, similar to what is observed in patients with early, autoimmune MS. However, in marked contrast to MS, single-cell RNAseq in prodromal PD revealed an increase in the frequency and cell numbers of CSF microglia-like macrophages expressing IL6-JAK-STAT3 and TNFα signaling pathways. Aspects of this inflammatory signature were shared with those of brain microglia of PD patients as well as CNS-dural sinus myeloid cells, border-associated macrophages, and muscularis macrophages from α-synuclein-expressing PD model mice. 23 These combined findings in humans and PD animal models reveal a myeloid-mediated TNFα inflammatory process in the CNS, perhaps arising from the gut during the prodromal stage of PD, suggesting a newly described pathologic process in disease etiology. Results Single-cell immune atlas of PBMCs and CSF in prodromal and early stage of PD To profile the immunological changes in prodromal and across PD stages, we generated a single-cell RNAseq immune cell atlas from paired peripheral blood mononuclear cells (PBMCs) and CSF cells. Participants were categorized into four groups: RBD (n=36), PD without RBD (described as PD; n=15), PD with RBD (described as PD-RBD; n=18), 27 patients with relapsing remitting MS, age-matched healthy controls (HC; n=15 for PD, n=7 for MS) (Supplementary Tables 1-3) who were separately compared to a previously published cohort of age matched healthy controls. Clinical and demographic information was collected, and PBMCs and CSF samples were obtained from all participants. The demographic and clinical characteristics of our study population were similar to other cohorts (Supplementary Table 2). The mean ages of the subjects in the four groups ranged from 64 to 89 years; 77% were males. Of the RBD group, 19 (53%) were hyposmic, as defined by an UPSIT score below the 15 th percentile for age and gender, 24 7 (19%) evidenced reduced dopamine transporter (DaT) binding in the striatum, and 25 (69%) showed Synuclein Aggregating Activity (SAA) in their CSF. In all, 26 (72%) of the RBD subjects met MDS Prodromal PD Likelihood Ratio criteria for prodromal PD, 25 and were thus defined as a “high-risk” population. Of the subjects who entered with a diagnosis of RBD, six were diagnosed with either PD or Lewy Body Dementia (LBD) during the course of the study. We first examined the CSF for evidence of inflammation by quantitative analysis of approximately ∼30 mL of CSF from each subject. Unexpectedly, subjects with RBD showed increased CSF cellularity compared to age-matched HC subjects (3.3 vs 1.4 cells/µL; p =0.029, ANOVA followed by Tukey-Kramer post-hoc test; Fig. 1a ), indicative of inflammation in the CNS of prodromal PD patients. We then performed droplet-based single-cell RNAseq with VDJ sequencing using the 10x Genomics Chromium platform. After stringent quality control, 523,521 cells from PBMCs (73 donors) and 259,560 cells from CSF (72 donors) were used for the analysis ( Figs. 1b-f , Supplementary Fig. 1a ). The average number of genes and percentage of mitochondrial genes detected per cell across all samples were 1,833 genes and 2.67% in PBMCs and 1,929 genes and 1.83% in CSF, respectively, reflecting high-quality single-cell transcriptomic data across both sample types. Based on the clusters defined by unsupervised clustering, we identified major immune cell types: B cells, myeloid cells, T cells, and NK cells in both PBMCs and CSF. Subclusters within these groups were further annotated based on known marker genes, resulting in 24 clusters in PBMCs and 20 clusters in CSF ( Figs. 1c-f , Supplementary Figs. 1b, c). The cell cluster annotations were validated using CellTypist 26 (Supplementary Figs. 1e,f). Download figure Open in new tab Supplementary Figure 1 Download figure Open in new tab Figure 1: Single-cell immune atlas of PBMCs and CSF in prodromal and early stage of PD. (a) CSF cell counts. (HC: 1.4 + 0.7; RBD: 3.3 + 2.3, PD: 2.1 + 1.8 cells/µL; p =0.029, ANOVA followed by Tukey-Kramer post-hoc test) (b) Schematic overview of the project workflow. (c-f) Uniform Manifold Approximation and Projection (UMAP) plots of single-cell data (c,e) and corresponding cell frequencies (d,f) for blood (c,d) and CSF (e,f). Error bars indicate 95% confidence intervals (e,f). In PBMCs, the proportions of immune cells were as follows: B cells 8.09%, myeloid cells 20.8%, T cells 59.6%, and NK cells 11.0% ( Fig. 1d ). In contrast, CSF showed distinctly different proportions consistent with our previous findings. 