Over the past few years, several fluid biomarker candidates have been proposed for frontotemporal dementia (FTD). We have previously identified CSF proteins that could separate individuals with genetic FTD from controls. However, it is unknown whether alterations in these CSF protein levels are associated with neurodegenerative processes. The aim of this study was to explore how these CSF biomarker candidates correlate with symptom severity as well as cortical and subcortical atrophy. The levels of fourteen proteins were measured in CSF from 202 individuals, 131 mutation carriers with mutations in C9orf72, GRN, or MAPT, and 71 controls, in a cross-sectional subset from the GENFI cohort. The association between the levels of these proteins and CDR plus NACC FTLD-NM sum-of-boxes, cortical thickness, and subcortical volumes were estimated in the mutation carriers. Elevated CSF levels of five out of fourteen proteins were associated with an increased CDR score in the mutation carriers. Additionally, elevated levels of three of these proteins, NEFM, PTPRN2 and SERPINA3, were associated with reduced cortical thickness and/or subcortical volume among all mutation carriers. Some mutation-specific associations were also observed, with SPP1 and CTSS being associated with CDR and atrophy only in MAPT mutation carriers, while NPTX2 was specific for GRN mutation carriers. As indicated by the association to brain atrophy, the proposed fluid biomarker candidates continue to show promise and additional studies will further elucidate their relationship to cortical atrophy in genetic FTD, and their potential as biomarkers for diagnosis, prognosis, and disease staging.

Background & Aims: Primary sclerosing cholangitis (PSC) is a rare cholestatic liver disease with heterogeneous phenotypes and progression. Autoimmune traits, such as the presence of autoantibodies, are suspected to drive its heterogeneity. Methods: We performed a proteome-scale autoantibody screen of IgG and IgA isotypes using >42,100 protein fragments. This was followed by a validation of 1,153 selected autoantibodies, in serum samples from 466 patients with PSC in a longitudinal setting using the SUPRIM cohort and 214 controls. Results: We identified autoantibodies associated with clinical phenotypes, biochemical and clinical severity, comorbidities, and disease progression (e.g. alkaline phosphatase and albumin level p <e-10, presence of hepatobiliary malignancies p <0.001, seroconversion before transplantation p <0.001). Rather than a single universal autoantibody marker, small patient subgroups were positive for various autoantibodies with variable specificity. Global analysis of autoantigen targets revealed an overrepresentation of proteins normally expressed in immune-privileged sites, including the brain, testis, and retina. When interrogating tissue-specific autoantigen co-expression linked to expression and splicing quantitative trait loci of PSC risk variants, the thyroid emerged as an additional relevant tissue. We also detected increased autoantibody diversity associated with PSC duration and end-stage disease, already observable several years before liver transplantation. Multiomics analysis across body compartments confirmed neuroendocrine dysregulation in PSC. Our results are provided as a resource for further studies. Conclusions: Overall, our data support the cryptic antigen and epitope-drifting autoimmune theories and indicate that neuroendocrine dysregulation may contribute to PSC pathogenesis. Impact and implications: From a proteome-scale profiling of the SUPRIM cohort, we provide a short list of autoantibodies associated with clinical phenotypes and progression, along with the peptide sequences used to capture them. We identify across multiple datasets neuroendocrine deregulations in primary sclerosing cholangitis and provide a short list of related key plasma proteins. These data and technical details should facilitate validation studies, investigations of related pathophysiological mechanisms and development of low-cost tools for diagnostic or prognostic purposes.

Background: Cognitive impairment is a recognised feature of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Despite advances in understanding cognitive impairment in ALS, no fluid biomarkers reliably predict these changes. Prior research in Alzheimer disease (AD) has demonstrated that CSF protein ratios enhance biomarker accuracy by mitigating inter-individual variability, improving diagnostic precision. In AD, ratios involving synaptic markers have shown stronger associations with cognitive outcomes than single proteins, motivating evaluation of a similar ratio-based approach in ALS. Methods: Building on findings from the AD field, we analysed 47 CSF proteins, suggested to be associated to neurodegeneration, in 66 patients with ALS and explored protein ratios to evaluate their utility in detecting cognitive impairment, hypothesising shared mechanisms between neurodegenerative diseases. Elastic net regression identified the most predictive protein pairs associated with cognitive impairment, assessed with the Edinburgh Cognitive and Behavioural ALS Screen (ECAS). Results: Elastic net identified seven single proteins (NEFM, NPTX2, GAP43, IGFBP4, IGFBP7, SPP1, CDH8) and eight protein pairs associated with ECAS total score. Ratios were generally more informative than individual proteins, with PTPRN2/GAP43 showing the strongest association with ECAS scores, indicating an enhanced ability to capture cognitive changes. Several of the proteins in the most predictive pairs have previously been implicated to associate to cognitive impairment in AD. Conclusion: Our findings indicate that protein ratios outperform single-protein analyses in detecting associations with cognitive impairment, aligning with advancements in AD research. By extending the concept of CSF protein ratios from AD to ALS, this study highlights shared pathological mechanisms and suggests that similar proteins are linked to cognitive dysfunction in both diseases.

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.

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.

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.

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.

The success of affinity proteomic methods is dependent on well-characterized affinity binders. Antibodies are the classic affinity binders, and the availability of antibodies is constantly increasing. However, their suitability for a given proteomic method is often context-dependent and requires evaluation of their binding selectivity. One powerful proteomic platform for characterizing the binding selectivity of antibodies is the planar antigen microarray. In this chapter, we describe our in-house developed procedures for generating high-density planar antigen microarrays with an antigen content of 42,100 unique protein fragments. We provide details regarding the assay protocol used for the assessment of the binding selectivity of polyclonal antibodies produced by the Human Protein Atlas.