{"paper_id":"18e51b54-fa8d-4bef-8038-835315cc898c","body_text":"Pediatric cerebrospinal fluid immune profiling distinguishes pediatric -onset multiple 1 \nsclerosis from other pediatric-onset acute neurological disorders 2 \nDiego A. Espinoza 1,2,3,+, Tobias Zrzavy 1,2,4,+, Gautier Breville 1,2,+, Simon Thebault 1,2,5, Amaar 3 \nMarefi2,6,7, Ina Mexhitaj 1,2, Mengyuan Kan 3,8, Micky Bacchus 6, Jessica Legaspi 6, Samantha 4 \nFernandez6, Anna Melamed6, Mallory Stubblebine6, Yeseul Kim1,2, Zachary Martinez9, Caroline 5 \nDiorio9, Andreas Schulte -Mecklenbeck10, Heinz Wiendl 11, Ayman Rezk 1,2, Rui Li 1,2,3,12, Sona 6 \nNarula2,6,7, Amy T. Waldman 2,6,7, Sarah E. Hopkins 2,6,7, Brenda Banwell 1,2,6,7*, Amit Bar -7 \nOr1,2,3.6,7,* 8 \n 9 \n1Center for Neuroinflammation and Experimental Therapeutics, Perelman School of Medicine, 10 \nUniversity of Pennsylvania, Philadelphia, PA, USA 11 \n2Department of Neurology, Perelman School of Medicine, University of Pennsylvania, 12 \nPhiladelphia, PA, USA 13 \n3Colton Center for Autoimmunity, Perelman School of Medicine, University of Pennsylvania, 14 \nPhiladelphia, PA, USA 15 \n4Department of Neurology, Medical University of Vienna, Vienna, Austria 16 \n5Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, 17 \nMontreal, Canada 18 \n6Division of Child Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA  19 \n7Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, 20 \nPhiladelphia, PA, USA 21 \n8Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, 22 \nUniversity of Pennsylvania, Philadelphia, PA, USA 23 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\n9Division of Oncology, Department of Pediatrics, Children's Hospital of Philadelphia, 24 \nPhiladelphia, PA, USA 25 \n10Department of Neurology with Institute of Translational Neurology, University Hospital of 26 \nMünster, University of Münster, Münster, Germany. 27 \n11Clinic for Neurology and Neurophysiology, University Medical Center  Freiburg, Freiburg, 28 \nGermany 29 \n12Department of Neurology  of the First affiliated  Hospital, Institute of Neuroscience , Fujian 30 \nMedical University, Fujian, China. 31 \n 32 \n+Co-first authors 33 \n*Co-corresponding authors  34 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nABSTRACT 35 \nThe cerebrospinal fluid (CSF) provides a unique glimpse into the central nervous system (CNS) 36 \ncompartment and offers insights into immune processes associated with both healthy immune 37 \nsurveillance as well as inflammatory disorders of the CNS. The latter include demyelinating 38 \ndisorders, such as multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein antibody -39 \nassociated disease (MOGAD) , that warrant different therapeutic approaches yet are not always 40 \nstraightforward to distinguish on clinical and imaging grounds alone. Here, we establish a 41 \ncomprehensive phenotypic landscape of the pediatric CSF immune compartment across a range of 42 \nnon-inflammatory and inflammatory neurological disorders, with a focus on better elucidating 43 \nCNS-associated immune mechanisms potentially involved in, and discriminating between, 44 \npediatric-onset MS (MS) and other pediatric -onset suspected neuroimmune disorders, including 45 \nMOGAD. We find that CSF from pediatric patients with non-inflammatory neurological disorders 46 \nis primarily composed of non-activated CD4+ T cells, with few if any B cells present. CSF from 47 \npediatric patients with acquired inflammatory demyelinating disorders is characterized by 48 \nincreased numbers of B cells compared to CSF of both patients with other inflammatory or non -49 \ninflammatory conditions.  Certain features, including particular increased frequencies of antibody-50 \nsecreting cells (ASCs) and decreased frequencies of CD14+ myeloid cells, distinguish MS from 51 \nMOGAD and other acquired inflammatory demyelinating disorders. 52 \n  53 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nINTRODUCTION 54 \nIn the non-inflammatory state, the central nervous system (CNS) contains immune cells involved 55 \nin steady -state immune surveillance 1, while in inflammatory disease states such as multiple 56 \nsclerosis (MS),  the abundance and profile of peripheral immune cells present within the CNS can 57 \nvary and may include pathogenic immune cells 1,2. Cerebrospinal fluid (CSF), which is produced 58 \nin the choroid plexus and drains into the subarachnoid granulations, is a fluid that envelops the 59 \nbrain in the subarachnoid space. It thus represents a CNS sub -compartment and may harbor cells 60 \nthat partially mirror disease processes occurring within the CNS tissue. To this end, prior studies 61 \nhave profiled CSF immune cells across neurological diseases including MS 3–6, Alzheimer’s 62 \ndisease7, and brain metastases 8, among others (recently reviewed in 9), to elucidate disease -63 \nimplicated immune mechanisms. However, while CSF cells from adult patients have been profiled 64 \nextensively 3–6,10–16, there exists limited data on the CSF cellular compartment in the pediatric age 65 \ngroup5, whether in health or across neurological disease states. The latter includes pediatric 66 \nacquired inflammatory demyelinating syndromes (ADS), a collection of disorders in which 67 \nimmune responses are implicated in mediating CNS demyelination. 68 \n 69 \nPediatric-onset ADS include pediatric-onset MS (MS) which accounts for approximately 25% of 70 \nall pediatric ADS diagnoses 17–23; the more recently described MOGAD which accounts for 71 \napproximately 30% of pediatric ADS diagnoses17; and individuals with ADS who do not meet MS, 72 \nMOGAD, or neuromyelitis-optica spectrum disorder (NMOSD)  diagnostic criteria ( collectively 73 \nreferred to here as ‘other ADS’). MS, and particularly MOGAD, can share considerable overlap in 74 \ntheir clinical presentation and disease course (with a relapsing course experienced in some patients 75 \nwith MOGAD). At the time of initial presentation, it may not be straightforward to distinguish the 76 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\ntwo conditions, and results of anti-MOG antibody testing that can be diagnostic for MOGAD may 77 \ntake days to weeks to become available. Like MS, patients with MOGAD can experience relapsing 78 \nepisodes of inflammatory demyelination. However, use of certain MS