27 B cells 0.53%, myeloid cells 23.8%, T cells 73.0%, and NK cells 2.59% ( Fig. 1f ). Compared to PBMCs, CSF had a higher proportion of T cells and myeloid cells, and a lower proportion of B cells and NK cells. At the subcluster level, specific differences were observed. For example, populations such as CD4 + Tnaive cells (15.9% in PBMC, 5.62% in CSF), CD16 + monocytes (3.56% in PBMC, 0.09% in CSF), and CD16 + NK cells (10.0% in PBMC, 0.34% in CSF) were less abundant in CSF, while clusters such as cDC2 (1.06% in PBMC, 3.98% in CSF) and CD4 + T central memory (Tcm) cells (12.3% in PBMC, 41.6% in CSF) were enriched. As we previously reported, in patients with CNS HIV 15 , microglia-like macrophages in the CSF (termed CSF Mac), which expressed C1QC and TREM2 specifically, were the most abundant myeloid cell population in CSF. These findings suggest that, while the CSF immune environment largely consists of circulating immune cells similar to those in PBMCs, the CSF exhibits significant differences in cell composition that likely represents a unique immune microenvironment linked to the brain and CNS borders. Thus, we successfully constructed a single-cell immune cell atlas of PBMCs and CSF in RBD and PD. Pronounced immunological changes in prodromal PD To identify PD-specific immune alterations, we analyzed our single-cell immune atlas to assess both shifts in cell frequencies and absolute cell numbers and alterations in gene expression. To evaluate changes in cell frequencies, we used a Bayesian model 28 to compare RBD, PD, and PD-RBD groups against age-matched healthy controls ( Figs. 2a-c ). RBD exhibits heterogeneity in features such as hyposmia and CSF SAA status, both of which are known to be associated with PD onset. 29 Using available demographic, clinical and imaging data, we further stratified RBD individuals based on prodromal PD likelihood using the Movement Disorders Society (MDS) online Prodromal PD Calculator. 25 This analysis revealed a significant increase in the CSF Mac population in the RBD high-probability group ( Figs. 2b,c ). Additionally, the absolute numbers of CSF Mac, CD4 + Tnaive, Tcm, and Tem cells in CSF were increased as compared to healthy controls ( Fig. 2d ) while there was a decrease in the relative proportion of CD4 + Tcm cells and CD8 + T effector memory/terminally differentiated effector memory (em/emra) cells in the RBD group (FDR<0.05 and <0.1 respectively). As previously described, 30 we observed an increase in CD4 + T naive cells in the peripheral blood of prodromal PD ( Supplementary Fig. 6 , FDR <0.05). Download figure Open in new tab Figure 2: Global characterization highlighted activation of CSF Macrophages in prodromal PD. (a,b) Relative cell frequency differences were analyzed in Blood (a) and CSF (b) for patients with RBD, PD, and PD-RBD using scCODA 28 (a Bayesian statistical tool) relative to age matched healthy controls (HC), with RBD stratified by PD risk score (right heatmaps). See materials and methods for the details. FDR thresholds: 0.05, ‘**’; 0.1, ‘*’. Color indicates fold change in relative cell frequency. The RBD high-risk group is outlined with dotted lines. (c) Relative CSF Mac cell frequency was significantly increased in both RBD low and high patients as compared to PD and PD RBD and age-matched controls. Shown in comparison are new onset, previously reported untreated 18 MS patients, where the relative CSF Mac cell frequency was lower compared to age-matched healthy controls. For MS, Statistical testing was performed using the Mann–Whitney U test for each cell population, followed by multiple testing corrections. The adjusted p-value was 1.12×10 -4 . (d) The absolute cell number of CSF Mac and T cell populations calculated by the proportion of the cell from scRNAseq and the cell count data. (e) Hallmark gene sets enriched in MS (upper) and RBD (lower) across cell types in