Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease
2020; Springer Nature; Volume: 16; Issue: 6 Linguagem: Inglês
10.15252/msb.20199356
ISSN1744-4292
AutoresJakob M. Bader, Philipp E. Geyer, Johannes Müller, Maximilian T. Strauss, Manja Koch, Frank Leypoldt, Pèter Köertvelyessy, Daniel Bittner, Carola G. Schipke, Enise I. Incesoy, Oliver Peters, Nikolaus Deigendesch, Mikael Simons, Majken K. Jensen, Henrik Zetterberg, Matthias Mann,
Tópico(s)Advanced Proteomics Techniques and Applications
ResumoArticle2 June 2020Open Access Transparent process Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease Jakob M Bader Jakob M Bader orcid.org/0000-0002-6575-0609 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Philipp E Geyer Philipp E Geyer orcid.org/0000-0001-7980-4826 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Johannes B Müller Johannes B Müller orcid.org/0000-0003-3454-2396 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Maximilian T Strauss Maximilian T Strauss orcid.org/0000-0003-3320-6833 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Manja Koch Manja Koch orcid.org/0000-0002-4794-4821 Departments of Nutrition & Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA Search for more papers by this author Frank Leypoldt Frank Leypoldt Institute of Clinical Chemistry, Faculty of Medicine, Kiel University, Kiel, Germany Department of Neurology, Faculty of Medicine, Kiel University, Kiel, Germany Search for more papers by this author Peter Koertvelyessy Peter Koertvelyessy Department of Neurology, Medical Faculty, Otto von Guericke University Magdeburg, Magdeburg, Germany Department of Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany Search for more papers by this author Daniel Bittner Daniel Bittner Department of Neurology, Medical Faculty, Otto von Guericke University Magdeburg, Magdeburg, Germany Search for more papers by this author Carola G Schipke Carola G Schipke Experimental & Clinical Research Center (ECRC), Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, & Berlin Institute of Health, Berlin, Germany Search for more papers by this author Enise I Incesoy Enise I Incesoy Department of Psychiatry, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin & Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany Search for more papers by this author Oliver Peters Oliver Peters Department of Psychiatry, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin & Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany German Center for Neurodegenerative Diseases, Berlin, Germany Search for more papers by this author Nikolaus Deigendesch Nikolaus Deigendesch Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland Search for more papers by this author Mikael Simons Mikael Simons German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Munich Cluster for Systems Neurology, Munich, Germany Search for more papers by this author Majken K Jensen Majken K Jensen Departments of Nutrition & Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA Department of Public Health, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Henrik Zetterberg Henrik Zetterberg Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden UK Dementia Research Institute at UCL, London, UK Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK Search for more papers by this author Matthias Mann Corresponding Author Matthias Mann [email protected] orcid.org/0000-0003-1292-4799 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jakob M Bader Jakob M Bader orcid.org/0000-0002-6575-0609 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Philipp E Geyer Philipp E Geyer orcid.org/0000-0001-7980-4826 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Johannes B Müller Johannes B Müller orcid.org/0000-0003-3454-2396 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Maximilian T Strauss Maximilian T Strauss orcid.org/0000-0003-3320-6833 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany Search for more papers by this author Manja Koch Manja Koch orcid.org/0000-0002-4794-4821 Departments of Nutrition & Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA Search for more papers by this author Frank Leypoldt Frank Leypoldt Institute of Clinical Chemistry, Faculty of Medicine, Kiel University, Kiel, Germany Department of Neurology, Faculty of Medicine, Kiel University, Kiel, Germany Search for more papers by this author Peter Koertvelyessy Peter Koertvelyessy Department of Neurology, Medical Faculty, Otto von Guericke University Magdeburg, Magdeburg, Germany Department of Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany Search for more papers by this author Daniel Bittner Daniel Bittner Department of Neurology, Medical Faculty, Otto von Guericke University Magdeburg, Magdeburg, Germany Search for more papers by this author Carola G Schipke Carola G Schipke Experimental & Clinical Research Center (ECRC), Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, & Berlin Institute of Health, Berlin, Germany Search for more papers by this author Enise I Incesoy Enise I Incesoy Department of Psychiatry, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin & Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany Search for more papers by this author Oliver Peters Oliver Peters Department of Psychiatry, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin & Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany German Center for