2024 Alzheimer’s disease facts and figures. Alzheimers Dement. 2024;20:3708-3821.
Sevigny J, Chiao P, Bussière T, Weinreb PH, Williams L, Maier M, Dunstan R, Salloway S, Chen T, Ling Y. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature. 2016;537:50–6.
Google Scholar
van Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M, Kanekiyo M, Li D, Reyderman L, Cohen S, et al. Lecanemab in early Alzheimer’s disease. N Engl J Med. 2023;388:9–21.
Google Scholar
Mintun MA, Lo AC, Duggan Evans C, Wessels AM, Ardayfio PA, Andersen SW, Shcherbinin S, Sparks J, Sims JR, Brys M. Donanemab in early Alzheimer’s disease. N Engl J Med. 2021;384:1691–704.
Google Scholar
Vaz M, Silva V, Monteiro C, Silvestre S. Role of aducanumab in the treatment of Alzheimer’s disease: challenges and opportunities. Clin Interv Aging. 2022;17:797–810.
Google Scholar
Kurkinen M. Lecanemab (Leqembi) is not the right drug for patients with Alzheimer’s disease. Adv Clin Exp Med. 2023;32:943–7.
Google Scholar
Manly JJ, Deters KD. Donanemab for Alzheimer disease—who benefits and who is harmed? JAMA. 2023;330:510–1.
Google Scholar
Hardy JA, Higgins GA. Alzheimer’s disease: the amyloid cascade hypothesis. Science. 1992;256:184–5.
Google Scholar
Aisen P, Bateman RJ, Crowther D, Cummings J, Dwyer J, Iwatsubo T, Kosco-Vilbois M, McDade E, Mohs R, Scheltens P, et al. The case for regulatory approval of amyloid-lowering immunotherapies in Alzheimer’s disease based on clearcut biomarker evidence. Alzheimers Dement. 2025;21:e14342.
Kepp KP, Robakis NK, Høilund-Carlsen PF, Sensi SL, Vissel B. The amyloid cascade hypothesis: an updated critical review. Brain. 2023;146:3969–90.
Google Scholar
DeTure MA, Dickson DW. The neuropathological diagnosis of Alzheimer’s disease. Mol Neurodegener. 2019;14:32.
Google Scholar
Kapasi A, DeCarli C, Schneider JA. Impact of multiple pathologies on the threshold for clinically overt dementia. Acta Neuropathol. 2017;134:171–86.
Google Scholar
Boyle PA, Yu L, Wilson RS, Leurgans SE, Schneider JA, Bennett DA. Person-specific contribution of neuropathologies to cognitive loss in old age. Ann Neurol. 2018;83:74–83.
Google Scholar
Yates JR, Ruse CI, Nakorchevsky A. Proteomics by mass spectrometry: approaches, advances, and applications. Annu Rev Biomed Eng. 2009;11:49–79.
Google Scholar
Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature. 2016;537:347–55.
Google Scholar
Bai B, Vanderwall D, Li Y, Wang X, Poudel S, Wang H, Dey KK, Chen PC, Yang K, Peng J. Proteomic landscape of Alzheimer’s disease: novel insights into pathogenesis and biomarker discovery. Mol Neurodegener. 2021;16:55.
Google Scholar
Bai B, Wang X, Li Y, Chen PC, Yu K, Dey KK, Yarbro JM, Han X, Lutz BM, Rao S, et al. Deep multilayer brain proteomics identifies molecular networks in Alzheimer’s disease progression. Neuron. 2020;105:975–91.
Google Scholar
Johnson ECB, Carter EK, Dammer EB, Duong DM, Gerasimov ES, Liu Y, Liu J, Betarbet R, Ping L, Yin L, et al. Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nat Neurosci. 2022;25:213–25.
Google Scholar
Johnson ECB, Bian S, Haque RU, Carter EK, Watson CM, Gordon BA, Ping L, Duong DM, Epstein MP, McDade E, et al. Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer’s disease. Nat Med. 2023;29:1979–88.
Google Scholar
Askenazi M, Kavanagh T, Pires G, Ueberheide B, Wisniewski T, Drummond E. Compilation of reported protein changes in the brain in Alzheimer’s disease. Nat Commun. 2023;14:4466.
Google Scholar
Drummond E, Kavanagh T, Pires G, Marta-Ariza M, Kanshin E, Nayak S, Faustin A, Berdah V, Ueberheide B, Wisniewski T. The amyloid plaque proteome in early onset Alzheimer’s disease and down syndrome. Acta Neuropathol Commun. 2022;10:53.
Google Scholar
Pichet Binette A, Gaiteri C, Wennstrom M, Kumar A, Hristovska I, Spotorno N, Salvado G, Strandberg O, Mathys H, Tsai LH, et al. Proteomic changes in Alzheimer’s disease associated with progressive abeta plaque and tau tangle pathologies. Nat Neurosci. 2024;27:1880–91.
Google Scholar
Ali M, Timsina J, Western D, Liu M, Beric A, Budde J, Do A, Heo G, Wang L, Gentsch J, et al. Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures across the Alzheimer disease continuum. Neuron. 2025;113:1363-1379-e1369.
Yarbro JM, Han X, Dasgupta A, Yang K, Liu D, Shrestha HK, Zaman M, Wang Z, Yu K, Lee DG, et al. Human and mouse proteomics reveals the shared pathways in Alzheimer’s disease and delayed protein turnover in the amyloidome. Nat Commun. 2025;16:1533.
Google Scholar
Kavanagh T, Halder A, Drummond E. Tau interactome and RNA binding proteins in neurodegenerative diseases. Mol Neurodegener. 2022;17:66.
Google Scholar
Shrestha H, et al. Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures in six neurodegenerative disorders. Sneak Peek. 2025. https://doi.org/10.2139/ssrn.5245669.
Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Johnstone R, Mohammed AK, Hamon C. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem. 2003;75:1895–904.
Google Scholar
Doerr A. Targeting with PRM. Nat Methods. 2012;9:950–950.
Google Scholar
Peterson AC, Russell JD, Bailey DJ, Westphall MS, Coon JJ. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics. 2012;11:1475–88.
Google Scholar
Petrera A, von Toerne C, Behler J, Huth C, Thorand B, Hilgendorff A, Hauck SM. Multiplatform approach for plasma proteomics: complementarity of olink proximity extension assay technology to mass spectrometry-based protein profiling. J Proteome Res. 2020;20:751–62.
Google Scholar
Gold L, Walker JJ, Wilcox SK, Williams S. Advances in human proteomics at high scale with the SOMAscan proteomics platform. N Biotechnol. 2012;29:543–9.
Google Scholar
Li J, Van Vranken JG, Pontano Vaites L, Schweppe DK, Huttlin EL, Etienne C, Nandhikonda P, Viner R, Robitaille AM, Thompson AH, et al. TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples. Nat Methods. 2020;17:399–404.