comparison to HC (materials and methods). Only positively associated gene sets were visualized. See Supplementary Fig. 2 for other comparisons. Panels include only clusters containing >1000 cells for blood and >500 cells for CSF. (lower) The same cell types were retained for MS. Download figure Open in new tab Supplementary Figure 2 We next examined gene expression changes within each cluster ( Supplementary Fig. 2a ). Compared to PBMCs, we observed more pronounced gene expression changes in CSF, especially in CD4 + T cells, CD14 + monocytes, and the CSF Mac population. Notably, when stratifying RBD patients by MDS Prodromal PD probability score, the RBD high-probability group exhibited the highest number of differentially expressed genes (DEGs). These findings suggest that both cell frequency and gene expression changes are most pronounced in the RBD high-probability group, with relatively smaller differences observed in PD and PD-RBD. Furthermore, in CSF, CD4 + T cells and CSF Mac demonstrated coordinated changes in both cell numbers and gene expression. To interpret these gene expression changes, we calculated scores for each cell population across conditions using Hallmark gene sets from the Molecular Signatures Database (MSigDB) ( Fig. 2e , Supplementary Figs. 2b-f, Supplementary Tables 4,5). In PBMCs, oxidative phosphorylation was broadly upregulated in T cells from RBD and PD-RBD patients, suggesting that peripheral circulating T cells maintain a homeostatic state, consistent with the observed increase in naive CD4 + T cells. In CSF, the most pronounced gene program changes were detected in myeloid populations, particularly in CSF Mac. In the RBD group, significant enrichment of pathways such as mitotic spindle ( Mean change =0.640, p adj =4.33×10 -302 ), inflammatory response ( Mean change =0.689, p adj =2.45×10 -189 ), IL6-JAK-STAT3 signaling ( Mean change =0.447, p adj =3.97×10 -166 ), and TNFα signaling via NF-κB ( Mean change =0.807, p adj =2.84×10 -96 ), were observed. These findings suggest heightened cellular proliferation with TNFα/IL6-JAK-STAT3 immune pathways in the CSF Mac population in prodromal PD. Inflammatory signature in MS versus RBD CSF A major paradox in the field of autoimmunity revolves around the TNFα pathway, presumably relating to the different genetic architectures of human autoimmune diseases. 31 , 32 Specifically, while anti-TNFα therapy is an effective treatment for Inflammatory Bowel Disease (IBD) and rheumatoid arthritis (RA), it paradoxically induces exacerbations in patients with MS. 33 , 34 Moreover, studies have shown an increased incidence of PD among persons with IBD but a highly significant decrease in the incidence of PD in IBD patients on anti-TNFα therapy. 20 , 21 Based on these observations, we hypothesized that the CSF Mac population in prodromal PD would be characterized by elevated TNFα signaling and exhibit a distinct gene expression profile compared to CSF from patients with MS. We analyzed a new single-cell dataset of CSF cells from 29 MS patients and 7 previously published age-matched healthy individuals. As recently reported in a different MS dataset, 18 we observed a marked reduction in the CSF Mac population that was confirmed with a recent meta-analysis of other MS datasets ( Fig. 2c ). 17 , 18 , 35 Gene set enrichment analysis revealed a pattern opposite to that observed in RBD, with strong enrichment of gene sets in T cells, B cells, and dendritic cells, while enrichment in CSF macrophages was minimal ( Fig. 2e , Supplementary Table 6). Notably, TNFα signaling via NF-κB showed a negative change in MS CSF Mac ( Mean change =-0.716, p adj =4.75×10 -5 ). Furthermore, we applied (1) a detailed analysis pipeline specialized for circulating CD4 + T cells 36 , 37 and (2) TCR-based estimation of T cell activity. 