Neurodegenerative Diseases, Berlin, Germany Search for more papers by this author Nikolaus Deigendesch Nikolaus Deigendesch Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland Search for more papers by this author Mikael Simons Mikael Simons German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Munich Cluster for Systems Neurology, Munich, Germany Search for more papers by this author Majken K Jensen Majken K Jensen Departments of Nutrition & Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA Department of Public Health, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Henrik Zetterberg Henrik Zetterberg Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden UK Dementia Research Institute at UCL, London, UK Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK Search for more papers by this author Matthias Mann Corresponding Author Matthias Mann [email protected] orcid.org/0000-0003-1292-4799 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Author Information Jakob M Bader1,‡, Philipp E Geyer1,2,‡, Johannes B Müller1, Maximilian T Strauss1, Manja Koch3, Frank Leypoldt4,5, Peter Koertvelyessy6,7, Daniel Bittner6, Carola G Schipke8, Enise I Incesoy9, Oliver Peters9,10, Nikolaus Deigendesch11, Mikael Simons12,13, Majken K Jensen3,14, Henrik Zetterberg15,16,17,18 and Matthias Mann *,1,2 1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany 2NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark 3Departments of Nutrition & Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA 4Institute of Clinical Chemistry, Faculty of Medicine, Kiel University, Kiel, Germany 5Department of Neurology, Faculty of Medicine, Kiel University, Kiel, Germany 6Department of Neurology, Medical Faculty, Otto von Guericke University Magdeburg, Magdeburg, Germany 7Department of Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany 8Experimental & Clinical Research Center (ECRC), Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, & Berlin Institute of Health, Berlin, Germany 9Department of Psychiatry, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin & Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany 10German Center for Neurodegenerative Diseases, Berlin, Germany 11Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland 12German Center for Neurodegenerative Diseases (DZNE), Munich, Germany 13Munich Cluster for Systems Neurology, Munich, Germany 14Department of Public Health, University of Copenhagen, Copenhagen, Denmark 15Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden 16Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden 17UK Dementia Research Institute at UCL, London, UK 18Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK ‡These authors contributed equally to this work ‡[Correction added on 28 September 2020, after first online publication: Projekt Deal funding statement has been added.] *Corresponding author. Tel: +49 8985782557; E-mail: [email protected] Molecular Systems Biology (2020)16:e9356https://doi.org/10.15252/msb.20199356 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Neurodegenerative diseases are a growing burden, and there is an urgent need for better biomarkers for diagnosis, prognosis, and treatment efficacy. Structural and functional brain alterations are reflected in the protein composition of cerebrospinal fluid (CSF). Alzheimer's disease (AD) patients have higher CSF levels of tau, but we lack knowledge of systems-wide changes of CSF protein levels that accompany AD. Here, we present a highly reproducible mass spectrometry (MS)-based proteomics workflow for the in-depth analysis of CSF from minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins by AD status (> 1,000 proteins, CV < 20%). Proteins with previous links to neurodegeneration such as tau, SOD1, and PARK7 differed most strongly by AD status, providing strong positive controls for our approach. CSF proteome changes in Alzheimer's disease prove to be widespread and often correlated with tau concentrations. Our unbiased screen also reveals a consistent glycolytic signature across our cohorts and a recent study. Machine learning suggests clinical utility of this proteomic signature. Synopsis A robust proteomic workflow quantifies more than 1,000 proteins in cerebrospinal fluid and reveals an Alzheimer's Disease-associated signature of more than 20 proteins across three independent cohorts. These include tau, superoxide dismutase 1, PARK7, YKL-40 and novel biomarker candidates. Proteomics workflow for quantification of more than 1,000 proteins from microliters of cerebrospinal fluid. More than 20 proteins consistently associated with Alzheimer's Disease across three cohorts comprising about 200 individuals in total. Alzheimer's Disease CSF signature of Tau, SOD1, PARK7, YKL-40, and glycolysis-related proteins. Introduction Alzheimer's disease (AD) is the most common type of dementia, and its prevalence is growing rapidly in aging societies (GBD 2016 Neurology Collaborators, 2019). In 2015, almost 47 million people worldwide were estimated to be affected by dementia, and the numbers are expected to reach 75 million by 2030, and 131 million by 2050, with the greatest increase expected in low-income and middle-income countries (Winblad et al, 2016). Patients with AD typically present with memory impairment and difficulty performing activities of daily living (Scheltens et al, 2016). However, symptoms may manifest decades after the