Parkinson's disease (PD) is a debilitating condition that affects millions of people worldwide, yet there are currently no reliable biomarkers for its diagnosis. Alpha-synuclein aggregation is a well-known hallmark of PD pathology, but the behavior and kinetics of these aggregates are poorly understood. To address this gap in knowledge, this study utilized several approaches to evaluate the potential of alpha-synuclein aggregates as potential biomarker for PD. Firstly, the aggregation behavior of alpha-synuclein (aSyn) oligomers and fibrils was evaluated using a Real-time quaking induced conversion (RT-QuIC) assay. This assay differentiated PD from healthy samples in human skin, brain homogenates, and cerebrospinal fluid (CSF) samples. Secondly, a symmetric ECL assay was used to quantify alpha-synuclein oligomers and fibrils. Finally, direct stochastic optical reconstruction microscopy (dSTORM) was utilized to visualize the size and shape of recombinant alpha-synuclein fibrils and RT-QuIC end products (fibrils) at high resolution. The results demonstrated that fast-aggregating samples had higher concentrations of aSyn oligomers in skin and other samples compared to slow-aggregating samples. Interestingly, human skin as a biological matrix showed a better and significant differentiation of healthy and PD samples, compared to brain or CSF indicating skin as a promising specimen for detecting alpha-synuclein aggregation. Preliminary analysis of the average length and width of alpha-synuclein aggregates showed promise, and further experiments with a larger sample size are required to prove whether different species of alpha-synuclein fibrils/oligomers exist in healthy and disease samples. These results provide a foundation for the continued investigation of alpha-synuclein aggregation as a potential biomarker for PD.

Background/Objectives: Neurodegenerative diseases are a major cause of morbidity and mortality worldwide, and their public health burden continues to increase. There is an urgent need to develop reliable and sensitive biomarkers to aid the timely diagnosis, disease progression monitoring, and therapeutic development for neurodegenerative disorders. Proteomic screening strategies, including antibody microarrays, are a powerful tool for biomarker discovery, but their findings should be confirmed using quantitative assays. The current study explored the feasibility of combining an exploratory proteomic strategy and confirmatory ligand-binding assays to screen for and validate biomarker candidates for neurodegenerative disorders. Methods: It analyzed cerebrospinal fluid (CSF) and plasma samples from patients with Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis and healthy controls using an exploratory antibody microarray and validatory ligand-binding assays. Results: The screening antibody microarray identified differentially expressed proteins between patients with neurodegenerative diseases and healthy controls, including cluster of differentiation 14 (CD14), osteopontin, and vascular endothelial growth factor 165b. Quantitative ligand-binding assays confirmed that CD14 levels were elevated in CSF of patients with Alzheimer’s disease (p = 0.0177), whereas osteopontin levels were increased in CSF of patients with Parkinson’s disease (p = 0.0346). Conclusions: The current study demonstrated the potential utility of combining an exploratory proteomic approach and quantitative ligand-binding assays to identify biomarker candidates for neurodegenerative disorders. To further validate and expand these findings, large-scale analyses using well-characterized samples should be conducted.

Introduction: Elezanumab (EZB), a monoclonal antibody specific to repulsive guidance molecule A (RGMa), is under investigation to treat acute ischemic stroke. Biomarker evidence in ischemic stroke and for the RGMa pathway is limited. Methods: This blinded, in-study analysis from a 52-week phase 2a, multicenter, randomized (1:1; EZB:placebo) clinical trial (NCT04309474) included patients aged 30-90 years with an acute ischemic stroke (onset ≤ 24 hours before baseline), and a NIHSS score of 7-21. Blinded analyses did not distinguish between treatment arms. Healthy adult controls were procured outside this trial. Blood samples were taken post-stroke across 52 weeks, or 1 time (baseline) for healthy adults. Plasma concentrations of neurodegenerative markers neurofilament light chain protein (NfL) and glial fibrillary acidic protein (GFAP) were measured (validated Simoa Human Neurology 4-Plex A assay; Quanterix). Protein, mRNA, and miRNA were assessed with omics approaches. Results: This preliminary analysis included 34 patients with stroke (mean [SD] age 67.1 [12.3] years, 58.8% male) and 31 healthy adults (61.8 [8.0] years, 71.0% male). The mean (SD) baseline NIHSS total score was 10.9 (4.3). NfL and GFAP concentrations were elevated on day 1 in patients with stroke vs healthy adults (healthy adults are included for reference only). Mean concentrations of NfL were 48.7 pg/mL vs 13.9 pg/mL, respectively; mean concentrations of GFAP were 746.0 pg/mL vs 137.2 pg/mL, respectively. In patients with stroke, agnostic to treatment, peak NfL and GFAP elevations occurred at day 28 and days 2-4, respectively; NfL and GFAP concentrations were lower at week 52 than day 1. Conclusions: Preliminary blinded analysis of plasma markers of neurodegeneration confirm increased NfL and GFAP levels during acute stroke, which diminish over time with different temporal patterns. Final unblinded results will provide a robust natural time course of specific biomarkers of neurodegeneration including GAP43. Forthcoming analyses evaluate broad panels of proteins, mRNA, and miRNA markers. Identifying relevant post stroke biomarkers with directed and exploratory omics may allow development of validated assays to assess efficacy and support decision making in clinical trials.

