Next-Generation Neuroimmunology: New Technologies to Understand Central Nervous System Autoimmunity
Authors: Gerd Meyer zu Hörste, Catharina C. Gross, Luisa Klotz, Nicholas Schwab, Heinz Wiendl
Journal: Trends in Immunology (2020)

Autoimmune diseases of the nervous system, particularly multiple sclerosis (MS), are only partly mimicked by commonly used animal models such as experimental autoimmune encephalomyelitis (EAE).Translational studies in patients are mostly based on: autopsy or biopsy material, cerebrospinal fluid (CSF), or peripheral blood. Disease-specific findings considerably depend on the compartment being analyzed.The advent of various immunotherapies has significantly enhanced our knowledge of phase-specific immunopathological mechanisms in the field of neuroimmunology; we can further learn (immune)pathogenesis from successful and unsuccessful therapeutic interventions.MS is specifically associated with complex alterations of various immune cells and parenchymal cells, contributing to the view that changes in immune regulatory networks can initiate and perpetuate the disease.Comparative, unbiased, and multidimensional high-resolution approaches are needed (e.g., using single cell transcriptomics) to reveal key pathogenic signatures in a compartment-specific manner in human nervous system autoimmunity.Orphan diseases such as Rasmussen encephalitis (RE) and Susac syndrome (SuS) can serve as paradigmatic disease entities to dissect defined mechanisms in comparison with more prevalent disorders. Understanding neuroimmunological disorders is essential for developing new diagnostic and therapeutic strategies. Rodent models have provided valuable insights, but are sometimes equated with their human counterparts. Here, we summarize how novel technologies may enable an improved human-focused view of immune mechanisms. Recent studies have applied these new technologies to the brain parenchyma, its surrounding cerebrospinal fluid, and peripheral immune compartments. Therapeutic interventions have also facilitated translational understanding in a reverse way. However, with improved technology, access to patient samples remains a rate-limiting step in translational research. We anticipate that next-generation neuroimmunology is likely to integrate, in the immediate future, diverse technical tools for optimal diagnosis, prognosis, and treatment of neuroimmunological disorders. Understanding neuroimmunological disorders is essential for developing new diagnostic and therapeutic strategies. Rodent models have provided valuable insights, but are sometimes equated with their human counterparts. Here, we summarize how novel technologies may enable an improved human-focused view of immune mechanisms. Recent studies have applied these new technologies to the brain parenchyma, its surrounding cerebrospinal fluid, and peripheral immune compartments. Therapeutic interventions have also facilitated translational understanding in a reverse way. However, with improved technology, access to patient samples remains a rate-limiting step in translational research. We anticipate that next-generation neuroimmunology is likely to integrate, in the immediate future, diverse technical tools for optimal diagnosis, prognosis, and treatment of neuroimmunological disorders. Recent research, preferentially from a 'human point-of-view', is integrating novel technologies and enabling a more defined understanding of disease mechanisms in autoimmune diseases of the central nervous system (CNS) (see Glossary). Animal models such as experimental autoimmune encephalomyelitis (EAE) enable the study of 'aspects' of human diseases; however, these models are poorly suited for drug testing or assessing disease etiology (reviewed in [1.Korn T. Kallies A. T cell responses in the central nervous system.Nat. Rev. Immunol. 2017; 17: 179-194Crossref PubMed Scopus (157) Google Scholar,2.Becher B. et al.Cytokine networks in neuroinflammation.Nat. Rev. Immunol. 