Google Scholar
Zuniga NR, Frost DC, Kuhn K, Shin M, Whitehouse RL, Wei T-Y, He Y, Dawson SL, Pike I, Bomgarden RD, et al. Achieving a 35-plex tandem mass tag reagent set through deuterium incorporation. J Proteome Res. 2024;23:5153–65.
Google Scholar
Wang Z, Yu K, Tan H, Wu Z, Cho JH, Han X, Sun H, Beach TG, Peng J. 27-plex tandem mass tag mass spectrometry for profiling brain proteome in Alzheimer’s disease. Anal Chem. 2020;92:7162–70.
Google Scholar
Sun H, Poudel S, Vanderwall D, Lee DG, Li Y, Peng J. 29-plex tandem mass tag mass spectrometry enabling accurate quantification by interference correction. Proteomics. 2022;22:e2100243.
Google Scholar
Liu D, Yang S, Kavdia K, Sifford JM, Wu Z, Xie B, Wang Z, Pagala VR, Wang H, Yu K, et al. Deep profiling of microgram-scale proteome by tandem mass tag mass spectrometry. J Proteome Res. 2021;20:337–45.
Google Scholar
Wang Z, Kavdia K, Dey KK, Pagala VR, Kodali K, Liu D, Lee DG, Sun H, Chepyala SR, Cho JH, et al: High-throughput and deep-proteome profiling by 16-plex tandem mass tag labeling coupled with two-dimensional chromatography and mass spectrometry. J Vis Exp. 2020,162. https://doi.org/10.3791/61684.
Nusinow DP, Szpyt J, Ghandi M, Rose CM, McDonald ER 3rd, Kalocsay M, Jané-Valbuena J, Gelfand E, Schweppe DK, Jedrychowski M, et al. Quantitative proteomics of the cancer cell line encyclopedia. Cell. 2020;180:387-402.e316.
Google Scholar
Ting L, Rad R, Gygi SP, Haas W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods. 2011;8:937–40.
Google Scholar
Niu M, Cho J-H, Kodali K, Pagala V, High AA, Wang H, Wu Z, Li Y, Bi W, Zhang H, et al. Extensive peptide fractionation and y1 ion-based interference detection method for enabling accurate quantification by isobaric labeling and mass spectrometry. Anal Chem. 2017;89:2956–63.
Google Scholar
Zaman M, Fu Y, Chen PC, Sun H, Yang S, Wu Z, Wang Z, Poudel S, Serrano GE, Beach TG, et al. Dissecting detergent-insoluble proteome in Alzheimer’s disease by TMTc-corrected quantitative mass spectrometry. Mol Cell Proteomics. 2023;22:100608.
Google Scholar
Guzman UH, Martinez-Val A, Ye Z, Damoc E, Arrey TN, Pashkova A, Renuse S, Denisov E, Petzoldt J, Peterson AC, et al. Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition. Nat Biotechnol. 2024;42:1855–66.
Google Scholar
Meier F, Brunner A-D, Frank M, Ha A, Bludau I, Voytik E, Kaspar-Schoenefeld S, Lubeck M, Raether O, Bache N, et al. diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition. Nat Methods. 2020;17:1229–36.
Google Scholar
Meier F, Beck S, Grassl N, Lubeck M, Park MA, Raether O, Mann M. Parallel Accumulation-Serial Fragmentation (PASEF): multiplying sequencing speed and sensitivity by synchronized scans in a trapped ion mobility device. J Proteome Res. 2015;14:5378–87.
Google Scholar
Meier F, Brunner AD, Koch S, Koch H, Lubeck M, Krause M, Goedecke N, Decker J, Kosinski T, Park MA, et al. Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer. Mol Cell Proteomics. 2018;17:2534–45.
Google Scholar
Bekker-Jensen DB, Martínez-Val A, Steigerwald S, Rüther P, Fort KL, Arrey TN, Harder A, Makarov A, Olsen JV. A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol Cell Proteomics. 2020;19:716–29.
Google Scholar
Kawashima Y, Nagai H, Konno R, Ishikawa M, Nakajima D, Sato H, Nakamura R, Furuyashiki T, Ohara O. Single-shot 10K proteome approach: over 10,000 protein identifications by data-independent acquisition-based single-shot proteomics with ion mobility spectrometry. J Proteome Res. 2022;21:1418–27.
Google Scholar
Ctortecka C, Clark NM, Boyle BW, Seth A, Mani DR, Udeshi ND, Carr SA. Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications. Nat Commun. 2024;15:5707.
Google Scholar
Heil LR, Damoc E, Arrey TN, Pashkova A, Denisov E, Petzoldt J, Peterson AC, Hsu C, Searle BC, Shulman N, et al. Evaluating the performance of the astral mass analyzer for quantitative proteomics using data-independent acquisition. J Proteome Res. 2023;22:3290–300.
Google Scholar
Stewart HI, Grinfeld D, Giannakopulos A, Petzoldt J, Shanley T, Garland M, Denisov E, Peterson AC, Damoc E, Zeller M, et al. Parallelized acquisition of orbitrap and astral analyzers enables high-throughput quantitative analysis. Anal Chem. 2023;95:15656–64.
Google Scholar
Demichev V, Szyrwiel L, Yu F, Teo GC, Rosenberger G, Niewienda A, Ludwig D, Decker J, Kaspar-Schoenefeld S, Lilley KS, et al. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat Commun. 2022;13:3944.
Google Scholar
Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N. Increasing the throughput of sensitive proteomics by plexDIA. Nat Biotechnol. 2023;41:50–9.
Google Scholar
Derks J, Slavov N. Strategies for increasing the depth and throughput of protein analysis by plexDIA. J Proteome Res. 2023;22:697–705.
Google Scholar
Thielert M, Itang EC, Ammar C, Rosenberger FA, Bludau I, Schweizer L, Nordmann TM, Skowronek P, Wahle M, Zeng WF. Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel. Mol Syst Biol. 2023;19:e11503.
Google Scholar
Nilsson J, Gobom J, Sjödin S, Brinkmalm G, Ashton NJ, Svensson J, Johansson P, Portelius E, Zetterberg H, Blennow K, Brinkmalm A. Cerebrospinal fluid biomarker panel for synaptic dysfunction in Alzheimer’s disease. Alzheimers Dement. 2021;13:e12179.
Tao QQ, Cai X, Xue YY, Ge W, Yue L, Li XY, Lin RR, Peng GP, Jiang W, Li S, et al. Alzheimer’s disease early diagnostic and staging biomarkers revealed by large-scale cerebrospinal fluid and serum proteomic profiling. Innovation. 2024;5:100544.
Google Scholar
Montoliu-Gaya L, Ashton NJ, Benedet AL, Rodriguez JL, Brinkmalm G, Karikari TK, Kern S, Rosa-Neto P, Zetterberg H, Blennow K. Mass spectrometric measurement of six site-specific tau phosphorylations in CSF and blood of Alzheimer’s disease patients. Alzheimers Dement. 2021;17:e055985.
