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Related Experiment Video

Updated: Jun 10, 2025

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Simple and robust high-throughput serum proteomics workflow with low-microflow LC-MS/MS.

Yoondam Seo1,2, Inseon Kang1, Hyeon-Jeong Lee1

  • 1Doping Control Center, Korea Institute of Science and Technology (KIST), Hwarang-Ro 14-Gil 5, Seongbuk-Gu, Seoul, 02792, Republic of Korea.

Analytical and Bioanalytical Chemistry
|October 18, 2024
PubMed
Summary

This study introduces a rapid, automated serum proteomics workflow for high-throughput biomarker discovery. The method achieves reproducible results with minimal sample preparation, aiding in the identification of proteins linked to chronic kidney disease.

Keywords:
Chronic kidney diseaseData-independent acquisitionHigh-throughputLow-microflow

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Area of Science:

  • Proteomics
  • Biomarker Discovery
  • Clinical Chemistry

Background:

  • Clinical proteomics advances protein identification and quantification in biofluids for biomarker discovery.
  • Serum proteomics requires improved throughput and reproducibility for large-scale clinical analysis.
  • High-throughput automated equipment is costly and has limited accessibility.

Purpose of the Study:

  • To develop a rapid, high-throughput, and cost-effective serum proteomics workflow.
  • To optimize a low-microflow LC-MS/MS method without automation.
  • To assess the method's reproducibility and stability for clinical sample analysis.

Main Methods:

  • A rapid, high-throughput, low-microflow LC-MS/MS workflow was developed, omitting depletion and desalting steps.
  • The method was applied to data-independent acquisition (DIA) analysis of 235 serum samples.
  • Quality control samples were used to assess reproducibility and stability of the 18-min DIA workflow.

Main Results:

  • The workflow consistently identified approximately 6000 peptides and 600 protein groups, including 33 FDA-approved biomarkers.
  • The 18-min DIA workflow demonstrated reproducibility and stability, even with 2 µL of serum.
  • Five dysregulated proteins were identified across various stages of chronic kidney disease in diabetes patients.

Conclusions:

  • The developed workflow offers a rapid, cost-effective, and reproducible approach for high-throughput serum proteomics.
  • This method facilitates biomarker discovery and analysis in large-scale clinical studies.
  • The workflow successfully identified potential protein biomarkers for chronic kidney disease progression.