38 Both approaches consistently suggested that T cells did not show evidence of global T cell activation in PD or RBD (Supplementary Note and Supplementary Fig. 6 - 12 ). Download figure Open in new tab Supplementary Figure 3 Download figure Open in new tab Supplementary Figure 4 Download figure Open in new tab Supplementary Figure 5 Download figure Open in new tab Supplementary Figure 6 Download figure Open in new tab Supplementary Figure 7 Download figure Open in new tab Supplementary Figure 8 Download figure Open in new tab Supplementary Figure 9 Download figure Open in new tab Supplementary Figure 10 Download figure Open in new tab Supplementary Figure 11 Download figure Open in new tab Supplementary Figure 12 Inflammatory signature in CSF Macrophages We conducted a detailed analysis of CSF Mac and found notable quantitative and qualitative changes. As previously described, the CSF Mac population shares transcriptomic similarity with microglia and expresses macrophage markers, such as C1QC , APOE , and TREM2, at higher levels compared to other myeloid populations ( Figs. 3a-c ). 15 , 17 , 18 Previous studies also suggested this population resembles border-associated macrophages. 16 , 17 To further analyze the tissue specificity of this CSF Mac population and its similarity with microglia, we integrated myeloid populations from both PBMCs and CSF with publicly available datasets of microglia 39 and cross-tissue myeloid cells, 26 comparing their expression profiles ( Supplementary Fig. 3 ). While the analysis as expected revealed that CSF Mac population was transcriptionally close to microglia and tissue-resident macrophages, distinct differences were also observed. For instance, SPP1 , P2RY12 , and TMEM119 were more highly expressed in microglia than in CSF Mac ( Supplementary Fig. 3 ). These findings highlight both the similarities and the divergence in expression patterns between CSF Mac and microglia. We next compared the gene expression of CSF Mac between controls and RBD. The analysis revealed upregulation of HLA class II molecules and their regulator CIITA , as well as increased expression of HIF1A , mitochondrial genes, AP1 family genes, JAK2 , TLR2 , and TNFRSF1A and TNFRSF1B in RBD ( Figs. 3d , Supplementary Table 7). These changes were consistent with hallmark gene set analyses, which showed elevated scores for pathways such as inflammatory response and IL6-JAK-STAT3 signaling in RBD patients ( Fig. 3e ). Both RNA seq and flow cytometry analysis revealed an increase in integrin alpha 4 subunit (ITGA4), a key molecule for CNS homing in CD16 + monocytes from the blood of RBD low-risk individuals, suggesting that monocyte migration to the CNS is more active during the prodromal phase of PD ( Fig. 3f ). The pattern of ITGA4 upregulation suggests that monocyte activation may occur relatively early in disease. Download figure Open in new tab Figure 3: CSF macrophages activation and linked gene patterns with PD brain microglia. (a) Myeloid cell populations in CSF on UMAP embeddings. (b) Dot plot depicting signature genes’ mean expression levels and percentage of cells expressing them across clusters. Marker genes for the plot were manually selected. (c) Heatmap showing the transcriptome correlation between myeloid cell populations. (d) Volcano plot showing the differentially expressed genes in CSF Macrophage (CSF Mac) between RBD vs HC. For the visualization, genes with mean expression >0.5 were selected. Genes with FDR <0.1, Log2 fold change >0.2 or <-0.2 were highlighted in red. (e) Sample-wise HALLMARK gene set activity in CSF Mac. *: FDR <0.05. (f) Mean Fluorescence Intensity (MFI) distribution of ITGA4 in peripheral blood CD16 + Monocytes. ( p =0.0147). (g and h) Heatmap showing scaled mean expression of representative genes for CSF Mac population (g) and from the Mac population isolated from the dura from human truncated αSYN expressing PD model mice (MI2BAC, labeled as truncated αSYN) and control mice (BAC, labeled as Ctrl). (h). The value was scaled for each gene. We further hypothesized that CSF Mac might be functionally linked to microglial changes occurring in the brains of PD patients. To test this hypothesis, we utilized publicly available single-nucleus RNAseq data profiling multiple brain regions from 100 donors, including 75 PD cases. 