Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort

Area of Science:

• Proteomics
• Biomarker Discovery
• Mass Spectrometry

Background:

• Plasma proteomics is crucial for disease research but traditionally requires extensive sample preparation.
• Data-dependent acquisition (DDA) has limitations in proteome coverage and depth for complex samples like plasma.

Purpose of the Study:

• To develop and validate a streamlined, cost-effective method for deep plasma proteome analysis.
• To improve proteome coverage and depth for biomarker discovery using prefractionation and data-independent acquisition (DIA).

Main Methods:

• Utilized moderate plasma prefractionation combined with data-independent acquisition (DIA).
• Employed standard depletion, sample preparation, and fractionation techniques.
• Performed 3 LC-MS/MS injections with a 360-minute DIA run time on a 20-patient COVID-19 cohort.

Main Results:

• Detected an average of 1321 proteins per patient and 2031 unique proteins across the cohort.
• Identified hundreds of differentially abundant proteins, including those at concentrations as low as 47 ng/L.
• Demonstrated that fractionation significantly improved protein quantification, enhancing proteome depth.

Conclusions:

• The combined prefractionation and DIA method offers a cost-effective approach for deep plasma proteome analysis.
• This technique expands the utility of plasma proteomics beyond traditional research settings for clinical applications.
• The enhanced proteome coverage facilitates comprehensive biomarker discovery for diseases such as COVID-19.