Analysis of Labeled and Non-Labeled Proteomic Data Using Progenesis QI for Proteomics

Significance of the Topic

This application note addresses the critical need for reliable, comprehensive software tools in quantitative proteomics. Mass spectrometry workflows generate complex, high-dimensional data sets that demand accurate peak detection, robust alignment, and complete data matrices. Progenesis QI for proteomics integrates labeled and label-free strategies, as well as data-dependent and data-independent acquisitions, to streamline analyses in biomarker discovery, disease research, and industrial QA/QC.

Study Objectives and Overview

• Evaluate the performance of Progenesis QI for proteomics in both labeled (dimethyl and SILAC-like) and label-free quantitation workflows.
• Demonstrate compatibility with data-dependent acquisition (DDA) and data-independent acquisition (HDMSE) on a SYNAPT G2-Si platform.
• Assess reproducibility of peak detection, alignment, and quantitation across technical replicates and protein spike-in experiments.

Methods and Instrumentation

Sample preparation covered three model systems: a cytosolic Escherichia coli digest spiked with bovine serum albumin, alcohol dehydrogenase, enolase, and glycogen phosphorylase B standards; dimethyl-labeled HL60 human B cells; and a Saccharomyces cerevisiae lysate spiked with the UPS1 standard at varying fmol levels. Chromatographic separation used nanoACQUITY UPLC with a Symmetry C18 trap and HSS T3 analytical column over a 90-minute gradient. Mass spectrometry employed both DDA and ion mobility-assisted HDMSE acquisition modes under defined collision energy settings. Data processing involved Progenesis QI for proteomics v2.0 for alignment, peak detection, and quantitation, with identification supported by ProteinLynx Global SERVER v3.0.2, Mascot v2.5, and ProteoLabels v1.0.

Instrumentation

• Mass Spectrometer: Waters SYNAPT G2-Si with ESI in positive mode (3.2 kV), cone voltage 30 V, m/z range 50–2000.
• Liquid Chromatography: nanoACQUITY UPLC; Symmetry C18 trap (180 µm x 20 mm, 5 µm); HSS T3 analytical column (75 µm x 150 mm, 1.8 µm); flow 300 nL/min; 0.1% formic acid in water and acetonitrile.
• Software: Progenesis QI for proteomics v2.0; ProteinLynx Global SERVER v3.0.2; Mascot v2.5; ProteoLabels v1.0; Spotfire v9.1.

Results and Discussion

• Peak Detection and Alignment: Six technical replicates of E. coli digest yielded an average of 28 793±458 features, with co-detection in the aggregate improving coverage to ~100%, compared to ~55% per individual run.
• Label-Free Quantitation (HDMSE): Spiking experiments with ADH internal standard enabled normalization and accurate quantitation of multiple protein spikes at expected ratios.
• Dimethyl Labeling Workflows: Co-detection of light/heavy peptide pairs showed consistent retention and ion mobility drift times. ProteoLabels facilitated precise peptide- and protein-level quantitation with tight ratio distributions across replicates.
• DDA Label-Free Quantitation: Analysis of UPS1 gamma-synuclein spiked into yeast lysate across a 200-fold range demonstrated reliable feature detection and linear peptide and protein responses down to 0.125 fmol.

Benefits and Practical Applications

Progenesis QI for proteomics offers complete data matrices without missing values, flexible experimental design, and consistent peak picking. These features enhance statistical power in differential analyses, support high-throughput QA/QC, and improve coverage in fractionated samples, aiding biomarker discovery and routine laboratory workflows.

Future Trends and Potential Applications

• Integration of ion mobility separation with next-generation quantitation workflows for deeper proteome coverage.
• Expansion to targeted assays and post-translational modification profiling with high precision.
• Coupling with machine learning algorithms for automated pattern recognition and predictive biomarker identification.

Conclusion

Progenesis QI for proteomics delivers a unified platform for robust analysis of labeled and label-free proteomic data acquired in DDA or DIA modes. Its co-detection strategy and aggregate alignment significantly improve data completeness, precision, and accuracy, establishing a versatile tool for diverse proteomic applications in research and industry.