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Introduction Heparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to β-amyloid (Aβ) and tau pathology in Alzheimer’s disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry-based proteomic approaches. Methods We employed heparin-affinity chromatography, followed by off-line fractionation and tandem mass tag mass spectrometry (TMT-MS), to enrich HBPs from plasma obtained from AD ( n  = 62) and control ( n  = 47) samples. These profiles were then correlated to Aβ, tau and phosphorylated tau (pTau) CSF biomarkers and plasma pTau181 from the same individuals, as well as a consensus brain proteome network to assess the overlap with AD brain pathophysiology. Results Heparin enrichment from plasma was highly reproducible, enriched well-known HBPs like APOE and thrombin, and depleted high-abundant proteins such as albumin. A total of 2865 proteins, spanning 10 orders of magnitude in abundance, were measured across 109 samples. Compared to the consensus AD brain protein co-expression network, we observed that specific plasma proteins exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes, highlighting the complex interplay between the two compartments. Elevated proteins in AD plasma, when compared to controls, included members of the matrisome module in brain that accumulate with Aβ deposits, such as SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB, and the APOE4 proteoform. Additionally, heparin-enriched proteins in plasma demonstrated significant correlations with conventional AD CSF biomarkers, including Aβ, total tau, pTau, and plasma pTau181. A panel of five plasma proteins classified AD from control individuals with an area under the curve (AUC) of 0.85. When combined with plasma pTau181, the panel significantly improved the classification performance of pTau181 alone, increasing the AUC from 0.93 to 0.98. This suggests that the heparin-enriched plasma proteome captures additional variance in cognitive dementia beyond what is explained by pTau181. Conclusion These findings support the utility of a heparin-affinity approach coupled with TMT-MS for enriching amyloid-associated proteins, as well as a wide spectrum of plasma biomarkers that reflect pathological changes in the AD brain.

Lewy body dementia (LBD), a class of disorders comprising Parkinson’s disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer’s disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson’s disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or “modules” of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.

Background Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease involving loss of motor neurons, typically results in death within 3–5 years of disease onset. Although roughly 10% of cases can be linked to a specific inherited mutation (e.g., C9orf72 hexanucleotide repeat expansion or SOD1 mutation), the cause(s) of most cases are unknown. Consequently, there is a critical need for biomarkers that reflect disease onset and progression across ALS subgroups. Methods We employed tandem mass tag mass spectrometry (TMT-MS) based proteomics on cerebrospinal fluid (CSF) to identify and quantify 2105 proteins from sporadic, C9orf72, and SOD1 ALS patients, asymptomatic C9orf72 expansion carriers, and controls ( N  = 101). To verify trends in our Emory University cohort we used data-independent acquisition (DIA-MS) on an expanded, four center cohort. This expanded cohort of 259 individuals included 50 sporadic ALS (sALS), 43 C9orf72 ALS, 22 SOD1 ALS, 72 asymptomatic gene carriers (59 C9orf72 and 13 SOD1) and 72 age-matched controls. We identified 2330 proteins and used differential protein abundance and network analyses to determine how protein profiles vary across disease subtypes in ALS CSF. Results Differential abundance and co-expression network analysis identified proteomic differences between ALS and control, as well as differentially abundant proteins between sporadic, C9orf72 and SOD1 ALS. A panel of proteins differentiated forms of ALS that are indistinguishable in a clinical setting. An additional panel differentiated asymptomatic from symptomatic C9orf72 and SOD1 mutation carriers, marking a pre-symptomatic proteomic signature of genetic forms of ALS. Leveraging this large, multicenter cohort, we validated our ALS CSF network and identified ALS-specific proteins and network modules. Conclusions This study represents a comprehensive analysis of the CSF proteome across sporadic and genetic causes of ALS that resolves differences among these ALS subgroups and also identifies proteins that distinguish symptomatic from asymptomatic gene carriers. These new data point to varying pathogenic pathways that result in an otherwise clinically indistinguishable disease.