Neurodegeneration is a complex area involving multiple pathways and often has a long prodromal phase. Reliable biomarkers are needed to allow earlier diagnosis, monitoring of the disease progression, and studying the impact of therapeutic approaches. Therefore, a screening was performed by Sciomics GmbH to identify potential protein biomarkers. The most promising analytes were selected, including cluster of differentiation 14 (CD14), osteopontin (OPN), vascular endothelial growth factor A (VEGF-A), cancer antigen 15-3 (CA15-3) and carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1). Using quantitative ligand-binding assays based on fluorescence or electrochemiluminescence these analytes were investigated regarding up- or downregulation in the presence of Alzheimer´s disease (AD), Parkinson´s disease (PD), Multiple Sclerosis (MS) or Amyotrophic Lateral Sclerosis (ALS) compared to healthy patients. The quantitative assays confirmed the screening in that CD14 levels were elevated in cerebrospinal fluid (CSF) of AD patients and that OPN levels were increased in CSF of PD patients. In addition, the following significant changes were observed: OPN was elevated in plasma of PD and MS patients, while it was decreased in CSF of MS patients. VEGF-A levels were increased in the plasma of ALS patients and decreased in CSF of MS patients. Increased CD14 levels in AD CSF are in agreement with studies suggesting that CD14, a co-receptor of toll like receptor 4 (TLR4) expressed on the surface of e.g. microglia, might mediate the interaction of fibrillar Aβ to TLR4 and thus induce inflammation. OPN has already been shown to be increased in CSF and blood of PD patients previously and it is suggested to be involved in PD pathogenesis due to its role in oxidative stress, apoptosis, mitochondrial dysfunction, and cytokine regulation. Other studies have shown increased levels of OPN in plasma of MS patients potentially due to its expression in activated T-cells. However, some studies also show increased MS CSF levels in contrast to our findings, where OPN levels are lower in MS CSF. Literature data for VEGF-A is more conflicting possibly due to different assays detecting different isoforms. To further solidify these findings, large-scale analyses using well characterized samples should be done in the future.

Alzheimer’s disease (AD) patients show sustained levels of inflammation in the brain and the peripheral immune system. It is not known how various peripheral blood mononuclear cells (PBMCs) differ in AD patients and whether those differences can act as biomarkers of AD. Here we performed a multi-omic profiling of PBMCs from AD patients and compared the composition of their cell type and cell state as well as their gene and protein expression to normal controls. Single-cell proteogenomics analysis was performed on PBMCs from 20 AD patients and 15 controls using Singleron Biotechnologies’ ESCAPE platform. Seven of the AD and four control samples were additionally analyzed for bulk protein expression using Sciomics’ scioDiscover platform. Bulk proteomics identified 100 proteins with a significant differential abundance between AD patients and controls. Data point to a higher platelet activation and degranulation, as well as changes in the EGFR / MAPK3 and VEGF signaling in AD. As an individual marker CD163 was identified at a higher abundance in AD PBMCs pointing to an increase in monocyte / macrophage activity. From the single-cell analysis we found that AD patients had significantly more CD14+ monocytes. We further found that the CD14+ monocytes could be split into seven clusters based on their gene expression with only two clusters having a significantly higher number of cells in AD patients. One of the overrepresented clusters showed high expression of Alarmin genes, suggesting an increased inflammatory environment, while the other cluster showed a higher level of HLA expression suggesting a state primed for activation. We found significant changes in both gene and protein expression in PBCMs from AD patients that indicate an increased inflammatory state . While there was a good overlap of findings between gene and protein expression, some of the changes are only seen at the protein level, while others are only observed at the level of gene expression. The combination of the two measurement techniques provides us with additional insights into inflammatory nature of the peripheral immune system in AD patients and provide hints at the mechanisms different cells use to generate those inflammatory signals.