Gao Y, Li J, Hu K, Wang S, Yang S, Ai Q, Yan J. Phosphoproteomic analysis of APP/PS1 mice of Alzheimer’s disease by DIA based mass spectrometry analysis with PRM verification. J Proteom. 2024;299:105157.
Camporesi E, Nilsson J, Vrillon A, Cognat E, Hourregue C, Zetterberg H, Blennow K, Becker B, Brinkmalm A, Paquet C, Brinkmalm G. Quantification of the trans-synaptic partners neurexin-neuroligin in CSF of neurodegenerative diseases by parallel reaction monitoring mass spectrometry. EBioMedicine. 2022;75:103793.
Google Scholar
Yang L, Xuan C, Yu C, Zheng P, Yan J. Diagnostic Model of Alzheimer’s disease in the elderly based on protein and metabolic biomarkers. J Alzheimers Dis. 2022;85:1163–74.
Google Scholar
McKetney J, Panyard DJ, Johnson SC, Carlsson CM, Engelman CD, Coon JJ. Pilot proteomic analysis of cerebrospinal fluid in Alzheimer’s disease. Proteomics Clin Appl. 2021;15:e2000072.
Google Scholar
Haslam DE, Li J, Dillon ST, Gu X, Cao Y, Zeleznik OA, Sasamoto N, Zhang X, Eliassen AH, Liang L. Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms. Proteomics. 2022;22:2100170.
Guo Y, You J, Zhang Y, Liu WS, Huang YY, Zhang YR, Zhang W, Dong Q, Feng JF, Cheng W, Yu JT. Plasma proteomic profiles predict future dementia in healthy adults. Nat Aging. 2024;4:247–60.
Google Scholar
Timsina J, Gomez-Fonseca D, Wang L, Do A, Western D, Alvarez I, Aguilar M, Pastor P, Henson RL, Herries E. Comparative analysis of Alzheimer’s disease cerebrospinal fluid biomarkers measurement by multiplex SOMAscan platform and immunoassay-based approach 1. J Alzheimers Dis. 2022;89:193–207.
Google Scholar
Dammer EB, Ping L, Duong DM, Modeste ES, Seyfried NT, Lah JJ, Levey AI, Johnson ECB. Multi-platform proteomic analysis of Alzheimer’s disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome. Alzheimers Res Ther. 2022;14:174.
Google Scholar
Gaetani L, Bellomo G, Parnetti L, Blennow K, Zetterberg H, Di Filippo M. Neuroinflammation and Alzheimer’s disease: a machine learning approach to CSF proteomics. Cells. 1930;2021:10.
Wik L, Nordberg N, Broberg J, Björkesten J, Assarsson E, Henriksson S, Grundberg I, Pettersson E, Westerberg C, Liljeroth E, et al. Proximity extension assay in combination with next-generation sequencing for high-throughput proteome-wide analysis. Mol Cell Proteomics. 2021;20:100168.
Google Scholar
Jiang Y, Zhou X, Ip FC, Chan P, Chen Y, Lai NCH, Cheung K, Lo RMN, Tong EPS, Wong BWY, et al. Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer’s disease screening and staging. Alzheimers Dement. 2022;18:88–102.
Google Scholar
Tokuoka SM, Hamano F, Kobayashi A, Adachi S, Andou T, Natsume T, Oda Y. Plasma proteomics and lipidomics facilitate elucidation of the link between Alzheimer’s disease development and vessel wall fragility. Sci Rep. 2024;14:19901.
Google Scholar
Rooney MR, Chen J, Ballantyne CM, Hoogeveen RC, Boerwinkle E, Yu B, Walker KA, Schlosser P, Selvin E, Chatterjee N, et al. Plasma proteomic comparisons change as coverage expands for SomaLogic and Olink. MedRxiv. 2024. https://doi.org/10.1101/2024.07.11.24310161.
Candia J. SomaScan Bioinformatics: Normalization, Quality Control, and Assessment of Pre-Analytical Variation. Methods Mol Biol. 2025;2929:107-27.
Candia J, Fantoni G, Delgado-Peraza F, Shehadeh N, Tanaka T, Moaddel R, Walker KA, Ferrucci L. Variability of 7K and 11K somascan plasma proteomics assays. J Proteome Res. 2024;23:5531–9.
Google Scholar
Katz DH, Robbins JM, Deng S, Tahir UA, Bick AG, Pampana A, Yu Z, Ngo D, Benson MD, Chen Z-Z, et al. Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods. Sci Adv. 2022;8:eabm5164.
Google Scholar
Candia J, Daya GN, Tanaka T, Ferrucci L, Walker KA. Assessment of variability in the plasma 7k SomaScan proteomics assay. Sci Rep. 2022;12:17147.
Google Scholar
Frohlich K, Fahrner M, Brombacher E, Seredynska A, Maldacker M, Kreutz C, Schmidt A, Schilling O. Data-Independent acquisition: a milestone and prospect in clinical mass spectrometry-based proteomics. Mol Cell Proteomics. 2024;23:100800.
Google Scholar
Nordmann TM, Mund A, Mann M. A new understanding of tissue biology from MS-based proteomics at single-cell resolution. Nat Methods. 2024;21:2220–2.
Google Scholar
Gatto L, Aebersold R, Cox J, Demichev V, Derks J, Emmott E, Franks AM, Ivanov AR, Kelly RT, Khoury L, et al. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nat Methods. 2023;20:375–86.
Google Scholar
Bhatia HS, Brunner A-D, Öztürk F, Kapoor S, Rong Z, Mai H, Thielert M, Ali M, Al-Maskari R, Paetzold JC. Spatial proteomics in three-dimensional intact specimens. Cell. 2022;185:5040-5058. e5019.
Google Scholar
Mund A, Coscia F, Kriston A, Hollandi R, Kovacs F, Brunner AD, Migh E, Schweizer L, Santos A, Bzorek M, et al. Deep visual proteomics defines single-cell identity and heterogeneity. Nat Biotechnol. 2022;40:1231–40.
Google Scholar
Hu B, He R, Pang K, Wang G, Wang N, Zhu W, Sui X, Teng H, Liu T, Zhu J, et al. High-resolution spatially resolved proteomics of complex tissues based on microfluidics and transfer learning. Cell. 2025;188:734-748 e722.
Google Scholar
Ghatak S, Diedrich JK, Talantova M, Bhadra N, Scott H, Sharma M, Albertolle M, Schork NJ, Yates JR 3rd, Lipton SA. Single-cell patch-clamp/proteomics of human Alzheimer’s disease iPSC-derived excitatory neurons versus isogenic wild-type controls suggests novel causation and therapeutic targets. Adv Sci. 2024;11:e2400545.
Liao L, Cheng D, Wang J, Duong DM, Losik TG, Gearing M, Rees HD, Lah JJ, Levey AI, Peng J. Proteomic characterization of postmortem amyloid plaques isolated by laser capture microdissection. J Biol Chem. 2004;279:37061–8.