Background Alzheimer's disease (AD) is a complex, multifactorial pathology characterized by high heterogeneity in biological alterations. New genetic and genomic resources are identifying multiple genetic risk factors for late‐onset Alzheimer's disease (LOAD). However, our understanding of the cellular and molecular mechanisms linking disease risk variants to various phenotypes remains limited. Therefore, it is essential to integrate information from multiple data modalities to thoroughly explore endophenotype networks and biological interactions related to the disease, thereby accelerating our understanding of heterogeneity in Alzheimer's disease. Method We obtained transcriptomics and proteomics data from whole hemibrain samples of mouse models harboring the genetic risk variants Abca7A1527G, Mthfr677C>T, and Plcg2M28L. These mouse model already carrying humanized amyloid‐beta, APOE4, and Trem2R47H alleles, all knocked into a C57BL/6J background. We included mouse models of multiple ages for both sexes. Using state‐of‐the‐art bioinformatics tools, we conducted multi‐omics analysis to identify molecular alterations in these mouse models. Furthermore, we systematically aligned the multimodal mouse data with relevant human study cohorts to determine the AD relevance of risk genes. Result The effects of these genetic variants recapitulated a variety of human gene and protein expression patterns observed in the LOAD study cohort. The Abca7 variant exhibited extracellular matrix, neuroimmune, and oligodendrocyte‐related gene signatures at an early age, correlating with postmortem LOAD cases compared to controls. By 18 months of age, the Mthfr variant exhibited vasculature, myelination, and synapse‐related gene and protein signatures, also correlating with postmortem LOAD cases relative to controls. The Plcg2 variant exhibited neuroimmune, endolysosome, and synapse‐related gene signatures and altered cell‐ECM interaction processes at the protein level, correlating with postmortem LOAD cases compared to controls. Conclusion We have characterized in vivo signatures of three genetic candidates for late‐onset Alzheimer's disease (LOAD), identifying alterations in specific LOAD‐related pathways for each variant. Our study highlights that assembling multi‐omics measurements reveals interrelated pathway alterations in Alzheimer's Disease (AD) and enables the identification of biomarker combinations that may inform clinical practice. Our approach provides a platform for further exploration into the causes and progression of AD by assessing animal models at different ages and/or with different combinations of LOAD risk variants.

Alzheimer's disease (AD) is a complex, multifactorial pathology characterized by high heterogeneity in biological alterations. New genetic and genomic resources are identifying multiple genetic risk factors for late-onset Alzheimer's disease (LOAD). However, our understanding of the cellular and molecular mechanisms linking disease risk variants to various phenotypes remains limited. Therefore, it is essential to integrate information from multiple data modalities to thoroughly explore endophenotype networks and biological interactions related to the disease, thereby accelerating our understanding of heterogeneity in Alzheimer's disease. We obtained transcriptomics and proteomics data from whole hemibrain samples of mouse models harboring the genetic risk variants Abca7 , Mthfr , and Plcg2 . These mouse model already carrying humanized amyloid-beta, APOE4, and Trem2 alleles, all knocked into a C57BL/6J background. We included mouse models of multiple ages for both sexes. Using state-of-the-art bioinformatics tools, we conducted multi-omics analysis to identify molecular alterations in these mouse models. Furthermore, we systematically aligned the multimodal mouse data with relevant human study cohorts to determine the AD relevance of risk genes. The effects of these genetic variants recapitulated a variety of human gene and protein expression patterns observed in the LOAD study cohort. The Abca7 variant exhibited extracellular matrix, neuroimmune, and oligodendrocyte-related gene signatures at an early age, correlating with postmortem LOAD cases compared to controls. By 18 months of age, the Mthfr variant exhibited vasculature, myelination, and synapse-related gene and protein signatures, also correlating with postmortem LOAD cases relative to controls. The Plcg2 variant exhibited neuroimmune, endolysosome, and synapse-related gene signatures and altered cell-ECM interaction processes at the protein level, correlating with postmortem LOAD cases compared to controls. We have characterized in vivo signatures of three genetic candidates for late-onset Alzheimer's disease (LOAD), identifying alterations in specific LOAD-related pathways for each variant. Our study highlights that assembling multi-omics measurements reveals interrelated pathway alterations in Alzheimer's Disease (AD) and enables the identification of biomarker combinations that may inform clinical practice. Our approach provides a platform for further exploration into the causes and progression of AD by assessing animal models at different ages and/or with different combinations of LOAD risk variants.

Background Neuropathological changes in Alzheimer's disease (AD) begin decades before cognitive symptoms appear, highlighting the need for early detection to enable prevention and treatment. The ATN framework (amyloid [A], tau [T], and neurodegeneration [N]) is widely used for classifying AD, but biomarker progression often varies due to factors such as genetics, sex, race, and environment. AD is also characterized by significant heterogeneity, with comorbidities like cerebrovascular disease and Lewy body disease complicating diagnosis and treatment. Molecular subtyping has emerged as a promising approach to address this complexity, yet its application across diverse populations remains limited. Method Following tandem mass tag labeling, a network‐based analysis was applied to the CSF proteome (n = 2,067 proteins) from 483 samples (245 control, and 238 AD) that included 130 samples from African Americans, to identify molecular subtypes of AD. Proteomic data were organized into 10 network modules associated with molecular pathways, functions, and brain cell types. Using clustering techniques, we identified six molecular subtypes comprised of both AD and control samples, and examined their relationships with age, sex, race, and established AD biomarkers (Aβ, tau, pTau). Validation was performed with independent proteomic datasets, and plasma‐CSF dilution experiments were conducted to explore the role of proteolytic enzymes in blood‐brain barrier (BBB) dysfunction on CSF tau and synaptic protein levels. Result We identified six CSF proteomic subtypes, which largely aligned with previously described categories defined by proteins enriched in neuronal hyperplasticity, immune activation and BBB integrity. African Americans and males were disproportionately represented in the BBB integrity subtype, which was characterized by low CSF tau, high CSF/serum albumin ratios, and reduced synaptic protein levels. This subtype was enriched in proteolytic enzymes such as thrombin, plasminogen, and matrix metalloproteases, which can cleave tau. Ex vivo plasma‐CSF dilution experiments confirmed that increasing plasma levels reduced CSF tau and synaptic proteins, likely due to proteolytic activity. Conclusion This study highlights network‐based approaches in identifying molecular subtypes of AD that account for clinical and pathological heterogeneity. The BBB integrity subtype highlights how biological traits such as concomitant comorbidities influence CSF biomarkers, providing insights into disease mechanisms and opportunities for diversity‐informed diagnostics and therapies.