40 The analysis revealed that genes upregulated in CSF Mac in RBD were also significantly upregulated in the primary motor cortex and prefrontal cortex of PD brains (Supplementary Figs. 4a, b). These results suggest that CSF Mac are not only activated during the prodromal phase of PD but also reflect microglial changes occurring within the PD brain. In addition, we attempted to examine myeloid populations in the CNS border region. As dural tissue was not available, we examined dural myeloid cells from the MI2BAC knock-in PD model mice, which express both human 1–120 truncated and full-length α-synuclein in catecholaminergic neurons under the control of the tyrosine hydroxylase promoter on a mouse α-synuclein–null background, thereby lacking endogenous murine α-synuclein expression 41 (Methods). The dural macrophages expressed CSF Mac marker genes such as Trem2 , Apoe , and C1qc ( Fig. 3g,h and Supplementary Fig. 4c ). We found that TNFα-related genes and MHC class II genes were upregulated in PD model mice, consistent with the changes observed in CSF Mac in prodromal PD. These data suggest that synchronized gene expression changes occur in brain parenchymal microglia, CNS border-associated macrophages, and CSF Mac in prodromal and clinical stages of PD. Genetic prioritization of cell populations highlighted myeloid population in CSF To determine whether integrating known genetic information with our immune cell atlas could aid in prioritizing relevant cell populations in PD, we analyzed both polygenic and rare variant components. First, we used scDRS 42 to integrate GWAS data associated with PD, 4 RBD, 43 and LBD 44 traits, evaluating the enrichment of polygenic signals in individual cell types (Supplementary Figs. 5a,b). The analysis revealed the strongest polygenic signal in CSF Mac for LBD. Note that LBD 44 is a mixed proteinopathy that demonstrates genetic, clinical, and pathological features in common with both PD and Alzheimer’s disease, and approximately half of patients with RBD will go on to develop dementia with Lewy Bodies rather than motor Parkinson’s disease, 45 , 46 consistent with it not being a pure synucleinopathy. For PD, significant associations were observed in cDC2 and pDC in blood and pDC in CSF. No significant associations were detected for RBD, although polygenic signals appeared to accumulate in myeloid populations within the CSF. Enhanced cell-cell communication in the immune landscape of RBD Next, we examined cell-cell interactions in the CSF using CellPhoneDB. 47 The ligand—receptor interaction prediction suggested that myeloid cells, including CSF Mac and cDC2, would exhibit robust ligand—receptor signals and that there would be more interactions between lymphocytes and myeloid cells than among lymphocytes alone ( Fig. 4a ). Focusing on the RBD group, where we observed the most pronounced immunological changes, we investigated cell-cell interaction pairs with substantial shifts in signaling dynamics. We found that ligands associated with signaling were moderately elevated ( FDR <0.2) in lymphocyte subsets such as CD4 Tcm, NK CD16, and memory B cells, whereas receptor signals were enhanced ( FDR <0.2) in myeloid subsets including cDC2 and CSF Mac ( Fig. 4b ). Notably, the strongest elevation in receptor mediated signals occurred among myeloid cells themselves, suggesting the presence of autonomous feedback activation. To explore these observations further, we examined autonomous ligand—receptor pairs in CSF Mac and identified several upregulated pairs in RBD, including C3 – C3AR1 , CD55 – ADGRE5 , and LGALS9 – HAVCR2 (Tim3), which are associated with immunoregulatory functions ( Fig. 4c ). In addition, we detected neurodegeneration-related signals mediated by amyloid precursor protein ( APP – CD74 , APP – SORL1 ) and prion protein ( PRNP – ADGRG6 ). Download figure Open in new tab Figure 4: Cell-cell interaction analysis showed enhanced receiver activity in CSF myeloid cell populations (a) Heatmap showing the number of interactions inferred by CellPhoneDB. 47 The x-axis shows the sender, and the y-axis shows the receiver. (b) The activated ligand-receptor pairs in RBD compared to HC (materials and methods). The upregulated sender signals (upper) and receiver signals (lower) are shown. Interactions with FDR <0.2 are shown in the black lines. Potentially upregulated interactions ( p <0.05) are shown in the dashed grey lines. (c) Heatmaps show fold change in disease conditions compared to healthy controls for representative CSF Mac ligand-receptor pairs. (d) Dot plot showing the expression of HAVCR2 across CSF myeloid cell populations (upper) and in CSF Mac population across conditions (lower). It is also known that aggregated α-synuclein can induce the activation of myeloid cells. 