Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by...

Area of Science:

• Proteomics
• Bioinformatics
• Mass Spectrometry

Background:

• Label-free proteome quantification (LFQ) is crucial in biomedical, agricultural, and environmental studies.
• Optimizing LFQ workflows is challenging due to numerous tool and manipulation chain combinations.
• Current evaluation methods yield varied results for precision, accuracy, and robustness.

Purpose of the Study:

• To develop a novel strategy for discovering LFQ workflows with simultaneously enhanced performance.
• To systematically assess the feasibility of improving LFQ precision, accuracy, and robustness concurrently.
• To provide guidance for optimizing mass-spectrometry-based LFQ techniques.

Main Methods:

• Integrated 18 quantification tools with 3,128 manipulation chains to create thousands of potential LFQ workflows.
• Systematically optimized multistep manipulation chains to collectively enhance LFQ performance.
• Validated the strategy using benchmark datasets from various quantification measurements and acquisition modes.

Main Results:

• Identified specific manipulation chains that simultaneously improve LFQ precision, accuracy, and robustness.
• Confirmed the feasibility of achieving concurrent enhancements in multiple performance criteria for LFQ.
• Developed an online tool (https://idrblab.org/anpela/) for collective assessment of LFQ workflow performance.

Conclusions:

• The proposed novel strategy effectively discovers high-performance LFQ workflows.
• Simultaneous improvement in LFQ precision, accuracy, and robustness is achievable.
• The developed strategy and online tool offer valuable guidance for mass-spectrometry-based proteomic quantification.