Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease
Open accessAbstract
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.
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20% of the upregulated proteins were part of the Hif1a downstream network predicted here and included the HIF1A target proteins ALDOA, SPP1, PLD3, PGK1, FABP3, LDHA and STMN1.
“Importantly, 20% of the upregulated proteins were part of the Hif1a downstream network predicted here and included the HIF1A target proteins ALDOA, SPP1, PLD3, PGK1, FABP3, LDHA and STMN1”
From Transcriptional signature in microglia associated with Aβ plaque phagocytosis (2021) · cites this paper as [78]
The Results section (“Neuronal and glycolytic signature in AD CSF”) identifies ALDOA, PGK1 and LDHA among proteins elevated in AD cerebrospinal fluid; another Results section reports elevated FABP3. The cited paper does not establish the stated 20% overlap or identify these proteins as HIF1A targets, and the passages do not establish elevation of SPP1, PLD3 or STMN1. The network described as “predicted here” appears to be the citing authors’ analysis, not a finding of this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRecent analyses showed a reproducible protein signature in 3 independent patient cohorts (total 197 patients) specifically in AD CSF relative to depression and mild cognitive impairment (MCI).
“Recent analyses showed a reproducible protein signature in 3 independent patient cohorts (total 197 patients) specifically in AD CSF relative to depression and mild cognitive impairment (MCI)78.”
From Transcriptional signature in microglia associated with Aβ plaque phagocytosis (2021) · cites this paper as [78]
The Results (Overview of study populations) report three cohorts totaling 197 individuals, and the Results (Replication of AD-associated proteins across cohorts) describe a consistent 40-protein AD CSF signature. The Berlin comparison included depression and subjective cognitive impairment (SCI) controls, not a group identified as mild cognitive impairment (MCI); the other cohorts had different non-AD controls. (LLM, conf=0.90)
Experimental — accuracy not yet measuredProteins with associations in APOE region featured known AD biomarkers, including four members of the 14-3-3 protein family and neurofilament light and heavy chains.
“Proteins with associations in this region featured known AD biomarkers, including four members of the 14-3-3 protein family and neurofilament light and heavy chains37,38.”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [37]
The Results sections on CSF proteome alterations and independent-cohort confirmation identify 14-3-3 proteins as associated with AD CSF. Our copy does not establish that four family members and neurofilament light and heavy chains were among proteins associated with the APOE region; searches for APOE and neurofilament did not find that link. Page numbers were not available in the indexed passages. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn two separate smaller analyses of AD CSF, many of the same AD-associated synaptic and metabolic biomarkers were identified, further supporting the reproducibility of these systems-based CSF marker panels.
“In two separate smaller analyses of AD CSF, many of the same AD-associated synaptic and metabolic biomarkers were identified, further supporting the reproducibility these systems-based CSF marker panels [78, 172].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [172]
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. (1997) · European Conference on Computational Learning Theory · doi:10.1007/3-540-59119-2_166 · Cited by 23,988 · see citing works
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785 · see citing works
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568 · see citing works
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537 · see citing works
Scikit-learn: Machine Learning in Python
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078195 · Cited by 8,539 · see citing works
The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P. G., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 7,977 · see citing works
Self-citationPérez-Riverol, Y., Csordas, A., Bai, J.-W., Bernal-Llinares, M., Hewapathirana, S., Kundu, D. J., Inuganti, A., Griss, J., Mayer, G., Eisenacher, M., et al. (2018) · Nucleic Acids Res. · doi:10.1093/nar/gky1106 · Cited by 6,216 · see citing works
Feigin, V. L., Nichols, E., Alam, T., Bannick, M. S., Beghi, E., Blake, N., Culpepper, W. J., Dorsey, E. R., Elbaz, A., Ellenbogen, R. G., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(18)30499-x · Cited by 5,644 · see citing works
Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., & Trojanowski, J. Q. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(09)70299-6 · Cited by 4,806 · see citing works
Overdependence on degraded gist memory in Alzheimer's disease.
Gallo, D. A., Shahid, K. R., Olson, M. A., Solomon, T. M., Schacter, D. L., & Budson, A. E. (2006) · Neuropsychology · doi:10.1037/0894-4105.20.6.625 · Cited by 4,756 · see citing works
Querfurth, H. W., & LaFerla, F. M. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0909142 · Cited by 4,569 · see citing works
Moms, J. C., Heyman, A., Mohs, R. C., Hughes, J. P., van Belle, G., Fillenbaum, G., Mellits, E. D., & Clark, C. (1989) · Neurology · doi:10.1212/wnl.39.9.1159 · Cited by 4,130 · see citing works
Neuropathological alterations in Alzheimer disease
Serrano-Pozo, A., Frosch, M. P., Masliah, E., & Hyman, B. T. (2011) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a006189 · Cited by 3,345 · see citing works
Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., DeKosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., et al. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70090-0 · Cited by 3,321 · see citing works
Scheltens, P., Blennow, K., Breteler, M. M. B., de Strooper, B., Frisoni, G. B., Salloway, S., & Van der Flier, W. M. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01124-1 · Cited by 3,125 · see citing works
Gillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877 · see citing works
Mutations in the DJ-1 Gene Associated with Autosomal Recessive Early-Onset Parkinsonism
Bonifati, V., Rizzu, P., van Baren, M. J., Schaap, O., Breedveld, G. J., Krieger, E., Dekker, M. C. J., Squitieri, F., Ibáñez, P., Joosse, M., et al. (2003) · Science · doi:10.1126/science.1077209 · Cited by 2,800 · see citing works
Mass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334 · see citing works
Self-citationBrain energy metabolism: focus on Astrocyte-neuron metabolic cooperation
Bélanger, M., Allaman, I., & Magistretti, P. J. (2011) · Cell Metabolism · doi:10.1016/j.cmet.2011.08.016 · Cited by 2,313 · see citing works
The clinical use of structural MRI in Alzheimer disease
Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.215 · Cited by 2,113 · see citing works