2017; 17: 49-59Crossref PubMed Scopus (324) Google Scholar]). Moreover, one of the major challenges in the field constitutes achieving integration of human data obtained using diverse methodologies and from different tissues. Thus, in this review, we discuss a human- and relevant biomaterial-focused view of CNS autoimmunity, centered around the application of emerging technologies. Different components of the nervous system can be affected by autoimmune diseases (Figure 1) Although target antigens and causative agents are in many cases unknown, autoimmune-mediated diseases of the CNS can be broadly classified as primarily affecting: (i) neuronal and/or synaptic structures, (ii) myelin and/or glia cells, or (iii) the CNS vasculature (Table 1). Examples of selected paradigmatic diseases from each group are discussed here. Unfortunately, rare (and therefore difficult to study) CNS autoimmune diseases tend to be mechanistically well defined, while more common diseases tend to be clinically defined and therefore often represent a mixture of diverse mechanistic entities. We posit that common diseases [such as, e.g., multiple sclerosis (MS)] are encountered frequently enough to be recognized as a recurring 'clinical' pattern and are thus more likely categorized based on such clinical phenomena. In rare diseases, the discovery of disease-defining biomarkers often sparks recognition and subsequent understanding of a rare disease, as in the case of antibodies against aquaporin-4 in neuromyelitis optica spectrum disease (NMOSD).Table 1Paradigmatic Autoimmune Diseases Affecting the Central Nervous SystemaStructure affectedDiseaseClinical characteristicsCellular/structural target(Potential) target antigen(s)Immune mediators/lineages involvedTreatment targets (irrespective of approval status)Glia/myelinMultiple sclerosisRelapses of disability, variable recovery in relapsing-remitting stage.Accumulating disability in secondary progressive stage. Primary progressive variants.Myelin and/or oligodendrocytes in RRMS. Axon and neurodegeneration in SPMS.UnknownCD8+, CD4+ T cells, B cells in parenchyma; B cells, NK cells and other lineages in CSF(i) Immuno-suppression, (ii) pleiotropic immunomodulation, (iii) leukocyte trafficking, (iv) leukocyte depletionNeuromyelitis optica spectrum disorderRelapses of disability especially affecting the optic nerve and spinal cord.Astrocytes.Aquaporin-4B cells(i) Inhibition of complement activation, (ii) B cell depletion, (iii) immunosuppressionNeurons/axons/synapsesRasmussen encephalitisIntractable seizures and inflammation of one hemisphere.Neurons.UnknownCD8+ T cells(i) Surgical removal of one hemisphere, (ii) inhibition of trafficking in rodent studiesAutoimmune encephalitisMemory deficits, altered mental status, psychiatric symptoms, focal neurological deficits, seizures.Neuronal/synaptic proteins.Diverse (e.g., NMDA-receptor)Antibodies, CD8+ T cells(i) B cell depletion, (ii) immunosuppressionNarcolepsyInability to stay awake, sudden loss of muscle tone (cataplexy), sleep disturbances.Hypocretin/orexin-producing neurons in the brainstem.Hypocretin/orexin?CD8+ T cells?No immune-related treatmentsVascular endotheliumSusac syndromeEncephalopathy (diffuse brain dysfunction), loss of vision, loss of hearing.Vascular endothelium in the brain, retina, inner ear.UnknownCD8+ T cells?(i) Unknown, (ii) inhibition of trafficking in rodent studiesa Abbreviations: NMDA, N-methyl-d-aspartic acid; RRMS, relapsing-remitting multiple sclerosis; SPMS, secondary progressive multiple sclerosis. Open table in a new tab a Abbreviations: NMDA, N-methyl-d-aspartic acid; RRMS, relapsing-remitting multiple sclerosis; SPMS, secondary progressive multiple sclerosis. A first example is that of (auto)immune reactions against neuronal/synaptic structures that can trigger pathogenesis, as in the case of autoimmune encephalitis (AIE) (Table 1). Moreover, Rasmussen encephalitis (RE) is a rare form of pediatric epilepsy that features inflammation uniquely restricted to one brain hemisphere and which may be mediated by CD8+ T cells, as evidenced by the strong clonal expansion of CD8+ T cells reported in the blood of RE patients [3.Schneider-Hohendorf T. et al.CD8(+) T-cell pathogenicity in Rasmussen encephalitis elucidated by large-scale T-cell receptor sequencing.Nat. Commun. 