ArticleAnnals of clinical and translational neurology2023
Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression.
Article in Annals of clinical and translational neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 21 citations in OpenAlex.
- Chitinases in Tear Fluid of Patients With Amyotrophic Lateral Sclerosis.European journal of neurology · 2026Article
- Proteomic profile of CSF obtained at the time of diagnosis determines amyotrophic lateral sclerosis progression and survival: CXCL7 levels in disease prognosis and survival.Brain pathology (Zurich, Switzerland) · 2026Article
- The Target ALS Global Natural History Study: Cross-platform proteomics to accelerate biofluid biomarker and drug target discovery in amyotrophic lateral sclerosis.medRxiv : the preprint server for health sciences · 2026Article
- The FindMNDBiomarker Program: Protein Changes in Motor Neuron Disease/Amyotrophic Lateral Sclerosis Postmortem Tissue and Biofluids.Annals of neurology · 2025Article
- Identification and validation of a tear fluid-derived protein biomarker signature in patients with amyotrophic lateral sclerosis.Acta neuropathologica communications · 2025Article
- Extracellular vesicles in TDP-43 proteinopathies: pathogenesis and biomarker potential.Molecular neurodegeneration · 2025Review
- Network analysis of the cerebrospinal fluid proteome reveals shared and unique differences between sporadic and familial forms of amyotrophic lateral sclerosis.Molecular neurodegeneration · 2025Article
- Recent Advances in Human Cerebrospinal Fluid Proteomics.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Unveiling the double-edged sword: SOD1 trimers possess tissue-selective toxicity and bind septin-7 in motor neuron-like cells.Structure (London, England : 1993) · 2024Article
- Shotgun Proteomics Links Proteoglycan-4Biomolecules · 2024Article
- State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia.Leukemia · 2024Article
- Review
- Quantitative proteomics and applications in covalent ligand discovery.Frontiers in chemical biology · 2024Article
- The complement system in neurodegenerative and inflammatory diseases of the central nervous system.Frontiers in neurology · 2024Review
- State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia.bioRxiv : the preprint server for biology · 2023Article
- Order from chaos: Using CSF proteomics to predict ALS progression.Annals of clinical and translational neurology · 2023Article
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Authors and funding
14 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveAmyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response.
methodsWe utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP.
resultsWe identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate.
interpretationWe identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.
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