Olsson, B., Lautner, R., Andreasson, U., Öhrfelt, A., Portelius, E., Bjerke, M., Hölttä, M., Rosén, C., Olsson, C., Strobel, G., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)00070-3 · Cited by 2,049 · see citing works
Self-citationHansson, O., Zetterberg, H., Buchhave, P., Londos, E., Blennow, K., & Minthon, L. (2006) · The Lancet Neurology · doi:10.1016/s1474-4422(06)70355-6 · Cited by 1,700 · see citing works
Self-citationDefeating Alzheimer's disease and other dementias: a priority for European science and society
Winblad, B., Amouyel, P., Andrieu, S., Ballard, C., Brayne, C., Brodaty, H., Cedazo-Minguez, A., Dubois, B., Edvardsson, D., Feldman, H., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)00062-4 · Cited by 1,676 · see citing works
Self-citationState of play in amyotrophic lateral sclerosis genetics
Renton, A. E., Chiò, A., & Traynor, B. J. (2013) · Nature Neuroscience · doi:10.1038/nn.3584 · Cited by 1,563 · see citing works
Mitchell, A. J. (2008) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2008.04.014 · Cited by 1,155 · see citing works
Johnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001 · see citing works
Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial
Ludwig, C., Gillet, L. C. J., Rosenberger, G., Amon, S., Collins, B. C., & Aebersold, R. H. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178126 · Cited by 999 · see citing works
Revisiting biomarker discovery by plasma proteomics
Geyer, P. E., Holdt, L. M., Teupser, D., & Mann, M. (2017) · Molecular Systems Biology · doi:10.15252/msb.20156297 · Cited by 917 · see citing works
Self-citationArévalo-Rodríguez, I., Smailagic, N., Figuls, M. R. I., Ciapponi, A., Sanchez-Perez, E., Giannakou, A., Pedraza, O. L., Bonfill, X., & Cullum, S. J. (2015) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd010783.pub2 · Cited by 897 · see citing works
Mapping Scores Onto Stages: Mini-Mental State Examination and Clinical Dementia Rating
Perneczky, R., Wagenpfeil, S., Komossa, K., Grimmer, T., Diehl, J., & Kurz, A. (2006) · The American Journal of Geriatric Psychiatry · doi:10.1097/01.jgp.0000192478.82189.a8 · Cited by 768 · see citing works
Plasma Proteome Profiling to Assess Human Health and Disease
Geyer, P. E., Kulak, N. A., Pichler, G., Holdt, L. M., Teupser, D., & Mann, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.02.015 · Cited by 734 · see citing works
Self-citationImproved discrimination of AD patients using β-amyloid <sub>(1-42)</sub> and tau levels in CSF
Hulstaert, F., Blennow, K., Ivanoiu, A., Schoonderwaldt, H. C., Riemenschneider, M., Deyn, P. P. D., Bancher, C., Cras, P., Wiltfang, J., Mehta, P. D., et al. (1999) · Neurology · doi:10.1212/wnl.52.8.1555 · Cited by 724 · see citing works
Cox, J., & Mann, M. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s16-s12 · Cited by 614 · see citing works
Self-citationCallister, S., Barry, R., Adkins, J., Johnson, E. T., Qian, W., Webb‐Robertson, B., Smith, R. D., & Lipton, M. S. W. (2006) · Journal of Proteome Research · doi:10.1021/pr050300l · Cited by 500 · see citing works
Bruderer, R., Bernhardt, O. M., Gandhi, T., Xuan, Y., Sondermann, J. R., Schmidt, M., Gómez‐Varela, D., & Reiter, L. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra117.000314 · Cited by 479 · see citing works
Sjögren, M., Vanderstichele, H., Ågren, H., Zachrisson, O., Edsbagge, M., Wikkelsø, C., Skoog, I., Wallin, A., Wahlund, L.-O., Marcusson, J., et al. (2001) · Clinical Chemistry · doi:10.1093/clinchem/47.10.1776 · Cited by 436 · see citing works
Higginbotham, L., Ping, L., Dammer, E. B., Duong, D. M., Zhou, M., Gearing, M., Johnson, E. C. B., Hajjar, I., Lah, J. J., Levey, A. I., et al. (2019) · doi:10.1101/806752 · Cited by 367 · see citing works
Higginbotham, L., Ping, L., Dammer, E. B., Duong, D. M., Zhou, M., Gearing, M., Hurst, C., Glass, J. D., Factor, S. A., Johnson, E. C. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz9360 · Cited by 367 · see citing works
Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies
Geyer, P. E., Voytik, E., Treit, P. V., Doll, S., Kleinhempel, A., Niu, L., Müller, J. B., Buchholtz, M., Bader, J. M., Teupser, D., et al. (2019) · EMBO Molecular Medicine · doi:10.15252/emmm.201910427 · Cited by 348 · see citing works
Self-citationReview and Meta-Analysis of Biomarkers and Diagnostic Imaging in Alzheimer's Disease
Bloudek, L. M., Spackman, D. E., Blankenburg, M., & Sullivan, S. D. (2011) · Journal of Alzheimer’s Disease · doi:10.3233/jad-2011-110458 · Cited by 282 · see citing works
Plasma proteome profiling discovers novel proteins associated with non‐alcoholic fatty liver disease
Niu, L., Geyer, P. E., Albrechtsen, N. J. W., Gluud, L. L., Santos, A., Doll, S., Treit, P. V., Holst, J. J., Knop, F. K., Vilsbøll, T., et al. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188793 · Cited by 278 · see citing works
Self-citationPerformance Evaluation of the Q Exactive HF-X for Shotgun Proteomics
Kelstrup, C. D., Bekker‐Jensen, D. B., Arrey, T. N., Hogrebe, A., Harder, A., & Olsen, J. V. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00602 · Cited by 256 · see citing works
Arévalo-Rodríguez, I., Smailagic, N., Figuls, M. R. I., Ciapponi, A., Sanchez-Perez, E., Giannakou, A., Pedraza, O. L., Bonfill, X., & Cullum, S. J. (2015) · BJPsych Advances · doi:10.1192/apt.21.6.362 · Cited by 245 · see citing works
Keshishian, H., Burgess, M. W., Gillette, M. A., Mertins, P., Clauser, K. R., Mani, D. R., Kuhn, E. W., Farrell, L. A., Gerszten, R. E., & Carr, S. A. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.046813 · Cited by 222 · see citing works
Ritchie, C. W., Smailagic, N., Noel-Storr, A. H., Ukoumunne, O. C., Ladds, E. C., & Martin, S. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd010803.pub2 · Cited by 208 · see citing works
Loss-less Nano-fractionator for High Sensitivity, High Coverage Proteomics
Kulak, N. A., Geyer, P. E., & Mann, M. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o116.065136 · Cited by 167 · see citing works
Self-citationMini Mental State Examination and Logical Memory scores for entry into Alzheimer’s disease trials
Chapman, K. R., Bing‐Canar, H., Alosco, M. L., Steinberg, E. G., Martin, B. M., Chaisson, C. E., Kowall, N. W., Tripodis, Y., & Stern, R. A. (2016) · Alzheimer s Research & Therapy · doi:10.1186/s13195-016-0176-z · Cited by 152 · see citing works
The diagnostic value of FDG and amyloid PET in Alzheimer’s disease—A systematic review
Rice, L., & Bisdas, S. (2017) · European Journal of Radiology · doi:10.1016/j.ejrad.2017.07.014 · Cited by 150 · see citing works
Ferreira, D., Perestelo‐Pérez, L., Westman, E., Wahlund, L., Sarría‐Santamera, A., & Serrano‐Aguilar, P. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00047 · Cited by 144 · see citing works
A multiregional proteomic survey of the postnatal human brain
Carlyle, B. C., Kitchen, R. R., Kanyo, J. E., Voss, E. Z., Pletikos, M., Sousa, A. M. M., Lam, T. T., Gerstein, M. B., Sestan, N., & Nairn, A. C. (2017) · Nature Neuroscience · doi:10.1038/s41593-017-0011-2 · Cited by 143 · see citing works
14-3-3 proteins in neurological disorders.