disease -modifying drugs 79 \nmay not be efficacious in prevention of MOGAD relapses and may even exacerbate its course 24–80 \n26, pointing to distinct immunopathophysiological mechanisms underlying these two inflammatory 81 \ndemyelinating disorders, and the importance of distinguishing between them. 82 \n 83 \nHere, we investigate the CSF immune cell compartment across a spectrum of pediatric -onset 84 \nneurological disorders, including new-onset MS and MOGAD. We first characterize the immune 85 \ncompartment in non-inflammatory CNS conditions across the pediatric age-span. We then identify 86 \nimmune cell features distinguishing ADS from non -inflammatory and other (non-demyelinating) 87 \ninflammatory disorders and further identify key cell -based immune features distinguishing MS 88 \nfrom both MOGAD and other forms of ADS at the time of incident clinical presentation. In 89 \naddition to establishing a foundational dataset describing the profile and dynamic nature of the 90 \ncellular composition within non-inflammatory pediatric CSF throughout childhood and 91 \nadolescence, we provide disease-specific insights into early CNS-associated inflammation in MS, 92 \nMOGAD, other ADS and other neuroimmune disorders. We expect our findings to represent a 93 \nuseful resource for future studies into the pediatric CNS immune compartment in health and 94 \ndisease.  95 \n  96 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nRESULTS 97 \nCSF immune cell profiling in the non-inflammatory pediatric CSF  98 \nWe first established a standardized 16-parameter flow cytometric platform to profile immune cells 99 \nfrom fresh pediatric CSF samples obtained at diagnostic lumbar punctures (Fig. 1A, see Methods). 100 \nThis platform enabled us to identify and characterize major cell lineages (e.g. T cell, B cells, 101 \nmyeloid cells), as well as subsets within these lineages, from small volumes of pediatric CSF (0.5-102 \n7 mL) (Supplementary Fig. 1). 103 \n  104 \nWith this platform established, we next set out to determine the landscape of pediatric CSF across 105 \nneurological conditions that are not considered primarily inflammatory in nature (i.e. non -106 \ninflammatory neurological disorders, or NIND, which include disorders such as primary headaches 107 \nand idiopathic intracranial hypertension, among others (Supplementary Table 1). To this end, we 108 \nanalyzed the CSF flow cytometric profiles of 33 patients with subsequently ascertained diagnoses 109 \nof NIND (Table 1). NIND CSF cell concentrations ranged from 0 .2-6.0 cells/uL (median 0.63 110 \ncells/uL, Fig. 1B). The most abundant cell types within the cell fraction of the NIND CSF were 111 \nCD3+ T cells ( Fig. 1C, median 67.3%, CD45+/CD14 -/CD19-/CD3+) and CD14+ myeloid cells 112 \n(median 12.9%, CD45+/CD14+). Natural killer (NK cells 4.3%, CD45+/CD14 -/CD3-/CD19-113 \n/CD56+), dendritic cells (DCs 1.6%, CD45+/CD14-/CD3-/CD19-/CD56-/HLA-DR+/CD11c+), B 114 \ncells ( 0.6%, CD45+/CD14-/CD3-/CD19+) and polymorphonuclear cells (PMNs 0.3%, 115 \nCD45+/SSChi, inclusive of neutrophils, eosinophils, and basophils ) were detected at lower  116 \nfrequencies. 117 \n 118 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nWithin the CD3+ T-cell compartment of NIND CSF ( Fig. 1D), most cells were CD4+ (median 119 \n67.2%), whereas CD8+ (median 23.6%) and double-negative (CD4-/CD8-; median 8.4%) T cells 120 \nwere less frequent . Double-positive (CD4+/CD8+) T cells were rarely observed (median 0.7%). 121 \nMost CD4+ and CD8+ T cells were memory T cells (based on CD27 and CD45RA  expression, 122 \nmedian 97.8% and 77.0%, respectively, Fig. 1E-F), with larger variability in memory CD8+ T cell 123 \nfrequencies across donors . This variability appeared to be explained, at least in part, by a clear 124 \ncorrelation between age and frequency of naive CD8+ T cells, such that youngest children with 125 \nNIND had high frequencies of naive CD8+ T cells, with gradual increases in the frequency of 126 \nmemory CD8+ T cells observed with increasing age ( Fig. 1G ). Total CD8+ T -cell counts  127 \n(cells/mL) remained stable over time (Supplementary Fig. 2A), indicating an age-associated shift 128 \nin the balance of naive and memory CD8+ T cells in the non-inflamed pediatric CSF.  We did not 129 \nobserve an age-associated pattern for naive and memory CD4+ T cells (Supplementary Fig. 2B). 130 \n 131 \nFurther assessment of the memory CD4+ and CD8+ T-cell compartments in patients with NIND 132 \nrevealed that memory CD4+ T cells were mostly CD27+/CD45RA -, consistent with a central -133 \nmemory (CM) phenotype (Supplementary Fig. 2C) with the remainder being primarily effector-134 \nmemory (EM) cells (CD27 -/CD45RA-), and few CD4+ TEMRA cells (CD45RA+/CD27 -) 135 \ndetected (Supplementary Fig. 2C). Most memory CD4+ T cells in NIND CSF were Th1-like 136 \n(CXCR3+/CCR6-, Supplementary Fig. 2D ). Few memory CD4+ T cells were activated 137 \n(CD38+/HLA-DR+, Supplementary Fig. 2E). Similarly, most memory CD8+ T cells in the NIND 138 \npatients had a CM phenotype (Supplementary Fig. 2F), and activated memory CD8+ T cells 139 \nmade up a minority of the memory CD8+ T-cell compartment (Supplementary Fig. 2G). 140 \n 141 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nOverall, pediatric NIND CSF was as expected of generally low cellularity, with most leukocytes 142 \nbeing CD4+ T cells, most of which were memory T cells with  a Th1-like phenotype and non -143 \nactivated. CD8+ T cells were similarly mostly memory T cells and non-activated. We found that 144 \nyounger age correlated with an increased frequency of naive CD8+, but not CD4+, T cells.  145 \n 146 \nElevations in B-cell counts highlight the increased cellularity of pediatric ADS CSF 147 \nHaving established the CSF immune cell landscape in pediatric NIND, we next compared CSF 148 \ncellular profiles of children with ADS (n = 33) to children with both NIND and a range of other 149 \ninflammatory and non -inflammatory neurological disorders (Table 1, further detail in 150 \nSupplementary Table 1 ). These disorders  included peripheral inflammatory neurological 151 \ndisorders (PIND,  n=7, which include inflammatory demyelinating disorders of the peripheral 152 \nnervous system such as Guillain -Barre syndrome), autoimmune encephalitidies (AIE  n = 6, , 153 \nautoimmune inflammatory disorders of the CNS that are not necessarily demyelinating, such as 154 \nNMDA-receptor encephalitis), and inherited disorders of white matter (IDWM, n = 4, genetic 155 \ndisorders resulting in abnormal growth or destruction of CNS white matter).  