Tau protein is a key target of interest in developing therapeutics for neurodegenerative diseases. Here, we sought to develop a method that quantifies extracellular tau protein concentrations in human cerebrospinal fluid (CSF) without antibody-based enrichment strategies. We demonstrate that the fit-for-purpose validated method in Alzheimer’s Disease CSF is limited to quasi quantitative measures of tau surrogate peptides. We also provide evidence that CSF total Tau measures by LC-MS are feasible in the presence of monoclonal therapeutic antibodies in human CSF. Our Tau LC-MS/MS method is a translational bioanalytical tool for assaying target engagement and pharmacodynamics for anti-tau antibody drug development campaigns.

Intraneuronal insoluble inclusions made of Tau protein are neuropathological hallmarks of Alzheimer Disease (AD). Cleavage of Tau by legumain (LGMN) has been proposed to be crucial for aggregation of Tau into fibrils. However, it remains unclear if LGMN-cleaved Tau fragments accumulate in AD Tau inclusions. Using an in vitro enzymatic assay and non-targeted mass spectrometry, we identified four putative LGMN cleavage sites at Tau residues N167-, N255-, N296- and N368. Cleavage at N368 generates variously sized N368-Tau fragments that are aggregation prone in the Thioflavin T assay in vitro. N368-cleaved Tau is not detected in the brain of legumain knockout mice, indicating that LGMN is required for Tau cleavage in the mouse brain in vivo. Using a targeted mass spectrometry method in combination with tissue fractionation and biochemical analysis, we investigated whether N368-cleaved Tau is differentially produced and aggregated in brain of AD patients and control subjects. In brain soluble extracts, despite reduced uncleaved Tau in AD, levels of N368-cleaved Tau are comparable in AD and control hippocampus, suggesting that LGMN-mediated cleavage of Tau is not altered in AD. Consistently, levels of activated, cleaved LGMN are also similar in AD and control brain extracts. To assess the potential accumulation of N368-cleaved Tau in insoluble Tau aggregates, we analyzed sarkosyl-insoluble extracts from AD and control hippocampus. Both N368-cleaved Tau and uncleaved Tau were significantly increased in AD as a consequence of pathological Tau inclusions accumulation. However, the amount of N368-cleaved Tau represented only a very minor component (< 0.1%) of insoluble Tau. Our data indicate that LGMN physiologically cleaves Tau in the mouse and human brain generating N368-cleaved Tau fragments, which remain largely soluble and are present only in low proportion in Tau insoluble aggregates compared to uncleaved Tau. This suggests that LGMN-cleaved Tau has limited role in the progressive accumulation of Tau inclusions in AD.

BACKGROUNDMetabolite identification studies are very resource intensive and also are rarely performed in early discovery. Here, we report the validation of an ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS) platform for generating high-throughput stability data with structure elucidation in a single injection.MATERIALS & METHODSTandem mass spectrometry spectra were obtained for quantitative analysis using a generic information-dependent acquisition method from pooled microsomal samples incubated at low compound concentrations.RESULTSA good correlation was observed between clearance determined using UPLC-HRMS and UPLC-triple-quadrupole analysis. Structural elucidation performed with MassMetaSite™ (Molecular Discovery, Perugia, Italy) software identified 85% of the major metabolites of eight marketed drugs and over 100 internal compounds under these conditions.CONCLUSIONFor the first time, a high-throughput quantitative-qualitative workflow was established using a cocktail approach for sample analysis with UPLC-HRMS in order to enable metabolite identification in early discovery projects.