Google Scholar
Lutz BM, Peng J. Deep profiling of the aggregated proteome in alzheimer’s disease: from pathology to disease mechanisms. Proteomes. 2018;6:46.
Google Scholar
Qin W, Cho KF, Cavanagh PE, Ting AY. Deciphering molecular interactions by proximity labeling. Nat Methods. 2021;18:133–43.
Google Scholar
Sun X, Sun H, Han X, Chen PC, Jiao Y, Wu Z, Zhang X, Wang Z, Niu M, Yu K, et al. Deep single-cell-type proteome profiling of mouse brain by nonsurgical AAV-mediated proximity labeling. Anal Chem. 2022;94:5325–34.
Google Scholar
Morderer D, Wren MC, Liu F, Kouri N, Maistrenko A, Khalil B, Pobitzer N, Salemi MR, Phinney BS, Bu G, et al. Probe-dependent Proximity Profiling (ProPPr) uncovers similarities and differences in phospho-tau-associated proteomes between tauopathies. Mol Neurodegener. 2025;20:32.
Google Scholar
Alvarez-Castelao B, Schanzenbacher CT, Hanus C, Glock C, Tom Dieck S, Dorrbaum AR, Bartnik I, Nassim-Assir B, Ciirdaeva E, Mueller A, et al. Cell-type-specific metabolic labeling of nascent proteomes in vivo. Nat Biotechnol. 2017;35:1196–201.
Google Scholar
Unsihuay D, Mesa Sanchez D, Laskin J. Quantitative mass spectrometry imaging of biological systems. Annu Rev Phys Chem. 2021;72:307–29.
Google Scholar
Black S, Phillips D, Hickey JW, Kennedy-Darling J, Venkataraaman VG, Samusik N, Goltsev Y, Schürch CM, Nolan GP. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc. 2021;16:3802–35.
Google Scholar
Keren L, Bosse M, Thompson S, Risom T, Vijayaragavan K, McCaffrey E, Marquez D, Angoshtari R, Greenwald NF, Fienberg H. MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci Adv. 2019;5:eaax5851.
Google Scholar
Xiong F, Ge W, Ma C. Quantitative proteomics reveals distinct composition of amyloid plaques in Alzheimer’s disease. Alzheimers Dement. 2019;15:429–40.
Google Scholar
Drummond E, Pires G, MacMurray C, Askenazi M, Nayak S, Bourdon M, Safar J, Ueberheide B, Wisniewski T. Phosphorylated tau interactome in the human Alzheimer’s disease brain. Brain. 2020;143:2803–17.
Google Scholar
Muraoka S, DeLeo AM, Sethi MK, Yukawa-Takamatsu K, Yang Z, Ko J, Hogan JD, Ruan Z, You Y, Wang Y, et al. Proteomic and biological profiling of extracellular vesicles from Alzheimer’s disease human brain tissues. Alzheimers Dement. 2020;16:896–907.
Google Scholar
Carlyle BC, Kandigian SE, Kreuzer J, Das S, Trombetta BA, Kuo Y, Bennett DA, Schneider JA, Petyuk VA, Kitchen RR, et al. Synaptic proteins associated with cognitive performance and neuropathology in older humans revealed by multiplexed fractionated proteomics. Neurobiol Aging. 2021;105:99–114.
Google Scholar
Krivinko JM, DeChellis-Marks MR, Zeng L, Fan P, Lopez OL, Ding Y, Wang L, Kofler J, MacDonald ML, Sweet RA. Targeting the post-synaptic proteome has therapeutic potential for psychosis in Alzheimer Disease. Commun Biol. 2023;6:598.
Google Scholar
Hesse R, Hurtado ML, Jackson RJ, Eaton SL, Herrmann AG, Colom-Cadena M, Tzioras M, King D, Rose J, Tulloch J, et al. Comparative profiling of the synaptic proteome from Alzheimer’s disease patients with focus on the APOE genotype. Acta Neuropathol Commun. 2019;7:214.
Google Scholar
Chen PC, Han X, Shaw TI, Fu Y, Sun H, Niu M, Wang Z, Jiao Y, Teubner BJW, Eddins D, et al. Alzheimer’s disease-associated U1 snRNP splicing dysfunction causes neuronal hyperexcitability and cognitive impairment. Nat Aging. 2022;2:923–40.
Google Scholar
Bai B, Hales CM, Chen P-C, Gozal Y, Dammer EB, Fritz JJ, Wang X, Xia Q, Duong DM, Street C, et al. U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer’s disease. Proc Natl Acad Sci USA. 2013;110:16562–7.
Google Scholar
Wesseling H, Mair W, Kumar M, Schlaffner CN, Tang S, Beerepoot P, Fatou B, Guise AJ, Cheng L, Takeda S, et al. Tau PTM profiles identify patient heterogeneity and stages of Alzheimer’s disease. Cell. 2020;183:1699-1713.e1613.
Google Scholar
Zhang Q, Ma C, Chin LS, Li L. Integrative glycoproteomics reveals protein N-glycosylation aberrations and glycoproteomic network alterations in Alzheimer’s disease. Sci Adv. 2020;6:eabc5802.
Google Scholar
Abreha MH, Dammer EB, Ping L, Zhang T, Duong DM, Gearing M, Lah JJ, Levey AI, Seyfried NT. Quantitative analysis of the brain ubiquitylome in Alzheimer’s disease. Proteomics. 2018;18:e1800108.
Google Scholar
Morshed N, Lee MJ, Rodriguez FH, Lauffenburger DA, Mastroeni D, White FM. Quantitative phosphoproteomics uncovers dysregulated kinase networks in Alzheimer’s disease. Nat Aging. 2021;1:550–65.
Google Scholar
Johnson ECB, Dammer EB, Duong DM, Ping L, Zhou M, Yin L, Higginbotham LA, Guajardo A, White B, Troncoso JC, et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med. 2020;26:769–80.
Google Scholar
Oakley H, Cole SL, Logan S, Maus E, Shao P, Craft J, Guillozet-Bongaarts A, Ohno M, Disterhoft J, Van Eldik L. Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations: potential factors in amyloid plaque formation. J Neurosci. 2006;26:10129–40.
Google Scholar
Eimer WA, Vassar R. Neuron loss in the 5XFAD mouse model of Alzheimer’s disease correlates with intraneuronal Aβ 42 accumulation and caspase-3 activation. Mol Neurodegener. 2013;8:1–12.
Saito T, Matsuba Y, Mihira N, Takano J, Nilsson P, Itohara S, Iwata N, Saido TC. Single app knock-in mouse models of Alzheimer’s disease. Nat Neurosci. 2014;17:661–3.