underlying pathology has initiated, including the deposition of amyloid plaques and development of neurofibrillary tangles (Jack et al, 2010). Biomarkers have become important diagnostic tools to define the presence and absence of dementia before onset of memory loss. While a research framework for defining AD based on beta amyloid (Aβ) deposition, pathologic tau, and neurodegeneration (ATN) has been proposed (Jack et al, 2018), clinical criteria for AD are not universally standardized and range from clinical presentation to brain imaging by MRI and PET to clinical chemistry analysis of Aβ1–42/Aβ1–40, total-tau (t-tau), and phosphorylated-tau (p-tau181) in cerebrospinal fluid (CSF; Frisoni et al, 2010; McKhann et al, 2011; Ferreira et al, 2014; Rice & Bisdas, 2017). Most research currently focuses on Aβ and tau, because they are the main components of amyloid plaques and neurofibrillary tangles (Serrano-Pozo et al, 2011). However, the search for a disease-modifying therapy has yet to show clinically relevant results and it is becoming increasingly clear that many additional pathological changes in multiple pathways occur in dementia. Thus, we propose an unbiased analysis of CSF proteins in participants with and without AD for a comprehensive search for novel diagnostic biomarkers. A set of reliable protein biomarkers rather than a single marker could also enable the development of highly specific tests for early disease detection in at-risk segments of the population. Ideally, such markers should identify unexpected biological pathways and new potential therapeutic targets for future development. Mass spectrometry (MS)-based proteomics has become a very powerful technology for the analysis of protein abundance levels, modifications, and interactions, with important discoveries in biological and biochemical research, including neuroscience (Aebersold & Mann, 2016; Hosp & Mann, 2017). MS-based proteomics is unbiased in the sense that it identifies and quantifies proteins in an untargeted manner. Additionally, the identification is extremely specific through the amino acid sequence information at the peptide level. These inherent features differentiate MS-based from affinity-based methods and should make MS an ideal tool for biomarker discovery; however, in body fluids this long-standing goal has not generally been realized so far. This has been due to a variety of technological and conceptual limitations, compromising reproducibility, the number of consistently quantified proteins and throughput (Geyer et al, 2017). For instance, a general issue in body fluid proteomics is the presence of highly abundant proteins such as albumin that hamper efficient identification of less abundant proteins. Previous workflows were laborious, typically quantified a few hundred proteins at most per sample and required hundreds of microliters of precious CSF, thereby limiting the availability of suitable samples (Dayon et al, 2018). Reproducibility was low with only a minority of proteins having clinically accepted coefficients of variation (CV) of < 20%. Furthermore, many proteins were not quantifiable in all study participants and validation in well-characterized study populations was lacking. Therefore, entire databases have been curated to navigate reported CSF proteome alterations across studies in the field of neurodegeneration including AD (Guldbrandsen et al, 2017). Recent technological advances enable substantially higher proteome coverage and better and more comprehensive protein quantitation. These developments include automated sample preparation, technological improvements in mass spectrometers, MS data acquisition, and processing software that synergize to enhance the overall analytical performance (Bruderer et al, 2017; Kelstrup et al, 2018). Based on these advances, we here developed a streamlined and highly reproducible workflow from sample preparation to data-independent MS acquisition (Ludwig et al, 2018) and an integrated analysis of the results for CSF. This workflow enabled us to clearly identify the established markers as well as a large number of consistent and biologically meaningful proteome changes across several independent cohorts. Results Overview of study populations We recently proposed a shift in the study design of clinical discovery proteomics termed "rectangular strategy" (Geyer et al, 2017). In the previous "triangular strategy" study design, selected samples were characterized with extensive workflows and a small number of candidates were then assessed in a larger number of individuals using targeted methods. However, these candidates often turned out to be specific to the discovery population and could not be validated in independent study populations. In contrast, in the "rectangular strategy", multiple studies are subjected to the same high proteome depth workflow, moving the discovery to the population-wide setting in order to discern pathological from study-specific effects. To implement the rectangular strategy, we analyzed three separate study populations of about 30 AD patients and about 30 or 50 controls, amounting to 197 individuals in total (Fig 1A). We refer to the study populations as cohorts throughout the manuscript, because each cross-sectional study was slightly different, conducted in distinct settings and geographical regions. One cohort originated from western Sweden, another from the German cities Magdeburg