48 These ligand—receptor pairs may modulate the activation state of CSF macrophages and influence the CNS microenvironment. In particular, increases in TIM-3 expression in models of Alzheimer’s Disease models leads to decreased clearance of amyloid plaques 49 indicating that TIM-3 expression may lead to decreased clearance of α-synuclein, leading to increases in myeloid cells activation ( Fig. 4d ). Collectively, these dynamic and comprehensive changes in cell-cell interactions appear to play a critical role in shaping the immune landscape during the prodromal stage of PD. An Immunological Gut–Brain Axis in Prodromal PD Building on our previous work demonstrating a gut–brain axis mediated by gut-derived T cells, 50 we analyzed paired PBMC, CSF, and gut samples from individuals with RBD, PD-RBD, MS, and HC using single-cell RNA and TCR-seq ( Fig. 5a , Supplementary Figs. 4d-e). Across tissues, shared TCR clones were predominantly observed among CD8 effector memory T cells, with clonal overlap detected between PBMC and gut, as well as between PBMC and CSF. Although less frequent, shared clones spanning the gut and CSF were also identified, suggesting a continuous T cell circulation axis linking the gut, peripheral blood, and central nervous system. Download figure Open in new tab Figure 5: Immunological gut-brain axis in prodromal PD. (a) Heatmap showing pairwise dissimilarity of T cell clonal repertoires across human tissues quantified using 1−Jaccard index . (b) Hallmark gene sets show higher activity in Macrophages in the muscularis externa (ME) in 3KL αS transgenic mice compared to WT (Methods). Only positively associated gene sets were visualized. The dashed line at the bottom indicates Padj = 0.05. The data was downloaded from GSE307000 23 . (c) Working model linking gut and CNS-border myeloid inflammation. Finally, a recent report suggested that intestinal macrophages modulate synucleinopathy in a murine α-synucleinopathy model. 23 Re-analysis of muscularis macrophages from these mice showed that intestinal macrophages display increased TNFα signaling pathway activity similar to CSF Mac in human RBD individuals ( Fig. 5b ). Taken together, these findings support a model in which gut α-synuclein aggregation and intestinal macrophage activation are immunologically linked to CSF and border-associated macrophages through shared TNFα-driven inflammatory programs, forming a coordinated gut–brain immune axis in prodromal PD ( Fig. 5c ). Discussion While emerging evidence suggests an underlying inflammatory component in PD, as demonstrated by the presence of α-synuclein autoreactive T cells in the circulation of patients with manifest and prodromal PD, pathological studies reveal only a minimal presence of CD8 + T cells in the substantia nigra of PD patients early in disease pathogenesis. 51 To elucidate early mechanisms of disease pathogenesis in PD, we performed single-cell RNAseq analysis of paired CSF and blood from subjects with different stages of PD and compared them to age-matched healthy subjects and patients with MS. We found a previously unrecognized increase in CSF immune cells that is most pronounced in patients with prodromal PD (RBD). Single-cell RNAseq analysis revealed increased frequencies of a population of microglia-like macrophages, termed CSF Mac, exhibiting inflammatory responses characterized by increased expression of IL6-JAK-STAT3 and TNFRSF1A and TNFRSF1B signaling pathways, with a relative lack of T cell activation in CSF or blood. This is in marked contrast to what is observed in MS CSF, where there is a loss of CSF microglia-like macrophages and an increase in activated T cells. Specifically, we compared immune populations with single-cell resolution in the CSF of subjects with new-onset MS and RBD. In contrast to MS CSF where we observed a decreased frequency of CSF Mac population, this population was increased in the CSF of patients with prodromal PD, with a pronounced TNF/IL-6/JAK-STAT signature. Of note, inhibition of the JAK/STAT pathway has been implicated in exerting protective effects in PD. 52 – 54 We recently observed that treatment of patients with MS with B cell depletion leads to an exponential increase of this CSF Mac population with increased secretion of TNFα by circulating monocytes. 