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Synthetic plasma pool cohort correction for affinity-based proteomics datasets allows multiple study comparison.

Dries Heylen1,2, Murih Pusparum2,3, Jurgis Kuliesius4

  • 1Data Science Institute, Theory Lab, Hasselt University, 3590 Diepenbeek, Belgium.

Briefings in Bioinformatics
|December 18, 2024
PubMed
Summary
This summary is machine-generated.

Quantitative proteomics using OLINK Target 96 enables disease research. A new Synthetic Plasma Pool Cohort Correction (SPOC) method allows accurate, cost-efficient data comparison across studies without resending samples.

Keywords:
biomarkersnormalizationprotein quantificationproteomics

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Area of Science:

  • Proteomics
  • Genomics
  • Biotechnology

Background:

  • Quantitative proteomics links genomics to human diseases by analyzing protein levels.
  • OLINK's Target 96 is a prominent affinity-based protein measurement method used in large cohorts like SCALLOP.
  • Current methods for comparing OLINK Target 96 data across independent cohorts are logistically challenging and costly.

Purpose of the Study:

  • To develop a robust and cost-efficient method for accurate quantitative comparison of OLINK Target 96 protein data across independent studies.
  • To address the limitations of the 'biological bridging sample' approach in multi-cohort proteomics collaborations.

Main Methods:

  • Development of the Synthetic Plasma Pool Cohort Correction (SPOC) approach.
  • Utilizing an OLINK-composed synthetic plasma sample for normalization.
  • Implementation in a federated data-sharing context, demonstrated with a sepsis use case.

Main Results:

  • The SPOC correction method provides accurate and cost-efficient normalization for OLINK Target 96 data.
  • The approach simplifies multi-cohort data comparison, overcoming logistical hurdles.
  • Successful illustration of the method's utility in a sepsis research scenario.

Conclusions:

  • The SPOC correction offers a practical solution for inter-cohort comparison of proteomics data.
  • This method enhances collaboration and data sharing in large-scale proteomics studies.
  • SPOC correction facilitates more accessible and efficient proteomic data analysis across diverse research settings.