and Kiel (obtained through Harvard T. H. Chan School of Public Health), and the third from Berlin. The overall median age was 70.0 ± 12.1 years (± SD) (Fig EV1A). However, the 16 non-AD control patients of the Kiel sub-cohort were younger (median 32.0 ± 17.1 years). In each of our cohorts, patients were classified as AD if the t-tau concentration was above 400 ng/l, and the Aβ1–42 concentration below 550 ng/l or the Aβ1–42/Aβ1–40 ratio below 0.065 as determined by ELISA measurement at the clinical collection site (Materials and Methods). Figure 1. Study overview and CSF proteome characterization Overview of the study populations (cohorts) and schematic proteomic workflow. The CSF of three cohorts comprising AD and control subjects was analyzed. The total number of subjects per cohort group is depicted. Light and dark shades represent female and male subjects, respectively. "Ctrl" refers to non-AD control subjects. Number of proteins identified and quantified passing the 1% FDR cutoffs in each sample. Horizontal lines show the mean and the error bars ± SD. The dashed line indicates the level of the meta-median (1,233 proteins) of the group medians of quantified proteins. Number of samples per group as shown in A). Data completeness curve. The number of proteins in the dataset (Y axis) depending on the minimum number of samples in which the proteins have each been quantified (X axis) is plotted. The arrows indicate 50%, 75%, and 100% data completeness. Median CSF protein abundance distribution as calculated from MS intensities of quantified peptides of each protein. The top ten most abundant proteins and hemoglobins are highlighted. Global correlation map of proteins generated by clustering the Pearson correlation coefficients of all possible protein combinations. The abundance of proteins with common regulation correlates across samples, and they therefore form a cluster. Prominent clusters are annotated with functional terms obtained from bioinformatics enrichment analysis. The position of tau (gene name MAPT) is labeled on the Y axis. The inset shows the color code for Pearson correlation coefficients. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Clinical information on cohort subjects A. Age of participants at the time point of CSF collection. Bars represent mean and standard deviation. Participant numbers are 29, 31, 26, 12, 16, 33, 26, and 24 for Sweden AD, Sweden biochemical controls, Magdeburg AD, Magdeburg biochemical controls, Berlin AD, Berlin depression controls, and Berlin subjective cognitive impairment (SCI) controls. B–F. CSF concentration of t-tau (B), p-tau181 (C), Aβ1–42 (D), Aβ1–40 (E), and the Aβ1–42/Aβ1–40 concentration ratio (F) as measured by ELISA. Bars represent mean and standard deviation. NA indicates that these data were not available. Participant numbers as in A) if data were available. G–I. CSF concentrations of t-tau plotted versus Aβ1–42 for samples of the Sweden (G), Magdeburg (H), and Berlin (I) cohorts. Samples classified as AD according to the biochemical criteria of this study colored in red, samples as non-AD in blue. J–K. CSF concentration of t-tau plotted versus the Aβ1–42/Aβ1–40 concentration ratio for samples of the Magdeburg (J) and Berlin (K) cohorts. Samples classified as AD according to the biochemical criteria of this study colored in red, samples as non-AD in blue. L. Mini-mental state examination (MMSE) scores, a measure of cognitive performance. Bars represent mean and standard deviation. These data were only available for the Berlin cohort. Participant numbers are 33, 26, and 24 for Berlin AD, Berlin depression controls, and Berlin subjective cognitive impairment (SCI) controls. Download figure Download PowerPoint The degree of separation of AD cases and controls by clinical AD CSF biomarker concentrations differed across cohorts. AD and non-AD were best separated in the Sweden cohort but the Magdeburg cohort also exhibited a good overall separation (Fig EV1B–K, Materials and Methods). In the Berlin cohort, however, AD and control groups overlapped to some degree regarding CSF Aβ1–42 and slightly regarding t-tau. Characterization of the CSF proteomics workflow Previously, we developed a streamlined Plasma Proteome Profiling pipeline, in which the proteins in one microliter of plasma are digested to peptides and purified for MS analysis in an automated system (Geyer et al, 2016). CSF contains much less protein than plasma, with about 0.17–0.70 g/l and 60–80 g/l total protein content, respectively (Seyfert et al, 2002; Laub et al, 2010). Nevertheless, we achieved a very robust workflow with high proteome depth from only a few microliter of sample that was not depleted of highly abundant proteins (Fig 1A and C). We adopted a data-independent acquisition strategy (DIA), both because it can achieve high data completeness (Gillet et al, 2012) and because it has been shown to perform excellently on the linear quadrupole-Orbitrap instruments employed here (Kelstrup et al, 2018). A DIA library of about 2,700 proteins was computationally merged from pooled AD and non-AD samples after separation into 24 fractions each and a direct-DIA search for all single-run samples (Materials and Methods). CSF proteomes were acquired by measuring single 100-min gradient runs for each patient. On average, we quantified 1,233 proteins per CSF sample (Fig 1B, Datasets EV1, EV2 and EV3). The data acquired with DIA had 