INTRODUCTION Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within non‐Hispanic White (NHW) populations. Here we provide an extensive survey of the proteomic landscape of AD across diverse racial/ethnic groups. METHODS Two cortical regions, from multiple centers, were harmonized by uniform neuropathological diagnosis. Among 998 unique donors, 273 donors self‐identified as African American, 229 as Latino American, and 434 as NHW. RESULTS While amyloid precursor protein and the microtubule‐associated protein tau demonstrated higher abundance in AD brains, no significant race‐related differences were observed. Further proteome‐wide and focused analyses (specific amyloid beta [Aβ] species and the tau domains) supported the absence of racial differences in these AD pathologies within the brain proteome. DISCUSSION Our findings indicate that the racial differences in AD risk and clinical presentation are not underpinned by dramatically divergent patterns in the brain proteome, suggesting that other determinants account for these clinical disparities. Highlights We present a large‐scale proteome (∼10,000 proteins) of DLPFC (998) and STG (244) across AD cases. About 50% of samples were from racially and ethnically diverse brain donors. Key AD proteins (amyloid and tau) correlated with CERAD and Braak stages. No significant race‐related differences in amyloid and tau protein levels were observed in AD brains. AD‐associated protein changes showed a strong correlation between the brain proteomes of African American and White individuals. This dataset advances understanding of ethnoracial‐specific AD pathways and potential therapies.

INTRODUCTION Alzheimer's disease (AD) shows clinical and molecular heterogeneity shaped by demographic and genetic factors. METHODS To resolve this heterogeneity, we performed a network‐based proteomic analysis of cerebrospinal fluid (CSF) from 431 individuals, including 111 African Americans, to identify protein co‐expression modules and define AD subtypes. RESULTS Ten co‐expression modules reflecting diverse pathways and cell types were identified, many linked to demographics and AD biomarkers. One subtype, enriched in African Americans and males, showed low CSF tau, elevated plasma proteins, and reduced synaptic proteins, features consistent with blood–brain barrier (BBB) dysfunction. This subtype also showed the highest levels of thrombin activity, capable of cleaving tau. Introducing plasma into CSF ex vivo recapitulated the BBB subtype signature, supporting a causal role for plasma proteases in tau and synaptic protein depletion. CONCLUSION These findings link BBB dysfunction and plasma proteases to CSF tau loss and highlight the need for diversity in AD‐biomarker research. Highlights Race and sex correlate with key AD proteomic network modules. We identify six proteomic subtypes with distinct demographic and AD biomarker profiles. Subtype 3 demonstrates an A+/T− phenotype and a profile suggestive of BBB dysfunction. Low CSF tau and neuronal proteins may stem from infiltrating plasma protease cleavage. Plasma spike‐in experiments show decreased endogenous CSF tau and neuronal proteins.

INTRODUCTION Multi‐omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked Black Americans (BA) and Latin Americans (LA), who are disproportionately affected by AD. METHODS To bridge this gap, Accelerating Medicines Partnership in Alzheimer's Disease (AMP‐AD) expanded brain multi‐omics profiling to multi‐ethnic donors. RESULTS We generated multi‐omics data and curated and harmonized phenotypic data from BA (n = 306), LA (n = 326), or BA and LA (n = 4) brain donors plus non‐Hispanic White (n = 252) and other (n = 20) ethnic groups, to establish a foundational dataset enriched for BA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSION The inclusion of traditionally underrepresented groups in multi‐omics studies is essential to discovering the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD. Highlights Accelerating Medicines Partnership in Alzheimer's Disease Diversity Initiative led brain tissue profiling in multi‐ethnic populations. Brain multi‐omics data is generated from Black American, Latin American, and non‐Hispanic White donors. RNA, whole genome sequencing and tandem mass tag proteomicsis completed and shared. Multiple brain regions including caudate, temporal and dorsolateral prefrontal cortex were profiled.