2016; 711153Crossref PubMed Scopus (67) Google Scholar]. Although the cause for its 'one-sided-ness' is still enigmatic, it is rather homogeneous in presentation and blood leukocytes from human RE patients alone induce RE-like CNS inflammation and seizures in immunodeficient NOD/LtSz-SCID-IL2Rγcnull mice [4.Kebir H. et al.Humanized mouse model of Rasmussen's encephalitis supports the immune-mediated hypothesis.J. Clin. Invest. 2018; 128: 2000-2009Crossref PubMed Scopus (17) Google Scholar]. This supports the concept that RE can serve as a 'paradigmatic' case of antineuronal immunity, which may be used to better understand overarching mechanisms in the future. A second example constitutes the field of disorders primarily affecting myelin/glia; this is dominated by research in the common disease MS [5.Reich D.S. et al.Multiple sclerosis.N. Engl. J. Med. 2018; 378: 169-180Crossref PubMed Scopus (1083) Google Scholar]. The target antigen(s) in MS still remain elusive and a single target antigen may not exist [6.Brandle S.M. et al.Distinct oligoclonal band antibodies in multiple sclerosis recognize ubiquitous self-proteins.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 7864-7869Crossref PubMed Scopus (110) Google Scholar]. NMOSD can mimic MS clinically, but unlike MS, is defined by antibodies against the astrocytic water channel aquaporin-4 and therefore likely represents an astrocytopathy [7.Wingerchuk D.M. et al.International consensus diagnostic criteria for neuromyelitis optica spectrum disorders.Neurology. 2015; 85: 177-189Crossref PubMed Scopus (2530) Google Scholar]. Although NMOSD (prevalence 1–10/100 000) is approximately tenfold less prevalent than MS, it demonstrates that diverse mechanisms can trigger similar clinical presentations (Table 1). A third example is that the vasculature of the CNS can be specifically targeted in CNS autoimmunity. For example, Susac syndrome (SuS) is a rare disorder characterized by diffuse brain dysfunction and loss of vision and hearing due to the immune-mediated occlusion of microvessels in the brain, retina, and inner ear [8.Jarius S. et al.Clinical, paraclinical and serological findings in Susac syndrome: an international multicenter study.J. Neuroinflammation. 2014; 11: 46Crossref PubMed Scopus (77) Google Scholar]. Although the human target antigen remains elusive, SuS likely represents a CD8+ T cell-mediated brain endotheliopathy based on observations that oligoclonal CD8+ T cells expand in the peripheral blood and cerebrospinal fluid (CSF) of human SuS patients and, furthermore, that granzyme-expressing CD8+ T cells are present in damaged microvessels of the CNS parenchyma in such patients [9.Gross C.C. et al.CD8 T cell-mediated endotheliopathy is a targetable mechanism of neuro-inflammation in Susac syndrome.Nat. Commun. 2019; 10: 5779Crossref PubMed Scopus (50) Google Scholar] (Table 1). Moreover, transfer of activated CD8+ T cells expressing a T cell receptor (TCR) specific for the influenza hemagglutinin (HA) into recipient mice expressing HA-antigen in brain endothelial cells driven by the Slco1c1 promoter (EC-HA+ versus EC-HA− mice) resulted in SuS-like pathology [9.Gross C.C. et al.CD8 T cell-mediated endotheliopathy is a targetable mechanism of neuro-inflammation in Susac syndrome.Nat. Commun. 2019; 10: 5779Crossref PubMed Scopus (50) Google Scholar]. Thus, here we integrate findings from new methodologies that examine these defined 'paradigmatic diseases' of CNS autoimmunity. As in many other fields of immunology, flow cytometry remains the technical mainstay in human CNS autoimmune diseases due to its relative simplicity and low cost (Figure 2). Recent years have seen a continuous increase in the number of parameters being analyzed simultaneously. The field is thus moving from few parameters towards more comprehensive and (semi)-unbiased approaches to evaluate 'immune-regulatory network' architectures, rather than preselected populations. This is paralleled by the development of analytical approaches (Box 1). A deep functional immunophenotyping matrix (10 × 13 colors) represents one example of a standardized and semi-unbiased immune cell profiling flow cytometry approach [10.Lohmann L. et al.Immune cell profiling during switching from natalizumab to fingolimod reveals differential effects on systemic immune-regulatory networks and on trafficking of non-T cell populations into the cerebrospinal fluid-results from the ToFingo successor study.Front. Immunol. 