Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease

Identification of plasma biomarkers for non-invasive diagnosis of hepatitis B cirrhosis

Hepatitis B virus (HBV) infection represents a major public health challenge due to its potential progression to liver cirrhosis and hepatocellular carcinoma, underscoring the importance of early diagnosis for effective management. This study aimed to identify plasma biomarkers for the non-invasive diagnosis of hepatitis B cirrhosis (HBC). We employed quantitative proteomic analysis via liquid chromatography-tandem mass spectrometry on plasma samples from 27 individuals, including 13 patients with HBC and 14 with chronic hepatitis B (CHB). Bioinformatics analysis of 963 identified proteins revealed 234 differential expressed proteins, comprising 115 upregulated and 119 downregulated proteins. Four candidate biomarkers-CHI3L1, IGFBP1, SHBG, and TIMP2-were subsequently selected and validated using ELISA in a cohort of 158 patients, all demonstrating elevated levels in HBC patients. The four-biomarker panel (4MP) demonstrated superior diagnostic performance, achieving an area under the curve (AUC) of 0.902 for distinguishing HBC from CHB. For differentiating decompensated HBC from CHB, the 4MP achieved an AUC of 0.993, with a sensitivity of 93.75 % and specificity of 98.73 %. Mostly, the 4MP also performed well in identifying severe HBC from non-severe HBC, achieving an AUC of 0.911, with a sensitivity of 81.25% and specificity of 93.65%. In conclusion, this study identifies four novel plasma biomarkers for HBC, highlighting their potential to enhance non-invasive diagnostic strategies for monitoring HBC progression.

Early transcriptional and cellular abnormalities in choroid plexus of a mouse model of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid-β plaques, tau hyperphosphorylation, and neuroinflammation. The choroid plexus (ChP), serving as the blood-cerebrospinal fluid-brain barrier, plays essential roles in immune response to stress and brain homeostasis. However, the cellular and molecular contributions of the ChP to AD progression remain inadequately understood.

METHODS

To elucidate the molecular abnormalities during the early stages of AD, we acquired single-cell transcription profiling of ChP from APP/PS1 mice with early-stage of Aβ pathology and litter-mate controls. The transcriptional alterations that occurred in each cell type were identified by differentially expressed genes, cell-cell communications and pseudotemporal trajectory analysis. The findings were subsequently validated by a series of in situ and in vitro assays.

RESULTS

We constructed a comprehensive atlas of ChP at single-cell resolution and identified six major cell types and immune subclusters in male mice. The majority of dysregulated genes were found in the epithelial cells of APP/PS1 mice in comparison to wild-type (WT) mice, and most of these genes belonged to down-regulated module involved in mitochondrial respirasome assembly, cilium organization, and barrier integrity. The disruption of the epithelial barrier resulted in the downregulation of macrophage migration inhibitory factor (MIF) secretion in APP/PS1 mice, leading to macrophage activation and increased phagocytosis of Aβ. Concurrently, ligands (e.g., APOE) secreted by macrophages and other ChP cells facilitated the entry of lipids into ependymal cells, leading to lipid accumulation and the activation of microglia in the brain parenchyma in APP/PS1 mice compared to WT controls.

CONCLUSIONS

Taken together, these data profiled early transcriptional and cellular abnormalities of ChP within an AD mouse model, providing novel insights of cerebral vasculature into the pathobiology of AD.

Protein co-aggregates of dense core amyloid plaques and CSF differ in rapidly progressive Alzheimer's disease and slower sporadic Alzheimer's disease

BACKGROUND

The rapidly progressive phenotype of Alzheimer's disease (rpAD) remains a rare and less-studied entity. Therefore, the replication of key results from the rpAD brain and cerebrospinal fluid (CSF) is lacking.

METHODS

A label-free quantitative LC-MS/MS analysis of proteins co-aggregating with core-amyloid β plaques in fresh frozen tissue (FFT) from medial temporal regions of rpAD ( n = 8 ) neuropathologically characterized at the National Prion Disease Pathology Surveillance Center (NPDPSC), compared with microdissected amyloid plaques from formalin-fixed, paraffin-embedded (FFPE) tissue blocks from patients with rpAD ( n = 22 ) previously published from the NPDPSC cohort, was performed. Matched rpAD CSF from the FFT cases were compared to a previously published proteomic evaluation of CSF in the AD subtype with rapid progression.

RESULTS

A total of 1841 proteins were characterized in the FFT study, of which 463 were consistently identified in every rpAD patient analyzed. One thousand two hundred eighty-three proteins were shared between the FFT and the prior FFPE study. FFT offered a more comprehensive proteomic profile than the prior FFPE study and prominently included the immune system pathways. Thirty-five proteins were shared in the FFT brain tissue, matched CSF from the same subjects, in which biological processes related to immune response were again notable. These results were validated against prior published proteomic CSF AD data with a faster rate of progression to identify the top 5 potential protein biomarkers of rapid progression in AD CSF.

CONCLUSIONS

These results support a distinct immune-related proteomic profile in both the brain and the CSF that can be explored as potential biomarkers in the future for the clinical diagnosis of rpAD.

A primer on copper biology in the brain

This primer aims to expose scientists who study the brain to the field of copper biology. We briefly discuss key copper homeostasis mechanisms and proteins and place these functions in the context of the brain and neurodevelopment. A small number of key copper genes are explored as representative examples of the importance of this metal to the brain. We show that these genes are expressed throughout the brain and their defects are linked to a diverse array of neurological phenotypes, which we discuss further in the context of several neurological and neurodegenerative diseases associated with dysregulation of copper. This review aims to expose interested scientists to the fundamental roles for copper in the brain, the primary proteins responsible for maintaining copper homeostasis in the brain, and the classic neurological diseases associated with this metal.

Coagulation functions and factors correlated with central nervous system infection in herpes zoster patients

Objective

Reports on central nervous system (CNS) infection caused by varicella-zoster virus (VZV) reactivation are increasing, but its pathogenesis remains unclear, which causes delayed diagnosis and treatment. Some studies suggested that hypercoagulability is involved in the pathogenesis of CNS infection of VZV. This study investigated the coagulation parameters of herpes zoster (HZ) and their correlations with the VZV-related CNS infection, and provided a reference for the early diagnosis and treatment.

Methods

We selected 123 consecutive patients, including 95 HZ cases and 28 VZV meningitis (VZVM) cases hospitalized due to HZ. Forty-seven patients who underwent physical examination in our hospital were used as Health controls (HCs) group. The coagulation parameters of the three groups were measured and compared, and the correlation between coagulation function parameters and CNS infection was analyzed by Logistic regression. the expression of coagulation factor in cerebrospinal fluid (CSF) proteomics of 28 VZVM patients and 11 HZ patients were analyzed.

Results

Compared with HCs group, plasma Fibrinogen (Fib) and D-dimer (DD) levels in HZ and VZVM group were significantly increased (P <0.01), while there were no significant differences in other parameters (P > 0.05). There was also no significant difference in the levels of coagulation parameters between the HZ and VZVM groups (P > 0.05). Proteomic analysis of CSF revealed that there was no difference in the expression levels of Fib, Antithrombin III (AT-III), and coagulation factors VII, IX, X, XI in the HZ and VZVM patients (P > 0.05). The expression levels of coagulation factors XII and XIIIa were higher in VZVM patients than those in HZ patients (P < 0.05 and P < 0.01, respectively).

Conclusion

In HZ and VZVM patients, a hypercoagulable state was observed with increased Fib and DD levels. However, hypercoagulation was not a risk factor for CNS infection, and there was no significant correlation between the elevated level and the severity of disease.

Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures across the Alzheimer disease continuum

Changes in β-amyloid (Aβ) and hyperphosphorylated tau (T) in brain and cerebrospinal fluid (CSF) precede Alzheimer's disease (AD) symptoms, making the CSF proteome a potential avenue to understand disease pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 analytes (2,029 unique proteins) dysregulated in AD. Of these, 865 (43%) were previously reported, and 1,164 (57%) are novel. The identified proteins cluster in four different pseudo-trajectories groups spanning the AD continuum and were enriched in pathways including neuronal death, apoptosis, and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfunction (mid stages), brain plasticity and longevity (mid stages), and microglia-neuron crosstalk (late stages). Using machine learning, we created and validated highly accurate and replicable (area under the curve [AUC] > 0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD.