Foote, M. M., & Zhou, Y. (2012) · PubMed · Cited by 140 · see citing works
Spies, P. E., Slats, D., Sjögren, M. J. C., Kremer, B. P. H., Verhey, F. R. J., Rikkert, M. G. M. O., & Verbeek, M. M. (2010) · Current Alzheimer Research · doi:10.2174/156720510791383796 · Cited by 135 · see citing works
Prediction of Alzheimer disease in subjects with amnestic and nonamnestic MCI
Vos, S. J. B., van Rossum, I. A., Verhey, F. R. J., Knol, D. L., Soininen, H. S., Wahlund, L., Hampel, H. J., Tsolaki, M., Minthon, L., Frisoni, G. B., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e318288690c · Cited by 133 · see citing works
Sathe, G., Na, C. H., Renuse, S., Madugundu, A. K., Albert, M., Moghekar, A., & Pandey, A. (2019) · PROTEOMICS – Clinical Applications · doi:10.1002/prca.201800105 · Cited by 126 · see citing works
Barthélemy, N. R., Fenaille, F., Hirtz, C., Sergeant, N., Schraen-Maschke, S., Vialaret, J., Buée, L., Gabelle, A., Junot, C., Lehmann, S., et al. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b01001 · Cited by 123 · see citing works
Comi, C., Carecchio, M., Chiocchetti, A., Nicola, S., Galimberti, D., Fenoglio, C., Cappellano, G., Monaco, F., Scarpini, E., & Dianzani, U. (2010) · Journal of Alzheimer's Disease · doi:10.3233/jad-2010-1309 · Cited by 120 · see citing works
Hosp, F., Gutiérrez-Ángel, S., Schaefer, M. H., Cox, J., Meissner, F., Hipp, M. S., Hartl, F. U., Klein, R., Dudanova, I., & Mann, M. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.10.097 · Cited by 117 · see citing works
Self-citationMaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides
Wichmann, C., Meier, F., Winter, S. V., Brunner, A., Cox, J., & Mann, M. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir118.001131 · Cited by 115 · see citing works
Self-citationAbbatiello, S. E., Mani, D. R., Schilling, B., MacLean, B., Zimmerman, L. J., Feng, X., Cusack, M. P., Sedransk, N., Hall, S. C., Addona, T. A., et al. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.027078 · Cited by 113 · see citing works
Baldacci, F., Lista, S., Cavedo, E., Bonuccelli, U., & Hampel, H. (2017) · Expert Review of Proteomics · doi:10.1080/14789450.2017.1304217 · Cited by 113 · see citing works
Fountoulakis, M., Cairns, N. J., & Lübec, G. (1999) · Springer eBooks · doi:10.1007/978-3-7091-6380-1_23 · Cited by 102 · see citing works
Alzheimer disease pathology and the cerebrospinal fluid proteome
Dayon, L., Núñez Galindo, A., Wojcik, J., Cominetti, O., Corthésy, J., Oikonomidi, A., Henry, H., Kussmann, M., Migliavacca, E., Severin, I., et al. (2018) · Alzheimer's Research & Therapy · doi:10.1186/s13195-018-0397-4 · Cited by 101 · see citing works
Niemantsverdriet, E., Ottoy, J., Somers, C., Roeck, E. E. D., Struyfs, H., Soetewey, F., Verhaeghe, J. A. J., den Bossche, T. V., Mossevelde, S. V., Goeman, J., et al. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170327 · Cited by 94 · see citing works
Albrechtsen, N. J. W., Geyer, P. E., Doll, S., Treit, P. V., Bojsen-Møller, K. N., Martinussen, C., Jørgensen, N. B., Torekov, S. S., Meier, F., Niu, L., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.012 · Cited by 83 · see citing works
Self-citationA Primer on Concepts and Applications of Proteomics in Neuroscience
Hosp, F., & Mann, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.025 · Cited by 77 · see citing works
Self-citationLaub, R., Baurin, S., Timmerman, D., Branckaert, T., & Strengers, P. F. W. (2010) · Vox Sanguinis · doi:10.1111/j.1423-0410.2010.01345.x · Cited by 74 · see citing works
Determinants of lumbar CSF protein concentration
Seyfert, S., Kunzmann, V., Schwertfeger, N., Koch, H. C., & Faulstich, A. (2002) · Journal of Neurology · doi:10.1007/s00415-002-0777-2 · Cited by 74 · see citing works
Molinuevo, J. L., Gispert, J. D., Dubois, B., Heneka, M. T., Lleó, A., Engelborghs, S., Pujol, J., de Souza, L. C., Alcolea, D. A., Jessen, F., et al. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-130203 · Cited by 59 · see citing works
Guldbrandsen, A., Farag, Y. M., Kroksveen, A. C., Oveland, E., Lereim, R. R., Opsahl, J. A., Myhr, K., Berven, F. S., & Barsnes, H. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o116.064477 · Cited by 47 · see citing works
Role of FABP3 as Biomarker in Alzheimer's Disease and Synucleinopathies
Sepe, F. N., Chiasserini, D., & Parnetti, L. (2018) · Future Neurology · doi:10.2217/fnl-2018-0003 · Cited by 38 · see citing works
Francelle, L., Galvan, L., Gaillard, M., Guillermier, M., Houitte, D., Bonvento, G., Petit, F., Jan, C., Dufour, N., Hantraye, P., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu571 · Cited by 37 · see citing works
Apolipoproteins and their subspecies in human cerebrospinal fluid and plasma
Koch, M., Furtado, J. D., Falk, K., Leypoldt, F., Mukamal, K. J., & Jensen, M. K. (2017) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2017.01.007 · Cited by 31 · see citing works
Self-citationRitchie, C. W., Smailagic, N., Ladds, E. C., Noel-Storr, A. H., Ukoumunne, O. C., & Martin, S. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd010803 · Cited by 31 · see citing works
Mateo, I., Llorca, J., Infante, J., Rodríguez‐Rodríguez, E., Berciano, J., & Combarros, O. (2008) · European Journal of Neurology · doi:10.1111/j.1468-1331.2008.02059.x · Cited by 28 · see citing works
