IMSC: Complete Characterization of a Cysteine- linked Antibody-Drug Conjugate Performed on a Hybrid Quadrupole-Orbitrap Mass Spectrometer with High Mass Range

Significance of the Topic

Label-free quantification is an essential approach in mass spectrometry-based proteomics, offering cost-effective and flexible analysis without isotope labeling. However, traditional workflows in Proteome Discoverer 2.1 have limited quantitative capabilities, especially for complex samples. Enhancing untargeted label-free methods bridges the gap with isotopically labeled workflows, improving accuracy and enabling comprehensive proteomic studies.

Objectives and Study Overview

This study introduces a novel untargeted label-free quantification workflow in Proteome Discoverer 2.2, leveraging an improved feature detection algorithm. The goals are to implement full quantitative functionality—such as ratio calculation, scaled abundances, and statistical replicates—and to compare performance against existing label-free methods (spectral counting and Top N peptide averaging) in Proteome Discoverer 2.1.

Materials and Methods

A benchmark dataset (PRIDE PXD003002) featuring Arabidopsis proteasome proteins spiked into an E. coli background and the Pandey human proteome dataset were processed. Three workflows were evaluated:

• Spectral counting using a Sequest™ HT-Percolator consensus workflow.
• Top N peptide quantification with the Precursor Ion Area Detector and Top N Peptides parameter set to 3.
• New untargeted workflow employing the Minora Feature Detector, RT-Aligner, and Feature Mapper nodes.

The Sequest HT search targeted Arabidopsis thaliana and Escherichia coli databases. Quantitative tables were exported to Excel for manual ratio calculations. Gap filling and retention time alignment ensured feature consistency across replicates.

Applied Instrumentation

The analyses were performed on a hybrid quadrupole-Orbitrap mass spectrometer with high mass range capability. Data acquisition supported comprehensive isotopic cluster detection and high-resolution chromatographic profiling.

Main Results and Discussion

The new feature detection algorithm extends the Minora engine to identify and quantify isotopic clusters irrespective of peptide spectral matches. In comparison:

• Spectral counting lacked quantitative precision for complex samples.
• Top N averaging provided abundance estimates but did not support ratio generation or error statistics.
• The untargeted Minora-based workflow delivered full quantitative outputs, including scaled abundance values, fold-change ratios, and standard errors across replicates.

Consensus reports now include three tabs—Consensus Features, LCMS Features, and LCMS Peaks—linking quantitative metrics to chromatographic traces for intuitive data exploration.

Benefits and Practical Applications of the Method

By enabling robust label-free quantification with comprehensive statistical analysis, the new workflow supports:

• Accurate differential protein expression studies in complex biological matrices.
• High-throughput comparative proteomics without costly labeling reagents.
• Improved data completeness through alignment and gap filling.

This approach benefits research, quality control, and biomarker discovery in pharmaceutical, clinical, and industrial settings.

Future Trends and Potential Applications

Advancements may include integration with ion mobility separation, machine learning for feature annotation, and compatibility with additional mass spectrometer platforms. Extending functionality to support real-time data acquisition and cloud-based processing will further accelerate proteomic workflows.

Conclusion

The enhanced untargeted label-free quantification workflow in Proteome Discoverer 2.2 overcomes limitations of previous methods, delivering full quantitative capability rivaling isotopic approaches. Its modular node-based design simplifies implementation and provides a versatile tool for diverse proteomic applications.

Reference

1. PRIDE dataset PXD003002: Arabidopsis proteasome proteins spiked into E. coli background, used for spectral counting evaluation.
2. Pandey A, et al. Human proteome dataset, PRIDE repository, applied for multidimensional separation quantification.