Proteome Atlas for Neurodegenerative Diseases

The proteome atlas framework represents a paradigm shift in how neurodegenerative diseases are classified and understood. Rather than relying solely on clinical phenotype and histopathological hallmarks, proteome atlases map the complete landscape of protein abundance, post-translational modifications, solubility shifts, and network co-expression patterns across disease subtypes. This molecular approach reveals hidden biological signatures that transcend traditional clinical boundaries, enabling more precise patient stratification, earlier diagnosis, and targeted therapeutic development.

Overview

Neurodegenerative diseases have historically been defined by their clinical presentation and post-mortem pathology. Alzheimer’s Disease is diagnosed by amyloid plaques and tau tangles, Parkinson’s Disease by alpha-synuclein Lewy bodies, and Frontotemporal Lobar Degeneration by TDP-43 or tau inclusions. However, this classification system fails to capture the considerable molecular heterogeneity within each disease category, the overlapping biological processes across diseases, and the preclinical stages where molecular changes precede clinical symptoms by years or decades.

The emergence of large-scale proteomics technologies — including mass spectrometry-based analysis of cerebrospinal fluid (CSF) and plasma, aptamer-based proteomics platforms (SomaScan, Olink), and proximity ligation assays — has enabled comprehensive protein quantification across thousands of analytes in patient cohorts. Proteome atlases integrate these data with network-based analyses (weighted gene co-expression network analysis, WGCNA) to identify disease-specific molecular signatures that can distinguish subtypes, predict progression, and highlight therapeutic targets.

Recent landmark studies have demonstrated the power of this approach in Frontotemporal Lobar Degeneration (Saloner 2025, Nat Neurosci), primary tauopathies (Kavanagh 2025, Acta Neuropathol), and Alzheimer’s Disease (Bai 2020, Wingo 2021, Hammerling 2025) ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference, 2Differences in the soluble and insoluble proteome between primary tauopathies2025 · Acta Neuropathol · PMID 40545554Open reference, 3Deep Multilayer Brain Proteomics of Human Alzheimer's Disease2020 · Alzheimer's Dementia · PMID 32761138Open reference, 4Large-scale CSF proteomics identifies neuronal dysfunction in Alzheimer's disease2021 · Nat Neurosci · PMID 34949826Open reference, 5CSF proteome network topology identifies AD subtypes2025 · Acta Neuropathol · PMID 40320089Open reference}.

CSF Proteome Atlas of Frontotemporal Lobar Degeneration

Study Design and Methodology

The landmark 2025 Nature Neuroscience study by Saloner and colleagues analyzed cerebrospinal fluid proteomes from 116 carriers of autosomal dominant FTLD mutations compared with 39 non-carrier controls ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}. The study used aptamer-based proteomics (SomaScan v4) to quantify over 4,000 proteins across the cohort. Researchers applied weighted gene co-expression network analysis (WGCNA) to identify 31 protein co-expression modules, which were then correlated with both cross-sectional clinical indicators (CDR+NACC-FTLD, CSF neurofilament light chain, bilateral frontotemporal volume) and longitudinal cognitive trajectory measures.

This approach treats the CSF proteome as an integrated network rather than a collection of individual biomarkers, capturing the coordinated biological processes that characterize disease states.

Disease-Specific Molecular Signatures

The WGCNA analysis revealed that each major genetic subtype of FTLD harbors a distinct proteomic signature:

• C9orf72 repeat expansion carriers: Show increased abundance of RNA splicing modules, reflecting the disruption of nucleocytoplasmic transport and RNA processing caused by GGGGCC repeat transcription and dipeptide repeat protein aggregation ^{6Proteostasis disruption in neurodegeneration2023 · Nat Rev Neurosci · PMID 37017450Open reference}.

• GRN (progranulin) mutation carriers: Also display increased RNA splicing modules, consistent with the known role of progranulin in lysosomal function and immune regulation.

• MAPT (tau) mutation carriers: Exhibit increased extracellular matrix (ECM) modules, reflecting the distinct tau pathology and neuronal vulnerability patterns in this genetic subtype.