Cerebrospinal fluid proteome profiling across the Alzheimer's disease continuum: a step towards solving the equation for 'X'

BACKGROUND

While the temporal profile of amyloid (Aβ) and tau cerebrospinal fluid (CSF) biomarkers along the Alzheimer's disease (AD) continuum is well-studied, chronological changes of CSF proteins reflecting other disease-relevant processes, denoted 'X' in the ATX(N) framework, remain poorly understood.

METHODS

Using an untargeted mass spectrometric approach termed tandem mass tag (TMT), we quantified over 1500 CSF proteins across the AD continuum in three independent cohorts, finely staged by Aβ/tau positron emission tomography (PET), fluid biomarkers, or brain biopsy. Weighted protein co-expression network analysis identified clusters of proteins robustly correlating in all three cohorts which sequentially changed with AD progression. Obtained protein clusters were correlated with fluid biomarker measurements (phosphorylated tau (p-tau) species including p-tau181, p-tau217, and p-tau205, as well as Aβ), Aβ/tau PET imaging, and clinical parameters to discern disease-relevant clusters which were modelled across the AD continuum.

RESULTS

Neurodegeneration-related proteins (e.g., 14-3-3 proteins, PPIA), derived from different brain cell types, strongly correlated with fluid as well as imaging biomarkers and increased early in the AD continuum. Among them, the proteins SMOC1 and CNN3 were highly associated with Aβ pathology, while the 14-3-3 proteins YWHAZ and YWHAE as well as PPIA demonstrated a strong association with both Aβ and tau pathology as indexed by PET. Endo-lysosomal proteins (e.g., HEXB, TPP1, SIAE) increased early in abundance alongside neurodegeneration-related proteins, and were followed by increases in metabolic proteins such as ALDOA, MDH1, and GOT1 at the mild cognitive impairment (MCI) stage. Finally, later AD stages were characterized by decreases in synaptic/membrane proteins (e.g., NPTX2).

CONCLUSIONS

Our study identified proxies of Aβ and tau pathology, indexed by PET, (SMOC1, YWHAE, CNN3) and highlighted the dynamic fluctuations of the CSF proteome over the disease course, identifying candidate biomarkers for disease staging beyond Aβ and tau.

Association of glycoprotein 1b and miR-26a-5p levels with platelet function in Alzheimer's disease

BackgroundAlterations in biochemical and molecular pathways in Alzheimer's disease (AD) may be evident in the brain, blood cells, and vessels. Platelets regulate blood hemostasis and play key roles in neurodegenerative diseases like AD. miR-26a-5p and GP1b may affect platelet functions (PF), with miR-26a-5p as a diagnostic/therapeutic target and GP1b linking vascular and neurological disorders in AD progression.ObjectiveThis study explores the roles of GP1b and hsa-miR-26a-5p in regulating PF in AD.Methods85 participants, including 43 AD, and 45 controls, were included. PF induced by ADP were assessed by optical density and white matter changes by MRI Axial FLAIR. Serum levels of von Willebrand Factor and GP1b were measured by ELISA. Platelet receptor expressions of CD62P and CD42b (GPIb) were measured by flow cytometry, and levels of hsa-miR-26a-5p and hsa-miR-24-3p by qRT-PCR.ResultsADP-induced PF was significantly reduced in AD (p = 0.016). Flow cytometry showed significantly low CD42b and high CD62P expression in AD, respectively (p < 0.0001, p = 0.014). Serum GP1b levels were significantly higher in AD (p = 0.018). Additionally, hsa-miR-26a-5p expression was significantly low in AD (p = 0.001), and a positive correlation was found between the expression levels of hsa-miR-24-3p and hsa-miR-26a-5p in both controls; and AD (r = 0.4149, p = 0.0051, 95% CI = 0.1256-0.6392; r = 0.6820, p = 0.0023, 95% CI 0.4728-0.8184).ConclusionsThis study highlights increased serum GP1b levels with decreased both platelet surface GP1b levels and hsa-miR-26a-5p expressions in AD. GP1b and hsa-miR-26a-5p might have essential roles on PF in AD.

Proteomic profiling in cerebrospinal fluid reveal biomarkers for shunt outcome in idiopathic normal-pressure hydrocephalus

BACKGROUND

The pathophysiology of idiopathic normal pressure hydrocephalus (iNPH) remains unclear, and the treatment strategy remains suboptimal. This study aims to identify biomarkers for shunt prognosis by cerebrospinal fluid (CSF) proteomic profiling.

METHODS

CSF samples collected from 37 iNPH patients from the discovery cohort and 12 iNPH patients from an independent validation cohort (71.9 ± 6.1 years (mean ± SD)), and 16 age-balanced controls (69.9 ± 7.6 years (mean ± SD)) were collected from September 2020 to December 2023. 53 CSF samples were analyzed using a mass spectrometry-based proteomic workflow. Clinical evaluations were performed on all iNPH patients, and 44 patients underwent ventriculoperitoneal shunting. Postoperative CSF were also collected from 10 iNPH patients who underwent shunting surgery. Bioinformatics, machine learning, and enzyme-linked immunosorbent assay (ELISA) were performed to identify CSF proteome changes related to pathophysiology in iNPH, and screen for biomarkers associated with shunt response.

RESULTS

39 and 285 proteins significantly increased and decreased in iNPH CSF compared to the control group. Gene ontology analysis revealed that the noticeably increased proteins were mainly associated with myeloid leukocyte migration and extracellular matrix organization, and significantly decreased proteins were primarily associated with axon development and synapse organization. Machine learning identified 6 candidate biomarkers that potentially predicted the response to shunt surgery. Among these, QPCT levels were found to be elevated in non-responders, while RBP4 levels were decreased, and both of these changes were validated through ELISA.

CONCLUSIONS

Our findings provide support for the hypothesis that the pathophysiology of iNPH is characterized by a state of neuroinflammation, extracellular matrix remodeling, and neurodegeneration, and CSF shunting can reverse such pathological state. Machine learning using preoperative proteomic profiles satisfactorily predicted the clinical outcome of the shunt procedure in iNPH. Future research targeting specific proteins in iNPH may be warranted to better comprehend the disease mechanism and design patient-tailored treatments.

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Platelets mirror changes in the frontal lobe antioxidant system in Alzheimer's disease

INTRODUCTION

Blood biomarkers reflecting Alzheimer's disease (AD) pathophysiology can improve diagnosis and treatment.

METHODS

We applied top-down proteomics to compare frontal lobe from 17 AD cases and 11 controls to blood platelets from a second independent study group of 124 AD patients, 61 with mild cognitive impairment (MCI), and 168 controls. Findings were immunologically validated.

RESULTS

Sixty AD-associated proteoforms were identified in frontal lobe, with 26 identically represented in platelets. Validation in platelet samples confirmed elevated glutathione S-transferase omega 1 (GSTO1) levels linked to single nucleotide polymorphism (SNP) rs4925 and increased superoxide dismutase 1 (SOD1) levels in AD. Bioinformatics revealed copper chaperone for superoxide dismutase (CCS) and glutathione peroxidase 1 (GPX1) as integral partners of these antioxidant enzymes. Both were detected to be reduced in frontal lobes and platelets in AD. SOD1 and CCS are already changed in MCI.

DISCUSSION

These four novel blood biomarkers, integrated with traditional AD biomarkers, may facilitate patient risk assessment and treatment, with SOD1 and CCS alterations in MCI offering early diagnostic potential.

