Accurate diagnosis and monitoring of neurodegenerative diseases require reliable biomarkers. Cerebrospinal fluid (CSF) proteins are promising candidates for reflecting brain pathology; however, their diagnostic utility may be compromised by natural variability between individuals, weakening their association with disease. Here, we measured the levels of 69 pre-selected proteins in cerebrospinal fluid using antibody-based suspension bead array technology in a multi-disease cohort of 499 individuals with neurodegenerative disorders including Alzheimer’s disease (AD), behavioral variant frontotemporal dementia, primary progressive aphasias, amyotrophic lateral sclerosis (ALS), corticobasal syndrome, primary supranuclear palsy, along with healthy controls. We identify significant inter-individual variability in overall CSF levels of brain-derived proteins, which could not be attributed to specific disease associations. Using linear modelling, we show that adjusting for median CSF levels of brain-derived proteins increases the diagnostic accuracy of proteins previously identified as altered in CSF in the context of neurodegenerative disorders. We further demonstrate a simplified approach for the adjustment using pairs of correlated proteins with opposite alteration in the diseases. With this approach, the proteins adjust for each other and further increase the biomarker performance through additive effect. When comparing the diseases, two proteins—neurofilament medium and myelin basic protein—showed increased levels in ALS compared to other diseases, and neurogranin showed a specific increase in AD. Several other proteins showed similar trends across the studied diseases, indicating that these proteins likely reflect shared processes related to neurodegeneration. Overall, our findings suggest that accounting for inter-individual variability is crucial in future studies to improve the identification and performance of relevant biomarkers. Importantly, we highlight the need for multi-disease studies to identify disease-specific biomarkers.

Background: The identification of responders to methotrexate (MTX) would optimize the therapy of patients with early rheumatoid arthritis (eRA). Our aim was to identify protein biomarkers for the prediction of the response to MTX.

Methods: We analysed patients with eRA ( N  = 135) from the Swedish Pharmacotherapy (SWEFOT) trial population (Trial registration number: NCT00764725). Baseline serum levels of 177 proteins with an inflammatory signature were profiled via 380 antibodies in a suspension bead array format. Protein levels were analysed for their associations with the achievement of a low 28-joint disease activity score (LDA = DAS28 ≤ 3.2) after 3 months of MTX therapy (primary outcome) or a good response according to the European Alliance of Associations for Rheumatology (EULAR) criteria (secondary outcome).

Results: Multivariable analysis revealed that the serum levels of two of the 177 proteins at baseline, matrix metalloproteinase 7 (MMP-7) and the alpha-chain of fibrinogen (FGA), were significantly different between patients who did and did not achieve LDA at 3 months. Among patients with low versus high levels of either MMP-7 or FGA, 60% versus 24% and 58% versus 22%, respectively, achieved LDA ( p  < 0.001). Among patients with low levels of both proteins, 79% achieved LDA at 3 months, whereas only 18% of those with high levels of both proteins achieved LDA at 3 months ( p  < 0.001). The results were similar when a secondary outcome was used.

Conclusions: Low levels of MMP-7 and FGA at baseline were associated with improved clinical outcomes in eRA patients. Validation of these results in another eRA cohort is now warranted, and if confirmed, it may facilitate clinical decision-making regarding whether to start with MTX in monotherapy or more potent alternatives.

Differential Scanning Fluorimetry (DSF) is a biophysical assay that is used to estimate protein stability in vitro. In a DSF experiment, the increased fluorescence of a solvatochromatic dye, such as Sypro Orange, is used to detect the unfolding of a protein during heating. However, Sypro Orange is only compatible with a minority of proteins (< 30 %), limiting the scope of this method. We recently reported that protein-adaptive DSF (paDSF) can partially solve this problem, wherein the protein is initially pre-screened against ∼300 chemically diverse dyes, termed the Aurora collection. While this approach significantly improves the number of targets amenable to DSF, it still fails to produce protein-dye pairs for some proteins. Here, we report the expansion of the dye collection to Aurora 2.0, which includes a total of 517 structurally diverse molecules and multiple new chemotypes. To assess performance, these dyes were screened against a panel of ∼100 proteins, which were selected, in part, to represent the most challenging targets (e.g. small size). From this effort, Aurora 2.0 achieved an impressive success rate of 94 %, including producing dyes for some targets that were not matched in the original collection. These findings support the idea that larger, more chemically diverse libraries improve the likelihood of detecting melting transitions across a wider range of proteins. We propose that Aurora 2.0 makes paDSF an increasingly powerful method for studying protein stability, ligand binding and other biophysical properties in high throughput.

Objective: Identification of those at high and low risk of disease relapse is a major unmet need in the management of patients with ANCA-associated vasculitis (AAV). Precise stratification would allow tailoring of immunosuppressive medication. We profiled the autoantibody repertoire of AAV patients in remission to identify novel autoantibodies associated with relapse risk. Methods: Plasma samples collected from 246 AAV patients in remission were screened for novel autoantibodies using in-house generated protein arrays including 42 000 protein fragments representing 18 000 unique human proteins. Patients were categorized based on the occurrence and frequency of relapses. We modelled the association between these antibodies and relapse occurrence using descriptive and high dimensional regression approaches. Results: We observed nine autoantibodies at higher frequency in samples from AAV patients experiencing multiple relapses compared with patients in long-term remission off therapy. LASSO analysis identified six autoantibodies that exhibited an association with relapse occurrence after sample collection. Antibodies targeting homeostatic iron regulator (HFE) and synaptotagmin 5 (SYT5) were identified as associated with relapse in both analyses. Conclusion: Through a broad protein array-based autoantibody screening, we identified two novel autoantibodies directed against HFE and SYT5 as candidate biomarkers of relapse in AAV.