A striking finding was that all three genetic subtypes show decreased synaptic/neuronal modules and decreased autophagy modules, indicating convergent endpoints of neurodegeneration despite divergent upstream causes ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}. This convergence has profound therapeutic implications — interventions targeting synaptic protection or autophagy enhancement may be broadly applicable across FTLD subtypes.

Cross-Platform Validation

The researchers validated their findings across independent cohorts, including the 4RTNI cohort (sporadic progressive supranuclear palsy-Richardson syndrome) and the BioFINDER 2 cohort (frontotemporal dementia spectrum clinical syndromes), using both SomaScan and Olink platforms ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}. This cross-platform replication confirms that the molecular signatures are robust and not artifacts of a single measurement technology.

The ability to validate findings in sporadic disease cohorts — not just genetic carriers — establishes the generalizability of proteome-based disease classification beyond the monogenic forms of FTLD.

Therapeutic Implications

The study identified “hub” proteins — highly connected nodes within the affected co-expression modules — as particularly promising for biomarker and therapeutic development ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}. These hub proteins represent the master regulators of disease-related biological processes. Network-based proteomics demonstrated potential for identifying replicable molecular pathways that could guide drug development for adults living with FTLD.

Tauopathy Proteome Atlas: Molecular Classification of Primary Tauopathies

Comparative Proteomics of CBD, Pick’s Disease, and PSP

A parallel breakthrough came from the 2025 Acta Neuropathologica study by Kavanagh and colleagues, which applied sarkosyl fractionation and mass spectrometry to post-mortem brain tissue from three primary tauopathies: corticobasal degeneration (CBD), Pick’s disease (PiD), and progressive supranuclear palsy (PSP) ^{2Differences in the soluble and insoluble proteome between primary tauopathies2025 · Acta Neuropathol · PMID 40545554Open reference}.

The study examined not just protein abundance, but also protein solubility — a critical distinction because disease-associated proteins often shift from soluble to insoluble compartments. The findings revealed that CBD and Pick’s disease showed the greatest proteomic similarity in both the soluble and insoluble fractions, while PSP exhibited the most divergent profile ^{2Differences in the soluble and insoluble proteome between primary tauopathies2025 · Acta Neuropathol · PMID 40545554Open reference}. This finding challenges the traditional grouping of these “4R tauopathies” and suggests that CBD and Pick’s disease share more biological overlap than CBD and PSP.

Critical Solubility Shifts

The study identified consistent solubility changes across the tauopathies affecting four major biological categories:

1. Lysosomal regulators: Unique lysosomal proteins become more insoluble in distinct tauopathies, suggesting that lysosomal dysfunction plays a disease-specific role in each condition. SORT1 (sortilin) was identified as highly insoluble in CBD and aggregates to different extents across tauopathies ^{2Differences in the soluble and insoluble proteome between primary tauopathies2025 · Acta Neuropathol · PMID 40545554Open reference}.

2. Postsynaptic proteins: synaptic proteins showed altered solubility, consistent with the well-documented synaptic loss in these conditions and suggesting postsynaptic targeting as a therapeutic strategy.

3. Extracellular matrix (ECM): ECM proteins displayed distinct solubility patterns across subtypes, with MAPT carriers in the FTLD study showing particularly elevated ECM modules ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}.

4. Mitochondrial proteins: Mitochondrial dysfunction is reflected in solubility shifts of mitochondrial proteins, consistent with the established role of energy metabolism disruption in neurodegeneration.

Biomarker Candidates from Tauopathy Proteomics

The Kavanagh study identified several proteins as promising biomarker candidates ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}0:

• SORT1 (Sortilin): Verified by immunofluorescence to aggregate in CBD tissue. SORT1 is involved in trafficking of proteins to lysosomes and the cell surface, and its aggregation in CBD specifically suggests a disease-specific pathological process.

• ROCK1 and JAK2: Show solubility shifts that affect key signaling pathways — ROCK1 in cytoskeletal regulation and JAK2 in cytokine signaling.

