A large-scale cerebrospinal fluid (CSF) proteomics study has identified a panel of proteins that may improve diagnosis of multiple sclerosis (MS), particularly in patients lacking classical biomarkers. Using high-throughput mass spectrometry, researchers at the Max Planck Institute of Biochemistry uncovered protein signatures that distinguish MS from other inflammatory neurological diseases.
Diagnosing neurological diseases can be challenging because symptoms and biological signals often overlap. In MS, clinicians typically rely on magnetic resonance imaging together with CSF analysis, including detection of oligoclonal antibody bands. However, these markers are absent in roughly one in ten patients, making diagnosis slower and less certain.
“To go one step further, we combined the latest advances in mass spectrometry hardware, software, and sample preparation and adapted the workflow to cerebrospinal fluid,” said Matthias Mann, director at the Max Planck Institute of Biochemistry, in a press release.
Using a high-throughput data-independent acquisition workflow, the team measured approximately 1,500 proteins per CSF sample across a cohort of more than 5,000 individuals with diverse neurological disorders.
“The breakthrough was achieving both objectives simultaneously: analyzing thousands of proteins while studying thousands of patients across many neurological diseases,” said Jakob Bader, first author of the study.
To probe disease-specific signals, the researchers then applied an expanded version of the workflow capable of quantifying more than 2,000 proteins per sample. Machine-learning analysis identified a 22-protein panel that improved differentiation between MS and other inflammatory diseases of the central nervous system, particularly in diagnostically challenging cases lacking oligoclonal bands.
Several proteins in the panel reflect distinct aspects of MS pathology, including B-cell activity, axonal damage, and immune signaling. The researchers subsequently translated the discovery findings into a targeted mass spectrometry assay designed for routine laboratory workflows.
Beyond diagnosis, the study also suggests that proteomic patterns in CSF at the time of diagnosis may contain prognostic information. In the analysis, protein signatures were associated with long-term disability outcomes and the likelihood of progression from relapsing to progressive disease.
The researchers say the approach could extend beyond multiple sclerosis to other neurological conditions where reliable molecular markers remain scarce.
“Proteins control almost all biological processes in the body and have long been the most important group of diagnostic markers,” said Mann. “With the methodology established here, we can now analyze the CSF proteome across large patient cohorts, which may provide a powerful route to identifying new biomarkers.”
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