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Related Experiment Video

Updated: Sep 14, 2025

A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status
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Systematic evaluation of analytical methods for CSF proteomics.

Aastha Aastha1, Leonardo Jose Monteiro Macedo Filho2, Michael Woolman3

  • 1University of Toronto.

Research Square
|July 25, 2025
PubMed
Summary
This summary is machine-generated.

Comparing cerebrospinal fluid (CSF) proteomics workflows revealed no single best method. Workflow selection for CSF biomarker discovery depends on sample volume, cost, and specific research questions for neuro-oncology.

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Area of Science:

  • Proteomics
  • Neuroscience
  • Biomarker Discovery

Background:

  • Cerebrospinal fluid (CSF) analysis offers insights into brain pathology.
  • Challenges in CSF proteomics include sample complexity and the need for optimized workflows.
  • Existing laboratory workflows for CSF proteomics have distinct advantages and limitations.

Purpose of the Study:

  • To benchmark five orthogonal sample-preparation strategies for CSF proteomics.
  • To evaluate workflows based on input volume, hands-on time, and reagent cost.
  • To guide the selection of appropriate CSF proteomics strategies for translational applications.

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 Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS).
  • Evaluation of proteomic depth, detection consistency, and identification of biological signatures.

Main Results:

  • Over 38,000 unique peptides and 3000 proteins were detected across all methods.
  • Seer yielded the greatest 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.
  • Workflow selection should be tailored to specific research objectives, sample volume constraints, and budget.
  • This comparative framework aids in matching CSF proteomics strategies to neuro-oncological research goals, accelerating biomarker translation.