18 Taken together, these data suggest fundamental differences in the activity of TNFα pathways between prodromal PD and MS. Moreover, these experimental results are consistent with large-scale epidemiologic investigations of patients with IBD, demonstrating an increased risk of PD in patients with IBD, and that anti-TNFα agents substantially decrease the incidence of new-onset PD in this population. 20 Our observations comparing PD and MS are in line with a fundamental observation of human inflammatory diseases. Specifically, the therapeutic efficacy found with blocking an inflammatory pathway in one autoimmune disease can lead to flare-ups in other autoimmune diseases. Two prominent examples include anti-IL-17 blockade, which has strong efficacy in treating psoriasis but has the risk of exacerbating IBD 55 and, as noted anti-TNFα blockade, an effective treatment in IBD and rheumatoid arthritis, can cause exacerbations in patients with MS. 34 These dichotomous responses are likely related to the different genetic architectures of autoimmune diseases, where a risk haplotype in one disease can be protective in another. 31 Compared to the significant changes we observed in CSF myeloid populations, relatively minor changes in T cell populations were noted in prodromal PD. Specifically, our analysis replicated previous studies 30 showing increases in circulating and CSF naïve CD4 + T cells in prodromal PD and PD with reductions in the proportion of CD4 + Tcm cells and CD8 + Temra cells in CSF. These results are in contrast to T and B cell populations in patients with early relapsing/remitting MS, which display markedly inflammatory signals in CSF T cells and in parenchymal lesions which are relatively absent in prodromal PD. While prior studies have identified α-synuclein–reactive T cells in patients with PD 8 , 10 , 12 , we did not observe evidence of global T cell expansion or activation in the CSF of prodromal PD. We hypothesize, based on PD α-synuclein mouse models, that these autoreactive T cells exhibit their pathogenic function in the gut and dural associated lymph node tissue. Notably, we identified shared T cell clones between the gut and CSF, suggesting that T cells may represent an immunologic link between these compartments in PD. This finding aligns with α-synuclein mouse models in which aggregated α-synuclein in the gut leads to activation of both myeloid cells and reactive T cells 56 . Furthermore, the accompanying paper demonstrates the presence of activated T cells and myeloid cells with a TNFα signature in dura, suggesting that the meningeal compartment is a key anatomical immune niche in PD. 48 In this framework based on observations in both animal models and in humans with prodromal PD, we hypothesize the disease originates from α-synuclein aggregation in the gut perhaps triggered by the microbiome inducing TNFα secreting macrophages in the muscularis layer of the gut leading to: 1) propagation of α-synuclein aggregates via the vagus nerve to the brain 57 ; and/or 2) trafficking of α-synuclein reactive T cells between gut and dural sinuses that then; 3) mediate a myeloid-driven inflammatory response characterized by TNF signaling across intestinal and then meningeal compartments into the CNS (figure S13). In summary, our integrated analysis of human CSF and blood, together with observations in mouse models, supports a role for myeloid-driven inflammation in early PD pathogenesis. We identified an increase in CSF immune cells in prodromal PD, marked by expansion of CSF macrophages with TNFα and JAK/STAT pathway inflammatory signatures, in contrast to the T-cell driven inflammation observed in MS. Although α-synuclein-reactive T cells have been previously identified, we did not find evidence