New Method for Label-Free Quantification in the Proteome Discoverer Framework

Significance of the Topic

The implementation of robust untargeted label-free quantification is critical for proteomic studies where isotope labeling is impractical or cost-prohibitive.

• Enables large-scale comparison of protein abundance without additional labeling steps
• Provides wide dynamic range and flexibility for discovery workflows
• Improves accessibility of quantitative proteomics in diverse research, QA/QC and industrial applications

Objectives and Study Overview

This study presents a new untargeted label-free quantification workflow in Thermo Scientific Proteome Discoverer and compares it against spectral counting and Top N peptide approaches.

• Evaluate quantification accuracy and sensitivity using an Arabidopsis proteasome spike-in in an E. coli background
• Demonstrate multi-dimensional separation and quantification on a human proteome dataset from Pandey et al.

Methodology

The new workflow leverages Proteome Discoverer 2.2 with the following key components:

• Minora Feature Detector for untargeted detection of isotopic clusters regardless of spectral matches
• RT-Aligner and Feature Mapper for retention time alignment and gap filling across runs
• Peptide and Protein Quantifier node for scaling and normalization

Comparison workflows included:

• Spectral counting via Sequest HT and basic consensus node without MSF merging
• Top N quantification using Precursor Ion Area Detector and Top 3 peptide summation

Main Results and Discussion

On the Arabidopsis/E. coli spike-in dataset, the Minora-based workflow delivered:

• Ratios closer to expected values across a wide concentration range
• Improved precision with lower standard deviations compared to spectral counting and Top N
• Quantification of a similar or greater number of proteins with accurate dynamic range

On the human proteome fractions, processing of five fractions yielded over 5100 proteins and 60 000 peptides, enabling identification of tissue-specific markers such as synapsins and tau in the frontal cortex sample. Scaled abundance profiles highlighted exclusive proteins with no missing values across runs, underlining the effectiveness of feature mapping.

Benefits and Practical Applications

The integrated workflow offers:

• Enhanced accuracy, precision and sensitivity for untargeted label-free proteomics
• Seamless integration of quantification, scaling and normalization within a single software environment
• Rapid discovery of biomarkers and profiling of complex samples without isotopic labels

Future Trends and Potential Applications

Potential developments include:

• Advanced spectral counting algorithms (emPAI-based) for improved count-based quantification
• Expanded application to large-scale clinical and multi-omics studies
• Machine learning integration for automated feature curation and deeper quantification
• Adaptation to emerging mass spectrometry platforms and data-independent acquisition workflows

Conclusion

The Minora feature detection workflow in Proteome Discoverer 2.2 substantially improves untargeted label-free quantification by delivering higher accuracy, precision and throughput than traditional spectral counting and Top N methods. Its integration with robust scaling, normalization and study management tools provides a powerful platform for quantitative proteomics of complex biological samples.

References

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