Google Scholar
Oddo S, Caccamo A, Shepherd JD, Murphy MP, Golde TE, Kayed R, Metherate R, Mattson MP, Akbari Y, LaFerla FM. Triple-transgenic model of Alzheimer’s disease with plaques and tangles: intracellular Aβ and synaptic dysfunction. Neuron. 2003;39:409–21.
Google Scholar
Ayyadevara S, Ganne A, Balasubramaniam M, Shmookler Reis RJ. Intrinsically disordered proteins identified in the aggregate proteome serve as biomarkers of neurodegeneration. Metab Brain Dis. 2022;37:147–52.
Google Scholar
Martá-Ariza M, Leitner DF, Kanshin E, Suazo J, Giusti Pedrosa A, Thierry M, Lee EB, Devinsky O, Drummond E, Fortea J, et al. Comparison of the amyloid plaque proteome in down syndrome, early-onset Alzheimer’s disease, and late-onset Alzheimer’s disease. Acta Neuropathol. 2025;149:9.
Google Scholar
Griffiths J, Schneegans E, Whitwell H, Qiu Z, Notman B, Cheung D, Willumsen N, Matthews PM, Grant SGN, Jackson JS. A synaptic-astrocytic proteomic signature associated with synaptopathy in Alzheimer’s Disease. bioRxiv. 2025. https://doi.org/10.1101/2025.01.23.634408.
Mohallem R, Schaser AJ, Aryal UK. Molecular signatures of neurodegenerative diseases identified by proteomic and phosphoproteomic analyses in aging mouse brain. Mol Cell Proteomics. 2024;23:100819.
Google Scholar
Suttapitugsakul S, Stavenhagen K, Donskaya S, Bennett DA, Mealer RG, Seyfried NT, Cummings RD. Glycoproteomics landscape of asymptomatic and symptomatic human Alzheimer’s disease brain. Mol Cell Proteomics. 2022;21:100433.
Google Scholar
Zhang Q, Ma C, Chin LS, Pan S, Li L. Human brain glycoform coregulation network and glycan modification alterations in Alzheimer’s disease. Sci Adv. 2024;10:eadk6911.
Google Scholar
Wang Y, Mandelkow E. Tau in physiology and pathology. Nat Rev Neurosci. 2016;17:5–21.
Google Scholar
Arakhamia T, Lee CE, Carlomagno Y, Duong DM, Kundinger SR, Wang K, Williams D, DeTure M, Dickson DW, Cook CN. Posttranslational modifications mediate the structural diversity of tauopathy strains. Cell. 2020;180(633–644):e612.
Poudel S, Vanderwall D, Yuan ZF, Wu Z, Peng J, Li Y. JUMPptm: Integrated software for sensitive identification of post-translational modifications and its application in Alzheimer’s disease study. Proteomics. 2022;23:e2100369.
Wenger K, Viode A, Schlaffner CN, van Zalm P, Cheng L, Dellovade T, Langlois X, Bannon A, Chang R, Connors TR, et al. Common mouse models of tauopathy reflect early but not late human disease. Mol Neurodegener. 2023;18:10.
Google Scholar
Drummond E, Nayak S, Faustin A, Pires G, Hickman RA, Askenazi M, Cohen M, Haldiman T, Kim C, Han X, et al. Proteomic differences in amyloid plaques in rapidly progressive and sporadic Alzheimer’s disease. Acta Neuropathol. 2017;133:933–54.
Google Scholar
Grau S, Baldi A, Bussani R, Tian X, Stefanescu R, Przybylski M, Richards P, Jones SA, Shridhar V, Clausen T. Implications of the serine protease HtrA1 in amyloid precursor protein processing. Proc Natl Acad Sci USA. 2005;102:6021–6.
Google Scholar
Chen S, Puri A, Bell B, Fritsche J, Palacios HH, Balch M, Sprunger ML, Howard MK, Ryan JJ, Haines JN, et al. HTRA1 disaggregates α-synuclein amyloid fibrils and converts them into non-toxic and seeding incompetent species. Nat Commun. 2024;15:2436.
Google Scholar
Lourenço F, Galvan V, Fombonne J, Corset V, Llambi F, Müller U, Bredesen D, Mehlen P. Netrin-1 interacts with amyloid precursor protein and regulates amyloid-β production. Cell Death Differ. 2009;16:655–63.
Google Scholar
Zamani E, Parviz M, Roghani M, Hosseini M, Mohseni-Moghaddam P, Nikbakhtzadeh M. Netrin-1 protects the SH-SY5Y cells against amyloid beta neurotoxicity through NF-κB/Nrf2 dependent mechanism. Mol Biol Rep. 2020;47:9271–7.
Google Scholar
Chen G, Kang SS, Wang Z, Ahn EH, Xia Y, Liu X, Sandoval IM, Manfredsson FP, Zhang Z, Ye K. Netrin-1 receptor UNC5C cleavage by active δ-secretase enhances neurodegeneration, promoting Alzheimer’s disease pathologies. Sci Adv. 2021;7:eabe4499.
Google Scholar
Balcomb K, Johnston C, Kavanagh T, Leitner D, Schneider J, Halliday G, Wisniewski T, Sunde M, Drummond E. SMOC1 colocalizes with Alzheimer’s disease neuropathology and delays Aβ aggregation. Acta Neuropathol. 2024;148:72.
Google Scholar
Levites Y, Dammer EB, Ran Y, Tsering W, Duong D, Abreha M, Gadhavi J, Lolo K, Trejo-Lopez J, Phillips J, et al. Integrative proteomics identifies a conserved Aβ amyloid responsome, novel plaque proteins, and pathology modifiers in Alzheimer’s disease. Cell Rep Med. 2024;5:101669.
Google Scholar
Zaman M, Yang S, Huang Y, Yarbro JM, Hao Y, Wang Z, Liu D, Harper KE, Soliman H, Hemphill A, et al. Midkine Attenuates Aβ Fibril Assembly and Amyloid Plaque Formation. bioRxiv. 2025. https://doi.org/10.1101/2025.03.20.644383.
Yasuhara O, Muramatsu H, Kim S, Muramatsu T, Maruta H, McGeer P. Midkine, a novel neurotrophic factor, is present in senile plaques of Alzheimer disease. Biochem Biophys Res Commun. 1993;192:246–51.
Google Scholar
Muramatsul H, Yokoi K, Chen L, Ichihara-Tanaka K, Kimura T, Muramatsu T. Midkine as a factor to counteract the deposition of amyloid β-peptide plaques: in vitro analysis and examination in knockout mice. Int Arch Med. 2011;4:1–9.
Esteve P, Rueda-Carrasco J, Inés Mateo M, Martin-Bermejo MJ, Draffin J, Pereyra G, Sandonís Á, Crespo I, Moreno I, Aso E, et al. Elevated levels of secreted-frizzled-related-protein 1 contribute to Alzheimer’s disease pathogenesis. Nat Neurosci. 2019;22:1258–68.