HIGHLIGHTS

Platelets mirror several Alzheimer's disease (AD)-dependent neuronal changes, valuable for blood tests. As a start, 60 AD-associated frontal lobe proteins were identified by top-down proteomics. Fifty percent of these 60 AD-affected brain proteins are represented identically in platelets. Among these, glutathione S-transferase omega 1 (GSTO1), superoxide dismutase 1 (SOD1), copper chaperone for superoxide dismutase (CCS), and glutathione peroxidase 1 (GPX1) are identically AD related in brain and platelets. SOD1 and its crucial activating partner CCS are altered in the platelets of patients with mild cognitive impairment.

omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation applied to bulk- and single cell proteomics data

The unprecedented speed and sensitivity of mass spectrometry (MS) unlocked large-scale applications of proteomics and even enabled proteome profiling of single cells. However, this fast-evolving field is hindered by a lack of scalable dimensionality reduction tools that can compensate for substantial batch effects and missingness across MS runs. Therefore, we present omicsGMF, a fast, scalable, and interpretable matrix factorization method, tailored for bulk and single-cell proteomics data. Unlike current workflows that sequentially apply imputation, batch correction, and principal component analysis, omicsGMF integrates these steps into a unified framework, dramatically enhancing data processing and dimensionality reduction. Additionally, omicsGMF provides robust imputation of missing values, outperforming bespoke state-of-the-art imputation tools. We further demonstrate how this integrated approach increases statistical power to detect differentially abundant proteins in the downstream data analysis. Hence, omicsGMF is a highly scalable approach to dimensionality reduction in proteomics, that dramatically improves many important steps in proteomics data analysis.

Application of the Human Proteome in Disease, Diagnosis, and Translation into Precision Medicine: Current Status and Future Prospects

This review summarizes the existing studies of human proteomics technology in the medical field with a focus on the development mechanism of a disease and its potential in discovering biomarkers. Through a systematic review of the relevant literature, we found the significant advantages and application scenarios of proteomics technology in disease diagnosis, drug development, and personalized treatment. However, the review also identifies the challenges facing proteomics technologies, including sample preparation of low-abundance proteins, massive amounts of data analysis, and how research results can be better used in clinical practice. Finally, this work discusses future research directions, including the development of more effective proteomics technologies, strengthening the integration of multi-source omics technologies, and promoting the application of AI in the human proteome.

Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer's disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) and six variations of a deep neural network (DNN) model-the DL model in the H2O package. Least Absolute Shrinkage and Selection Operator (LASSO) selected 35 proteomic biomarkers from the pool. Based on grid search, the DNN model with an activation function of "Rectifier With Dropout" with 2 layers and 32 of 35 selected proteomic biomarkers revealed the best model with the highest accuracy of 0.995 and an F1 Score of 0.996, while among seven traditional ML methods, XGBoost was the best with an accuracy of 0.986 and an F1 Score of 0.985. Several biomarkers were correlated with the APOE-ε4 genotype, polygenic hazard score (PHS), and three clinical cerebrospinal fluid biomarkers (Aβ42, tTau, and pTau). Bioinformatics analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed several molecular functions and pathways associated with the selected biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, and regulation of lipid localization. The results showed that the DL model may represent a promising tool in the prediction of MCI. These plasma proteomic biomarkers may help with early diagnosis, prognostic risk stratification, and early treatment interventions for individuals at risk for MCI.

Modified Decision Tree with Custom Splitting Logic Improves Generalization across Multiple Brains' Proteomic Data Sets of Alzheimer's Disease

Many factors negatively affect a generalization of the findings in discovery proteomics. They include differentiation between patient cohorts, a variety of experimental conditions, etc. We presented a machine-learning-based workflow for proteomics data analysis, aiming at improving generalizability across multiple data sets. In particular, we customized the decision tree model by introducing a new parameter, min_groups_leaf, which regulates the presence of the samples from each data set inside the model's leaves. Further, we analyzed a trend for the feature importance's curve as a function of the novel parameter for feature selection to a list of proteins with significantly improved generalization. The developed workflow was tested using five proteomic data sets obtained for post-mortem human brain samples of Alzheimer's disease. The data sets consisted of 535 LC-MS/MS acquisition files. The results were obtained for two different pipelines of data processing: (1) MS1-only processing based on DirectMS1 search engine and (2) a standard MS/MS-based one. Using the developed workflow, we found seven proteins with expression patterns that were unique for asymptomatic Alzheimer patients. Two of them, Serotransferrin TRFE and DNA repair nuclease APEX1, may be potentially important for explaining the lack of dementia in patients with the presence of neuritic plaques and neurofibrillary tangles.

A comprehensive view of the molecular features within the serum and serum EV of Alzheimer's disease

Conventional Alzheimer's disease research mainly focuses on cerebrospinal fluid, which requires an invasive sampling procedure. This method carries inherent risks for patients and could potentially lower patient compliance. EVs (Extracellular Vesicles) and blood are two emerging noninvasive mediators reflecting the pathological changes of Alzheimer's disease. Integrating the two serum proteomic information based on DIA (Data Independent Acquisition) is conducive to the comparison of serological research strategies, mining pathological information of AD, and evaluating the potential of EVs and blood in the diagnosis of AD. We generated a combined proteomic data resource of 39 serum samples derived from patients with AD and from age-matched controls (AMC) and identified 639 PGs (protein groups) in serum samples and 714 PGs in serum EV samples. The differentially expressed protein groups identified in both serum and serum EV provide a reflective profile of the pathological characteristics associated with AD. The combined strategy performed well, identifying 40 potential diagnostic markers with AUC values above 0.85, including two molecular diagnostic models that achieved an effectiveness score of 0.991.

Identification of novel proteins associated with intelligence by integrating genome-wide association data and human brain proteomics

While genome-wide association studies (GWAS) have identified genetic variants associated with intelligence, their biological mechanisms remain largely unexplored. This study aimed to bridge this gap by integrating intelligence GWAS data with human brain proteomics and transcriptomics. We conducted proteome-wide (PWAS) and transcriptome-wide (TWAS) association studies, along with enrichment and protein-protein interaction (PPI) network analyses. PWAS identified 44 genes in the human brain proteome that influence intelligence through protein abundance regulation (FDR P <  0.05). Causal analysis revealed 36 genes, including GPX1, involved in the cis-regulation of protein abundance (P <  0.05). In independent PWAS analyses, 17 genes were validated, and 10 showed a positive correlation with intelligence (P <  0.05). TWAS revealed significant SNP-based heritability for mRNA in 28 proteins, and cis-regulation of mRNA levels for 20 genes was nominally associated with intelligence (FDR P <  0.05). This study identifies key genes that bridge genetic variants and protein-level mechanisms of intelligence, providing novel insights into its biological pathways and potential therapeutic targets.

Uncovering dark mass in population proteomics: Pan-analysis of single amino acid polymorphism relevant to cognition and aging

Human proteome data across populations have been analyzed extensively to reveal protein quantitative associations with physiological or pathological states, while the single amino acid polymorphism (SAP) has been rarely investigated. In this work, we introduce a pan-SAP workflow that relies on pan-database searching independent of individual genome sequencing. Using ten cohorts comprising 2,004 individuals related to cognition disorder and aging, we quantify the SAP sites in key proteins, such as apolipoprotein E (APOE) in plasma and cerebrospinal fluid at the proteome level. Specifically, the quantification of heterozygous APOE-C112R, including its abundance and ratio, provides insights into the dosage effect and relationship with cognition disorder, which cannot be interpreted at the genomic level. Furthermore, our approach could precisely track age-related changes in APOE-C112R levels. Taken together, this pan-SAP workflow uncovered existing but hidden SAPs in multi-populations, connecting SAP quantification to disease progression and paving the way for broader proteomic investigations in complex diseases.

Biomarkers of synaptic degeneration in Alzheimer's disease

Synapse has been considered a critical neuronal structure in the procession of Alzheimer's disease (AD), attacked by two pathological molecule aggregates (amyloid-β and phosphorylated tau) in the brain, disturbing synaptic homeostasis before disease manifestation and subsequently causing synaptic degeneration. Recently, evidence has emerged indicating that soluble oligomeric amyloid-β (AβO) and tau exert direct toxicity on synapses, causing synaptic damage. Synaptic degeneration is closely linked to cognitive decline in AD, even in the asymptomatic stages of AD. Therefore, the identification of novel, specific, and sensitive biomarkers involved in synaptic degeneration holds significant promise for early diagnosis of AD, reducing synaptic degeneration and loss, and controlling the progression of AD. Currently, a range of biomarkers in cerebrospinal fluid (CSF), such as synaptosome-associated protein 25 (SNAP-25), synaptotagmin-1, growth-associated protein-43 (GAP-43), and neurogranin (Ng), along with functional brain imaging techniques, can detect variations in synaptic density, offering high sensitivity and specificity for AD diagnosis. However, these methods face challenges, including invasiveness, high cost, and limited accessibility. In contrast, biomarkers found in blood or urine provide a minimally invasive, cost-effective, and more accessible alternative to traditional diagnostic methods. Notably, neuron-derived exosomes in blood, which contain synaptic proteins, show variations in concentration that can serve as indicators of synaptic injury, providing an additional, less invasive approach to AD diagnosis and monitoring.