156 \n 157 \nWe first compared the absolute CSF cell counts of all children with ADS to children with NIND, 158 \nPIND, AIE, and IDWM. We observed a statistically significant increase in the cellularity of ADS 159 \nCSF compared to NIND, PIND, and AIE CSF (Fig. 2A ). We next examined how individual 160 \nlineages contributed to the increased cellularity in ADS CSF. We found that counts of total CD3+ 161 \nT cells, CD14+ myeloid cells, NK cells, DCs, PMNs, and B cells showed statistically significant  162 \nincreases in ADS CSF when compared to NIND CSF  (Fig 2B). Similarly, cell counts for these 163 \nlineages tended to be increased when comparing ADS CSF to PIND, AIE, and IDWM CSF, though 164 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\ndifferences did not reach statistical significance for all comparisons (Fig. 2B). Of all examined 165 \nlineages, the B-cell lineage exhibited the greatest relative enrichment in ADS CSF, with a greater 166 \nthan tenfold increase of B-cell counts in ADS CSF relative to B-cell counts in NIND, PIND, AIE, 167 \nor IDWM CSF (Fig. 2C). 168 \n 169 \nMS CSF is distinguished from  MOGAD and  other ADS by a high frequency of antibody 170 \nsecreting cells and a decreased frequency of CD14+ myeloid cells 171 \nHaving identified a pronounced B-cell enrichment in the CSF of children with ADS (Fig. 2B-C), 172 \nwe next assessed individual ADS diagnoses ( MS, MOGAD, and other ADS). We observed that 173 \nCSF B-cell counts were increased in each of MS, MOGAD, and other ADS CSF when 174 \nindependently compared to NIND CSF ( Supplementary Table 2). Among these,  B-cell 175 \nfrequencies were particularly elevated in MS CSF in comparison to MOGAD and other ADS CSF 176 \n(Fig. 3A); B-cell absolute counts also appeared elevated in MS CSF in comparison to MOGAD 177 \nand other ADS CSF, though this comparison did not reach statistical significance  for MS vs. 178 \nMOGAD, in part likely due to the large range of CSF B-cell and total cell counts observed in 179 \nMOGAD patients (Fig. 3A, Supplementary Fig. 3A). We next sought to determine whether levels 180 \nof antibody-secreting cells (ASCs), a B -cell subset that includes plasmablasts  and plasma cells , 181 \ndiffered between MS, MOGAD, and other ADS CSF. We found that both ASC frequencies and 182 \ncounts were increased in MS CSF when compared to MOGAD and other ADS (Fig. 3B). In fact, 183 \nASCs were frequently absent in  MOGAD (absent in 5/10 samples) or other ADS CSF (absent in 184 \n5/10 samples) while almost always detected in MS CSF (Fig. 3B). Finally, frequencies and counts 185 \nof B cells excluding ASCs (non -ASC B cells) appeared elevated in MS when compared to 186 \nMOGAD and other ADS (Supplementary Fig. 3B-C). Together these findings point to elevated 187 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nB-cell and particularly ASC levels as a feature distinguishing MS CSF not only from non -188 \ninflammatory disease controls but also from MOGAD and other ADS (Fig. 3D). 189 \n 190 \nWe further observed that frequencies of CSF CD14+ myeloid cells were lower in CSF of MS 191 \ncompared to both MOGAD and other ADS patients (Fig. 3C-D), which appeared to reflect 192 \nelevations in CD14+ myeloid cell counts in the CSF of MOGAD and the other ADS patients, 193 \nrelative to NIND CSF (Fig. 3C, Supplementary Table 2).  194 \n 195 \nCD3+ T-cell frequencies were enriched in MS CSF relative to MOGAD and other ADS CSF  196 \n(Supplementary Fig. 3B), though no particular subset of T-cells clearly contributed this difference 197 \n(Supplementary Fig. 5A-B).  NK cell and DC frequencies  and counts appeared to be similar 198 \nacross the three groups ( Supplementary Fig. 3B-C). PMN frequencies appeared, at times, 199 \nelevated in MOGAD and other ADS CSF, while largely absent in MS (Supplementary Fig. 3B). 200 \nTo explore this phenomenon further, we set a threshold (defined as >2 times the mean PMN 201 \nfrequency in NIND CSF ) and assessed whether the presence of CSF PMNs above this threshold 202 \nwas associated with MOGAD/other AD S compared to MS (Supplementary Fig. 3D). Indeed, 203 \ndetection of CSF PMNs above this threshold  was associated with a diagnosis of other 204 \nADS/MOGAD and essentially excluded MS (Supplementary Fig. 3E). 205 \n 206 \nPotential for the ASC to CD14+ myeloid cell ratio to distinguish MS CSF from MOGAD and 207 \nother ADS CSF 208 \nSince an increase in ASC frequencies and a decrease in CD14+ myeloid cell frequencies appeared 209 \nto best differentiate between CSF of MS versus MOGAD or other ADS patients ( Fig. 3D, 210 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nSupplementary Fig. 4B), we considered whether the ratio of ASC to CD14+ myeloid cell  211 \nfrequencies could be utilized to discriminate MS from MOGAD and other ADS, at the individual 212 \npatient level. As expected, we found that the CSF ASC to CD14+ myeloid cell ratios (AMRs) were 213 \nelevated in children with MS, both in comparison to MOGAD and to other ADS  patients (p < 214 \n0.001, Fig. 4A ). A previously reported ratio, termed the neuroinflammatory composite score 6 215 \n(NCS), similarly incorporated several cellular measures, using both CSF (B-cell, CD14+ myeloid 216 \ncell, CD56+ CD3+ NKT -cell frequencies and total cell counts) and blood (CD56 dim NK-cell 217 \nfrequencies) to distinguish between neuroinflammatory diagnoses (high composite scores), and 218 \nnon-inflammatory controls (lower composite scores). We utilized a CSF-only version of the NCS 219 \n(coNCS, see Methods) and similarly found that this coNCS was also elevated in patients with MS, 220 \nin comparison to MOGAD and other ADS (Fig. 4B). We found similar AMR and coNCS patterns 221 \ndistinguishing between patients who had received systemic glucocorticoid and/or intravenous 222 \nimmunoglobulin (IVIG) treatment prior to sampling versus those who had not (Supplementary 223 \nFig. S5A-B). We next assessed the performance of the AMR and the coNCS as classifiers for MS, 224 \nwhen compared to either MOGAD, other ADS, or both  by constructing receiver-operating 225 \ncharacteristic (ROC) curves and found that both the AMR and coNCS performed well at 226 \ndiscriminating MS from MOGAD and other ADS, with AUCs > 0.7 (Fig. 4C). The AMR, however, 227 \nappeared superior to the coNCS in discriminating MS vs MOGAD (AUC=0.95, 0.80, respectively) 228 \nand MS vs MOGAD/other ADS (AUC=0.94, 0.88, respectively). The AMR also provided 229 \nexcellent performance at distinguishing MS from other ADS (AUC=0.93), though the coNCS 230 \noutperformed the AMR in this regard. (AUC=0.96). We were able to generate the full NCS in a 231 \nsubset of patients  for whom we also had available blood  CD56dim NK -cell frequencies  and 232 \nsimilarly observed that the full NCS was higher in MS relative to MOGAD and other ADS 233 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\n(Supplementary Fig. 6A) and overall performed similarly to the AMR (Supplementary Fig. 6B). 