Schipke, C. G., Prokop, S., Heppner, F. L., Heuser, I., & Peters, O. (2011) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000322588 · Cited by 24 · see citing works
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Organ aging signatures in the plasma proteome track health and disease
Oh, H. S.-H., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06802-1 · Cited by 634 · see citing works
Johnson, E. C. B., Carter, E. K., Dammer, E. B., Duong, D. M., Gerasimov, E. S., Liu, Y., Liu, J., Betarbet, R., Ping, L., Yin, L., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00999-y · Cited by 561 · see citing works
Artificial intelligence for proteomics and biomarker discovery
Mann, M., Kumar, C., Zeng, W., & Strauss, M. T. (2021) · Cell Systems · doi:10.1016/j.cels.2021.06.006 · Cited by 390 · see citing works
Self-citationHigginbotham, L., Ping, L., Dammer, E. B., Duong, D. M., Zhou, M., Gearing, M., Hurst, C., Glass, J. D., Factor, S. A., Johnson, E. C. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz9360 · Cited by 367 · see citing works
Transcriptional signature in microglia associated with Aβ plaque phagocytosis
Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23111-1 · Cited by 357 · see citing works
Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery
Bai, B., Vanderwall, D., Li, Y., Wang, X., Poudel, S., Wang, H., Dey, K. K., Chen, P., Yang, K., & Peng, J. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00474-z · Cited by 252 · see citing works
Dammer, E. B., Ping, L., Duong, D. M., Modeste, E. S., Seyfried, N. T., Lah, J. J., Levey, A. I., & Johnson, E. C. B. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-022-01113-5 · Cited by 188 · see citing works
MS-Based Proteomics of Body Fluids: The End of the Beginning
Bader, J. M., Albrecht, V., & Mann, M. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100577 · Cited by 171 · see citing works
Self-citationTargeting synapse function and loss for treatment of neurodegenerative diseases
Dejanovic, B., Sheng, M., & Hanson, J. E. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00823-1 · Cited by 168 · see citing works
Kitata, R. B., Yang, J., & Chen, Y. (2022) · Mass Spectrometry Reviews · doi:10.1002/mas.21781 · Cited by 165 · see citing works
Proteome profiling of cerebrospinal fluid reveals biomarker candidates for Parkinson’s disease
Karayel, Ö., Winter, S. V., Padmanabhan, S., Kuras, Y. I., Vu, D. T., Tuncali, I., Merchant, K., Wills, A.-M. A., Scherzer, C. R., & Mann, M. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100661 · Cited by 158 · see citing works
Self-citationUrinary proteome profiling for stratifying patients with familial Parkinson’s disease
Winter, S. V., Karayel, Ö., Strauss, M. T., Padmanabhan, S., Surface, M., Merchant, K., Alcalay, R. N., & Mann, M. (2021) · EMBO Molecular Medicine · doi:10.15252/emmm.202013257 · Cited by 149 · see citing works
Self-citationMessner, C. B., Demichev, V., Wang, Z., Hartl, J., Kustatscher, G., Mülleder, M., & Ralser, M. (2022) · PROTEOMICS · doi:10.1002/pmic.202200013 · Cited by 134 · see citing works
Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia
Winchester, L. M., Harshfield, E. L., Shi, L., Badhwar, A., Khleifat, A. A., Clarke, N., Dehsarvi, A., Lengyel, I., Lourida, I., Madan, C. R., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13390 · Cited by 130 · see citing works
Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau
Rayaprolu, S., Higginbotham, L., Bagchi, P., Watson, C. M., Zhang, T., Levey, A. I., Rangaraju, S., & Seyfried, N. T. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-00840-3 · Cited by 118 · see citing works
Clark, C., Dayon, L., Masoodi, M., Bowman, G. L., & Popp, J. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00814-7 · Cited by 116 · see citing works
Western, D., Timsina, J., Wang, L., Wang, C., Yang, C., Phillips, B., Wang, Y., Liu, M., Ali, M., Beric, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01972-8 · Cited by 111 · see citing works
Kulenkampff, K., Perez, A.-M. W., Sormanni, P., Habchi, J., & Vendruscolo, M. (2021) · Nature Reviews Chemistry · doi:10.1038/s41570-021-00254-9 · Cited by 110 · see citing works
Li, K. W., Gonzalez‐Lozano, M. A., Koopmans, F., & Smit, A. B. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.564446 · Cited by 102 · see citing works
Cerebrospinal Fluid–Basic Concepts Review
Czarniak, N., Kamińska, J., Matowicka-Karna, J., & Koper‐Lenkiewicz, O. M. (2023) · Biomedicines · doi:10.3390/biomedicines11051461 · Cited by 99 · see citing works
Xing, X., Cai, L., Ouyang, J., Wang, F., Li, Z., Liu, M., Wang, Y., Zhou, Y., Hu, E., Huang, C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-44255-2 · Cited by 99 · see citing works
del Campo, M., Peeters, C. F. W., Johnson, E. C. B., Vermunt, L., Hok‐A‐Hin, Y. S., van Nee, M. M., Chen‐Plotkin, A. S., Irwin, D. J., Hu, W. T., Lah, J. J., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00300-1 · Cited by 97 · see citing works
Trigeminal ganglion neurons are directly activated by influx of CSF solutes in a migraine model