Google Scholar
Rueda-Carrasco J, Martin-Bermejo MJ, Pereyra G, Mateo MI, Borroto A, Brosseron F, Kummer MP, Schwartz S, López-Atalaya JP, Alarcon B, et al. SFRP1 modulates astrocyte-to-microglia crosstalk in acute and chronic neuroinflammation. EMBO Rep. 2021;22:e51696.
Google Scholar
Martens YA, Zhao N, Liu C-C, Kanekiyo T, Yang AJ, Goate AM, Holtzman DM, Bu G. ApoE cascade hypothesis in the pathogenesis of Alzheimer’s disease and related dementias. Neuron. 2022;110:1304–17.
Google Scholar
Kaji S, Berghoff SA, Spieth L, Schlaphoff L, Sasmita AO, Vitale S, Büschgens L, Kedia S, Zirngibl M, Nazarenko T, et al. Apolipoprotein E aggregation in microglia initiates Alzheimer’s disease pathology by seeding β-amyloidosis. Immunity. 2024;57:2651-2668.e2612.
Google Scholar
Hüttenrauch M, Ogorek I, Klafki H, Otto M, Stadelmann C, Weggen S, Wiltfang J, Wirths O. Glycoprotein NMB: a novel Alzheimer’s disease associated marker expressed in a subset of activated microglia. Acta Neuropathol Commun. 2018;6:108.
Google Scholar
Marques AR, Gabriel TL, Aten J, van Roomen CP, Ottenhoff R, Claessen N, Alfonso P, Irún P, Giraldo P, Aerts JM. Gpnmb is a potential marker for the visceral pathology in Niemann-Pick type C disease. PLoS One. 2016;11:e0147208.
Google Scholar
Liu M, Zhu J, Zheng J, Han X, Jiang L, Tong X, Ke Y, Guo Z, Huang W, Cong J, et al. GPNMB and ATP6V1A interact to mediate microglia phagocytosis of multiple types of pathological particles. Cell Rep. 2025;44:115343.
Google Scholar
Hu Y, Wang X, Zhao Z, Liu M, Ren X, Xian X, Liu C, Wang Q. The downregulation of ITGAX exacerbates amyloid-β plaque deposition in Alzheimer’s disease by increasing polarization of M1 microglia. J Alzheimers Dis. 2024;100:657–73.
Google Scholar
Rangaraju S, Dammer EB, Raza SA, Rathakrishnan P, Xiao H, Gao T, Duong DM, Pennington MW, Lah JJ, Seyfried NT, Levey AI. Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease. Mol Neurodegener. 2018;13:24.
Google Scholar
Zhu B, Liu Y, Hwang S, Archuleta K, Huang H, Campos A, Murad R, Piña-Crespo J, Xu H, Huang TY. Trem2 deletion enhances tau dispersion and pathology through microglia exosomes. Mol Neurodegener. 2022;17:58.
Google Scholar
Dhandapani R, Neri M, Bernhard M, Brzak I, Schweizer T, Rudin S, Joller S, Berth R, Kernen J, Neuhaus A, et al. Sustained Trem2 stabilization accelerates microglia heterogeneity and Aβ pathology in a mouse model of Alzheimer’s disease. Cell Rep. 2022;39:110883.
Google Scholar
Cullell N, Caruana G, Elias-Mas A, Delgado-Sanchez A, Artero C, Buongiorno MT, Almería M, Ray NJ, Correa SAL, Krupinski J. Glymphatic system clearance and Alzheimer’s disease risk: a CSF proteome-wide study. Alzheimers Res Ther. 2025;17:31.
Google Scholar
Yadav H, Bakshi A, Anamika, Singh V, Paul P, Murugan NA, Maurya SK. Co-localization and co-expression of Olfml3 with Iba1 in brain of mice. J Neuroimmunol. 2024;394:578411
Al Barashdi MA, Ali A, McMullin MF, Mills K. Protein tyrosine phosphatase receptor type C (PTPRC or CD45). J Clin Pathol. 2021;74:548–52.
Google Scholar
Miyake K, Yamashita Y, Ogata M, Sudo T, Kimoto M. RP105, a novel B cell surface molecule implicated in B cell activation, is a member of the leucine-rich repeat protein family. J Immunol. 1995;154:3333–40.
Google Scholar
Divanovic S, Trompette A, Atabani SF, Madan R, Golenbock DT, Visintin A, Finberg RW, Tarakhovsky A, Vogel SN, Belkaid Y, et al. Negative regulation of Toll-like receptor 4 signaling by the Toll-like receptor homolog RP105. Nat Immunol. 2005;6:571–8.
Google Scholar
Chen Y, Zhou Y, Bai Y, Jia K, Zhang H, Chen Q, Song M, Dai Y, Shi J, Chen Z, et al. Macrophage-derived CTSS drives the age-dependent disruption of the blood-CSF barrier. Neuron. 2025;113:1082–97.
Google Scholar
Lin L, Wu Z, Luo H, Huang Y. Cathepsin-mediated regulation of alpha-synuclein in Parkinson’s disease: a Mendelian randomization study. Front Aging Neurosci. 2024;16:1394807.
Google Scholar
Seyfried NT, Dammer EB, Swarup V, Nandakumar D, Duong DM, Yin L, Deng Q, Nguyen T, Hales CM, Wingo T, et al. A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease. Cell Syst. 2017;4:60-72.e64.
Google Scholar
Hong S, Beja-Glasser VF, Nfonoyim BM, Frouin A, Li S, Ramakrishnan S, Merry KM, Shi Q, Rosenthal A, Barres BA, et al. Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science. 2016;352:712–6.
Google Scholar
Shi QQ, Chowdhury S, Ma R, Le KX, Hong S, Caldarone BJ, Stevens B, Lemere CA. Complement C3 deficiency protects against neurodegeneration in aged plaque-rich APP/PS1 mice. Sci Transl Med. 2017;9:eaaf6295.
Google Scholar
Hurst C, Pugh DA, Abreha MH, Duong DM, Dammer EB, Bennett DA, Herskowitz JH, Seyfried NT. Integrated proteomics to understand the role of neuritin (NRN1) as a mediator of cognitive resilience to Alzheimer’s disease. Mol Cell Proteomics. 2023;22:100542.
Google Scholar
Antonell A, Lladó A, Altirriba J, Botta-Orfila T, Balasa M, Fernández M, Ferrer I, Sánchez-Valle R, Molinuevo JL. A preliminary study of the whole-genome expression profile of sporadic and monogenic early-onset Alzheimer’s disease. Neurobiol Aging. 2013;34:1772–8.
Google Scholar
Beckmann ND, Lin W-J, Wang M, Cohain AT, Charney AW, Wang P, Ma W, Wang Y-C, Jiang C, Audrain M, et al. Multiscale causal networks identify VGF as a key regulator of Alzheimer’s disease. Nat Commun. 2020;11:3942.