Gas-phase fractionation DDA promotes in-depth DIA phosphoproteome analysis

Data-independent acquisition (DIA) is a promising method for quantitative proteomics. Library-based DIA database searching against project-specific data-dependent acquisition (DDA) spectral libraries is the gold standard. These libraries are constructed using material-consuming pre-fractionation two dimensional DDA analysis. The alternative to this is library-free DIA analysis. Limited sample amounts restrict the use of fractionation to build spectral libraries for post-translational modifications (PTMs) DIA analysis. We present the use of gas-phase fractionation (GPF) DDA data to improve the depth of library-free DIA identification for the phosphoproteome, called GPF-DDA hybrid DIA. This method fully utilizes the remnants of samples post-DIA analysis and leverages both library-based and -free DIA database searching. GPF-DDA hybrid DIA analyzes phosphopeptides surplus sample after DIA analysis using a number of DDA injections with each scanning different mass-to-charge (m/z) windows, instead of preforming traditional off-line fractionation-based DDA. The GPF-DDA data is integrated into the library-free DIA database search to create a hybrid library, enhancing phosphopeptide identification. Two GPF-DDA injections proved to increase 18 % phosphopeptide and 13 % phosphosite identification in HEK293 cell lines, while five injections resulted in up to 28 % phosphopeptide and 21 % phosphosite increases compared to library-free DIA analysis alone. We used GPF-DDA hybrid DIA phosphoproteomics to characterize lung tissue upon direct (smoke induced) and indirect (sepsis induced) acute lung injury (ALI) in mice. The differentially expressed phosphosites (DEPsites) in direct ALI were found in proteins related to mRNA processing and RNA. DEPsites in indirect ALI were enriched in proteins related to microtubule polymerization, positive regulation of microtubule polymerization and fibroblast migration. This study demonstrates that GPF-DDA hybrid DIA analysis workflow can indeed promote depth of DIA analysis of phosphoproteome and could be extended to DIA analysis of other PTMs.

Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics

Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography-mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.

An accessible workflow for high-sensitivity proteomics using parallel accumulation-serial fragmentation (PASEF)

Deep and accurate proteome analysis is crucial for understanding cellular processes and disease mechanisms; however, it is challenging to implement in routine settings. In this protocol, we combine a robust chromatographic platform with a high-performance mass spectrometric setup to enable routine yet in-depth proteome coverage for a broad community. This entails tip-based sample preparation and pre-formed gradients (Evosep One) combined with a trapped ion mobility time-of-flight mass spectrometer (timsTOF, Bruker). The timsTOF enables parallel accumulation-serial fragmentation (PASEF), in which ions are accumulated and separated by their ion mobility, maximizing ion usage and simplifying spectra. Combined with data-independent acquisition (DIA), it offers high peak sampling rates and near-complete ion coverage. Here, we explain how to balance quantitative accuracy, specificity, proteome coverage and sensitivity by choosing the best PASEF and DIA method parameters. The protocol describes how to set up the liquid chromatography-mass spectrometry system and enables PASEF method generation and evaluation for varied samples by using the py_diAID tool to optimally position isolation windows in the mass-to-charge and ion mobility space. Biological projects (e.g., triplicate proteome analysis in two conditions) can be performed in 3 d with ~3 h of hands-on time and minimal marginal cost. This results in reproducible quantification of 7,000 proteins in a human cancer cell line in quadruplicate 21-min injections and 29,000 phosphosites for phospho-enriched quadruplicates. Synchro-PASEF, a highly efficient, specific and novel scan mode, can be analyzed by Spectronaut or AlphaDIA, resulting in superior quantitative reproducibility because of its high sampling efficiency.

Targeting PAAN/MIF nuclease activity in parthanatos-associated brain diseases

Current FDA-approved drugs for neurodegenerative diseases primarily aim to reduce pathological protein aggregation or alleviate symptoms by enhancing neurotransmitter signaling. However, outcomes remain suboptimal and often fail to modify the course of neurodegenerative diseases. Acute neurologic injury that occurs in stroke and traumatic brain injury (TBI) also suffer from inadequate therapies to prevent neuronal cell death, resulting from both the acute insult and the subsequent reperfusion injury following recanalization of the occlusion in stroke. Approaches to prevent neuronal loss in neurodegenerative disease and acute neurologic injury hold significant therapeutic promise. Parthanatos is a cell death pathway that is activated and plays an integral role in these neurologic disorders. Parthanatos-associated apoptosis-inducing factor nuclease (PAAN), also known as macrophage migration inhibitory factor (MIF) nuclease, is the final executioner in the parthanatic cell death cascade. We posit that inhibiting parthanatos by blocking MIF nuclease activity offers a promising and precise strategy to prevent neuronal cell death in both chronic neurodegenerative disease and acute neurologic injury. In this chapter, we discuss the role of MIF's nuclease activity - distinct from its other enzymatic activities - in driving cell death that occurs in various neurological diseases. We also delve into the discovery, screening, structure, and function of MIF nuclease inhibitors, which have demonstrated neuroprotection in Parkinson's disease (PD) cell and mouse models. This analysis includes essential future research directions and queries that need to be considered to advance the clinical development of MIF nuclease inhibitors. Ultimately, our discussion aims to inspire drug development centered around inhibiting MIF's nuclease activity, potentially resulting in transformative, disease-modifying therapeutics.

Proteomic patterns associated with ketamine response in major depressive disorders

BACKGROUND

Major depressive disorder (MDD) is characterized by persistent feelings of sadness and loss of interest. Ketamine has been widely used to treat MDD owing to its rapid effect in relieving depressive symptoms. Importantly, not all patients respond to ketamine treatment. Identifying sub-populations who will benefit from ketamine, as well as those who may not, prior to treatment initiation, would significantly advance precision medicine in patients with MDD.

METHODS

Here, we used mass spectrometry-based plasma proteomics to analyze matched pre- and post-ketamine treatment samples from a cohort of 30 MDD patients whose treatment outcomes and demographic and clinical characteristics were considered.

RESULTS

Ketamine responders and non-responders were identified according to their individual outcomes after two weeks of treatment. We analyzed proteomic alterations in post-treatment samples from responders and non-responders and identified a collection of six proteins pivotal to the antidepressive effect of ketamine. Subsequent co-regulation analysis revealed that pathways related to immune response were involved in ketamine response. By comparing the proteomic profiles of samples from the same individuals at the pre- and post-treatment time points, dynamic proteomic rearrangements induced by ketamine revealed that immune-related processes were activated in association with its antidepressive effect. Furthermore, receiver operating characteristic curve analysis of pre-treatment samples revealed three proteins with strong predictive performance in determining the response of patients to ketamine before receiving treatment.

CONCLUSIONS

These findings provide valuable knowledge about ketamine response, which will ultimately lead to more personalized and effective treatments for patients.

TRIAL REGISTRATION

The study was registered in the Chinese Clinical Trials Registry (ChiCTR-OOC-17012239) on May 26, 2017.

Recent Advances in Human Cerebrospinal Fluid Proteomics

Cerebrospinal fluid (CSF) proteomics has become an alternative that allows zooming-in where pathophysiological alterations are taking place, detecting protein mediators that might eventually be considered as potential biomarkers in neurological or psychiatric diseases. From a technological point of view, mass-spectrometry-based-proteomics as well as antibody/aptamer-based platforms allow the simultaneous monitoring of secreted and circulating proteins in a broad concentration range in a robust manner, generating innovative facets in biomarker validation. This chapter highlights recent discoveries in the field of CSF protein analysis, covering sample preparation methods for proteomic workflows, shotgun proteomics, subproteomics, and other omics applied to CSF.

Head-to-Head Comparison of Aptamer- and Antibody-Based Proteomic Platforms in Human Cerebrospinal Fluid Samples from a Real-World Memory Clinic Cohort

High-throughput proteomic platforms are crucial to identify novel Alzheimer's disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan® 7k) and antibody-based (Olink® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and inter-platform reproducibility were evaluated through correlations between two independent SomaScan® assays analyzing the same samples, and between SomaScan® and Olink® results. Association analyses were performed between proteomic measures, CSF biological traits, sample demographics, and AD endophenotypes. Our 12-category metric of reproducibility combining correlation analyses identified 2428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.