2018; 9: 1560Crossref PubMed Scopus (20) Google Scholar].Box 1Basic Analytical Approaches for High-Dimensional Single Cell DataThe technical ability to query a large number of parameters in large numbers of individual cells has led to an ever increasing number and complexity of 'single cell datasets'. This was paralleled by an evolution of analytical approaches. We outline a few key developments of this rapidly evolving field. Overall, we observe a convergence of tools and approaches originating from flow cytometry on the one hand and from single cell transcriptomics on the other hand. The relative strengths and weaknesses of each technique have been reviewed previously [83.Galli E. et al.The end of omics? High dimensional single cell analysis in precision medicine.Eur. J. Immunol. 2019; 49: 212-220Crossref PubMed Scopus (22) Google Scholar].Common analytical approaches for many 'single cell' datasets include dimensionality reduction techniques. Among others, these include principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP) (reviewed in [84.Palit S. et al.Meeting the challenges of high-dimensional data analysis in immunology.Front. Immunol. 2019; 10: 1515Crossref PubMed Scopus (46) Google Scholar]). All these approaches aim to facilitate understanding by reducing highly multidimensional data into a two-dimensional space. Additional approaches include the 'spanning-tree progression analysis of density-normalized events' (SPADE) algorithm that generates branched tree-like structures from flow cytometry data [85.Qiu P. et al.Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE.Nat. Biotechnol. 2011; 29: 886-891Crossref PubMed Scopus (695) Google Scholar]. In a SPADE tree, nodes represent cell clusters and connecting edges represent relationships and similarity. In FlowSOM, so-called self-organizing maps (SOM) are trained that aim to preserve the topological information of the original input space. Such dimensionality reduction and clustering techniques try to answer: Which cell types are present? Which transcriptional features distinguish cell types and conditions? In essence, they thus facilitate understanding the cellular heterogeneity in a given tissue.Another set of analytical approaches is preferentially suited to understand cellular differentiation as a continuous phenomenon. Rather than being clustered, cells are ordered in a pseudo-temporal order named 'pseudotime' using tools such as Monocle [86.Trapnell C. et al.The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.Nat. Biotechnol. 2014; 32: 381-386Crossref PubMed Scopus (2437) Google Scholar] and Wishbone [87.Setty M. et al.Wishbone identifies bifurcating developmental trajectories from single-cell data.Nat. Biotechnol. 2016; 34: 637-645Crossref PubMed Scopus (347) Google Scholar] (reviewed in [88.Saelens W. et al.A comparison of single-cell trajectory inference methods.Nat. Biotechnol. 2019; 37: 547-554Crossref PubMed Scopus (530) Google Scholar]). These approaches aim to answer: Can we understand the development or differentiation of cells in the given tissue? This has found more widespread use in transcriptomics than in flow cytometry-based studies.We see one of the key future challenges of the field in optimally integrating multiple sources (e.g., different sites, different tissues, etc.) and types of data (e.g., RNA, protein, antigen receptor information) into a comprehensive characterization of cells; we find this currently best implemented in the Seurat package [89.Butler A. et al.Integrating single-cell transcriptomic data across different conditions, technologies, and species.Nat. Biotechnol. 2018; 36: 411-420Crossref PubMed Scopus (4156) Google Scholar]. The technical ability to query a large number of parameters in large numbers of individual cells has led to an ever increasing number and complexity of 'single cell datasets'. This was paralleled by an evolution of analytical approaches. We outline a few key developments of this rapidly evolving field. Overall, we observe a convergence of tools and approaches originating from flow cytometry on the one hand and from single cell transcriptomics on the other hand. The relative strengths and weaknesses of each technique have been reviewed previously [83.Galli E. et al.The end of omics? High dimensional single cell analysis in precision medicine.Eur. J. Immunol. 