Rasmussen, M. K., Møllgård, K., Bork, P. A. R., Weikop, P., Esmail, T., Drici, L., Albrechtsen, N. J. W., Carlsen, J. F., Huynh, N. P. T., Ghitani, N., et al. (2024) · Science · doi:10.1126/science.adl0544 · Cited by 95 · see citing works
Self-citationKoopmans, F., Li, K. W., Klaassen, R. V., & Smit, A. B. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00513 · Cited by 94 · see citing works
Dayon, L., Cominetti, O., & Affolter, M. (2022) · Expert Review of Proteomics · doi:10.1080/14789450.2022.2070477 · Cited by 90 · see citing works
Zeng, X., Lafferty, T. K., Sehrawat, A., Chen, Y., Ferreira, P. C. L., Bellaver, B., Povala, G., Kamboh, M. I., Klunk, W. E., Cohen, A. D., et al. (2024) · Molecular Neurodegeneration · doi:10.1186/s13024-024-00753-5 · Cited by 87 · see citing works
del Campo, M., Vermunt, L., Peeters, C. F. W., Sieben, A., Hok‐A‐Hin, Y. S., Lleó, A., Alcolea, D. A., van Nee, M. M., Engelborghs, S., van Alphen, J. L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41122-y · Cited by 75 · see citing works
Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data
Torun, F. M., Winter, S. V., Doll, S., Riese, F. M., Vorobyev, A., Müller, J., Geyer, P. E., & Strauss, M. T. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00473 · Cited by 67 · see citing works
Self-citationMyelin in Alzheimer’s disease: culprit or bystander?
Maitre, M., Jeltsch-David, H., Okechukwu, N. G., Klein, C., Patte-Mensah, C., & Mensah-Nyagan, A.-G. (2023) · Acta Neuropathologica Communications · doi:10.1186/s40478-023-01554-5 · Cited by 66 · see citing works
Guo, Y. X., Chen, S.-D., You, J., Huang, S.-Y., Chen, Y., Zhang, Y., Wang, L., He, X., Deng, Y., Zhang, Y.-R., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01924-6 · Cited by 65 · see citing works
Common Factors in Neurodegeneration: A Meta-Study Revealing Shared Patterns on a Multi-Omics Scale
Ruffini, N., Klingenberg, S., Schweiger, S., & Gerber, S. (2020) · Cells · doi:10.3390/cells9122642 · Cited by 65 · see citing works
Haque, R., Watson, C. M., Liu, J., Carter, E. K., Duong, D. M., Lah, J. J., Wingo, A. P., Roberts, B. R., Johnson, E. C. B., Saykin, A. J., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adg4122 · Cited by 64 · see citing works
Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes
Ammar, C., Schessner, J. P., Willems, S., Michaelis, A. C., & Mann, M. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100581 · Cited by 64 · see citing works
Self-citationWebel, H. E., Niu, L., Nielsen, A. B., Locard‐Paulet, M., Mann, M., Jensen, L. J., & Rasmussen, S. (2024) · Nature Communications · doi:10.1038/s41467-024-48711-5 · Cited by 63 · see citing works
Self-citationModeste, E. S., Ping, L., Watson, C. M., Duong, D. M., Dammer, E. B., Johnson, E. C. B., Roberts, B. R., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00638-z · Cited by 62 · see citing works
Alzheimer’s disease: a review on the current trends of the effective diagnosis and therapeutics
Manap, A. S. A., Almadodi, R., Sultana, S., Sebastian, M. G., Kavani, K. S., Lyenouq, V. E., & Shankar, A. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1429211 · Cited by 62 · see citing works
Ali, M., Timsina, J., Western, D., Liu, M., Beric, A., Budde, J., Do, A., Heo, G., Wang, L., Gentsch, J., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.02.014 · Cited by 61 · see citing works
Dammer, E. B., Shantaraman, A., Ping, L., Duong, D. M., Gerasimov, E. S., Ravindran, S. P., Guðmundsdóttir, V., Frick, E. A., Gómez, G., Walker, K. A., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adn3504 · Cited by 61 · see citing works
ARDD 2020: from aging mechanisms to interventions
Mkrtchyan, G. V., Abdelmohsen, K., Andreux, P. A., Bagdonaite, I., Barzilai, N., Brunak, S., Cabreiro, F., de Cabo, R., Campisi, J., Cuervo, A. M., et al. (2020) · Aging · doi:10.18632/aging.202454 · Cited by 59 · see citing works
Self-citationMethods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias
Teunissen, C. E., Kimble, L. E., Bayoumy, S., Bolsewig, K., Burtscher, F., Coppens, S., Das, S., Gogishvili, D., Gomes, B. F., José, N. G. D. S., et al. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100629 · Cited by 58 · see citing works
Self-citationWatson, C. M., Dammer, E. B., Ping, L., Duong, D. M., Modeste, E. S., Carter, E. K., Johnson, E. C. B., Levey, A. I., Lah, J. J., Roberts, B. R., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02158-3 · Cited by 58 · see citing works
Platelet biomarkers for a descending cognitive function: A proteomic approach
Yu, H., Liu, Y., He, B., He, T., Chen, C., He, J., Yang, X., & Wang, J. (2021) · Aging Cell · doi:10.1111/acel.13358 · Cited by 56 · see citing works
Anastasi, F., Masciandaro, S. M., Carratore, R. D., Dell’Anno, M. T., Signore, G., Falleni, A., McDonnell, L. A., & Bongioanni, P. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22062951 · Cited by 55 · see citing works
CSF proteomics in autosomal dominant Alzheimer’s disease highlights parallels with sporadic disease
van der Ende, E. L., Veld, S. G. J. G. I. ‘., Hanskamp, I., van der Lee, S. J., Dijkstra, J. I. R., Hok‐A‐Hin, Y. S., Blujdea, E. R., Swieten, J. C. V., Irwin, D. J., Chen‐Plotkin, A. S., et al. (2023) · Brain · doi:10.1093/brain/awad213 · Cited by 55 · see citing works
