Multiplex Panel Detects Glial and Inflammatory Biomarker Signatures in Sporadic and C9orf72-ALS

Circulating protein profiling identifies prognostic biomarkers in amyotrophic lateral Sclerosis

A proteomic signature for prognostic stratification in amyotrophic lateral sclerosis

Proteomic profiling of baseline CSF and serum from HDClarity identifies signatures for Huntington disease staging and stratification

Background Sensitive biomarkers that objectively stage Huntington disease (HD) are needed to improve participant stratification and facilitate the enrichment of clinical trials with biologically and clinically homogeneous populations. The HDClarity study, an international longitudinal biofluid collection initiative for HD, provides a unique resource for large-scale proteomic profiling of matched CSF and serum samples spanning the disease spectrum. Here, we leveraged baseline proteomic data from HDClarity to characterize protein signatures associated with HD stage and clinical severity, compare measurements across analytical platforms and biofluid compartments, and identify candidate multi-protein panels for disease staging. Methods Baseline proteomic data generated using Olink Explore (∼3,000 proteins) and SomaScan v4.1 (∼7,000 proteins) were analyzed in matched CSF and serum samples from 315 HD gene-expansion carriers and 92 non-HD controls. A total of 2,119 proteins overlapped between Olink and SomaScan, enabling assessment of cross-platform concordance, while CSF-serum relationships were evaluated using all available protein measurements within each assay. Covariate-adjusted linear regression models were used to assess disease stage-associated differences in protein abundance, while partial correlation analyses evaluated relationships between protein abundance, clinical severity in HD gene-expansion carriers, and estimated years to disease onset in premanifest participants. A nested machine-learning pipeline incorporating univariate feature ranking, penalized regression-based feature selection, and repeated cross- validation was used to derive compact multi-protein classifiers for HD staging. Results Cross-platform and CSF-serum correlations were highly protein-dependent, with some analytes showing strong concordance and others exhibiting weak or inverse relationships. These findings highlight substantial heterogeneity in biomarker behaviour across analytical platforms and biofluids. Adjusted models identified both known HD-associated markers (NEFL, GFAP, CHI3L1) and less well-characterized proteins in CSF and serum whose baseline abundance differed across HD-Integrated Staging System (HD-ISS) and clinical stages. Partial correlation analyses revealed additional candidate biomarkers associated with clinical severity and estimated time to disease onset. Machine-learning models derived compact CSF and serum protein panels that accurately classified participants across HD-ISS stages 0 and 1, as well as the transition from premanifest to early manifest disease. Conclusions This study provides the first large-scale orthogonal comparison of matched CSF and serum proteomes in HDClarity, establishing robust baseline proteomic signatures across the HD continuum. Our findings demonstrate the importance of considering both analytical platform and biofluid when interpreting protein biomarkers and identify compact protein panels with potential utility for objective disease staging, patient stratification, and clinical trial enrichment in HD. Trial Registration Not applicable. One Sentence Summary Caron et al. analyzed matched baseline CSF and serum proteomic data from the HDClarity study generated using two orthogonal proteomic platforms, identifying reproducible multi-protein panels capable of staging and stratifying Huntington disease.

Serum SERPINA3 as a Candidate Non-Invasive Biomarker for Neuromyelitis Optica Spectrum Disorder: Proteomic Discovery and Same-Center Validation

SERPINA3 is therefore an exploratory adjunctive serum biomarker candidate rather than a stand-alone diagnostic test; prospective external validation in clinically representative cohorts is required.

Cross-disease LC-MS/MS plasma proteomics identifies reproducible shared and disease-enriched biomarker signatures in neurodegenerative disorders.

Neurodegenerative diseases (NDDs) exhibit considerable molecular heterogeneity, making it difficult to pinpoint robust, disease-specific biomarkers. Although proteomic studies have deepened our understanding of individual disorders, systematic cross-disease comparisons with cross-platform validation remain scarce, especially for rare conditions like spinal and bulbar muscular atrophy (SBMA). To address this gap, we conducted a comparative plasma proteomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in 264 participants across major neurodegenerative and related diagnostic groups, including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), SBMA, and cognitively healthy controls. This unified framework allowed us to capture both disease-specific and shared protein signatures across neurodegenerative conditions. Candidate proteins were then validated in the UK Biobank (Olink Explore) and the Global Neurodegeneration Proteomics Consortium (SomaScan). Of 23 proteins assessed in the UK Biobank, four unique proteins (yielding six disease-protein associations) showed nominally significant and directionally concordant changes; of 20 proteins represented by 27 probes tested in the Global Neurodegeneration Proteomics Consortium, seven proteins reached nominal significance, all with full directional concordance across both cohorts. Notably, IGFBP2 was consistently elevated in AD and PD across independent datasets, pointing to shared metabolic dysregulation, while ADIPOQ showed parallel increases in the same conditions, reinforcing convergent shifts in energy metabolism. By contrast, CRTAC1 and COMP were selectively reduced in motor neuron diseases, suggesting disease-enriched alterations in extracellular matrix composition. Taken together, our findings provide a cross-disease, cross-platform framework for uncovering reproducible proteomic biomarkers and shed light on both overlapping and distinct molecular pathways in neurodegeneration.

Integrated multiomics profiling reveals MS-specific molecular signatures linked to progression and response to nasal anti-CD3 (Foralumab) therapy 2267524

Chronic neuroinflammation in multiple sclerosis (MS) can persist independently of relapse activity, revealing mechanisms of progression not addressed by current therapies. Dysregulated T-cell activity plays a central role in this process and is the main target of nasal Foralumab immunotherapy. Here, we aimed to identify CNS-related biomarkers in the CSF of MS patients linked to disease progression and to define the molecular effects of Foralumab in secondary progressive (SP) MS. We integrated single-cell and proteomic data from PBMC (n = 4) and CSF (n = 23) of MS patients on anti-CD20 therapy or untreated. Data-independent acquisition (DAI) proteomics was performed in CSF of relapsing-remitting (RR) and SP patients. CSF proteomic profiling revealed distinct molecular signatures between RR and SP patients. RR samples showed higher levels of immune activation markers (PTPRC, RGS10) and proteins associated with neuronal plasticity and axonal injury (NPTX2, NETO1). In contrast, SP samples were enriched for fibroblast-related proteins involved in tissue injury, fibrosis, and extracellular matrix (ECM) remodeling (FAP, COL8A1), along with elevated cytotoxicity markers (CTSW) and components of the complement cascade. Foralumab treatment significantly reduced the expression of proteins linked to cytotoxic activity (LAMP1), interferon signaling (IFNAR1), tissue injury/fibrosis (COBA1), and NF-κB—dependent inflammation (SIGLEC14, LY86). Notably, increased TGFB1 expression was observed in both CSF and PBMCs, suggesting a regulatory and immunomodulatory effect of the therapy. We identified accessible biomarkers distinguishing RR and SP patients and proteins linked to disease progression and found that nasal Foralumab ameliorates MS by increasing TGFB1 and suppressing T cell cytotoxicity and inflammation. n/a Computational and Systems Immunology (COMP)