234 \nFinally, we also evaluated the NCS companion “MS score”, which incorporates ASC presence and 235 \nIgG synthesis rates . We found that  it performed relatively poorly in our dataset (correctly 236 \nclassifying only 7 of 15 MS diagnoses ; Supplementary Table 3). Overall, the two -parameter 237 \nAMR we describe here provided excellent performance at discriminating MS CSF from MOGAD 238 \nand other ADS CSF and performed at least as well as a previously reported neuro-immunological 239 \nclassifier reliant on additional parameters (NCS). 240 \n 241 \nInteractive exploration of pediatric CSF immune profiles  242 \nWe next developed a public, web-based R-Shiny application for further analysis and visualization 243 \nof pediatric CSF immune -profiles across a broader range of disease states  244 \n(https://diegoalexespi.shinyapps.io/pedscsf/). By extending our CSF profiling to patients with viral 245 \nencephalitis (VE) and patients with CNS -involved hemophagocytic lymphohistiocytosis (HLH), 246 \nfor example, we are able to observe a breadth of CD8+ T -cell activation states ( Supplementary 247 \nFig. S7), such that patients with VE and HLH harbored elevated levels of CD8+ T-cell activation 248 \nthat are rarely observed in other disease states including other ADS , MOGAD, and MS  249 \n(Supplementary Fig. S7). Such exploratory analyses can provide preliminary insight into further 250 \nstudies of the CSF immune compartment across disease states. 251 \n  252 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nDISCUSSION 253 \nData on CSF cellular immune profiles in pediatric neurological disorders has been exceptionally 254 \nlimited. Here, we present a flow cytometric characterization of CSF immune cells across a 255 \nspectrum of pediatric neurological disorders, identifying age -associated profiles in non -256 \ninflammatory neurological diseases that likely reflect steady-state immune surveillance, as well as 257 \ndisease-specific cellular signatures across a range of  inflammatory states. We find that under 258 \nnormal, non-inflammatory conditions, pediatric CSF is not entirely acellular, with small numbers 259 \nof immune cells predominantly composed of T cells, and with a CD8+ T-cell compartment that 260 \nevolves with age. B cells are scarce or entirely absent in the CSF of non-inflammatory states while   261 \nthe CSF of children diagnosed with a range of ADS, exhibit notable increases in the number of B 262 \ncells present. Among ADS presentations, a distinguishing feature of the CSF immune cell profile 263 \nof children with MS compared to MOGAD and other ADS, is an increased frequency of ASCs and 264 \na decreased of frequency of CD14+ myeloid cells. Overall, these data represent the first 265 \ncharacterization of pediatric CSF across both noninflammatory states and inflammatory 266 \nneurological disorders and provides insight into normal immune surveillance as well as putative 267 \nmechanisms of pediatric CNS disease.  268 \n 269 \nOur characterization of the CSF profile across the pediatric age-span in non -inflammatory 270 \ndisorders provides us insight into age -associated patterns of CNS immune surveillance in the 271 \nimmunological steady-state. We find that most cells participating in CNS immune surveillance are 272 \nCD4+ CM T cells across the pediatric age -span. These findings in the pediatric population are 273 \noverall consistent with prior reports in adults that identified CM CD4+ and CD8+ T cells and 274 \nCXCR3+ T cells as the primary cellular components of the NIND CSF immune compartment, with 275 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nminimal to no B cells detected 27–29. We extend these observations by finding that, across the 276 \npediatric age -span, CD8+ T cells shift from a naive (antigen -inexperienced) to memory 277 \n(presumably antigen-experienced) phenotype with increasing age . This trend aligns with the 278 \nobservation that adult NIND CSF is largely devoid of naive CD8+ T cells 27. Since the migration 279 \nof immune cells from the periphery into the CNS is a regulated process and not a simple reflection 280 \nof the periphery (as both CD4+ and CD8+ T cells are primarily naive throughout childhood30), this 281 \nobservation implies that naive CD8+ T cells earlier in life are endowed with a capacity to migrate 282 \ninto the CNS. One explanation is that these naive CD8+ T cells are “tissue-residency poised31” and 283 \nmature into CD8+ tissue-resident memory T cells within the CNS that eventually saturate the CNS 284 \nCD8+ tissue resident T-cell niche. Most parenchymal T cells in human brain  (across health and 285 \ndisease states ) appear to be CD8+ rather than CD4+, and express tissue residency markers 32, 286 \nconsistent with the above phenomenon  observed in CD8+ but not CD4+ T cells.  It is also possible 287 \nthat what we  observe as “naive” CD8+ T cells in the CSF, instead reflect a collection of antigen-288 \nexperienced memory CD8+ T cells that share markers with naive CD8+ T cells33,34. In any event, 289 \nit is evident that there exists an evolving recruitment of CD8+ T-cells of different phenotypes into 290 \nthe CNS across the pediatric age span, likely reflecting in part tandem immunological changes in 291 \nCD8+ T-cell compartments beyond the CNS35–37. 292 \n 293 \nWe observe a significant elevation in CSF B-cell counts in children with ADS, beyond elevations 294 \nobserved for other lineages, and relative to both non-inflammatory and other inflammatory states. 