Dataset Information

Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease.

ABSTRACT: Alzheimer's disease (AD) lacks protein biomarkers reflective of its diverse underlying pathophysiology, hindering diagnostic and therapeutic advancements. Here, we used integrative proteomics to identify cerebrospinal fluid (CSF) biomarkers representing a wide spectrum of AD pathophysiology. Multiplex mass spectrometry identified ~3500 and ~12,000 proteins in AD CSF and brain, respectively. Network analysis of the brain proteome resolved 44 biologically diverse modules, 15 of which overlapped with the CSF proteome. CSF AD markers in these overlapping modules were collapsed into five protein panels representing distinct pathophysiological processes. Synaptic and metabolic panels were decreased in AD brain but increased in CSF, while glial-enriched myelination and immunity panels were increased in brain and CSF. The consistency and disease specificity of panel changes were confirmed in >500 additional CSF samples. These panels also identified biological subpopulations within asymptomatic AD. Overall, these results are a promising step toward a network-based biomarker tool for AD clinical applications.

SUBMITTER: Higginbotham L

PROVIDER: S-EPMC7577712 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease.

Higginbotham Lenora L Ping Lingyan L Dammer Eric B EB Duong Duc M DM Zhou Maotian M Gearing Marla M Hurst Cheyenne C Glass Jonathan D JD Factor Stewart A SA Johnson Erik C B ECB Hajjar Ihab I Lah James J JJ Levey Allan I AI Seyfried Nicholas T NT

Science advances 20201021 43

Alzheimer's disease (AD) lacks protein biomarkers reflective of its diverse underlying pathophysiology, hindering diagnostic and therapeutic advancements. Here, we used integrative proteomics to identify cerebrospinal fluid (CSF) biomarkers representing a wide spectrum of AD pathophysiology. Multiplex mass spectrometry identified ~3500 and ~12,000 proteins in AD CSF and brain, respectively. Network analysis of the brain proteome resolved 44 biologically diverse modules, 15 of which overlapped wi ...[more]

PMID: 33087358

Similar Datasets

Project description:IntroductionAlzheimer's disease (AD) is the most common cause of dementia, characterized by progressive cognitive decline. Protein biomarkers of AD brain pathology, including ?-amyloid and Tau, are reflected in cerebrospinal fluid (CSF), yet the identification of additional biomarkers linked to other brain pathophysiologies remains elusive. We recently reported a multiplex tandem-mass tag (TMT) CSF proteomic analysis of nearly 3000 proteins, following depletion of highly abundant proteins and off-line fractionation, across control and AD cases. Of these, over 500 proteins were significantly increased or decreased in AD, including markers reflecting diverse biological functions in brain. Here, we use a targeted mass spectrometry (MS) approach, termed parallel reaction monitoring (PRM), to quantify select CSF biomarkers without pre-depletion or fractionation to assess the reproducibility of our findings and the specificity of changes for AD versus other causes of cognitive impairment.MethodWe nominated 41 proteins (94 peptides) from the TMT CSF discovery dataset, representing a variety of brain cell-types and biological functions, for label-free PRM analysis in a replication cohort of 88 individuals that included 20 normal controls, 37 clinically diagnosed AD cases and 31 cases with non-AD cognitive impairment. To control for technical variables, isotopically labeled synthetic heavy peptide standards were added into each of the 88 CSF tryptic digests. Furthermore, a peptide pool, representing an equivalent amount of peptide from all samples, was analyzed (n?=?10) across each batch. Together, this approach enabled us to assess both the intra- and inter-sample differences in peptide signal response and retention time.ResultsDespite differences in sample preparation, quantitative MS approaches and patient samples, 25 proteins, including Tau, had a consistent and significant change in AD in both the discovery and replication cohorts. Validated CSF markers with low coefficient of variation included the protein products for neuronal/synaptic (GDA, GAP43, SYN1, BASP1, YWHAB, YWHAZ, UCHL1, STMN1 and MAP1B), glial/inflammation (SMOC1, ITGAM, CHI3L1, SPP1, and CHIT1) and metabolic (PKM, ALDOA and FABP3) related genes. Logistical regression analyses revealed several proteins with high sensitivity and specificity for classifying AD cases from controls and other non-AD dementias. SMOC1, YWHAZ, ALDOA and MAP1B emerged as biomarker candidates that could best discriminate between individuals with AD and non-AD cognitive impairment as well as Tau/?-amyloid ratio. Notably, SMOC1 levels in postmortem brain are highly correlated with AD pathology even in the preclinical stage of disease, indicating that CSF SMOC1 levels reflect underlying brain pathology specific for AD.ConclusionCollectively these findings highlight the utility of targeted MS approaches to quantify biomarkers associated with AD that could be used for monitoring disease progression, stratifying patients for clinical trials and measuring therapeutic response.

Project description:Alzheimer's disease is biologically heterogeneous, and detailed understanding of the processes involved in patients is critical for development of treatments. CSF contains hundreds of proteins, with concentrations reflecting ongoing (patho)physiological processes. This provides the opportunity to study many biological processes at the same time in patients. We studied whether Alzheimer's disease biological subtypes can be detected in CSF proteomics using the dual clustering technique non-negative matrix factorization. In two independent cohorts (EMIF-AD MBD and ADNI) we found that 705 (77% of 911 tested) proteins differed between Alzheimer's disease (defined as having abnormal amyloid, n = 425) and controls (defined as having normal CSF amyloid and tau and normal cognition, n = 127). Using these proteins for data-driven clustering, we identified three robust pathophysiological Alzheimer's disease subtypes within each cohort showing (i) hyperplasticity and increased BACE1 levels; (ii) innate immune activation; and (iii) blood-brain barrier dysfunction with low BACE1 levels. In both cohorts, the majority of individuals were labelled as having subtype 1 (80, 36% in EMIF-AD MBD; 117, 59% in ADNI), 71 (32%) in EMIF-AD MBD and 41 (21%) in ADNI were labelled as subtype 2, and 72 (32%) in EMIF-AD MBD and 39 (20%) individuals in ADNI were labelled as subtype 3. Genetic analyses showed that all subtypes had an excess of genetic risk for Alzheimer's disease (all P > 0.01). Additional pathological comparisons that were available for a subset in ADNI suggested that subtypes showed similar severity of Alzheimer's disease pathology, and did not differ in the frequencies of co-pathologies, providing further support that found subtypes truly reflect Alzheimer's disease heterogeneity. Compared to controls, all non-demented Alzheimer's disease individuals had increased risk of showing clinical progression (all P < 0.01). Compared to subtype 1, subtype 2 showed faster clinical progression after correcting for age, sex, level of education and tau levels (hazard ratio = 2.5; 95% confidence interval = 1.2, 5.1; P = 0.01), and subtype 3 at trend level (hazard ratio = 2.1; 95% confidence interval = 1.0, 4.4; P = 0.06). Together, these results demonstrate the value of CSF proteomics in studying the biological heterogeneity in Alzheimer's disease patients, and suggest that subtypes may require tailored therapy.

Dataset Information

Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease.

Publications

Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets