A Dual Workflow to Improve the Proteomic Coverage in Plasma Using Data-Independent Acquisition-MS

Area of Science:

• Proteomics
• Analytical Chemistry
• Biochemistry

Background:

• Plasma is a critical matrix for clinical and proteomic studies.
• Data-independent acquisition (DIA) mass spectrometry allows simultaneous quantification of numerous plasma proteins.
• High-abundance protein depletion enhances proteome coverage but risks nonspecific removal of interacting proteins.

Purpose of the Study:

• To investigate the complementarity of protein identification and quantification between depleted and undepleted plasma fractions.
• To systematically define quantitative parameters for plasma proteins.
• To develop a decision-making tool for optimizing plasma proteomic workflows.

Main Methods:

• Utilized antibody-based depletion columns for plasma processing.
• Performed DIA mass spectrometry on depleted and undepleted plasma samples.
• Defined quantitative parameters including linearity cutoff (r² > 0.8), lower limit of quantification (LLOQ), measurement range, and limit of detection (LOD) for 757 proteins.

Main Results:

• Demonstrated significant complementarity in protein identification and quantification between depleted and undepleted plasma fractions.
• Established robust quantitative metrics for a large set of plasma proteins.
• Developed the web-based tool, PlasmaPilot, to guide experimental design.

Conclusions:

• The analysis of both depleted and undepleted plasma fractions provides complementary proteomic information.
• Quantitative parameters are crucial for assessing protein detectability and reliability.
• PlasmaPilot aids researchers in deciding whether IgY14 mediated depletion is appropriate for specific protein targets.