295 \nThis elevation of B cells in ADS CSF could reflect a trafficking of peripheral B cells into the CNS 296 \nwhere they may then participate in mechanisms associated with inflammatory demyelination; B 297 \ncells are indeed present both in MS and MOGAD brain lesions 38,39. The observation that B-cell 298 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nnumbers are elevated in both MS and MOGAD CSF does not necessarily mean they participate in 299 \nthese diseases through the same mechanism. In fact, B-cell depleting therapies, which are highly 300 \nefficacious in preventing relapse activity in MS40–42, have had mixed results in preventing relapse 301 \nactivity in  MOGAD25,26. Thus, it is plausible that these cells play distinct functional roles in 302 \ndifferent inflammatory demyelinating disease processes. Future studies could further delineate 303 \nwhether and how B cells in MS and MOGAD CSF differ with regards to, for example, their pro- 304 \nand/or anti-inflammatory functions. 305 \n 306 \nWe found a particular enrichment of ASCs (the antibody -secreting B -cell subset) in MS  CSF 307 \ncompared to other ADS and MOGAD. While ASC elevations have previously been  described in 308 \nMS CSF, prior studies have typically compared MS to non-inflammatory controls rather than to 309 \nother inflammatory states 4,43–45. Here, our direct comparisons with MOGAD and  other ADS 310 \nallowed us to demonstrate that increased presence of ASCs is a relatively distinct feature of MS  311 \nCSF, rather than a feature of all ADS  CSF. While peripheral ASCs may generate circulating 312 \nantibodies to CNS proteins in certain ADS, such as the generation of circulating antibodies to 313 \nMOG in MOGAD, the contribution of  ASCs to disease in the MS CNS has not been fully 314 \nelucidated and their function within the CNS may be distinct from that of peripheral ASCs . Of 315 \ninterest is whether and how the enrichment of ASCs in MS CSF may be associated somehow with 316 \npresence (or possibly generation of) meningeal lymphoid aggregates, a feature also reported in the 317 \nbrains of patients with M S46 but not in MOGAD or other AD S. These aggregates , which are 318 \ncomposed of T cells, B cells, plasma cells, and myeloid cells, are thought to contribute to chronic 319 \nCNS-compartmentalized inflammation and progressive disease47,48, also a feature of  MS, but not 320 \nMOGAD49. If the presence of these ASCs in pediatric MS CSF were indeed associated with the 321 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\npresence or development of meningeal lymphoid aggregates, this could imply that the cellular 322 \nmachinery and anatomical structures associated with chronic CNS -compartmentalized 323 \ninflammation in MS may already be developing very early in the MS disease spectrum.   324 \n 325 \nWe also identify decreased numbers of CD14+ myeloid cells in the CSF (and a concomitant 326 \nincreased ratio of ASCs to CD14+ myeloid cells) as a feature distinguishing MS from MOGAD 327 \nand other ADS. Further work could dissect the pro/anti-inflammatory states of myeloid cells in the 328 \nCSF of ADS patients including MOGAD, and  assess their potential interactions with  B-cell 329 \nsubsets, as such interactions are implicated in the context of CNS inflammatory disease 48. 330 \nRegardless of the processes that underlie the different CSF ratios of ASC to CD14+ myeloid cells, 331 \nthe ratio in our cohort distinguishes between MS and non-MS ADS including MOGAD and could 332 \nbe further investigated for its potential to provide an MS disease-specific CSF signature at time of 333 \ninitial CSF evaluation.  The utility of this ratio may prove  complementary to the previously 334 \ndeveloped CSF and blood “neuroinflammatory composite score  (NCS)” that has been  used in 335 \nadults to distinguish between neuroinflammatory syndromes and non-inflammatory syndromes6. 336 \n 337 \nWe note that CSF CD4+ T cell subset profiles did not clearly distinguish between MS and other 338 \nADS or MOGAD. While prior reports in adults identified elevated frequencies of CD4+ regulatory 339 \nT cells (Tregs) in MS CSF when compared to idiopathic intracranial hypertension (IIH) 340 \ncontrols4,50, we did not find elevated Treg frequencies in MS CSF when compared to either NIND 341 \nor to MOGAD or other ADS. Plausible explanations for this discrepancy include the more 342 \nheterogenous cohort of NINDs included in our study and the differing ages of the patients across 343 \nstudies. While T cells observed in white matter lesions of MOGAD39 have been shown to be 344 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nprimarily CD4+ (in contrast to MS lesions, which are  CD8+ T-cell dominant38,51), we did not find 345 \ndifferences in CSF frequencies of CD4+ T cells or CD8+ T cells between MS and MOGAD  346 \npatients.  347 \n 348 \nWhile we were able to identify a CSF immune profile that distinguishes MS from MOGAD (and 349 \nother ADS), we did not identify a unique CSF immune profile that clearly distinguished MOGAD 350 \npatients. In prior studies52,53 PMNs (i.e. neutrophils or eosinophils) have been found to be elevated 351 \nin MOGAD CSF .  Here we identify that elevated frequencies of PMNs (while potentially 352 \nexclusionary for diagnosis of MS), is  a potential feature of both MOGAD and other ADS . The 353 \npresence of PMNs  in the CSF has been historically associated with anti -pathogen responses 354 \n(typically to bacterial or also viral species) during CNS infection, yet the development of MOGAD 355 \nor other ADS have not been firmly linked to a specific viral infection (including EBV 54). It is 356 \nplausible that the presence of PMNs in select MOGAD and other ADS CSF samples could hint at 357 \nsome anti-pathogen (potentially anti -viral) immune processes that, in parallel, also manifest as 358 \nanti-self, inflammatory CNS demyelination processes.  