Tao, Q.-Q., Cai, X., Xue, Y.-Y., Ge, W., Yue, L., Li, X.-Y., Lin, R.-R., Peng, G.-P., Jiang, W., Li, S., et al. (2024) · The Innovation · doi:10.1016/j.xinn.2023.100544 · Cited by 55 · see citing works
Mitochondrial dysfunction in Alzheimer’s disease - a proteomics perspective
Abyadeh, M., Gupta, V., Chitranshi, N., Gupta, V., Wu, Y., Saks, D., Wall, R. V., Fitzhenry, M. J., Basavarajappa, D. K., You, Y., et al. (2021) · Expert Review of Proteomics · doi:10.1080/14789450.2021.1918550 · Cited by 50 · see citing works
Identifying interactions in omics data for clinical biomarker discovery using symbolic regression
Christensen, N. J., Demharter, S., Machado, M., Pedersen, L., Salvatore, M., Stentoft-Hansen, V., & Iglesias, M. T. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac405 · Cited by 49 · see citing works
Multi‐cohort profiling reveals elevated CSF levels of brain‐enriched proteins in Alzheimer’s disease
Bergström, S., Remnestål, J., Yousef, J., Olofsson, J., Markaki, I., Carvalho, S., Corvol, J., Kultima, K., Kilander, L., Löwenmark, M., et al. (2021) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51402 · Cited by 48 · see citing works
Self-citationReactive astrocytes secrete the chaperone HSPB1 to mediate neuroprotection
Yang, F., Beltran-Lobo, P., Sung, K., Goldrick, C., Croft, C. L., Nishimura, A., Hedges, E., Mahiddine, F., Troakes, C., Golde, T. E., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk9884 · Cited by 46 · see citing works
Mesenchymal and Neural Stem Cell-Derived Exosomes in Treating Alzheimer’s Disease
Wang, H., Huber, C. C., & Li, X. (2023) · Bioengineering · doi:10.3390/bioengineering10020253 · Cited by 44 · see citing works
Liu, P., Li, L., He, F., Meng, F., Liu, X., Su, Y., Su, X., Luo, B., & Peng, G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241814225 · Cited by 42 · see citing works
A region‐resolved proteomic map of the human brain enabled by high‐throughput proteomics
Tüshaus, J., Sakhteman, A., Lechner, S., The, M., Mucha, E., Krisp, C., Schlegel, J., Delbridge, C., & Küster, B. (2023) · The EMBO Journal · doi:10.15252/embj.2023114665 · Cited by 41 · see citing works
Differential proteomic profile of lumbar and ventricular cerebrospinal fluid
Rostgaard, N., Olsen, M. H., Ottenheijm, M. E., Drici, L., Simonsen, A. H., Plomgaard, P., Gredal, H., Poulsen, H. H., Zetterberg, H., Blennow, K., et al. (2023) · Fluids and Barriers of the CNS · doi:10.1186/s12987-022-00405-0 · Cited by 39 · see citing works
Self-citationOrgan-specific metabolic pathways distinguish prediabetes, type 2 diabetes, and normal tissues
Diamanti, K., Cavalli, M., Pereira, M. J., Pan, G., Castillejo-López, C., Kumar, C., Mundt, F., Komorowski, J., Deshmukh, A. S., Mann, M., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100763 · Cited by 39 · see citing works
Self-citationMikolajewicz, N., Yee, P. P., Bhanja, D., Trifoi, M. D., Miller, A. M., Métellus, P., Bagley, S. J., Balaj, L., Filho, L. D. M., Zacharia, B. E., et al. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.23.01621 · Cited by 38 · see citing works
Biomarkers of synaptic degeneration in Alzheimer’s disease
Cheng, Q., Fan, Y., Zhang, P., Liu, H., Han, J., Yu, Q., Wang, X., Wu, S., & Lu, Z. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102642 · Cited by 37 · see citing works
Jiang, Y., Uhm, H., Ip, F. C., Ouyang, L., Lo, R. M. N., Cheng, E. Y. L., Cao, X., Tan, C. M. C., Law, B. C. H., Ortiz‐Romero, P., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13676 · Cited by 36 · see citing works
Self-citationSkowronek, P., Wallmann, G., Wahle, M., Willems, S., & Mann, M. (2025) · Nature Protocols · doi:10.1038/s41596-024-01104-w · Cited by 35 · see citing works
Self-citationA new generation of AD biomarkers: 2019 to 2021
Hawksworth, J. I., Fernández, E., & Gevaert, K. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101654 · Cited by 34 · see citing works
Deep and fast label-free Dynamic Organellar Mapping
Schessner, J. P., Albrecht, V., Davies, A. K., Sinitcyn, P. G., & Borner, G. H. H. (2023) · Nature Communications · doi:10.1038/s41467-023-41000-7 · Cited by 33 · see citing works
Bandara, E. M. S., Asih, P. R., Pedrini, S., Hone, E., Fernando, W. M. A. D. B., & Martins, R. N. (2025) · Molecular Neurobiology · doi:10.1007/s12035-025-05219-3 · Cited by 33 · see citing works
Kononikhin, A. S., Zakharova, N. V., Semenov, S. D., Bugrova, A. E., Brzhozovskiy, A. G., Indeykina, M. I., Fedorova, Y. B., Колыхалов, И. В., Strelnikova, P. A., Ikonnikova, A. Y., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23147907 · Cited by 32 · see citing works
Pei, J., Palanisamy, C. P., Jayaraman, S., Natarajan, P. M., Umapathy, V. R., Roy, J. R., Thalamati, D., Ahalliya, R. M., Gopalakrishnan, V. K., & Mironescu, M. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102359 · Cited by 31 · see citing works
Discovery of novel CSF biomarkers to predict progression in dementia using machine learning
Gogishvili, D., Vromen, E. M., Hertog, S. K.-D., Lemstra, A. W., Pijnenburg, Y. A. L., Visser, P. J., Tijms, B. M., del Campo, M., Initiative, T. A. D. N., Abeln, S., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-33045-x · Cited by 29 · see citing works
