IMSC: New Method for Label-Free Quantication in the Proteome Discoverer Framework

Significance of the Topic

Accurate label‐free quantification is essential in proteomics when isotopic labels are impractical. Existing methods in Proteome Discoverer rely on spectral counting and “Top N” peptide averaging, which limit ratio calculations and dynamic range. A robust untargeted label‐free workflow addresses these gaps, enabling precise quantification across complex biological samples.

Objectives and Study Overview

This study introduces and validates a new feature‐detection–based quantification workflow integrated into Proteome Discoverer 2.2. It compares performance against spectral counting and “Top N” approaches using a known Arabidopsis proteasome spiked in E. coli and a human proteome dataset, assessing accuracy, sensitivity, and protein coverage.

Methodology and Instrumentation

• Software platform: Thermo Fisher Proteome Discoverer (v2.1 and pre‐release v2.2)
• Search engine: SEQUEST HT coupled with Percolator for peptide identification
• Quantification nodes: Minora Feature Detector, RT‐Aligner, Feature Mapper, Consensus workflow, Precursor Ion Area Detector for “Top N”
• Data types: Label‐free LC‐MS runs, multidimensional fractions of human proteome
• Sample sets: Arabidopsis proteasome in E. coli background; human brain tissue fractions

Main Results and Discussion

• The new feature‐detection approach delivers ratios significantly closer to theoretical values at low protein loads, outperforming spectral counting and “Top N” methods in both accuracy and dynamic range
• Precision improves with the requirement of a single PSM per peptide across runs, boosting quantified protein numbers by 20–40% compared to prior workflows
• Scaled abundance visualization and normalization to E. coli proteins streamline comparative analysis without explicit ratio calculations
• Application to human cortex fractions identifies over 5,000 proteins, highlighting neural markers with exclusive expression patterns

Benefits and Practical Applications

• Enhanced sensitivity and coverage for complex proteomic samples without the need for labels
• Seamless integration into existing Proteome Discoverer workflows with study management and data‐visualization tools
• Scalable to multidimensional LC separations and supportive of biomarker discovery, tissue‐specific expression studies, and QA/QC pipelines

Future Trends and Applications

• Incorporation of emPAI or other absolute quantification strategies to complement relative scaling
• Extension to data‐independent acquisition (DIA) workflows and integration with cloud‐based analysis platforms
• Automated multi‐factor normalization schemes leveraging internal standards and machine‐learning‐driven feature detection

Conclusion

The novel Minora‐based untargeted label‐free quantification workflow in Proteome Discoverer 2.2 offers significant improvements in quantification accuracy, precision, and protein coverage. Its integration with scaling and visualization tools empowers researchers to analyze complex proteomic datasets more effectively.

References

• Gemperline DC et al. Proteomics. 2016;16:920–924.
• Kim MS et al. Nature. 2014;509:575–581.