359 \n 360 \nOur study has several limitations. While we profile d children as proximal as possible to clinical 361 \ndisease onset, the underlying disease processes may already be well underway prior to clinical 362 \nmanifestations, which may be particularly true in MS  where evidence for injury manifesting as 363 \nelevated serum neurofilament light chain (NfL) levels is documented well prior to clinical disease 364 \nonset55. Another limitation is absence of CSF profiles from children with neuromyelitis optica 365 \nspectrum disorder (NMOSD), which is in the differential diagnosis of children with incident ADS 366 \npresentations. Even though it is relatively uncommon in the pediatric age -range, NMOSD is 367 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nthought to involve pathogenic antibodies, and it will be important to assess in the future whether 368 \nCSF ASC levels are elevated or not and how the ASC to CD14+ myeloid cell ratio manifests in 369 \nsuch children. Given that relapsing disease is a shared feature between NMOSD and MS (and some 370 \nchildren with MOGAD), defining these profiles in NMOSD could provide additional insight into 371 \nthe pathophysiology of antibody-mediated ADS and disease chronicity. Finally, our cohort consists 372 \nof children who underwent diagnostically-necessary lumbar punctures, which may not be wholly 373 \nrepresentative of all MS, MOGAD, or other ADS diagnoses. The diagnostic criteria for these 374 \ndisorders do not always necessitate CSF analyses, and the set of children who do undergo lumbar 375 \npunctures may be enriched for those whose presentations elicit diagnostic uncertainty. Clear 376 \nimmune profile patterns nevertheless emerged in our data, such as those clearly separating MS 377 \nfrom MOGAD and other ADS. 378 \n 379 \nIn sum, our findings provide insights into CSF immune cell profiles in the context of both normal 380 \nimmune surveillance and CNS non-inflammatory and inflammatory diseases, across the pediatric 381 \nage-span. Our findings identify CSF cellular features  that distinguish pediatric-onset MS from 382 \nMOGAD and other ADS and may imply that some features of chronic inflammation are present 383 \nearly in the disease course. While no cellular features clearly delineated MOGAD from other ADS, 384 \nthe profiles observed in MOGAD and other ADS, as well as in MS, could reflect biological 385 \nheterogeneity that may have implications for a patient’s clinical course . For example, a patient’s 386 \nresponsiveness to treatment(s) and risk of future relapse. Future studies could determine such 387 \n“endophenotypes” by acquiring these CSF measures at the time of presentation  and associating 388 \nthem with prospectively ascertained diagnoses, as has been done for peripheral blood profiles in 389 \nadult MS56. As flow cytometric profiling of CSF samples becomes more widely available (driven 390 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nby newly-developed CSF fixation protocols leveraged for multicenter studies 57,58), CSF immune 391 \nprofiling has the potential to play a n important complementary role in providing improved  392 \ndiagnosis and hence care of patients as part of a larger precision neuroimmunology approaches59.   393 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nMETHODS 394 \nPatient eligibility and recruitment 395 \nPatients were recruited over a period of 3 years from inpatient and outpatient facilities at the  396 \nChildren’s Hospital of Philadelphia (CHOP). Patients were eligible and approached for enrollment 397 \n(subject to staff availability) if they were undergoing a diagnostic lumbar puncture (LP) for a 398 \nsuspected neurological condition . Written informed consent was obtained from all donors. All 399 \nprotocols and consents were approved by the University of Pennsylvania Institutional Review 400 \nBoard. 401 \n 402 \nAcquisition, transport, and analysis of CSF samples 403 \nA total of 0.5-7 mLs of CSF per participant were obtained. CSF samples were collected in 15 mL 404 \npolypropylene tube s at the time of LP, placed on ice  and immediately transported to a single 405 \nlaboratory facility for prompt same-day processing and analysis. CSF from one HLH patient was 406 \ncollected from an external ventricular drain. Standardized operating procedures were consistent 407 \nwith consensus protocols for CSF analysis60. 408 \n 409 \nFlow cytometry of CSF cells 410 \nUpon arrival to the laboratory, 20 uL of CSF was placed on the Blood/Hemoglobin region of a 411 \nChemstrip (Chemstrip 10 SG, Cat. No. 11895362160) to evaluate for blood and/or hemoglobin 412 \ncontamination in the CSF sample. Samples with Chemstrip readings of ≥50 Erythrocytes/uL using 413 \neither the blood or hemoglobin reading were not evaluated further. Evaluable samples were 414 \nimmediately spun at 4º C for 10 minutes at 400 RCF. CSF supernatant was then removed from the 415 \ncell pellet, and the cell pellet was resuspended in 200 uL cold 1X PBS. 10 uL of sample was added 416 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nto 10 uL of Methylene Blue and utilized for cell counting using a hemocytometer. Following cell 417 \ncounting, 800 uL of cold 1X PBS was added to the sample, and the sample was spun again at 4º C 418 \nfor 10 minutes at 400 RCF. Supernatant was discarded and the cell pellet was resuspended in 100 419 \nuL of cold 1X PBS for flow cytometry staining with a cocktail of 16 antibodies targeting CD45, 420 \nCD14, CD3, CD19, CD56, HLA-DR, CD4, CD8, CD38, CD27, CD45RA, CD11c, CD127, CD25, 421 \nCXCR3, and CCR6 ( Supplementary Table 4) for 30 minutes at 4ºC protected from light. 422 \nFollowing the 30-minute stain, 900 uL of 1X cold PBS was added to the sample before another 423 \nspin at 4º C for 10 minutes at 400 RCF. Supernatant was discarded and cell pellet was resuspended 424 \nin 200-300 uL of 1X cold PBS and placed on ice prior to immediate analysis on an LSR Fortessa. 425 \nRoutinely, paired whole blood was stained and run on the Fortessa side-by-side with the CSF. BD 426 \nFACSDiva software was utilized to collect flow cytometry data on the LSR Fortessa. Single-stain 427 \ncompensation tubes with beads were run prior to data collection and utilized for compensation on 428 \nFlowJo software. After gating, frequencies were exported from FlowJo into .csv files for 429 \ndownstream statistical analysis. 430 \n 431 \nStatistical analysis 432 \nStatistical analysis was performed utilizing R software. When statistical comparisons were 433 \nperformed between conditions, a Wilcoxon -rank-sum test was utilized. The R packages dplyr, 434 \nggplot2, magrittr, tibble, tidyr, ggpubr, pROC, and cowplot were utilized for statistical analysis and 435 \nfigure generation. 