• MAPT (tau): Peptide-level solubility analysis revealed a bias to 4R tau in the CBD insoluble fraction, providing molecular confirmation of the isoform composition of pathological tau in each disease.

ProPPr Analysis of Phospho-Tau-Associated Proteomes

Complementing the Kavanagh study, Morderer and colleagues (2025, Brain) used probe-dependent proximity profiling (ProPPr) to map the protein interaction landscape of phospho-tau across tauopathies ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}1. ProPPr uses engineered peroxidase enzymes to label proteins in proximity to a bait protein (phospho-tau) in fixed tissue. This approach uncovered both similarities and differences in the phospho-tau-associated proteome between CBD, PSP, and Alzheimer’s Disease.

The spatial resolution of ProPPr revealed that some tau-associated proteins cluster in specific subcellular compartments in disease-specific patterns, while others show more widespread changes. This spatial dimension of proteomic analysis adds a new layer of information beyond simple abundance or solubility measurements.

Alzheimer’s Disease Proteome Atlases

Deep Brain Proteomics

Large-scale proteomics has also transformed understanding of Alzheimer’s Disease. The landmark study by Bai and colleagues (2020) performed deep proteomic profiling of human Alzheimer’s Disease brain tissue, identifying over 10,000 proteins and revealing coordinated changes in specific biological pathways ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}2. The study found that synaptic proteins, mitochondrial proteins, and proteins involved in proteostasis showed the most pronounced changes in Alzheimer’s Disease, providing a molecular readout of the processes that underlie cognitive decline.

Wingo and colleagues (2021, Nat Neurosci) applied WGCNA to large-scale CSF proteomics in Alzheimer’s Disease, identifying proteomic signatures of neuronal dysfunction that correlated with established biomarkers (amyloid-beta 42, phospho-tau, neurofilament light) and clinical outcomes ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}3.

AD Subtype Classification via CSF Proteomics

Hammerling and colleagues (2025) analyzed CSF proteome network topology to identify Alzheimer’s Disease subtypes with distinct biological signatures ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}4. The study found that network-based analysis outperformed traditional biomarker-based classification in predicting clinical progression, suggesting that the coordinated biological processes captured by proteomic networks carry more prognostic information than individual biomarkers.

Plasma Proteome Atlas for Neurodegeneration

Large-scale plasma proteomics has emerged as a complementary approach to CSF analysis, offering the advantage of accessible sampling. Whelan and colleagues (2024, Nat Med) created an atlas of the plasma proteome for brain diseases, demonstrating that plasma protein signatures can classify neurodegenerative disease subtypes with high accuracy ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}5. Hanssen and colleagues (2024, Nat Rev Neurol) further established the utility of plasma proteomics for neurodegenerative disease classification and progression monitoring

Molecular Classification Framework

The convergence of findings across diseases reveals a molecular classification framework that transcends traditional clinical boundaries. The following diagram illustrates how proteome atlases classify neurodegenerative diseases by their molecular signatures:

flowchart TD
    A["Neurodegenerative<br/>Proteome Atlas"] --> B["CSF/Plasma<br/>Proteomics"]
    A --> C["Brain Tissue<br/>Proteomics"]
    B --> D["Co-Expression<br/>Network Analysis"]
    C --> D
    D --> E["Molecular<br/>Signatures"]

    E --> F["Protein Solubility<br/>Shifts"]
    E --> G["Module Abundance<br/>Changes"]
    E --> H["Post-Translational<br/>Modifications"]

    F --> I["Lysosomal<br/>Dysfunction"]
    F --> J["Synaptic<br/>Loss"]
    F --> K["ECM<br/>Remodeling"]

    G --> L["RNA Splicing<br/>Modules"]
    G --> M["Synaptic/Neuronal<br/>Modules"]
    G --> N["Autophagy<br/>Modules"]