A Six-Gene Signature Related to Liquid-Liquid Phase Separation for Diagnosis of Alzheimer's Disease

BACKGROUND

Liquid-liquid phase separation (LLPS) has been increasingly recognized as a crucial mechanism in the pathogenesis of various neurodegenerative disorders, including Alzheimer's disease (AD). There remains a paucity of effective diagnostic biomarkers for this condition. This study aims to develop and validate a novel LLPS-related molecular signature to enhance the diagnostic accuracy and early detection of AD.

METHODS

LLPS-related genes were identified from online databases and subjected to bioinformatic analyses, including protein-protein interaction (PPI) network analysis and least absolute shrinkage and selection operator (LASSO) regression. Based on the optimal LLPS-related genes, a diagnosis risk model was constructed, and the diagnostic ability was evaluated using a receiver operator characteristic (ROC) curve. To elucidate the biological functions of the identified LLPS-related genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted.

RESULTS

A total of 149 LLPS-related genes were screened, which were found to be involved in functions related to oxidative stress, apoptosis, and cancer progression. The 149 genes were refined to six optimal candidates through PPI network analysis and LASSO regression: Activator of HSP90 ATPase Activity 1 (AHSA1), Eukaryotic Translation Initiation Factor 2 Alpha Kinase 2 (EIF2AK2), Heat Shock Protein Family A (Hsp70) Member 4 (HSPA4), Notch Receptor 1 (NOTCH1), Superoxide Dismutase 1 (SOD1), and Thioredoxin (TXN). Based on the six optimal genes, a diagnostic risk model was constructed, and the diagnostic ability was verified to be promising in AD both in training, internal validation, and two external validation datasets, with area under ROC curve (AUC) above 0.8. Furthermore, significant correlations were observed between the expression of these genes and tumor immune cell infiltration.

CONCLUSIONS

A six-gene diagnosis model was constructed and verified to exhibit robust diagnostic ability in AD.

Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer's disease

The integration of quantitative trait loci (QTLs) with disease genome-wide association studies (GWASs) has proven successful in prioritizing candidate genes at disease-associated loci. QTL mapping has been focused on multi-tissue expression QTLs or plasma protein QTLs (pQTLs). We generated a cerebrospinal fluid (CSF) pQTL atlas by measuring 6,361 proteins in 3,506 samples. We identified 3,885 associations for 1,883 proteins, including 2,885 new pQTLs, demonstrating unique genetic regulation in CSF. We identified CSF-enriched pleiotropic regions on chromosome (chr)3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron specificity and neurological development. We integrated our associations with Alzheimer's disease (AD) through proteome-wide association study (PWAS), colocalization and Mendelian randomization and identified 38 putative causal proteins, 15 of which have drugs available. Finally, we developed a proteomics-based AD prediction model that outperforms genetics-based models. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.

Associations of plasma SMOC1 and soluble IL6RA levels with the progression from mild cognitive impairment to dementia

Despite the central role attributed to neuroinflammation in the etiology and pathobiology of Alzheimer's disease (AD), the direct link between levels of inflammatory mediators in blood and cerebrospinal fluid (CSF) compartments, as well as their potential implications for AD diagnosis and progression, remains inconclusive. Moreover, there is debate on whether inflammation has a protective or detrimental effect on disease onset and progression. Indeed, distinct immunological mechanisms may govern protective and damaging effects at early and late stages, respectively. This study aims to (i) identify inflammatory mediators demonstrating robust correlations between peripheral and central nervous system (CNS) compartments by means of plasma and CSF analysis, respectively, and (ii) assess their potential significance in the context of AD and disease progression from mild cognitive impairment (MCI) to dementia. To achieve this, we have examined the inflammatory profile of a well-defined subcohort comprising 485 individuals from the Ace Alzheimer Center Barcelona (ACE). Employing a hierarchical clustering approach, we thoroughly evaluated the intercompartmental correlations of 63 distinct inflammation mediators, quantified in paired CSF and plasma samples, using advanced SOMAscan technology. Of the array of mediators investigated, only six mediators (CRP, IL1RAP, ILRL1, IL6RA, PDGFRB, and YKL-40) exhibited robust correlations between the central and peripheral compartments (proximity scores <400). To strengthen the validity of our findings, these identified mediators were subsequently validated in a second subcohort of individuals from ACE (n = 873). The observed plasma correlations across the entire cohort consistently have a Spearman rho value above 0.51 (n = 1,360, p < 1.77E-93). Of the high CSF-plasma correlated proteins, only soluble IL6RA (sIL6RA) displayed a statistically significant association with the conversion from MCI to dementia. This association remained robust even after applying a stringent Bonferroni correction (Cox proportional hazard ratio [HR] = 1.936 per standard deviation; p = 0.0018). This association retained its significance when accounting for various factors, including CSF amyloid (Aβ42) and Thr181-phosphorylated tau (p-tau) levels, age, sex, baseline Mini-Mental State Examination (MMSE) score, and potential sampling biases identified through principal component analysis (PCA) modeling. Furthermore, our study confirmed the association of both plasma and CSF levels of SPARC-related modular calcium-binding protein 1 (SMOC1) with amyloid and tau accumulation, indicating their role as early surrogate biomarkers for AD pathology. Despite the lack of a statistically significant correlation between SMOC1 levels in CSF and plasma, both acted as independent biomarkers of disease progression (HR > 1.3, p < 0.002). In conclusion, our study unveils that sIL6RA and SMOC1 are associated with MCI progression. The absence of correlations among inflammatory mediators between the central and peripheral compartments appears to be a common pattern, with only a few intriguing exceptions.

CSF proteins of inflammation, proteolysis and lipid transport define preclinical AD and progression to AD dementia in cognitively unimpaired individuals

This preclinical AD CSF proteome study identified a panel of 12-CSF markers detecting amyloid positivity and clinical progression to AD with high accuracy; some of these CSF proteins related to immune function, neurotrophic processes, energy metabolism and endolysosomal functioning (e.g., ITGB2, CLEC5A, IGFBP-1, CST3) changed before amyloid positivity is established.

Evaluation of cerebrospinal fluid (CSF) and interstitial fluid (ISF) mouse proteomes for the validation and description of Alzheimer's disease biomarkers

BACKGROUND

Mass spectrometry (MS)-based cerebrospinal fluid (CSF) proteomics is an important method for discovering biomarkers of neurodegenerative diseases. CSF serves as a reservoir for interstitial fluid (ISF), and extensive communication between the two fluid compartments helps to remove waste products from the brain.

NEW METHOD

We performed proteomic analyses of both CSF and ISF fluid compartments using intracerebral microdialysis to validate and detect novel biomarkers of Alzheimer's disease (AD) in APPtg and C57Bl/6J control mice.

RESULTS

We identified up to 625 proteins in ISF and 4483 proteins in CSF samples. By comparing the biofluid profiles of APPtg and C57Bl/6J mice, we detected 37 and 108 significantly up- and downregulated candidates, respectively. In ISF, 7 highly regulated proteins, such as Gfap, Aldh1l1, Gstm1, and Txn, have already been implicated in AD progression, whereas in CSF, 9 out of 14 highly regulated proteins, such as Apba2, Syt12, Pgs1 and Vsnl1, have also been validated to be involved in AD pathogenesis. In addition, we also detected new interesting regulated proteins related to the control of synapses and neurotransmission (Kcna2, Cacng3, and Clcn6) whose roles as AD biomarkers should be further investigated.

COMPARISON WITH EXISTING METHODS

This newly established combined protocol provides better insight into the mutual communication between ISF and CSF as an analysis of tissue or CSF compartments alone.

CONCLUSIONS

The use of multiple fluid compartments, ISF and CSF, for the detection of their biological communication enables better detection of new promising AD biomarkers.