436 \n 437 \nNeuroinflammatory composite score 438 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nA CSF-only neuroinflammatory composite score (coNCS, adapted from 6) was calculated with the 439 \nfollowing formula and used in Fig. 4B: 440 \n!\"𝑐𝑒𝑙𝑙𝑠\n𝑢𝐿 + 1+ ∗ 𝐵\t𝑐𝑒𝑙𝑙𝑠 ∗ 100 0 /[𝑀𝑜𝑛𝑜𝑐𝑦𝑡𝑒𝑠 ∗ 𝑁𝐾𝑇\t𝑐𝑒𝑙𝑙𝑠] 441 \nWhere cells/uL = CSF cell concentration calculated from hemocytometer manual count, B cells = 442 \nCSF B cell frequency as % of CSF lymphocytes, Monocytes = CSF CD14+ myeloid cell frequency 443 \nas % of CSF cells, and NKT cells = CSF CD56+ CD3+ T-cell frequency as % of CSF lymphocytes. 444 \nIf either the denominator or numerator were 0, the coNCS was set to 0. For Supplementary Fig. 445 \nS6B, the full NCS (with blood CD56dim NK) was utilized when blood CD56dim NK cell 446 \nfrequencies were available: 447 \n!\"𝑐𝑒𝑙𝑙𝑠\n𝑢𝐿 + 1+ ∗ 𝐵\t𝑐𝑒𝑙𝑙𝑠 ∗ 100 0 /[𝐶𝐷56𝑑𝑖𝑚\t𝑁𝐾𝑐𝑒𝑙𝑙𝑠!\"##$ ∗ 𝑀𝑜𝑛𝑜𝑐𝑦𝑡𝑒𝑠 ∗ 𝑁𝐾𝑇\t𝑐𝑒𝑙𝑙𝑠] 448 \nWhere CD56dim NK cellsblood = blood CD56dim NK cell frequency as % of blood lymphocytes 449 \nand all other parameters identical to the coNCS. If either the denominator or numerator were 0, 450 \nthe full NCS was set to 0. 451 \n 452 \nPatient diagnoses and categorical classification 453 \nPatient diagnoses were ascertained in prospective follow -up by pediatric neurologists with 454 \nextensive experience in pediatric neuroimmunology, and based on all available clinical and clinical 455 \nlaboratory data (but blinded to the CSF flow cytometry results). When possible, these diagnoses 456 \nwere then binned into five categories: non -inflammatory neurological disorders (NINDs), 457 \nperipheral inflammatory neurological disorders (PIND), autoimmune encephalitidies (AIE), 458 \ninherited disorders of white matter (IDWM) . For acquired demyelinating syndromes (ADS) , 459 \ndiagnoses were further characterized by presenting phenotype . The classification of specific 460 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\ndiagnoses into categories is available within Supplementary Table 1. Within the ADS category, 461 \nMOGAD was diagnosed utilizing the 2023 International MOGAD Panel proposed criteria 61 and 462 \nMS was diagnosed utilizing the 2017 McDonald criteria62. For children diagnosed with other ADS 463 \nor MOGAD, CSF samples were determined to have been procured with a median of 4.5  days or 464 \n12 days, respectivley,  from the onset of most recent episode of clinically active disease  465 \n(Supplementary Table 5). For children diagnosed with MS, 4 of 15 were found to be in remission 466 \nat the time of sampling, defined as the absence of clinical symptoms for at least 3 months prior to 467 \nLP (Supplementary Table 5); for those with active clinical disease, CSF samples were determined 468 \nto have been procured with a median of 6 days since the onset of most recent episode of clinically 469 \nactive disease (Supplementary Table 5).  In Supplementary Fig. S5, other ADS, MOGAD, and 470 \nCSF samples were further classified as treated if they had received any systemic glucocorticoid 471 \nand/or IVIG treatment within the last 30 days prior to LP. Children with other ADS, MOGAD, or 472 \nMS with any history of MS disease-modifying therapies were excluded from all analyses.  473 \n 474 \n 475 \n  476 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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The Lancet Neurology. 2018;17(2):162-173. doi:10.1016/S1474-669 \n4422(17)30470-2 670 \n 671 \nACKNOWLEDGMENTS 672 \nThis study was supported in part through the Melissa and Paul Anderson fund and Rosenblum 673 \ndonation (ABO, BB) with DAE supported in part by NIH Medical Scientist Training Program 674 \nT32 GM07170, T32 G000046, F31AI167501, the Blavatnik Family Fellowship in Biomedical 675 \nResearch at the University of Pennsylvania, and the Penn Colton Center for Autoimmunity. TZ 676 \nwas supported in part by FWF Schrödinger grant (Nr: J4524). We would also like to thank the 677 \nChildren’s Hospital of Philadelphia Division of Child Neurology for their help in procuring CSF 678 \nsamples. Figure 1A was created with Biorender.com. 679 \n 680 \nAUTHOR CONTRIBUTIONS 681 \nDAE performed analysis. DAE, TZ, GB, ST, IM, YK, and ZM performed experiments. DAE, TZ, 682 \nGB, ST, IM, MB, JL, SF, AnM, and MS assisted in sample transport and patient consenting. AmM 683 \nascertained patient diagnoses. DAE and MK designed the web-based R-shiny app for exploration 684 \nof data. TZ, IM, CD, AS-M, HW, AR, and RL aided with analysis, protocol optimization, and panel 685 \ndevelopment. ATW, SEH, and SN aided with patient recruitment and lumbar punctures. BB and 686 \nABO supervised the project. All authors contributed to the writing of the manuscript. 687 \n 688 \nCOMPETING INTERESTS 689 \nThe authors declare no conflict of interest related to this study. 690 \n  691 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nFIGURES AND FIGURE LEGENDS 692 \n  693 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\n 694 \nFigure 1: Pediatric NIND CSF is primarily composed of memory CD4+ and CD8+ T cells, 695 \nwith an age -associated increase in CD8+ memory T cell frequency (A)  Schematic of CSF 696 \nsample procurement and flow cytometry data acquisition. (B) Cell count range (cells/mL) of NIND 697 \nCSF (n=33). (C) Frequencies of CD3+ T cells, CD14+ myeloid cells, NK cells, DCs, PMNs, and 698 \nB cells in NIND CSF. (D) Frequencies of T-cell subsets, as percent of CD3+ T cells, in NIND CSF. 699 \n(E) Frequencies of CD4+ naive and memory T cells, as percent of CD4+ T cells, in NIND CSF. 700 \n(F) Frequencies of CD8+ naive and memory T cells, as percent of CD8+ T cells, in NIND CSF 701 \nand (G) across the NIND age span.  702 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\n 703 \nFigure 2: An increase in CSF B-cell counts best differentiates ADS from both other 704 \ninflammatory and non-inflammatory neurological disease cohorts (A) Cell counts (cells/mL) 705 \nin NIND CSF (n=33), PIND CSF (n=7), AIE CSF (n=6), IDWM CSF (n=4), and ADS CSF (n=35). 