    I --> O["Therapeutic Targets:<br/>Lysosomal Enhancement"]
    J --> P["Therapeutic Targets:<br/>Synaptic Protection"]
    K --> Q["Therapeutic Targets:<br/>ECM Modulation"]
    L --> R["Therapeutic Targets:<br/>RNA Splicing Repair"]
    M --> P
    N --> S["Therapeutic Targets:<br/>Autophagy Induction"]

    style A fill:#0a1929,stroke:#333
    style O fill:#1a0a1f,stroke:#333
    style P fill:#1a0a1f,stroke:#333
    style Q fill:#1a0a1f,stroke:#333
    style R fill:#1a0a1f,stroke:#333
    style S fill:#1a0a1f,stroke:#333

    click A "/mechanisms/proteome-atlas-neurodegenerative-diseases" "Proteome Atlas"
    click I "/mechanisms/lysosome-dysfunction" "Lysosomal Dysfunction"
    click J "/mechanisms/synaptic-dysfunction-neurodegeneration" "Synaptic Dysfunction"
    click L "/mechanisms/rna-metabolism-als-ftd" "RNA Metabolism in ALS/FTLD"
    click N "/mechanisms/protein-aggregation-mechanisms" "Protein Aggregation"
    click S "/mechanisms/protein-aggregation-mechanisms" "Autophagy Pathways"

Disease Subtype Molecular Signatures

Alzheimer’s Disease Subtypes

The molecular signatures of Alzheimer’s Disease subtypes include:

• Amyloid-predominant type: Characterized by elevated amyloid-beta aggregations, early amyloid PET positivity, and relatively slower progression. The proteomic signature reflects amyloid-driven pathophysiology with downstream tau pathology.

• Tau-predominant type: Shows elevated phospho-tau species (p-tau 181, p-tau 217) with more aggressive progression. The proteomic signature reveals greater synaptic loss and mitochondrial dysfunction.

• TAR DNA-binding protein 43 (TDP-43) predominant type: AD with limbic-predominant age-related TDP-43 encephalopathy (LATE) shows a distinct molecular signature with emphasis on hippocampal vulnerability and memory impairment out of proportion to amyloid/tau burden.

• Cerebral amyloid angiopathy (CAA) type: Shows vascular amyloid deposition with distinct proteomic signatures involving blood-brain barrier proteins and vascular matrix remodeling.

FTLD Subtypes

The proteomic signatures for Frontotemporal Lobar Degeneration subtypes are now well-characterized:

• C9orf72 subtype: RNA splicing modules elevated, dipeptide repeat proteins detected in proteome, nucleocytoplasmic transport proteins altered.

• GRN subtype: RNA splicing and lysosomal modules both elevated, reflecting the dual roles of progranulin in RNA metabolism and lysosomal function.

• MAPT subtype: Extracellular matrix and cytoskeletal modules elevated, consistent with the microtubule-stabilizing function of tau protein.

• TAU subtype: 4R tau-specific proteomic signatures with distinct solubility patterns between CBD, Pick’s disease, and PSP.

Parkinson’s Disease and Synucleinopathies

Emerging proteomic studies of Parkinson’s Disease and related synucleinopathies have identified:

• Alpha-synuclein oligomers: The key pathogenic proteomic signature in Parkinson’s Disease, Dementia with Lewy Bodies, and Multiple System Atrophy.

• Lysosomal dysfunction signature: Consistent with the genetic evidence from GBA mutations and the role of lysosomal pathways in alpha-synuclein clearance.

• Mitochondrial dysfunction signature: Reflecting the established role of mitochondrial pathology in Parkinson’s Disease through PINK1, PRKN, and other genes.

Amyotrophic Lateral Sclerosis (ALS) and FTD Overlap

The proteomic signatures at the ALS-FTLD interface reveal:

• TDP-43 pathology: Shared across the majority of ALS and many FTLD cases, with concordant proteomic signatures of RNA metabolism disruption.

• FUS pathology: In a subset of ALS and FTLD-FUS, distinct proteomic signatures involving stress granule proteins and nuclear import factors.

• C9orf72 repeat expansion: The most common genetic cause of both conditions, showing the characteristic RNA splicing module elevation in both diseases.