2019; 49: 212-220Crossref PubMed Scopus (22) Google Scholar]. Common analytical approaches for many 'single cell' datasets include dimensionality reduction techniques. Among others, these include principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP) (reviewed in [84.Palit S. et al.Meeting the challenges of high-dimensional data analysis in immunology.Front. Immunol. 2019; 10: 1515Crossref PubMed Scopus (46) Google Scholar]). All these approaches aim to facilitate understanding by reducing highly multidimensional data into a two-dimensional space. Additional approaches include the 'spanning-tree progression analysis of density-normalized events' (SPADE) algorithm that generates branched tree-like structures from flow cytometry data [85.Qiu P. et al.Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE.Nat. Biotechnol. 2011; 29: 886-891Crossref PubMed Scopus (695) Google Scholar]. In a SPADE tree, nodes represent cell clusters and connecting edges represent relationships and similarity. In FlowSOM, so-called self-organizing maps (SOM) are trained that aim to preserve the topological information of the original input space. Such dimensionality reduction and clustering techniques try to answer: Which cell types are present? Which transcriptional features distinguish cell types and conditions? In essence, they thus facilitate understanding the cellular heterogeneity in a given tissue. Another set of analytical approaches is preferentially suited to understand cellular differentiation as a continuous phenomenon. Rather than being clustered, cells are ordered in a pseudo-temporal order named 'pseudotime' using tools such as Monocle [86.Trapnell C. et al.The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.Nat. Biotechnol. 2014; 32: 381-386Crossref PubMed Scopus (2437) Google Scholar] and Wishbone [87.Setty M. et al.Wishbone identifies bifurcating developmental trajectories from single-cell data.Nat. Biotechnol. 2016; 34: 637-645Crossref PubMed Scopus (347) Google Scholar] (reviewed in [88.Saelens W. et al.A comparison of single-cell trajectory inference methods.Nat. Biotechnol. 2019; 37: 547-554Crossref PubMed Scopus (530) Google Scholar]). These approaches aim to answer: Can we understand the development or differentiation of cells in the given tissue? This has found more widespread use in transcriptomics than in flow cytometry-based studies. We see one of the key future challenges of the field in optimally integrating multiple sources (e.g., different sites, different tissues, etc.) and types of data (e.g., RNA, protein, antigen receptor information) into a comprehensive characterization of cells; we find this currently best implemented in the Seurat package [89.Butler A. et al.Integrating single-cell transcriptomic data across different conditions, technologies, and species.Nat. Biotechnol. 2018; 36: 411-420Crossref PubMed Scopus (4156) Google Scholar]. In human MS, peripheral blood leukocytes show no major compositional changes [11.Han S. et al.Comprehensive immunophenotyping of cerebrospinal fluid cells in patients with neuroimmunological diseases.J. Immunol. 2014; 192: 2551-2563Crossref PubMed Scopus (78) Google Scholar], although signs of T cell activation, for example, have been reported (reviewed in [12.Alvermann S. et al.Immunophenotyping of cerebrospinal fluid cells in multiple sclerosis: in search of biomarkers.JAMA Neurol. 2014; 71: 905-912Crossref PubMed Scopus (42) Google Scholar]). One assumption is, that MS alters blood leukocytes on a qualitative (functional) level rather than altering gross cell type abundances, supported by a recent cytometry by time-of-flight (CyTOF) study (discussed later) [13.Galli E. et al.GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis.Nat. Med. 2019; 25: 1290-1300Crossref PubMed Scopus (102) Google Scholar]. Specifically, blood leukocyte phenotyping revealed that CD56bright natural killer (NK) cell-dependent killing of CD155-expressing, potentially autoreactive CD4+ T cells, was reduced in MS compared with healthy controls [14.Gross C.C. et al.Impaired NK-mediated regulation of T-cell activity in multiple sclerosis is reconstituted by IL-2 receptor modulation.