706 \n(B) Cell counts (cells/mL) for CD3+ T cells, CD14+ myeloid cells, NK cells, DCs, PMNs, and B 707 \ncells across the same cohorts as A. (C) log10 of the fold change of median cell counts, comparing 708 \nthe median cell count in ADS to the median cell count in other cohorts for each lineage. For Fig. 709 \n2A-B, Wilcoxon -rank-sum test used to compare ADS to NIND, PIND, AIE, and IDWM 710 \nindependently (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001 ). NIND = non -711 \ninflammatory neurological disease, PIND = peripheral inflammatory neurological disease, AIE = 712 \nautoimmune encephalitidies, IDWM = inherited disorders of white matter, ADS = acquired 713 \ndemyelinating syndromes.  714 \n 715 \n  716 \n****\n*\n*\nCells\nNINDPINDAIEIDWMADS\n103\n104\n105\n106\ncells/mL\nA\n****\n***** * *\n*\n*\n*\n**\n****** * *\nDCs PMNs B cells\nCD3+ T cells CD14+ myeloid cells NK cells\nNINDPINDAIEIDWMADS NINDPINDAIEIDWMADS NINDPINDAIEIDWMADS\nNINDPINDAIEIDWMADS NINDPINDAIEIDWMADS NINDPINDAIEIDWMADS\n0\n101\n102\n103\n104\n0\n101\n102\n103\n104\n101\n102\n103\n104\n0\n101\n102\n103\n104\n102\n103\n104\n105\n0\n101\n102\n103\n104\ncells/mL\nB log10(count fold−change)\n0.0 0.5 1.0 1.5 2.0\nB cells\nPMNs\nDCs\nNK cells\nCD14+ myeloid cells\nCD3+ T cells\nlog10 fold−change of medians\nlog10(ADS/comparator)\nLineage\nComparisons\nADS vs. NIND\nADS vs. PIND\nADS vs. AIE\nADS vs. IDWM\nnot significant\nC\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\n 717 \nFigure 3: Pediatric MS CSF exhibits increased ASC frequencies and decreased CD14+ 718 \nmyeloid cell frequencies when compared to MOGAD and other ADS. (A) B-cell, (B) ASC, and 719 \n(C) CD14+ myeloid cell frequencies and counts (cells/mL) in NIND CSF (n=33), PIND CSF 720 \n(n=7), AIE CSF (n=6), IDWM CSF (n=4), other ADS CSF (n=10), MOGAD CSF (n=10), and MS 721 \nCSF (n=15). (D) log10 of the fold change of median frequencies, comparing the median frequency 722 \nin MS to the median frequency in other ADS and MOGAD for each cell population (lineage or 723 \nsubset). For Fig. 3A-C, Wilcoxon-rank-sum test used to compare MS to MOGAD, MS to  other 724 \nADS, and MOGAD to other ADS (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). 725 \nASC = antibody secreting cell, NIND = non-inflammatory neurological disease, PIND = peripheral 726 \ninflammatory neurological disease, AIE = autoimmune encephalitidies, IDWM = inherited 727 \ndisorders of white matter, other ADS = non-MS/non-MOGAD acquired demyelinating syndromes, 728 \nMOGAD = myelin oligodendrocyte glycoprotein antibody -associated disease, MS = multiple 729 \nsclerosis.   730 \n****\n*\nB cells\nNINDPIND AIEIDWM\nother ADSMOGAD\nMS\n0\n5\n10\n15\n% of cells\nA\n**\n***\nASCs\nNINDPIND AIEIDWM\nother ADSMOGAD\nMS\n0.0\n2.5\n5.0\n7.5% of cells\nB\n***\n***\nCD14+ myeloid cells\nNINDPIND AIEIDWM\nother ADSMOGAD\nMS\n0\n20\n40\n60\n% of cells\nC\n***\nB cells\nNINDPIND AIEIDWM\nother ADSMOGAD\nMS\n0\n101\n102\n103\ncells/mL\n**\n**\nASCs\nNINDPIND AIEIDWM\nother ADSMOGAD\nMS\n0\n101\n102\n103\ncells/mL\n**\nCD14+ myeloid cells\nNINDPIND AIEIDWM\nother ADSMOGAD\nMS\n101\n102\n103\n104\ncells/mL\nlog10(frequency fold−change)\n−2 −1 0 1 2\nCD14+ myeloid cells\nPMNs\nDCs\nNK cells\nCD3+ T cells\nnon−ASC B cells\nB cells\nASCs\nlog10 fold−change of medians\nlog10(MS/comparator)\nCell population\nComparisons\nMS vs. MOGAD\nMS vs. other ADS\nnot significant\nD\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\n 731 \nFigure 4: Ratio of CSF ASC to CSF CD14+ myeloid cell frequencies differentiates MS from 732 \nMOGAD and other ADS (A) Ratio of ASC frequencies to CD14+ myeloid cell frequencies 733 \n(AMR) and ( B) CSF-only neuroinflammatory composite score s (coNCS) across NIND CSF 734 \n(n=33), PIND CSF (n=7), AIE CSF (n=6), IDWM CSF (n=4), and other ADS CSF (n=10), 735 \nMOGAD CSF (n=10), and MS CSF (n=15).  (C) Receiver operating characteristic (ROC) curves 736 \nbuilt for AMR or coNCS classifiers for the MS -vs-MOGAD/other ADS comparison, MS -vs-737 \nMOGAD comparison, and MS -vs-other ADS comparison, along with each corresponding area 738 \nunder the curve (AUC). For Fig. 4A-B, Wilcoxon-rank-sum used to compare MS to MOGAD, MS 739 \n***\n***\nASC/CD14+ Myeloid Ratio\nNIND PIND AIE IDWM\nother ADSMOGAD\nMS\n−2\n−1\n0\n1\nlog10(AMR+0.01)\nA\n****\n*\nCSF−only Neuroinflammatory Composite Score\nNIND PIND AIE IDWM\nother ADSMOGAD\nMS\n−2\n0\n2\n4\nlog10(coNCS+0.01)\nB\nAMR AUC = 0.95\ncoNCS AUC = 0.80\nAMR AUC = 0.94\ncoNCS AUC = 0.88\nAMR AUC = 0.93\ncoNCS AUC = 0.96\nMS−vs−MOGAD MS−vs−MOGAD/other ADS MS−vs−other ADS\n0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00\n0.0\n0.8\nFalse positive rate\nTrue positive rate\nROC\nAMR\ncoNCS\nC\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nto other ADS, and MOGAD to other ADS (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p 740 \n< 0.0001). AMR = ASC to CD14+ myeloid cell ratio, coNCS = CSF -only neuroinflammatory 741 \ncomposite score, NIND = non -inflammatory neurological disease, PIND = peripheral 742 \ninflammatory neurological disease, AIE = autoimmune encephalitidies, IDWM = inherited 743 \ndisorders of white matter, other ADS = non-MS/non-MOGAD acquired demyelinating syndromes, 744 \nMOGAD = myelin oligodendrocyte glycoprotein antibody -associated disease, MS = multiple 745 \nsclerosis.  746 \n  747 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint \n\nCategory Median \nAge \nAge \nRange \nMale \nSex \nFemale \nSex \nDays from clinical \nepisode onset to \nsampling (median for \nADS groups only) \nDays from clinical \nepisode onset to \nsampling (range for \nADS groups only) \nNumber in \nremission \n(MS only) \nNIND 12.2 2.6 - 19.4 21 12 -- -- -- \nPIND 16.8 3.5 - 19.4 3 4 -- -- -- \nAIE 14 8.7 - 19 2 4 -- -- -- \nIDWM 10.1 1.3 - 17.2 4 0 -- -- -- \nother ADS 9.3 0.7 - 17.9 8 2 4.5 1-40 -- \nMOGAD 9.3 1.8 - 16.2 5 5 12 1-23 -- \nMS 16.9 11.6 - 19.1 4 11 6 3-41 4 \n 748 \nTable 1 Aggregated patient metadata by age and sex. NIND = non-inflammatory neurological 749 \ndisease, PIND = peripheral inflammatory neurological disease, AIE = autoimmune encephalitidies, 750 \nIDWM = inherited disorders of white matter, other ADS = non -MS/non-MOGAD acquired 751 \ndemyelinating syndromes, MOGAD = myelin oligodendrocyte glycoprotein antibody -associated 752 \ndisease, MS = multiple sclerosis 753 \n 754 \n 755 \n 756 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted March 1, 2025. ; https://doi.org/10.1101/2025.02.27.637541doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}