of global T cell activation in CSF. However, we did detect shared T cell clones between gut and CSF, alongside shared macrophage inflammatory responses in intestinal and meningeal compartments. Within this framework, TNFα pathway-associated myeloid populations emerge as a central effector in PD pathogenesis, providing a mechanistic rationale for therapeutic targeting of this pathway in prodromal PD. Based on these data, we have initiated a clinical trial of an anti-TNFα monoclonal antibody in patients with prodromal PD (ClinicalTrials.gov: NCT06996652 ). Funding This work was supported by Aligning Science Across Parkinson’s (ASAP: D.A.H., L.Z., and M.G.S.). D.A.H. also receives research support from Hoffmann-La Roche Pharmaceuticals and Genentech. The study is funded by the joint efforts of the Michael J. Fox Foundation for Parkinson’s Research (MJFF) and the ASAP initiative. MJFF administers the grant (ASAP-000529) on behalf of ASAP and itself. L.Z. is supported by NIH grants (DP2 DA056169, R56 AG074015). Y.Y. is supported by JSPS Overseas Research Fellowships. D.A.H. is supported by NIH grants (P01 AI073748, U19 AI089992 U24 AI11867, R01 AI22220, UM 1HG009390, P01 AI039671, P50 CA121974, R01 CA227473), Race to Erase MS and National MS Society. D.A.H. and J.M.C. are supported by a grant from the Marcus Foundation (Grant # AWD0011411) Conflict of interests D.A.H. has received research funding from Bristol-Myers Squibb, Sanofi, Genentech, and Hoffman LaRoche. He has been a consultant for Repertoire Inc, Bristol Myers Squibb, Genentech, Novartis Pharmaceuticals, and Sanofi Genzyme. J.M.C has received consulting fees from VanquaBio, Inc. Author Contributions L.Z., Y.Y., M.E.D., J.M.C., and D.A.H. conceptualized the study. L.Z., J.M., A.R., and H.W. performed the experiments. Y.Y., M.E.D., B.Z., L.Z., J.P.S, and V.R. analyzed the data. Q.W., M.G.S., K.J.N., D.A.P., and M.C. generated mouse scRNAseq data. Y.Y., L.Z., J.M.C., and D.A.H. wrote the manuscript. A.R., H.W., V.R., N.P., E.E.L., J.M.C., and D.A.H. collected clinical samples. Y.Y., L.Z., J.M.C., T.S.S., and D.A.H. supervised the project. Data and Materials Availability Codes will be available on Github ( https://github.com/yyoshiaki/2025_PD_PBMCCSF_scRNAseq ) upon acceptance. Raw fastq files will be available in the ASAP CRN cloud for the PD and RBD cohort, and dbGAP and GEO for the MS cohort upon acceptance. The processed scRNA-seq data will be available in CZ CELLxGENE upon acceptance. Acknowledgement We also acknowledge Yale Center for Genome Analysis for 10x Genomics library preparation and Illumina sequencing and Dr. Brian Koo for assistance in patient collections. The illustrations were generated using BioRender.com . Funder Information Declared Aligning Science Across Parkinson's , ASAP-000529 Hoffmann-La Roche Pharmaceuticals and Genentech National Institutes of Health, https://ror.org/01cwqze88 , DP2 DA056169 , R56 AG074015 , P01 AI073748 , U19 AI089992 , U24 AI11867 , R01 AI22220 Footnotes Added new data and figures with revisions in manuscripts. Reference 1. ↵ Kalia , L. V. & Lang , A. E . 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Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Myeloid-Driven Inflammation in Prodromal Parkinson’s Disease Yoshiaki Yasumizu , M. Elizabeth Deerhake , Jeonghyeon Moon , Nicholas Buitrago-Pocasangre , Anthony Russo , Haowei Wang , Biqing Zhu , John P. Seibyl , Vijaya Reddy , Qianchang Wang , Karen J Neish , Maria Grazia Spillantini , David A. Posner , Menna Clatworthy , Tomokazu S. Sumida , Erin E. Longbrake , Jesse M. Cedarbaum , Le Zhang , David A. Hafler bioRxiv 2025.05.16.654530; doi: https://doi.org/10.1101/2025.05.16.654530 Share This Article: Copy Citation Tools Myeloid-Driven Inflammation in Prodromal Parkinson’s Disease Yoshiaki Yasumizu , M. Elizabeth Deerhake , Jeonghyeon Moon , Nicholas Buitrago-Pocasangre , Anthony Russo , Haowei Wang , Biqing Zhu , John P. Seibyl , Vijaya Reddy , Qianchang Wang , Karen J Neish , Maria Grazia Spillantini , David A. Posner , Menna Clatworthy , Tomokazu S. Sumida , Erin E. Longbrake , Jesse M. Cedarbaum , Le Zhang , David A. 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