A framework for quality control in quantitative proteomics

Area of Science:

• Proteomics
• Analytical Chemistry
• Biotechnology

Background:

• Bottom-up proteomics workflows require rigorous quality control (QC) to ensure data reproducibility and accuracy.
• Variability in sample preparation, instrument performance, and quantitative analysis can compromise proteomics data integrity.
• A systematic approach to QC is essential throughout the entire proteomics workflow, from experimental design to data analysis.

Purpose of the Study:

• To present adaptable quality control (QC) measures for bottom-up proteomics.
• To demonstrate the application of QC strategies for assessing sample preparation, system function, and quantitative analysis.
• To provide a framework for ensuring high-quality proteomics data collection and analysis.

Main Methods:

• Utilized system suitability samples measured longitudinally with targeted methods across three instrument platforms.
• Incorporated internal quality controls (QCs) at protein and peptide levels for sample preparation and system failure differentiation.
• Employed external QC samples for consistency verification during batch correction and normalization.
• Integrated rapid analysis software (Skyline), longitudinal QC metrics (AutoQC), and data deposition platforms (PanoramaWeb).

Main Results:

• System suitability samples effectively identified severe system failures and tracked instrument function over extended periods.
• Internal QCs differentiated between sample preparation issues and system failures.
• External QCs confirmed the consistency and quantitative potential of experimental results.
• The integrated QC approach facilitated rapid quality assessment and optimized data collection.

Conclusions:

• An integrated quality control strategy is crucial for reliable bottom-up proteomics.
• The proposed QC measures enhance the assessment of sample preparation, system performance, and quantitative accuracy.
• This approach enables efficient use of instrument time for generating high-quality proteomics data.
• The presented methods serve as a valuable starting point for laboratories seeking to improve proteomics data quality.