100% completeness for 385 proteins (26%), 75% for 1,050 proteins (71%), and 50% for 1,288 proteins (87%) (Fig 1C). The quantified protein intensities spanned over six orders of magnitude, in which the top ten most abundant proteins contributed 65% of total protein intensity of the entire 1,484 proteins in our dataset (Fig 1D). To achieve such CSF proteome depth, extensive fractionation and depletion of abundant proteins often combined with isobaric labeling were previously required, with its associated disadvantages (preprint: Higginbotham et al, 2019; Sathe et al, 2019). For a single-shot CSF proteomics workflow that is amenable to high-throughput and large cohorts, this presents an unprecedented depth at high data completeness. We investigated intra- and inter-assay variability of our automated CSF pipeline by repeated sample preparation (Materials and Methods), which revealed high reproducibility with over 1,000 proteins having inter-assay CVs below 20% (Fig EV2A and B, Datasets EV4 and EV5). This level of variability is much smaller than the proteome differences between subjects, as assessed by calculating the inter-individual variability within the cohorts. Here, only 225 proteins had a CV below 20% (Fig EV2C). Click here to expand this figure. Figure EV2. Robustness of the analytical workflow and enrichment analysis of the tau-containing cluster in the global correlation map A–C. Comparison of inter-participant variation and technical assay variation. Coefficients of variation (CVs) were determined in a separate experiment for intra-plate CVs (A) and inter-plate CVs (B) to benchmark protein quantitation. Biological CVs were calculated from the main study data (C). Proteins with a CV below 20% are highlighted in blue. Numbers of proteins above and below this CV cutoff are given above and below the cutoff line, respectively. The CV experiment data resulted from an independent protein search, and thus, the total number of identified proteins is not identical to the main study. The data show that technical variation is much smaller than inter-participant variation. D. Annotation enrichment results for the tau (MAPT)-associated cluster in Fig 1E. Enrichment in the cluster over the entire background CSF proteome vs. enrichment significance (−log10 of Benjamini–Hochberg-adjusted P-values). Terms of interest with links to neurons are highlighted in black. Download figure Download PowerPoint The availability of a large set of 197 CSF samples prompted us to investigate the relationship between different proteins in order to functionally interpret co-regulation of proteins that cluster with each other or with clinical parameters. The global protein correlation map (Wewer Albrechtsen et al, 2018) resulting from more than a million protein–protein comparisons highlighted eight main clusters of proteins which follow common functions or themes (Dataset EV6). For instance, neuronal annotation terms such as the gene ontology cellular compartments (GOCC) terms neuron projection, axon, and synapse were selectively enriched in the second largest cluster (Figs 1E and EV2D). Identification of neuronal proteins in the CSF highlights that proteins originating in the central nervous system accumulate in the CSF, thus making the CSF reflective of physiological or pathological proteome alteration in this organ. Another cluster was enriched in blood plasma proteins relating to humoral immunity, the complement system or coagulation. Vascular proteins have been reported to be increased in AD brains while decreased in AD CSF (preprint: Higginbotham et al, 2019). However, apart from disease-associated effects such as a modulation of the blood–brain barrier, apparent alterations of blood protein abundances in CSF may be caused by blood contamination during CSF sampling which is hard to avoid entirely. Proteins are likely blood contaminants in CSF if they exhibit the same abundance profile across samples as known blood proteins and occur in the same abundance ratio to these blood proteins in CSF as in blood. Conversely, if a protein also found in blood does not correlate with the blood proteins, it may still be a genuine biomarker for AD. The global correlation map presents an efficient approach to distinguish biomarkers from contaminants (Geyer et al, 2019). Here, CSF signatures of proteins biologically relevant to AD clearly separated from protein clusters that are at higher risk to be contamination-associated (Fig 1E). Proteomics detects differences in CSF t-tau in individuals with or without AD and neuronal and widespread novel proteome alterations In the Sweden and the Magdeburg/Kiel cohorts, AD was associated with drastic CSF proteome alterations, with 540 and 453 proteins significantly (P < 0.05) differing by AD status, respectively. These changes encompassed up- and down-regulated proteins, and significant proteins had a median absolute fold change of about 1.3-fold in both studies. The extensive brain atrophy apparent upon autopsy and the widespread brain proteome alterations harmonize well with the observed substantial alterations in the CSF proteome in AD and other neurodegenerative diseases (Hosp et al, 2017; preprint: Higginbotham et al, 2019). In all three cohorts, tau (gene name MAPT) was the most significantly or among the most significantly altered proteins between individuals with or without AD, wit
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