Biomarker Discovery from Proteome Atlases

Hub Proteins as Biomarker Candidates

The network-based approach to proteomics identifies “hub proteins” — highly connected nodes within disease-associated co-expression modules — as particularly valuable biomarkers. These hub proteins represent master regulators of disease biology, making them more likely to reflect the integrated state of the biological system than individual markers.

Key hub proteins identified across studies include:

• Neurofilament light chain (NfL): Consistently elevated across neurodegenerative diseases in both CSF and plasma, reflecting axonal degeneration. NfL is now established as a cross-disease marker of neurodegeneration severity ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}6.

• GFAP (glial fibrillary acidic protein): Elevated in diseases with astrocyte activation, particularly Alzheimer’s Disease and FTLD.

• YKL-40 (chitinase-3-like protein 1): Marker of neuroinflammation and glial activation, elevated across multiple neurodegenerative conditions.

• TREM2 (Triggering Receptor Expressed on Myeloid Cells 2): Microglial activation marker with distinct patterns in different diseases and disease stages.

Disease-Specific Biomarker Candidates

• SORT1 (Sortilin): Highly insoluble in CBD specifically, making it a candidate biomarker for CBD vs. other tauopathies.

• ROCK1, JAK2: Solubility-shifted in tauopathies, potential markers of disease-specific pathway dysregulation.

• Synaptic proteins (GAP43, neurogranin, SNAP25): Decreased in synaptic/neuronal modules across diseases, reflecting synaptic loss as a common endpoint.

• Autophagy proteins (p62/SQSTM1, LC3): Decreased in autophagy modules, reflecting the proteostasis failure that characterizes neurodegeneration.

Fluid Biomarker Integration

Proteome atlases enable the integration of multiple fluid biomarkers into composite scores that capture disease biology more comprehensively than single markers. The AT(N) framework for Alzheimer’s Disease — based on amyloid (A), tau (T), and neurodegeneration (N) biomarkers — represents an early implementation of this approach ^{1Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration2025 · Nat Neurosci · PMID 40380000Open reference}7. Proteome atlases extend this framework by identifying additional biological axes (lysosomal function, synaptic integrity, inflammatory state, vascular function) that can be captured by fluid biomarkers.

Treatment Acceleration Through Proteome Atlases

Target Identification

Proteome atlases accelerate treatment discovery through several mechanisms:

1. Molecular subtype identification: By classifying diseases by their molecular signatures rather than clinical phenotype, proteome atlases enable more precise patient stratification for clinical trials. This allows enrichment of trial populations with patients most likely to respond to mechanism-specific interventions.

2. Convergence point identification: Despite the diversity of upstream causes (genetic mutations, environmental factors, aging), proteome atlases reveal convergent biological processes (synaptic loss, autophagy failure, neuroinflammation) that represent shared therapeutic targets applicable across multiple diseases and subtypes.

3. Network-based target prioritization: Hub proteins within disease-associated networks represent high-value therapeutic targets because modulating them is likely to affect multiple downstream processes. The proteome atlas approach identifies which hub proteins are most central to disease biology in each subtype.

4. Biomarker-driven trial design: Proteomic biomarkers enable monitoring of target engagement and disease modification in clinical trials, allowing more efficient go/no-go decisions and adaptive trial designs.

Therapeutic Strategies Implied by Proteome Atlas Findings

The molecular signatures identified by proteome atlases suggest several therapeutic strategies:

• Synaptic protection: Given the universal decrease in synaptic/neuronal modules across diseases, interventions that protect synapses or promote synaptic regeneration represent a broadly applicable approach.

• Autophagy enhancement: The consistent autophagy module decrease across FTLD subtypes and Alzheimer’s Disease points to autophagy induction as a disease-modifying strategy.

• Lysosomal modulation: Disease-specific lysosomal signatures (SORT1 in CBD, GBA-related pathways in Parkinson’s Disease) suggest lysosomal enhancement as a targeted approach.

• RNA splicing repair: Elevated RNA splicing modules in C9orf72 and GRN-related FTLD suggest that targeted RNA splicing modifiers could address the underlying biology in these genetic subtypes.

