Proteomic landscape of Alzheimer's disease: emerging technologies, advances and insights (2021 - 2025)

Abstract

The advancements of proteomics technologies are shaping Alzheimer's disease (AD) research, revealing new molecular insights and improving biomarker discovery. Here, we summarize major AD proteomics studies since our 2021 review, focusing on disease mechanisms and biomarker identification. Enhanced sensitivity and throughput in proteome profiling have been driven by mass spectrometry (MS)-based approaches and affinity-based platforms (e.g., Olink and SomaScan). Emerging techniques, including single-cell, spatial, and single-molecule proteomics, provide unprecedented resolution in studying cellular heterogeneity and pathological microenvironments (e.g., amyloidome). Multi-cohort analyses of AD brain tissues have revealed consensus protein alterations (n = 866), identifying novel disease-associated proteins validated in functional studies (e.g., MDK/PTN, NTN1, SMOC1, GPNMB, NPTX2, NRN1, VGF, and U1 snRNP). Proteomic studies of AD biofluids have identified distinct disease subtypes, offering candidate proteins for early detection. Comparisons between human tissues and AD mouse models highlight shared pathways in amyloid pathology while underscoring limitations in recapitulating human disease. Combining proteomics with genomics enables protein quantitative trait locus (pQTL) analysis in AD, linking genetic risk factors to protein expression changes. Discrepancies between proteome and transcriptome suggest altered protein turnover in AD. Overall, AD proteomics continues to provide mechanistic insights into disease progression and potential biomarkers for precision medicine.

Keywords: Alzheimer’s disease; Biomarker; Mass spectrometry; Multi-omics; Neurodegenerative disease; Pathogenesis; Proteome; Proteomics.

Fig. 1

Evolution of proteomics methodologies in AD research. This schematic timeline highlights major technological advances in mass spectrometry and proteomics, categorized into pre-2000, 2000–2020, and post-2020, alongside key AD research events, particularly those related to proteomics. In 1897, J.J. Thomson pioneered early MS by demonstrating that cathode rays were composed of particles (later identified as electrons) and developing techniques to measure the mass-to-charge ratio of ions, laying the foundation for modern MS. The progression of proteomics approaches, including MS-based data-dependent acquisition (DDA), data-independent acquisition (DIA), parallel reaction monitoring (PRM), and affinity-based platforms such as Olink and SomaScan, has enabled deeper and higher-throughput proteome profiling. The transition from the DDA method, such as tandem-mass-tag (TMT), to the DIA method has improved sensitivity and throughput. Meanwhile, proteomic analysis of AD samples has evolved from single-protein studies to proteome-wide investigations across large patient cohorts

Fig. 2

MS-based and affinity-based proteomics approaches for AD. Comparative overview of proteomic strategies employed in AD research, categorized into MS-based and affinity-based methods. MS-based approaches (e.g., DDA and DIA) provide unbiased analysis with deep proteome coverage, while affinity-based platforms (e.g., Olink and SomaScan) facilitate high-throughput detection of targeted proteins using antibodies or aptamers. Each method's advantages, such as sensitivity, throughput and multiplexing capacity, are contrasted with limitations, including missing values, quantification issues, prior target knowledge requirements, and potential cross-reactivity. Created in BioRender. Yarbro, J. [24] https://BioRender.com/ya7seph

Fig. 3

Current single-cell and spatial proteomics methodologies used in AD research. Overview of sample preparation techniques for single-cell and spatial proteomics, detailing sorting, labeling, and imaging methods. Sorting techniques such as fluorescence-activated cell sorting (FACS) and mass cytometry enable cell-type-specific proteomics, while labeling methods (e.g., enzyme-based and chemical-based proximity labeling) allow identification of molecular interactions. Imaging-based spatial proteomics techniques, including mass spectrometry imaging (MSI) and multiplexed immunofluorescence (IF), preserve spatial context and resolve cellular heterogeneity in AD pathology. Created in BioRender. Yarbro, J. [24] https://BioRender.com/rcz3b6q

Fig. 4

Multi-layered proteomic integration for AD pathogenesis. Illustration of the major proteomic layers investigated in AD, including whole proteome changes, post-translational modifications (PTMs), aggregated proteome, spatial/single-cell proteomics, and protein turnover. Bulk proteomic analyses provide insights into global alterations, while PTM analyses uncover dysregulated signaling events. Aggregated proteomics identifies proteins enriched in pathological inclusions (plaques, tangles), and spatial/single-cell approaches reveal cell-type-specific responses and microenvironmental interactions. Turnover proteomics enables tracking of temporal protein homeostasis disruptions, contributing to AD pathogenesis. Created in BioRender. Yarbro, J. [24] https://BioRender.com/9plux6n

Fig. 5

Comparative analysis of biofluid candidate biomarkers in AD. A Overview of CSF, blood, and other biofluid candidate biomarkers for AD detection and monitoring, highlighting their respective advantages and limitations. Key proteins identified in each biofluid category are listed, excluding classical markers such as Aβ peptides and total/phospho‐tau. B Workflow for the integrated ranking analysis of CSF candidate biomarkers. C Integrated ranking of CSF candidate biomarkers based on recent six proteomics studies in order: Johnson et al., [19]; Van Zalm et al., [154]; Del Campo et al., [155]; Del Campo et al., [156]; Del Campo et al., [157]; and Tijms et al., 2024 [158]. Created in BioRender. Yarbro, J. [24] https://BioRender.com/k2lsawa