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Related Experiment Video

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Comparative evaluation of analytical methods for CSF proteomics.

Aastha Aastha1,2, Leonardo Jose Monteiro De Macedo Filho3, Michael Woolman2

  • 1Department of Medical Biophysics, University of Toronto, Toronto, Canada.

Clinical Proteomics
|November 29, 2025
PubMed
Summary

Choosing the right cerebrospinal fluid (CSF) proteomics workflow is crucial for brain pathology research. This study benchmarks five methods, revealing that no single approach fits all needs, emphasizing workflow selection based on specific research goals.

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Area of Science:

  • Proteomics
  • Biomarker Discovery
  • Neuro-oncology

Background:

  • Cerebrospinal fluid (CSF) is vital for understanding brain pathology.
  • Challenges in CSF proteomics include sample complexity and workflow optimization.
  • Existing laboratory workflows for CSF proteomics have distinct advantages.

Purpose of the Study:

  • To benchmark five orthogonal sample-preparation strategies for CSF proteomics.
  • To determine the most suitable workflow for CSF analysis in neuro-oncology.
  • To provide a comparative framework for selecting CSF proteomics methods.

Main Methods:

  • Benchmarking of five CSF sample-preparation strategies: MStern, Proteograph™ nanoparticle enrichment (Seer), N-glycopeptide capture (N-Gp), and two extracellular-vesicle (EV) fractions (P20-EV, P150-EV).
  • Analysis of CSF from 19 patients with central nervous system lymphoma using 82 LC-MS/MS experiments.
  • Evaluation of proteomic depth, bias, and biological niche enrichment for each method.

Main Results:

  • Over 38,000 unique peptides and 3000 proteins were detected across all methods.
  • Seer yielded the highest proteomic depth (~17,000 peptides), followed by P20-EV (~9,000).
  • Each method highlighted distinct biological niches: P20-EVs (mitochondrial), N-Gp (lysosomal/plasma membrane), and Seer (nuclear).

Conclusions:

  • No single CSF proteomics workflow is universally optimal; selection depends on sample volume, cost, and research question.
  • The comparative framework aids investigators in matching CSF proteomics strategies to neuro-oncological objectives.
  • Optimized workflow selection can accelerate the translation of CSF biomarkers into clinical assays.