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: E2973-E2982Crossref PubMed Scopus (124) Google Scholar]. NK cell-frequencies have also been shown to increase in the blood of MS patients following therapies such as natalizumab (anti-α4-integrin antibody), blocking emigration of leukocytes into the CNS [10.Lohmann L. et al.Immune cell profiling during switching from natalizumab to fingolimod reveals differential effects on systemic immune-regulatory networks and on trafficking of non-T cell populations into the cerebrospinal fluid-results from the ToFingo successor study.Front. Immunol. 2018; 9: 1560Crossref PubMed Scopus (20) Google Scholar]; and alemtuzumab (anti-CD52 lymphocyte-depleting antibody) [15.Gross C.C. et al.Dimethyl fumarate treatment alters circulating T helper cell subsets in multiple sclerosis.Neurol. Neuroimmunol. Neuroinflamm. 2016; 3e183Crossref PubMed Scopus (104) Google Scholar]. Reversion of NK cell changes by MS treatments provides circumstantial evidence that NK cells may participate in MS pathogenesis. Blood leukocyte flow cytometry in NMOSD revealed an expansion of peripheral B cells and T follicular helper cells (a subset known to promote B cell responses) in comparison with nondiseased controls via an antibody-mediated disease mechanism [16.Ip P.-P. et al.Differentiation of remitting neuromyelitis optica spectrum disorders from multiple sclerosis by integrating parameters from serum proteins and lymphocyte subsets.J. Neuroimmunol. 2018; 318: 45-52Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar,17.Zhao C. et al.Increased circulating T follicular helper cells are inhibited by rituximab in neuromyelitis optica spectrum disorder.Front. Neurol. 2017; 8: 104Crossref PubMed Scopus (11) Google Scholar]. Research in human CNS autoimmune diseases is also facilitated by analyzing the CSF: a protective liquid that surrounds the CNS and transports leukocytes [18.Engelhardt B. et al.The movers and shapers in immune privilege of the CNS.Nat. Immunol. 2017; 18: 123-131Crossref PubMed Scopus (464) Google Scholar]. It can be collected for diagnostic purposes in human patients at modest risk. While the noncellular fraction of the CSF is mostly an ultrafiltrate of the blood, cells in the CSF are almost exclusively leukocytes, albeit at a 1000-fold lower concentration than in the blood and harboring a distinct composition and phenotype [11.Han S. et al.Comprehensive immunophenotyping of cerebrospinal fluid cells in patients with neuroimmunological diseases.J. Immunol. 2014; 192: 2551-2563Crossref PubMed Scopus (78) Google Scholar]. Experimental approaches to analyze the noncellular fraction of blood and CSF and other bodily fluids are outlined in Box 2.Box 2Analyzing Metabolism and SolutesAside from 'cell-focused techniques', methods to quantify soluble biomarkers have been established for many years and CSF constitutes a relevant biomaterial for analyzing solutes (see Figure 2 in main text). Mass spectrometry allows the identification of proteins without prior knowledge of the proteome of a given tissue. CSF mass spectrometry has been performed using samples from a multitude of neurological diseases, a selection of which is discussed here.Numerous CSF proteomics studies in MS have been conducted. These studies articulated new hypotheses of the pathogenesis of the disease and identified new CSF biomarkers (reviewed in, e.g., [90.Teunissen C.E. et al.Body fluid biomarkers for multiple sclerosis--the long road to clinical application.Nat. Rev. Neurol. 2015; 11: 585-596Crossref PubMed Scopus (69) Google Scholar]). For example, neurofilament chains in serum and CSF serve as a marker of neurodegeneration in inflammatory and neurodegenerative CNS diseases and are currently reaching late stages of clinical development [91.Khalil M. et al.Neurofilaments as biomarkers in neurological disorders.Nat. Rev. Neurol. 2018; 14: 577-589Crossref PubMed Scopus (783) Google Scholar].Another emerging and promising technology is metabolomics, which quantifies individual intermediates (e.g., lactate) in metabolic pathways. In MS, metabolomics showed discrete metabolite profiles of CSF [92.Reinke S.N. et al.Metabolomic profiling in multiple sclerosis: insights into biomarkers and pathogenesis.Mult. Scler. 