Google Scholar
Zhou J, Wade SD, Graykowski D, Xiao MF, Zhao B, Giannini LAA, Hanson JE, van Swieten JC, Sheng M, Worley PF, Dejanovic B. The neuronal pentraxin Nptx2 regulates complement activity and restrains microglia-mediated synapse loss in neurodegeneration. Sci Transl Med. 2023;15:eadf0141.
Google Scholar
van Zalm PW, Ahmed S, Fatou B, Schreiber R, Barnaby O, Boxer A, Zetterberg H, Steen JA, Steen H. Meta-analysis of published cerebrospinal fluid proteomics data identifies and validates metabolic enzyme panel as Alzheimer’s disease biomarkers. Cell Rep Med. 2023;4:101005.
Google Scholar
Del Campo M, Vermunt L, Peeters CFW, Sieben A, Hok AHYS, Lleo A, Alcolea D, van Nee M, Engelborghs S, van Alphen JL, et al. CSF proteome profiling reveals biomarkers to discriminate dementia with Lewy bodies from Alzheimer s disease. Nat Commun. 2023;14:5635.
Google Scholar
Del Campo M, Quesada C, Vermunt L, Peeters CFW, Hok AHYS, Trieu C, Braber AD, Verberk IMW, Visser PJ, Tijms BM, et al. CSF proteins of inflammation, proteolysis and lipid transport define preclinical AD and progression to AD dementia in cognitively unimpaired individuals. Mol Neurodegener. 2024;19:82.
Google Scholar
Del Campo M, Peeters CFW, Johnson ECB, Vermunt L, Hok AHYS, van Nee M, Chen-Plotkin A, Irwin DJ, Hu WT, Lah JJ, et al. CSF proteome profiling across the Alzheimer’s disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels. Nat Aging. 2022;2:1040–53.
Google Scholar
Tijms BM, Vromen EM, Mjaavatten O, Holstege H, Reus LM, van der Lee S, Wesenhagen KEJ, Lorenzini L, Vermunt L, Venkatraghavan V, et al. Cerebrospinal fluid proteomics in patients with Alzheimer’s disease reveals five molecular subtypes with distinct genetic risk profiles. Nat Aging. 2024;4:33–47.
Google Scholar
Teunissen CE, Verberk IMW, Thijssen EH, Vermunt L, Hansson O, Zetterberg H, van der Flier WM, Mielke MM, Del Campo M. Blood-based biomarkers for Alzheimer’s disease: towards clinical implementation. Lancet Neurol. 2022;21:66–77.
Google Scholar
Wang S, Xie S, Zheng Q, Zhang Z, Wang T, Zhang G. Biofluid biomarkers for Alzheimer’s disease. Front Aging Neurosci. 2024;16:1380237.
Google Scholar
Chatterjee P, Pedrini S, Stoops E, Goozee K, Villemagne VL, Asih PR, Verberk IMW, Dave P, Taddei K, Sohrabi HR, et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease. Transl Psychiatry. 2021;11:27.
Google Scholar
Chatterjee P, Pedrini S, Doecke JD, Thota R, Villemagne VL, Dore V, Singh AK, Wang P, Rainey-Smith S, Fowler C, et al. Plasma Abeta42/40 ratio, p-tau181, GFAP, and NfL across the Alzheimer’s disease continuum: a cross-sectional and longitudinal study in the AIBL cohort. Alzheimers Dement. 2023;19:1117–34.
Google Scholar
Palmqvist S, Stomrud E, Cullen N, Janelidze S, Manuilova E, Jethwa A, Bittner T, Eichenlaub U, Suridjan I, Kollmorgen G, et al. An accurate fully automated panel of plasma biomarkers for Alzheimer’s disease. Alzheimers Dement. 2023;19:1204–15.
Google Scholar
Gonzalez-Ortiz F, Turton M, Kac PR, Smirnov D, Premi E, Ghidoni R, Benussi L, Cantoni V, Saraceno C, Rivolta J, et al. Brain-derived tau: a novel blood-based biomarker for Alzheimer’s disease-type neurodegeneration. Brain. 2023;146:1152–65.
Google Scholar
Brickman AM, Manly JJ, Honig LS, Sanchez D, Reyes-Dumeyer D, Lantigua RA, Lao PJ, Stern Y, Vonsattel JP, Teich AF, et al. Plasma p-tau181, p-tau217, and other blood-based Alzheimer’s disease biomarkers in a multi-ethnic, community study. Alzheimers Dement. 2021;17:1353–64.
Google Scholar
Wang H, Dey KK, Chen PC, Li Y, Niu M, Cho JH, Wang X, Bai B, Jiao Y, Chepyala SR, et al. Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease. Mol Neurodegener. 2020;15:43.
Google Scholar
Dey KK, Wang H, Niu M, Bai B, Wang X, Li Y, Cho JH, Tan H, Mishra A, High AA, et al. Deep undepleted human serum proteome profiling toward biomarker discovery for Alzheimer’s disease. Clin Proteomics. 2019;16:16.
Google Scholar
Lacar B, Ferdosi S, Alavi A, Stukalov A, Venkataraman GR, de Geus M, Dodge H, Wu CY, Kivisakk P, Das S, et al. Identification of novel biomarkers for Alzheimer’s disease and related dementias using unbiased plasma proteomics. bioRxiv. 2024. https://doi.org/10.1101/2024.01.05.574446.
Cai H, Pang Y, Wang Q, Qin W, Wei C, Li Y, Li T, Li F, Wang Q, Li Y, et al. Proteomic profiling of circulating plasma exosomes reveals novel biomarkers of Alzheimer’s disease. Alzheimers Res Ther. 2022;14:181.
Google Scholar
Soares Martins T, Marcalo R, da Cruz ESCB, Trindade D, Catita J, Amado F, Melo T, Rosa IM, Vogelgsang J, Wiltfang J, et al. Novel exosome biomarker candidates for Alzheimer’s disease unravelled through mass spectrometry analysis. Mol Neurobiol. 2022;59:2838–54.
Google Scholar
Francois M, Karpe A, Liu JW, Beale D, Hor M, Hecker J, Faunt J, Maddison J, Johns S, Doecke J, et al. Salivaomics as a potential tool for predicting Alzheimer’s disease during the early stages of neurodegeneration. J Alzheimers Dis. 2021;82:1301–13.
Google Scholar
Eldem E, Barve A, Sallin O, Foucras S, Annoni JM, Schmid AW, Alberi Auber L. Salivary proteomics identifies transthyretin as a biomarker of early dementia conversion. J Alzheimers Dis Rep. 2022;6:31–41.
Google Scholar
Contini C, Fadda L, Lai G, Masala C, Olianas A, Castagnola M, Messana I, Iavarone F, Bizzarro A, Masullo C, et al. A top-down proteomic approach reveals a salivary protein profile able to classify Parkinson’s disease with respect to Alzheimer’s disease patients and to healthy controls. Proteomics. 2024;24:e2300202.