Platelet biomarkers identifying mild cognitive impairment in type 2 diabetes patients
Yu, H., Liu, Y., He, T., Zhang, Y., He, J., Li, M., Jiang, B., Gao, Y., Chen, C., Ke, D., et al. (2021) · Aging Cell · doi:10.1111/acel.13469 · Cited by 28 · see citing works
Quality Control—A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome
Rozanova, S., Uszkoreit, J., Schork, K., Serschnitzki, B., Eisenacher, M., Tönges, L., Barkovits, K., & Marcus, K. (2023) · Biomolecules · doi:10.3390/biom13030491 · Cited by 28 · see citing works
Sharma, M., Pal, P., & Gupta, S. K. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102483 · Cited by 27 · see citing works
Li, Y., Chen, Z., Wang, Q., Lv, X., Cheng, Z., Wu, Y., Tang, F., Shen, Y., & Gao, F. (2022) · Journal of Neurology · doi:10.1007/s00415-022-11476-2 · Cited by 27 · see citing works
Boeddrich, A., Haenig, C., Neuendorf, N., Blanc, E., Ivanov, A., Kirchner, M., Schleumann, P., Bayraktaroğlu, I., Richter, M., Molenda, C., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01206-2 · Cited by 27 · see citing works
Self-citationMass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross‐Linking
Lu, H., Zhu, Z., Fields, L. G., Zhang, H., & Li, L. (2024) · Mass Spectrometry Reviews · doi:10.1002/mas.21908 · Cited by 26 · see citing works
Dietmann, A., Wenz, E., van der Meer, J., Ringli, M., Warncke, J. D., Edwards, E., Schmidt, M. H., Bernasconi, C., Nirkko, A. C., Strub, M., et al. (2021) · Journal of Sleep Research · doi:10.1111/jsr.13296 · Cited by 26 · see citing works
Weiner, S., Sauer, M., Visser, P. J., Tijms, B. M., Vorontsov, E. A., Blennow, K., Zetterberg, H., & Gobom, J. (2022) · Clinical Proteomics · doi:10.1186/s12014-022-09354-0 · Cited by 26 · see citing works
Self-citationStrefeler, A., Jan, M., Quadroni, M., Teav, T., Rosenberg, N., Chatton, J., Guex, N., Gallart‐Ayala, H., & Ivanišević, J. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01162-4 · Cited by 26 · see citing works
Krance, S. H., Wu, C., Chan, A. C. Y., Kwong, S., Song, B. X., Xiong, L. Y., Ouk, M., Chen, M. H., Zhang, J., Yung, A., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220360 · Cited by 25 · see citing works
Cerebrospinal Fluid and Brain Proteoforms of the Granin Neuropeptide Family in Alzheimer’s Disease
Quinn, J. P., Ethier, E. C., Novielli, A., Malone, A., Ramirez, C. E., Salloum, L., Trombetta, B. A., Kivisäkk, P., Bremang, M., Selzer, S., et al. (2023) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.2c00341 · Cited by 24 · see citing works
Foresight in clinical proteomics: current status, ethical considerations, and future perspectives
Mundt, F., Albrechtsen, N. J. W., Mann, S. P., Treit, P. V., Ghodgaonkar-Steger, M., O’Flaherty, M. C., Raijmakers, R., Vizcaíno, J. A., Heck, A. J. R., & Mann, M. (2023) · Open Research Europe · doi:10.12688/openreseurope.15810.1 · Cited by 24 · see citing works
Self-citationMun, D., Budhraja, R., Bhat, F. A., Zenka, R. M., Johnson, K. L., Moghekar, A., & Pandey, A. (2023) · PROTEOMICS · doi:10.1002/pmic.202200507 · Cited by 23 · see citing works
Llano, D. A., Devanarayan, P., & Devanarayan, V. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.07.015 · Cited by 22 · see citing works
Leveraging the CSF proteome toward minimally-invasive diagnostics surveillance of brain malignancies
Mikolajewicz, N., Khan, S., Trifoi, M. D., Skakdoub, A., Ignatchenko, V., Mansouri, S., Zuccato, J., Zacharia, B. E., Glantz, M., Zadeh, G. M., et al. (2022) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdac161 · Cited by 22 · see citing works
Đorđević, N., Dierks, C., Hantikainen, E. M., Farztdinov, V. M., Amari, F., Hernandes, V. V., Grandi, A. D., Domingues, F. S., Shomroni, O., Textoris‐Taube, K., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-00856-0 · Cited by 22 · see citing works
Signatures of glial activity can be detected in the CSF proteome
Eninger, T., Müller, S. A., Bacioglu, M., Schweighauser, M., Lambert, M., Maia, L. F., Neher, J. J., Hornfeck, S. M., Obermüller, U., Kleinberger, G., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2119804119 · Cited by 22 · see citing works
Fibulin-2 is an extracellular matrix inhibitor of oligodendrocytes relevant to multiple sclerosis
Ghorbani, S., Li, C., Lozinski, B. M., Moezzi, D., D’Mello, C., Dong, Y., Visser, F., Li, H., Silva, C., Khakpour, M., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci176910 · Cited by 21 · see citing works
Xie, Y., Chen, X., Xu, M., & Zheng, X. (2025) · Biomedicines · doi:10.3390/biomedicines13030681 · Cited by 20 · see citing works
Fu, W., & Ho, P. C. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103058 · Cited by 20 · see citing works
Identification of molecular signatures associated with sleep disorder and Alzheimer’s disease
Liang, L., Yan, J., Huang, X., Zou, C., Chen, L., Li, R., Xie, J., Pan, M., Zou, D., & Liu, Y. M. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.925012 · Cited by 20 · see citing works
Paciotti, S., Wojdała, A. L., Bellomo, G., Toja, A., Chipi, E., Piersma, S. R., Pham, T. V., Gaetani, L., Jiménez, C. R., Parnetti, L., et al. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01269-8 · Cited by 19 · see citing works