• Extracellular matrix modulation: Elevated ECM modules in MAPT mutation carriers suggest that matrix remodeling interventions could address this specific biological process.

Multi-Disease Proteome Atlas Integration

The next frontier in neurodegenerative disease proteomics is the integration of disease-specific atlases into a unified multi-disease framework. Such an integration would enable:

• Cross-disease molecular comparisons: Identifying shared vs. disease-specific biological processes across the full spectrum of neurodegenerative diseases.

• Patient stratification across diseases: Classifying patients not by their clinical diagnosis but by their molecular profile, potentially revealing that patients with different clinical labels share more molecular similarity than patients with the same label.

• Platform-independent classification: Developing classification systems that are robust across different proteomic measurement platforms (SomaScan, Olink, mass spectrometry).

• Longitudinal proteomic trajectories: Mapping how molecular signatures change over the disease course, from preclinical stages through mild cognitive impairment to dementia.

Cross-Linking to Related Pages

The proteome atlas framework connects to numerous related pages across the wiki:

Disease Pages

• Alzheimer’s Disease — CSF proteome subtypes, AD-specific molecular signatures

• Frontotemporal Dementia — CSF proteome atlas, FTLD subtype classification

• Parkinson’s Disease — Synuclein proteomics, lysosomal dysfunction signatures

• Corticobasal Degeneration — Tauopathy proteomics, SORT1 aggregation

• Progressive Supranuclear Palsy — Tauopathy proteomics, PSP divergence

• ALS — ALS-FTD overlap, TDP-43 proteomics

Biomarker Pages

• Neurofilament Light Chain — Cross-disease neurodegeneration marker

• CSF Biomarkers — CSF proteome methodology

• Alzheimer’s Biomarkers — AT(N) framework, fluid biomarker integration

• Alpha-Synuclein Seeding Assays — Synucleinopathy proteomics

Gene/Protein Pages

• C9orf72 — RNA splicing module elevation, dipeptide repeat proteins

• GRN (Progranulin) — Lysosomal and RNA metabolism dysfunction

• MAPT (Tau Protein) — 4R tau proteomics, extracellular matrix remodeling

• TREM2 — Microglial activation, disease-modifying variant

• GBA — Lysosomal dysfunction in PD, alpha-synuclein clearance

Mechanism Pages

• Protein Aggregation Mechanisms — Proteostasis failure, solubility shifts

• ER Stress and Unfolded Protein Response — Proteostasis pathways

• Synaptic Dysfunction in Neurodegeneration — Synaptic module decrease

• Neuroinflammation Mechanisms — Microglial activation signatures

• Tauopathies Comparison Matrix — Cross-tauopathy molecular comparison

Therapeutic Pages

• Anti-Aggregation Therapies — Targeting disease-specific protein aggregation

• Lysosomal Enhancement Therapies — Targeting lysosomal dysfunction

• Synaptic Protection Strategies — Neuroprotective approaches

Future Directions

The proteome atlas approach is rapidly evolving with several key developments on the horizon:

1. Single-cell proteomics: Technologies that enable proteomic profiling at single-cell resolution will reveal cell-type-specific molecular signatures within the brain, adding a new dimension to atlas-based classification.

2. Spatial proteomics: Techniques like ProPPr and imaging mass cytometry enable spatial mapping of protein expression, revealing how molecular signatures vary across brain regions and cell types.

3. Longitudinal proteomics: Repeated sampling of the same patients over time will enable mapping of proteomic trajectory changes, identifying critical windows for therapeutic intervention.

4. Integration with genomics: Combining proteomic data with genetic data will reveal how genetic variants affect protein abundance and function, enabling causal target identification.

5. Multi-omics integration: Integrating proteomics with transcriptomics, epigenomics, and metabolomics will provide a more complete picture of disease biology.

The proteome atlas represents a fundamental advance in the molecular understanding of neurodegenerative diseases, moving the field from clinical classification based on pathological hallmarks toward biology-driven classification based on measurable molecular signatures. This shift has profound implications for diagnosis, patient stratification, clinical trial design, and therapeutic development.