2014; 20: 1396-1400Crossref PubMed Scopus (69) Google Scholar]. Other studies found the CSF metabolome to distinguish MS and NOSD [93.Kim H.-H. et al.Metabolomic profiling of CSF in multiple sclerosis and neuromyelitis optica spectrum disorder by nuclear magnetic resonance.PLoS One. 2017; 12e0181758PubMed Google Scholar], as well as between secondary progressive MS and relapsing-remitting MS [94.Novakova L. et al.Sulfatide isoform pattern in cerebrospinal fluid discriminates progressive MS from relapsing-remitting MS.J. Neurochem. 2018; 146: 322-332Crossref PubMed Scopus (9) Google Scholar]. One can envision that the differential diagnosis of nervous system autoimmune diseases might be supported by a panel of proteins or metabolites in the future. Recently, CSF metabolomics also identified an established treatment of MS (teriflunomide) as inhibiting the respiratory chain and thereby suppressing the proliferation of high-affinity T cells [95.Klotz L. et al.Teriflunomide treatment for multiple sclerosis modulates T cell mitochondrial respiration with affinity-dependent effects.Sci. Transl. Med. 2019; 11eaao5563Crossref PubMed Scopus (66) Google Scholar]. This illustrates the potential of the method in identifying possible mechanisms of treatment. Aside from 'cell-focused techniques', methods to quantify soluble biomarkers have been established for many years and CSF constitutes a relevant biomaterial for analyzing solutes (see Figure 2 in main text). Mass spectrometry allows the identification of proteins without prior knowledge of the proteome of a given tissue. CSF mass spectrometry has been performed using samples from a multitude of neurological diseases, a selection of which is discussed here. Numerous CSF proteomics studies in MS have been conducted. These studies articulated new hypotheses of the pathogenesis of the disease and identified new CSF biomarkers (reviewed in, e.g., [90.Teunissen C.E. et al.Body fluid biomarkers for multiple sclerosis--the long road to clinical application.Nat. Rev. Neurol. 2015; 11: 585-596Crossref PubMed Scopus (69) Google Scholar]). For example, neurofilament chains in serum and CSF serve as a marker of neurodegeneration in inflammatory and neurodegenerative CNS diseases and are currently reaching late stages of clinical development [91.Khalil M. et al.Neurofilaments as biomarkers in neurological disorders.Nat. Rev. Neurol. 2018; 14: 577-589Crossref PubMed Scopus (783) Google Scholar]. Another emerging and promising technology is metabolomics, which quantifies individual intermediates (e.g., lactate) in metabolic pathways. In MS, metabolomics showed discrete metabolite profiles of CSF [92.Reinke S.N. et al.Metabolomic profiling in multiple sclerosis: insights into biomarkers and pathogenesis.Mult. Scler. 2014; 20: 1396-1400Crossref PubMed Scopus (69) Google Scholar]. Other studies found the CSF metabolome to distinguish MS and NOSD [93.Kim H.-H. et al.Metabolomic profiling of CSF in multiple sclerosis and neuromyelitis optica spectrum disorder by nuclear magnetic resonance.PLoS One. 2017; 12e0181758PubMed Google Scholar], as well as between secondary progressive MS and relapsing-remitting MS [94.Novakova L. et al.Sulfatide isoform pattern in cerebrospinal fluid discriminates progressive MS from relapsing-remitting MS.J. Neurochem. 2018; 146: 322-332Crossref PubMed Scopus (9) Google Scholar]. One can envision that the differential diagnosis of nervous system autoimmune diseases might be supported by a panel of proteins or metabolites in the future. Recently, CSF metabolomics also identified an established treatment of MS (teriflunomide) as inhibiting the respiratory chain and thereby suppressing the proliferation of high-affinity T cells [95.Klotz L. et al.Teriflunomide treatment for multiple sclerosis modulates T cell mitochondrial respiration with affinity-dependent effects.Sci. Transl. Med. 2019; 11eaao5563Crossref PubMed Scopus (66) Google Scholar]. This illustrates the potential of the method in identifying possible mechanisms of treatment. Flow cytometry of CSF samples from MS patients has identified an intra-CSF (synonymously 'intrathecal') expansion of B lineage c