Google Scholar
Rajendran K, Krishnan UM. Biomarkers in Alzheimer’s disease. Clin Chim Acta. 2024;562:119857.
Google Scholar
Lee S, Kim E, Moon CE, Park C, Lim JW, Baek M, Shin MK, Ki J, Cho H, Ji YW, Haam S. Amplified fluorogenic immunoassay for early diagnosis and monitoring of Alzheimer’s disease from tear fluid. Nat Commun. 2023;14:8153.
Google Scholar
Karkkainen V, Hannonen S, Rusanen M, Lehtola JM, Saari T, Uusitalo H, Leinonen V, Thiede B, Kaarniranta K, Koivisto AM, Utheim T. Tear fluid reflects the altered protein expressions of Alzheimer’s disease patients in proteins involved in protein repair and clearance system or the regulation of cytoskeleton. J Alzheimers Dis. 2024. https://doi.org/10.1177/13872877241295315.
Yuan C, Shen J, Huang Y, Wu M, Zheng Y, Guo T, Xu X. Tear proteomic signature associated with mild cognitive impairment and dementia: an exploratory analysis. Alzheimers Dement. 2024;20:e089992.
Joshi N, Garapati K, Ghose V, Kandasamy RK, Pandey A. Recent progress in mass spectrometry-based urinary proteomics. Clin Proteomics. 2024;21:14.
Google Scholar
Wang Y, Sun Y, Wang Y, Jia S, Qiao Y, Zhou Z, Shao W, Zhang X, Guo J, Zhang B, et al. Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning. Alzheimers Res Ther. 2023;15:191.
Google Scholar
Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol. 2006;24:971–83.
Google Scholar
Yang C, Farias FHG, Ibanez L, Suhy A, Sadler B, Fernandez MV, Wang F, Bradley JL, Eiffert B, Bahena JA, et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci. 2021;24:1302–12.
Google Scholar
Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, Gunnarsdottir K, Helgason A, Oddsson A, Halldorsson BV, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53:1712–21.
Google Scholar
Eldjarn GH, Ferkingstad E, Lund SH, Helgason H, Magnusson OT, Gunnarsdottir K, Olafsdottir TA, Halldorsson BV, Olason PI, Zink F, et al. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature. 2023;622:348–58.
Google Scholar
Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, Burgess S, Jiang T, Paige E, Surendran P, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558:73–9.
Google Scholar
Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622:329–38.
Google Scholar
Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M. Global quantification of mammalian gene expression control. Nature. 2011;473:337–42.
Google Scholar
Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, Gilad Y. Genomic variation. Impact of regulatory variation from RNA to protein. Science. 2015;347:664–7.
Google Scholar
Liu Y, Beyer A, Aebersold R. On the dependency of cellular protein levels on mRNA abundance. Cell. 2016;165:535–50.
Google Scholar
Wingo AP, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, Dammer EB, Robins C, Beach TG, Reiman EM, et al. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nat Genet. 2021;53:143–6.
Google Scholar
Sung YJ, Yang C, Norton J, Johnson M, Fagan A, Bateman RJ, Perrin RJ, Morris JC, Farlow MR, Chhatwal JP, et al. Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer’s disease. Sci Transl Med. 2023;15:eabq5923.
Google Scholar
Western D, Timsina J, Wang L, Wang C, Yang C, Phillips B, Wang Y, Liu M, Ali M, Beric A, et al. Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease. Nat Genet. 2024;56:2672–84.
Google Scholar
Deming Y, Filipello F, Cignarella F, Cantoni C, Hsu S, Mikesell R, Li Z, Del-Aguila JL, Dube U, Farias FG, et al. The MS4A gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk. Sci Transl Med. 2019;11:eaau2291.
Google Scholar
Hu T, Liu Q, Dai Q, Parrish RL, Buchman AS, Tasaki S, Seyfried NT, Wang Y, Bennett DA, De Jager PL, et al. Proteome-wide association studies using summary pQTL data of three tissues identified 30 risk genes of Alzheimer’s disease dementia. medRxiv. 2024. https://doi.org/10.1101/2024.03.28.24305044.
Li W, Dasgupta A, Yang K, Wang S, Hemandhar-Kumar N, Chepyala SR, Yarbro JM, Hu Z, Salovska B, Fornasiero EF, et al. Turnover atlas of proteome and phosphoproteome across mouse tissues and brain regions. Cell. 2025;188:2267–87.
Google Scholar
Zhou Y, Fang J, Bekris LM, Kim YH, Pieper AA, Leverenz JB, Cummings J, Cheng F. AlzGPS: a genome-wide positioning systems platform to catalyze multi-omics for Alzheimer’s drug discovery. Alzheimers Res Ther. 2021;13:1–13.
Fernandez MV, Liu M, Beric A, Johnson M, Cetin A, Patel M, Budde J, Kohlfeld P, Bergmann K, Lowery J. Genetic and multi-omic resources for Alzheimer disease and related dementia from the knight Alzheimer disease research center. Sci data. 2024;11:768.
Google Scholar
Iturria-Medina Y, Adewale Q, Khan AF, Ducharme S, Rosa-Neto P, O’Donnell K, Petyuk VA, Gauthier S, De Jager PL, Breitner J. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity. Sci Adv. 2022;8:eabo6764.
Google Scholar
Eteleeb AM, Novotny BC, Tarraga CS, Sohn C, Dhungel E, Brase L, Nallapu A, Buss J, Farias F, Bergmann K. Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer’s disease. PLoS Biol. 2024;22:e3002607.
Google Scholar
Mann M, Kumar C, Zeng W-F, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 2021;12:759–70.
Google Scholar
Ji Y, Lotfollahi M, Wolf FA, Theis FJ. Machine learning for perturbational single-cell omics. Cell Syst. 2021;12:522–37.
Google Scholar
Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell. 2024;187:4488–519.
Google Scholar
Ctortecka C, Mechtler K. The rise of single-cell proteomics. Anal Sci Adv. 2021;2:84–94.
Google Scholar
Christopher JA, Stadler C, Martin CE, Morgenstern M, Pan Y, Betsinger CN, Rattray DG, Mahdessian D, Gingras A-C, Warscheid B. Subcellular proteomics. Nat Rev Methods Primers. 2021;1:32.
Google Scholar
Mathys H, Boix CA, Akay LA, Xia Z, Davila-Velderrain J, Ng AP, Jiang X, Abdelhady G, Galani K, Mantero J. Single-cell multiregion dissection of Alzheimer’s disease. Nature. 2024;632:858–68.
Google Scholar
Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: a review. Biotechnol Adv. 2021;49:107739.
Google Scholar
Cohen AA, Ferrucci L, Fülöp T, Gravel D, Hao N, Kriete A, Levine ME, Lipsitz LA, Olde Rikkert MG, Rutenberg A. A complex systems approach to aging biology. Nat Aging. 2022;2:580–91.
Google Scholar
de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer. 2024;24:171–91.
Google Scholar