Defining Alzheimer's Disease through Proteomic CSF Profiling

Alzheimer disease (AD) is the main cause of dementia, and its complexity is not yet completely understood. Proteomic profiles can provide useful information to explore the pathways involved and the heterogeneity among AD patients. A proteomic analysis was performed in cerebrospinal fluid (CSF) samples from mild cognitive impairment due to AD (MCI-AD) and control individuals; both groups were classified by amyloid β42/amyloid β40 levels in CSF (data available in BioStudies database (S-BSST1456)). The analysis based on PLS regression and volcano plot identified 7 proteins (FOLR2, PPP3CA, SMOC2, STMN1, TAGLN3, TMEM132B, and UCHL1) mainly related to protein phosphorylation, structure maintenance, inflammation, and protein degradation. Enrichment analysis revealed the involvement of different biological processes related to neuronal mechanisms and synapses, lipid and carbohydrate metabolism, immune system and inflammation, vascular, hormones, and response to stimuli, and cell signaling and adhesion. In addition, the proteomic profile showed some association with the levels of AD biomarkers in CSF. Regarding the subtypes, two MCI-AD subgroups were identified: one could be related to synapsis and neuronal functions and the other to innate immunity. The study of the proteomic profile in the CSF of AD patients reflects the heterogeneity of biochemical pathways involved in AD.

Deciphering the role of miRNAs in Alzheimer's disease: Predictive targeting and pathway modulation - A systematic review

Alzheimer's Disease (AD), a multifaceted neurodegenerative disorder, is increasingly understood through the regulatory lens of microRNAs (miRNAs). This review comprehensively examines the pivotal roles of miRNAs in AD pathogenesis, shedding light on their influence across various pathways. We delve into the biogenesis and mechanisms of miRNAs, emphasizing their significant roles in brain function and regulation. The review then navigates the complex landscape of AD pathogenesis, identifying key genetic, environmental, and molecular factors, with a focus on hallmark pathological features like amyloid-beta accumulation and tau protein hyperphosphorylation. Central to our discussion is the intricate involvement of miRNAs in these processes, highlighting their altered expression patterns in AD and subsequent functional implications, from amyloid-beta metabolism to tau pathology, neuroinflammation, oxidative stress, and synaptic dysfunction. The predictive analysis of miRNA targets using computational methods, complemented by experimental validations, forms a crucial part of our discourse, unraveling the contributions of specific miRNAs to AD. Moreover, we explore the therapeutic potential of miRNAs as biomarkers and in miRNA-based interventions, while addressing the challenges in translating these findings into clinical practice. This review aims to enhance understanding of miRNAs in AD, offering a foundation for future research directions and novel therapeutic strategies.

Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer's disease

BACKGROUND

Blood-based biomarkers are gaining grounds for the detection of Alzheimer's disease (AD) and related disorders (ADRDs). However, two key obstacles remain: the lack of methods for multi-analyte assessments and the need for biomarkers for related pathophysiological processes like neuroinflammation, vascular, and synaptic dysfunction. A novel proteomic method for pre-selected analytes, based on proximity extension technology, was recently introduced. Referred to as the NULISAseq CNS disease panel, the assay simultaneously measures ~ 120 analytes related to neurodegenerative diseases, including those linked to both core (i.e., tau and amyloid-beta (Aβ)) and non-core AD processes. This study aimed to evaluate the technical and clinical performance of this novel targeted proteomic panel.

METHODS

The NULISAseq CNS disease panel was applied to 176 plasma samples from 113 individuals in the MYHAT-NI cohort of predominantly cognitively normal participants from an economically underserved region in southwestern Pennsylvania, USA. Classical AD biomarkers, including p-tau181, p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were independently measured using Single Molecule Array (Simoa) and correlations and diagnostic performances compared. Aβ pathology, tau pathology, and neurodegeneration (AT(N) statuses) were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and an MRI-based AD-signature composite cortical thickness index, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA and neuroimaging-determined AT(N) biomarkers.

RESULTS

NULISA concurrently measured 116 plasma biomarkers with good technical performance (97.2 ± 13.9% targets gave signals above assay limits of detection), and significant correlation with Simoa assays for the classical biomarkers. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878-0.983). Fourteen markers were significantly decreased in Aβ-PET + participants, including TIMP3, BDNF, MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET + participants. Novel plasma biomarkers with tau PET-dependent longitudinal changes included proteins associated with neuroinflammation, synaptic function, and cerebrovascular integrity, such as CHIT1, CHI3L1, NPTX1, PGF, PDGFRB, and VEGFA; all previously linked to AD but only reliable when measured in cerebrospinal fluid. The autophagosome cargo protein SQSTM1 exhibited significant association with neurodegeneration after adjusting age, sex, and APOE ε4 genotype.

CONCLUSIONS

Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes, consistent with the recently revised biological and diagnostic framework. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

High-resolution proteomics and machine-learning identify protein classifiers of honey made by Sicilian black honeybees (Apis mellifera ssp. sicula)

Apis mellifera ssp. sicula, also known as the Sicilian black honeybee, is a Slow Food Presidium that produces honey with outstanding nutraceutical properties, including high antioxidant capacity. In this study, we used high-resolution proteomics to profile the honey produced by sicula and identify protein classifiers that distinguish it from that made by the more common Italian honeybee (Apis mellifera ssp. ligustica). We profiled the honey proteome of genetically pure sicula and ligustica honeybees bred in the same geographical area, so that chemical differences in their honey only reflected the genetic background of the two subspecies, rather than botanical environment. Differentially abundant proteins were validated in sicula and ligustica honeys of different origin, by using the so-called "rectangular strategy", a proteomic approach commonly used for biomarker discovery in clinical proteomics. Then, machine learning was employed to identify which proteins were the most effective in distinguishing sicula and ligustica honeys. This strategy enabled the identification of two proteins, laccase-5 and venome serine protease 34 isoform X2, that were fully effective in predicting whether honey was made by sicula or ligustica honeybees. In conclusion, we profiled the proteome of sicula honey, identified two protein classifiers of sicula honey in respect to ligustica, and proved that the rectangular strategy can be applied to uncover biomarkers to ascertain food authenticity.

Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer's disease

Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.

Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross-Linking

The exploration of protein structure and function stands at the forefront of life science and represents an ever-expanding focus in the development of proteomics. As mass spectrometry (MS) offers readout of protein conformational changes at both the protein and peptide levels, MS-based structural proteomics is making significant strides in the realms of structural and molecular biology, complementing traditional structural biology techniques. This review focuses on two powerful MS-based techniques for peptide-level readout, namely limited proteolysis-mass spectrometry (LiP-MS) and cross-linking mass spectrometry (XL-MS). First, we discuss the principles, features, and different workflows of these two methods. Subsequently, we delve into the bioinformatics strategies and software tools used for interpreting data associated with these protein conformation readouts and how the data can be integrated with other computational tools. Furthermore, we provide a comprehensive summary of the noteworthy applications of LiP-MS and XL-MS in diverse areas including neurodegenerative diseases, interactome studies, membrane proteins, and artificial intelligence-based structural analysis. Finally, we discuss the factors that modulate protein conformational changes. We also highlight the remaining challenges in understanding the intricacies of protein conformational changes by LiP-MS and XL-MS technologies.

Connecting genomic and proteomic signatures of amyloid burden in the brain

Background

Alzheimer's disease (AD) has a high heritable component characteristic of complex diseases, yet many of the genetic risk factors remain unknown. We combined genome-wide association studies (GWAS) on amyloid endophenotypes measured in cerebrospinal fluid (CSF) and positron emission tomography (PET) as surrogates of amyloid pathology, which may be helpful to understand the underlying biology of the disease.

Methods

We performed a meta-analysis of GWAS of CSF Aβ42 and PET measures combining six independent cohorts (n=2,076). Due to the opposite effect direction of Aβ phenotypes in CSF and PET measures, only genetic signals in the opposite direction were considered for analysis (n=376,599). Polygenic risk scores (PRS) were calculated and evaluated for AD status and amyloid endophenotypes. We then searched the CSF proteome signature of brain amyloidosis using SOMAscan proteomic data (Ace cohort, n=1,008) and connected it with GWAS results of loci modulating amyloidosis. Finally, we compared our results with a large meta-analysis using publicly available datasets in CSF (n=13,409) and PET (n=13,116). This combined approach enabled the identification of overlapping genes and proteins associated with amyloid burden and the assessment of their biological significance using enrichment analyses.