Alzheimer's disease and related dementias have a multifactorial aetiology and heterogeneous biology. The current study aims to identify different biological signatures in a deeply phenotyped memory clinic patient population. In this cross-sectional study, we analysed 49 pre-specified proteins using a multiplex antibody-based suspension bead array in 278 CSF samples from the real-world research database and biobank at the Karolinska University Hospital Memory Clinic, Solna, Sweden. Patients with a clinical diagnosis of subjective cognitive decline (N = 151), mild cognitive impairment (N = 61), Alzheimer's disease (N = 47), or other diagnoses (N = 19; vascular dementias, alcohol-related dementia, unspecified dementias, or other amnesias) were included. Principal component analyses were performed, and resulting principal components (PCs) were tested for associations with clinical variables and Alzheimer's disease biomarkers (CSF biomarkers beta-amyloid 42, beta-amyloid 42/40, phosphorylated tau 181, phosphorylated tau 181/beta-amyloid 42). PC 1 (explaining 52% of the variance between patients) was associated with the clinical Alzheimer's disease CSF biomarkers beta-amyloid 42, phosphorylated tau 181, and total tau but not with Alzheimer's disease-related neurodegeneration imaging markers, cognitive performance, or clinical diagnosis. PC 2 (explaining 9% of the variance) displayed an inflammatory profile with high contributions of chitinase 3 like 1 (CHI3L1) and triggering receptor expressed on myeloid cells 2 (TREM2) and significant correlation to CSF free light chain kappa. In contrast to PC 1, PC 3 (explaining 5% of the variance) showed associations with all the clinical Alzheimer's disease CSF biomarkers, the imaging markers, cognitive impairment and clinical diagnosis. Serpin family A member 3 (SERPINA3), chitinase 1 (CHIT1), and neuronal pentraxin 2 (NPTX2) contributed most to PC 3. PC 4 (explaining 4% of the variance) exhibited an inflammatory profile distinct from PC 2, with the largest contributions from TREM2, leucine-rich alpha-2-glycoprotein 1 (LRG1) and complement C9. The component was associated with peripheral inflammation. We found that CSF protein profiles in a memory clinic cohort reflect molecular differences across diagnostic groups. Our results emphasize that real-world memory clinic patients can have different ongoing biological processes despite receiving the same diagnosis. In the future, this information could be utilized to identify patient endotypes and uncover precision biomarkers and novel therapeutic targets.

Disturbed cerebral autoregulation (represented by a positive pressure reactivity index [PRx]), elevated intracranial pressure (ICP), and decreased cerebral perfusion pressure (CPP) are key treatment targets following severe traumatic brain injury (sTBI). This study investigated neuroinflammation as a potential mechanism underlying these intracranial disturbances. Plasma samples from 11 sTBI patients (from a prior Phase II drug trial) were analyzed for 174 proteins using an antibody-based suspension bead array, with intervention effects accounted for where possible. Dimensionality reduction techniques, including principal component analysis (PCA) and supervised methods, were applied to protein data, informed by physiological variables (ICP, CPP, and PRx). PCA revealed distinct protein clustering patterns related to ICP >20 mmHg and PRx > 0, with PC1 linked to patient ID, time from injury, and intervention, and PC2/PC3 significantly associated with PRx dose (p < 0.001). Markers relating to inflammation of the vascular system comprised 20% of the top 50 proteins influencing PC2, implicating complement inflammation in these processes. Notably, MASP-2 (p = 0.027) and complement factor I (p = 0.039) were significantly associated with PRx dose in a mixed-effects model. These findings suggest that vascular inflammation, particularly complement activation, may contribute to intracranial physiological disturbances in sTBI, highlighting the complement pathway as a potential target for further investigation.

Malaria presents with varying degrees of severity. To improve clinical management and prevention, it is crucial to understand the pathogenesis and host response. We analyzed 1,463 plasma proteins during and after acute Plasmodium falciparum malaria in adult travelers and linked responses to peripheral immune cells by integrating with single-cell RNA sequencing (RNA-seq) data from a subset of donors. We identified extensive perturbations in over 250 proteins with diverse origins, including many not previously analyzed in malaria patients, such as hormones, circulating receptors, and intracellular or membrane-bound proteins from affected tissues. The protein profiles clustered participants according to disease severity, enabling the identification of a compressed 11-protein signature enriched in severe malaria. Conceptually, this study advances our understanding of malaria by linking systemic proteomic changes to immune cell communication and organ-specific responses. This resource, which includes an interactive platform to explore data, opens new avenues for hypothesis generation, biomarker discovery, and therapeutic target identification.

Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal. The Psych-STRATA consortium addresses this research gap through a seven-step approach. First, transdiagnostic biosignatures of SCZ, BD and MDD are identified by GWAS and multi-modal omics signatures associated with treatment outcome and TR (steps 1 and 2). In a next step (step 3), a randomized controlled intervention study is conducted to test the efficacy and safety of an early intensified pharmacological treatment. Following this RCT, a combined clinical and omics-based algorithm will be developed to estimate the risk for TR. This algorithm-based tool will be designed for early detection and management of TR (step 4). This algorithm will then be implemented into a framework of shared treatment decision-making with a novel mental health board (step 5). The final focus of the project is based on patient empowerment, dissemination and education (step 6) as well as the development of a software for fast, effective and individualized treatment decisions (step 7). The project has the potential to change the current trial and error treatment approach towards an evidence-based individualized treatment setting that takes TR risk into account at an early stage.