Radiation Therapy in Alzheimer's Disease: A Systematic Review
Kaul, D., Ehret, F., Roohani, S., Jendrach, M., Buthut, M., Acker, G., Anwar, M., Zips, D., Heppner, F. L., & Prüß, H. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.11.044 · Cited by 19 · see citing works
Górska, A. M., Santos‐García, I., Eiriz, I., Brüning, T., Nyman, T. A., & Pahnke, J. (2024) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2024.110239 · Cited by 19 · see citing works
Navas-Carrillo, D., Rivera‐Caravaca, J. M., Sampedro-Andrada, A., & Orenes‐Piñero, E. (2020) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2020.1833298 · Cited by 19 · see citing works
SOD1 is a Possible Predictor of COVID-19 Progression as Revealed by Plasma Proteomics
Xu, B., Lei, Y., Ren, X., Yin, F., Wu, W., Sun, Y., Wang, X., Sun, Q., Yang, X., Wang, X., et al. (2021) · ACS Omega · doi:10.1021/acsomega.1c01375 · Cited by 19 · see citing works
Proteomics Landscape of Alzheimer’s Disease
Jain, A., & Sathe, G. J. (2021) · Proteomes · doi:10.3390/proteomes9010013 · Cited by 18 · see citing works
Proteomics of the astrocyte secretome reveals changes in their response to soluble oligomeric Aβ
Matafora, V., Gorb, A., Yang, F., Noble, W. J., Bachi, A., Perez‐Nievas, B. G., & Jiménez-Sánchez, M. (2023) · Journal of Neurochemistry · doi:10.1111/jnc.15875 · Cited by 18 · see citing works
Zhang, S., Ghalandari, B., Chen, Y., Wang, Q., Liu, K., Sun, X., Ding, X., Song, S., Jiang, L., & Ding, X. (2024) · Advanced Materials · doi:10.1002/adma.202401559 · Cited by 18 · see citing works
Unraveling Pathophysiological Mechanisms of Parkinson’s Disease: Contribution of CSF Biomarkers
Farotti, L., Paoletti, F. P., Simoni, S., & Parnetti, L. (2020) · Biomarker Insights · doi:10.1177/1177271920964077 · Cited by 18 · see citing works
Early Diagnosis of Alzheimer’s Disease: Moving Toward a Blood-Based Biomarkers Era
Tao, Q., Lin, R.-R., & Wu, Z. (2023) · Clinical Interventions in Aging · doi:10.2147/cia.s394821 · Cited by 18 · see citing works
Höhn, L., Hußler, W., Richter, A., Smalla, K., Birkl-Toeglhofer, A. M., Birkl, C. R., Vielhaber, S., Leber, S. L., Gundelfinger, E. D., Haybaeck, J., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065532 · Cited by 17 · see citing works
van Zalm, P., Ahmed, S., Fatou, B., Schreiber, R., Barnaby, O. S., Boxer, A. L., Zetterberg, H., Steen, J. A. J., & Steen, H. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101005 · Cited by 17 · see citing works
Self-citationPuerta, R., Cano, A. C., García‐González, P., García‐Gutiérrez, F., Capdevila, M., de Rojas, I., Olivé, C., Blázquez-Folch, J., Sotolongo‐Grau, Ó., Miguel, A., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms26010286 · Cited by 17 · see citing works
Du, X., Luo, L., Huang, Q., & Zhang, J. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153325 · Cited by 17 · see citing works
Torres-Sangiao, E., Giddey, A. D., Rodríguez, C. L., Tang, Z., Liu, X., & Soares, N. C. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.850374 · Cited by 16 · see citing works
- Authors
- Bader, J. M., Geyer, P. E., Müller, J. B., Strauss, M. T., Koch, M., Leypoldt, F., Koertvelyessy, P., Bittner, D., Schipke, C. G., Incesoy, E. I., Peters, O., Deigendesch, N., Simons, M., Jensen, M. K., Zetterberg, H., & Mann, M.
- Affiliations
- Max Planck Institute of Biochemistry (DE)
- University of Copenhagen (DK)
- Harvard University (US)
- Christian-Albrechts-Universität zu Kiel (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
- Humboldt-Universität zu Berlin (DE)
- German Center for Neurodegenerative Diseases (DE)
- University Hospital of Basel (CH)
- Sahlgrenska University Hospital (SE)
- Corresponding author
- Mann, Matthias
- ORCID
- Venue
- Molecular Systems Biology
- Published
- 2020-06-01
- Volume
- 16
- Issue
- 6
- Pages
- e9356-e9356
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC7266499
- OpenAlex
- W3030835098
- Cited by
- 286
- References
- 78
- Citations by year
- 2026: 30 · 2025: 54 · 2024: 55 · 2023: 68 · 2022: 44 · 2021: 27 · 2020: 8
- FWCI
- 22.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
- Multimedialen Archiv und Publikationsserver der Christian-Albrechts-Universität zu Kiel (Christian-Albrechts-Universität zu Kiel) (submittedVersion, cc-by)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- DZNE Pub (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Funders
- Novo Nordisk Fonden (NNF15CC0001, NNF15OC0016692)
- EC|Horizon 2020 Framework Programme (825694)
- Deutsche Forschungsgemeinschaft (412136960)
- H2020 European Research Council (ERC‐2012‐SyG_318987)
- Funding from the Harvard T.H. Chan School of Public Health Dean's Challenge Program sponsored by the McLennan Family Fund
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, cerebrospinal fluid proteome, biomarkers, tau protein, SOD1, DJ-1, neurodegeneration, mass spectrometry-based proteomics, machine learning, clinical utility
- MeSH terms
- Machine Learning, Alzheimer Disease, Glycolysis, Humans, Nerve Degeneration, Neurons, Reproducibility of Results, Cohort Studies, Biomarkers, tau Proteins, Proteome, Proteomics
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