Results

After filtering the meta-GWAS, we observed genome-wide significance in the rs429358-APOE locus and nine suggestive hits were annotated. We replicated the APOE loci using the large CSF-PET meta-GWAS and identified multiple AD-associated genes as well as the novel GADL1 locus. Additionally, we found a significant association between the AD PRS and amyloid levels, whereas no significant association was found between any Aβ PRS with AD risk. CSF SOMAscan analysis identified 1,387 FDR-significant proteins associated with CSF Aβ42 levels. The overlap among GWAS loci and proteins associated with amyloid burden was very poor (n=35). The enrichment analysis of overlapping hits strongly suggested several signalling pathways connecting amyloidosis with the anchored component of the plasma membrane, synapse physiology and mental disorders that were replicated in the large CSF-PET meta-analysis.

Conclusions

The strategy of combining CSF and PET amyloid endophenotypes GWAS with CSF proteome analyses might be effective for identifying signals associated with the AD pathological process and elucidate causative molecular mechanisms behind the amyloid mobilization in AD.

Unraveling the Protective Role of Oleocanthal and Its Oxidation Product, Oleocanthalic Acid, against Neuroinflammation

Neuroinflammation is a critical aspect of various neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. This study investigates the anti-neuroinflammatory properties of oleocanthal and its oxidation product, oleocanthalic acid, using the BV-2 cell line activated with lipopolysaccharide. Our findings revealed that oleocanthal significantly inhibited the production of pro-inflammatory cytokines and reduced the expression of inflammatory genes, counteracted oxidative stress induced by lipopolysaccharide, and increased cell phagocytic activity. Conversely, oleocanthalic acid was not able to counteract lipopolysaccharide-induced activation. The docking analysis revealed a plausible interaction of oleocanthal, with both CD14 and MD-2 leading to a potential interference with TLR4 signaling. Since our data show that oleocanthal only partially reduces the lipopolysaccharide-induced activation of NF-kB, its action as a TLR4 antagonist alone cannot explain its remarkable effect against neuroinflammation. Proteomic analysis revealed that oleocanthal counteracts the LPS modulation of 31 proteins, including significant targets such as gelsolin, clathrin, ACOD1, and four different isoforms of 14-3-3 protein, indicating new potential molecular targets of the compound. In conclusion, oleocanthal, but not oleocanthalic acid, mitigates neuroinflammation through multiple mechanisms, highlighting a pleiotropic action that is particularly important in the context of neurodegeneration.

Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of Frontotemporal Dementia

Diagnosis of Frontotemporal dementia (FTD) and the specific underlying neuropathologies (frontotemporal lobar degeneration; FTLD- Tau and FTLD-TDP) is challenging, and thus fluid biomarkers are needed to improve diagnostic accuracy. We used proximity extension assays to analyze 665 proteins in cerebrospinal fluid (CSF) samples from a multicenter cohort including patients with FTD (n = 189), Alzheimer's Disease dementia (AD; n = 232), and cognitively unimpaired individuals (n = 196). In a subset, FTLD neuropathology was determined based on phenotype or genotype (FTLD-Tau = 87 and FTLD-TDP = 68). Forty three proteins were differentially regulated in FTD compared to controls and AD, reflecting axon development, regulation of synapse assembly, and cell-cell adhesion mediator activity pathways. Classification analysis identified a 14- and 13-CSF protein panel that discriminated FTD from controls (AUC: 0.96) or AD (AUC: 0.91). Custom multiplex panels confirmed the highly accurate discrimination between FTD and controls (AUCs > 0.96) or AD (AUCs > 0.88) in three validation cohorts, including one with autopsy confirmation (AUCs > 0.90). Six proteins were differentially regulated between FTLD-TDP and FTLD-Tau, but no reproducible classification model could be generated (AUC: 0.80). Overall, this study introduces novel FTD-specific biomarker panels with potential use in diagnostic setting.

Alzheimer's disease: a review on the current trends of the effective diagnosis and therapeutics

The most prevalent cause of dementia is Alzheimer's disease. Cognitive decline and accelerating memory loss characterize it. Alzheimer's disease advances sequentially, starting with preclinical stages, followed by mild cognitive and/or behavioral impairment, and ultimately leading to Alzheimer's disease dementia. In recent years, healthcare providers have been advised to make an earlier diagnosis of Alzheimer's, prior to individuals developing Alzheimer's disease dementia. Regrettably, the identification of early-stage Alzheimer's disease in clinical settings can be arduous due to the tendency of patients and healthcare providers to disregard symptoms as typical signs of aging. Therefore, accurate and prompt diagnosis of Alzheimer's disease is essential in order to facilitate the development of disease-modifying and secondary preventive therapies prior to the onset of symptoms. There has been a notable shift in the goal of the diagnosis process, transitioning from merely confirming the presence of symptomatic AD to recognizing the illness in its early, asymptomatic phases. Understanding the evolution of disease-modifying therapies and putting effective diagnostic and therapeutic management into practice requires an understanding of this concept. The outcomes of this study will enhance in-depth knowledge of the current status of Alzheimer's disease's diagnosis and treatment, justifying the necessity for the quest for potential novel biomarkers that can contribute to determining the stage of the disease, particularly in its earliest stages. Interestingly, latest clinical trial status on pharmacological agents, the nonpharmacological treatments such as behavior modification, exercise, and cognitive training as well as alternative approach on phytochemicals as neuroprotective agents have been covered in detailed.

Boronic Acid-Rich Lanthanide Metal-Organic Frameworks Enable Deep Proteomics with Ultratrace Biological Samples

Label-free proteomics is widely used to identify disease mechanism and potential therapeutic targets. However, deep proteomics with ultratrace clinical specimen remains a major technical challenge due to extensive contact loss during complex sample pretreatment. Here, a hybrid of four boronic acid-rich lanthanide metal-organic frameworks (MOFs) with high protein affinity is introduced to capture proteins in ultratrace samples jointly by nitrogen-boronate complexation, cation-π and ionic interactions. A MOFs Aided Sample Preparation (MASP) workflow that shrinks sample volume and integrates lysis, protein capture, protein digestion and peptide collection steps into a single PCR tube to minimize sample loss caused by non-specific absorption, is proposed further. MASP is validated to quantify ≈1800 proteins in 10 HEK-293T cells. MASP is applied to profile cerebrospinal fluid (CSF) proteome from cerebral stroke and brain damaged patients, and identified ≈3700 proteins in 1 µL CSF. MASP is further demonstrated to detect ≈9600 proteins in as few as 50 µg mouse brain tissues. MASP thus enables deep, scalable, and reproducible proteome on precious clinical samples with low abundant proteins.

Proteomics profiling of extracellular vesicle for identification of potential biomarkers in Alzheimer's disease: A comprehensive review

The intricate origins and diverse symptoms of Alzheimer's disease (AD) pose significant challenges for both diagnosis and treatment. Exosomes and microvesicles, which carry disease-specific cargo from a variety of central nervous system cell types, have emerged as promising reservoirs of biomarkers for AD. Research on the screening of possible biomarkers in Alzheimer's disease using proteomic profiling of EVs is systematically reviewed in this comprehensive review. We highlight key methodologies employed in EV isolation, characterization, and proteomic analysis, elucidating their advantages and limitations. Furthermore, we summarize the evolving landscape of EV-associated biomarkers implicated in AD pathogenesis, including proteins involved in amyloid-beta metabolism, tau phosphorylation, neuroinflammation, synaptic dysfunction, and neuronal injury. The literature review highlights the necessity for robust validation strategies and standardized protocols to effectively transition EV-based biomarkers into clinical use. In the concluding section, this review delves into potential future avenues and technological advancements pivotal in crafting EV-derived biomarkers applicable to AD diagnostics and prognostics. This review contributes to our comprehension of AD pathology and the advancement of precision medicine in neurodegenerative diseases, hinting at a promising era in AD precision medicine.

Proteomic profile and predictive markers of outcome in patients with subarachnoid hemorrhage

BACKGROUND

The molecular mechanisms underlying development of posthemorrhagic hydrocephalus (PHH) following subarachnoid hemorrhage (SAH) remain incompletely understood. Consequently, treatment strategies tailored towards the individual patient remain limited. This study aimed to identify proteomic cerebrospinal fluid (CSF) biomarkers capable of predicting shunt dependency and functional outcome in patients with SAH in order to improve informed clinical decision making.

