Improvements in LFQ for reproducible quantification of proteomic experiments: how DDA outperforms DIA

Significance of the Topic

The accurate and reproducible quantification of proteins in complex biological samples is central to proteomics research and applications. Label-free quantification (LFQ) enables high-throughput analysis without the need for isotopic labels. While Data Independent Acquisition (DIA) has gained attention for consistent sampling, Data Dependent Acquisition (DDA) remains the benchmark for peptide identification depth and inter-experimental reproducibility when combined with advanced LFQ software.

Objectives and Study Overview

This study compares high-resolution accurate-mass (HRAM) quadrupole-Orbitrap DDA against HRAM quadrupole-Orbitrap DIA to:

• Assess sensitivity and the total number of peptides and proteins identified and quantified.
• Evaluate quantitative reproducibility across replicate analyses.
• Measure the impact of chromatographic column length (50 cm vs. 75 cm) on identification depth and LFQ performance.

Methodology and Used Instrumentation

• Sample: HeLa protein digest spiked with HRM peptide standards.
• Chromatography: Thermo Scientific EASY-nLC 1200 with 50 cm or 75 cm Acclaim PepMap™ EASY-Spray columns at 55 °C and a 2 h gradient (5–44% acetonitrile, 0.1% formic acid).
• Mass Spectrometry: Thermo Scientific Q Exactive™ HF MS operated in DDA or DIA mode.
• Data Processing (DDA): Proteome Discoverer™ 2.2.0.96 with SEQUEST® HT, Minora feature detector, RT aligner, Feature Mapper, Percolator algorithm (1% FDR).
• Data Processing (DIA): Spectronaut™ 9.0 for MS1-based quantitation using the library built from DDA runs.

Main Results and Discussion

• DDA outperformed DIA in the number of peptide spectral matches (PSMs), peptide and protein identifications, and quantifiable peptides with CV<20% across replicates.
• The 75 cm column increased PSMs, peptides, proteins, and quantifiable features compared to the 50 cm column in both acquisition modes.
• The Minora-based LFQ workflow integrated in Proteome Discoverer improved inter-experimental reproducibility and obviated the need for a pre-built spectral library, streamlining the analysis.
• The apparent wider dynamic range with the shorter column in certain plots was attributed to roll-up methods rather than true performance differences.

Benefits and Practical Applications

• Library-free DDA-LFQ maximizes proteome coverage and quantitative accuracy with fewer preparatory steps than DIA workflows.
• Integrated scaling, normalization, and study management within Proteome Discoverer enhances data consistency and throughput.
• Adaptable to QA/QC, biomarker discovery, and industrial process control where reproducible quantitation is critical.

Future Trends and Potential Applications

• Development of longer and more efficient chromatographic phases to further deepen proteome coverage.
• Integration of machine learning for peak picking, alignment, and normalization to boost LFQ precision.
• Expansion of label-free workflows to single-cell proteomics and clinical diagnostics requiring robust quantitation.

Conclusion

The integration of HRAM DDA with an untargeted LFQ workflow based on the Minora algorithm delivers superior depth, sensitivity, and reproducibility compared to DIA approaches. Longer chromatography columns further amplify these benefits, offering a streamlined, library-free solution for high-throughput proteomic quantitation.

References

• No external literature references were provided in the original text.