A Framework for Quality Control in Quantitative Proteomics

Area of Science:

• Proteomics
• Analytical Chemistry
• Biotechnology

Background:

• Bottom-up proteomics workflows require rigorous quality evaluation.
• Reproducibility and variability are critical concerns in proteomics data analysis.

Purpose of the Study:

• To present adaptable quality control (QC) measures for bottom-up proteomics.
• To ensure the quality, reproducibility, and variability of proteomics data from planning to analysis.

Main Methods:

• Utilized system suitability samples for longitudinal monitoring of instrument performance across platforms.
• Incorporated internal quality controls (QCs) at protein and peptide levels to assess sample preparation and differentiate system vs. sample issues.
• Employed external QC samples for consistency verification during batch correction and normalization.

Main Results:

• Demonstrated the use of system suitability samples to identify severe system failures and track instrument function over extended periods.
• Showcased how internal QCs help distinguish sample preparation problems from instrument malfunctions.
• Validated the utility of external QCs in ensuring quantitative potential before phenotype assessment.

Conclusions:

• An integrated QC approach combining targeted methods, longitudinal metrics, and data deposition platforms (Skyline, AutoQC, PanoramaWeb) is proposed.
• This strategy facilitates rapid quality assessment, optimizing the use of instrument time for high-quality data collection.
• The proposed QC framework serves as a valuable starting point for research groups aiming to enhance proteomics data integrity.