METHODS

Ventricular CSF samples were collected twice from 23 patients with SAH who required external ventricular drain (EVD) insertion (12 patients with successful EVD weaning, 11 patients in need of permanent CSF shunting due to development of PHH). The paired CSF samples were collected acutely after ictus and later upon EVD removal. Cisternal CSF samples were collected from 10 healthy control subjects undergoing vascular clipping of an unruptured aneurysm. All CSF samples were subjected to mass spectrometry-based proteomics analysis. Proteomic biomarkers were quantified using area under the curve (AUC) estimates from a receiver operating curve (ROC).

RESULTS

CSF from patients with SAH displayed a distinct proteomic profile in comparison to that of healthy control subjects. The CSF collected acutely after ictus from patients with SAH was moreover distinct from that collected weeks later but appeared similar in the weaned and shunted patient groups. Sixteen unique proteins were identified as potential predictors of shunt dependency, while three proteins were identified as potential predictors of functional outcome assessed six months after ictus with the modified Rankin Scale.

CONCLUSIONS

We here identified several potential proteomic biomarkers in CSF from patients with SAH capable of predicting (i) shunt dependency and thus development of PHH and (ii) the functional outcome assessed six months after ictus. These proteomic biomarkers may have the potential to aid clinical decision making by predicting shunt dependency and functional outcome following SAH.

Modeling the Simultaneous Dynamics of Proteins in Blood Plasma and the Cerebrospinal Fluid in Human In Vivo

The analysis of protein dynamics or turnover in patients has the potential to reveal altered protein recycling, such as in Alzheimer's disease, and to provide informative data regarding drug efficacy or certain biological processes. The observed protein dynamics in a solid tissue or a fluid is the net result of not only protein synthesis and degradation but also transport across biological compartments. We report an accurate 3-biological compartment model able to simultaneously account for the protein dynamics observed in blood plasma and the cerebrospinal fluid (CSF) including a hidden central nervous system (CNS) compartment. We successfully applied this model to 69 proteins of a single individual displaying similar or very different dynamics in plasma and CSF. This study puts a strong emphasis on the methods and tools needed to develop this type of model. We believe that it will be useful to any researcher dealing with protein dynamics data modeling.

Trigeminal ganglion neurons are directly activated by influx of CSF solutes in a migraine model

Classical migraine patients experience aura, which is transient neurological deficits associated with cortical spreading depression (CSD), preceding headache attacks. It is not currently understood how a pathological event in cortex can affect peripheral sensory neurons. In this study, we show that cerebrospinal fluid (CSF) flows into the trigeminal ganglion, establishing nonsynaptic signaling between brain and trigeminal cells. After CSD, ~11% of the CSF proteome is altered, with up-regulation of proteins that directly activate receptors in the trigeminal ganglion. CSF collected from animals exposed to CSD activates trigeminal neurons in naïve mice in part by CSF-borne calcitonin gene-related peptide (CGRP). We identify a communication pathway between the central and peripheral nervous system that might explain the relationship between migrainous aura and headache.

Effect of dynamic exclusion and the use of FAIMS, DIA and MALDI-mass spectrometry imaging with ion mobility on amyloid protein identification

Amyloidosis is a disease characterized by local and systemic extracellular deposition of amyloid protein fibrils where its excessive accumulation in tissues and resistance to degradation can lead to organ failure. Diagnosis is challenging because of approximately 36 different amyloid protein subtypes. Imaging methods like immunohistochemistry and the use of Congo red staining of amyloid proteins for laser capture microdissection combined with liquid chromatography tandem mass spectrometry (LMD/LC-MS/MS) are two diagnostic methods currently used depending on the expertise of the pathology laboratory. Here, we demonstrate a streamlined in situ amyloid peptide spatial mapping by Matrix Assisted Laser Desorption Ionization-Mass Spectrometry Imaging (MALDI-MSI) combined with Trapped Ion Mobility Spectrometry for potential transthyretin (ATTR) amyloidosis subtyping. While we utilized the standard LMD/LC-MS/MS workflow for amyloid subtyping of 31 specimens from different organs, we also evaluated the potential introduction in the MS workflow variations in data acquisition parameters like dynamic exclusion, or testing Data Dependent Acquisition combined with High-Field Asymmetric Waveform Ion Mobility Spectrometry (DDA FAIMS) versus Data Independent Acquisition (DIA) for enhanced amyloid protein identification at shorter acquisition times. We also demonstrate the use of Mascot's Error Tolerant Search and PEAKS de novo sequencing for the sequence variant analysis of amyloidosis specimens.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Evaluating the associations between intelligence quotient and multi-tissue proteome from the brain, CSF and plasma

Intelligence quotient is a vital index to evaluate the ability of an individual to think rationally, learn from experience and deal with the environment effectively. However, limited efforts have been paid to explore the potential associations of intelligence quotient traits with the tissue proteins from the brain, CSF and plasma. The information of protein quantitative trait loci was collected from a recently released genome-wide association study conducted on quantification data of proteins from the tissues including the brain, CSF and plasma. Using the individual-level genotypic data from the UK Biobank cohort, we calculated the polygenic risk scores for each protein based on the protein quantitative trait locus data sets above. Then, Pearson correlation analysis was applied to evaluate the relationships between intelligence quotient traits (including 120 330 subjects for 'fluid intelligence score' and 38 949 subjects for 'maximum digits remembered correctly') and polygenic risk scores of each protein in the brain (17 protein polygenic risk scores), CSF (116 protein polygenic risk scores) and plasma (59 protein polygenic risk scores). The Bonferroni corrected P-value threshold was P < 1.30 × 10-4 (0.05/384). Finally, Mendelian randomization analysis was conducted to test the causal relationships between 'fluid intelligence score' and pre-specific proteins from correlation analysis results. Pearson correlation analysis identified significant association signals between the protein of macrophage-stimulating protein and fluid intelligence in brain and CSF tissues (P brain = 1.21 × 10-8, P CSF = 1.10 × 10-7), as well as between B-cell lymphoma 6 protein and fluid intelligence in CSF (P CSF = 1.23 × 10-4). Other proteins showed close-to-significant associations with the trait of 'fluid intelligence score', such as plasma protease C1 inhibitor (P CSF = 4.19 × 10-4, P plasma = 6.97 × 10-4), and with the trait of 'maximum digits remembered correctly', such as tenascin (P plasma = 3.42 × 10-4). Additionally, Mendelian randomization analysis results suggested that macrophage-stimulating protein (Mendelian randomization-Egger: β = 0.54, P = 1.64 × 10-61 in the brain; β = 0.09, P = 1.60 × 10-12 in CSF) had causal effects on fluid intelligence score. We observed functional relevance of specific tissue proteins to intelligence quotient and identified several candidate proteins, such as macrophage-stimulating protein. This study provided a novel insight to the relationship between tissue proteins and intelligence quotient traits.

Proteomic analysis of Alzheimer's disease cerebrospinal fluid reveals alterations associated with APOE ε4 and atomoxetine treatment

Alzheimer's disease (AD) is currently defined by the aggregation of amyloid-β (Aβ) and tau proteins in the brain. Although biofluid biomarkers are available to measure Aβ and tau pathology, few markers are available to measure the complex pathophysiology that is associated with these two cardinal neuropathologies. Here, we characterized the proteomic landscape of cerebrospinal fluid (CSF) changes associated with Aβ and tau pathology in 300 individuals using two different proteomic technologies-tandem mass tag mass spectrometry and SomaScan. Integration of both data types allowed for generation of a robust protein coexpression network consisting of 34 modules derived from 5242 protein measurements, including disease-relevant modules associated with autophagy, ubiquitination, endocytosis, and glycolysis. Three modules strongly associated with the apolipoprotein E ε4 (APOE ε4) AD risk genotype mapped to oxidant detoxification, mitogen-associated protein kinase signaling, neddylation, and mitochondrial biology and overlapped with a previously described lipoprotein module in serum. Alterations of all three modules in blood were associated with dementia more than 20 years before diagnosis. Analysis of CSF samples from an AD phase 2 clinical trial of atomoxetine (ATX) demonstrated that abnormal elevations in the glycolysis CSF module-the network module most strongly correlated to cognitive function-were reduced by ATX treatment. Clustering of individuals based on their CSF proteomic profiles revealed heterogeneity of pathological changes not fully reflected by Aβ and tau.