High-throughput high-resolution data-independent acquisition workflow for accurate label-free quantitation

Significance of the Topic

Quantitative proteomics delivers critical insight into global protein expression and modifications, supporting predictive models and advancing disease research. Robust, accurate label-free quantitation (LFQ) is essential for comparing protein abundances across complex samples and large cohorts.

Study Objectives and Overview

This work evaluates a high-resolution data-independent acquisition (HR-DIA) workflow on a Thermo Scientific Orbitrap Exploris 240 mass spectrometer combined with a Vanquish Neo UHPLC system and a µPAC Neo UHPLC column using a 30 min gradient. Two-proteome and three-proteome mixtures mimic biological matrices to assess LFQ accuracy, precision, proteome coverage, reproducibility, and long-term robustness for large-scale proteomics.

Methodology and Instrumentation

Sample design

• Two-proteome mix: 800 ng HeLa background spiked with 24–192 ng E. coli peptides (ratio 1:8 to 1:2).
• Three-proteome mix: 325 ng HeLa background spiked with E. coli (ratio 1:4) and yeast (ratio 1:0.5) peptides.

LC-MS parameters

• Vanquish Neo UHPLC, µPAC Neo column (50 cm, 75 µm ID), 350 nL/min, 30 min gradient.
• Orbitrap Exploris 240, HR-DIA with 60 000/15 000 resolution, 12 m/z windows, 400–900 m/z precursor range, 30% NCE.

Data analysis

• Spectronaut 16/17 directDIA and DIA-NN v1.8.1 library-free processing.
• Protein and peptide identifications filtered at 1% FDR, cross-run normalization applied.

Main Results and Discussion

Accuracy and precision

• Two-proteome mix: E. coli ratios closely matched theoretical values with narrow distributions; peptide CVs < 10% across replicates.
• Three-proteome mix: Accurate quantitation for human (~ 3 000 proteins), E. coli (~ 500 proteins) and yeast, with clear linearity over abundance ranges.

Proteome coverage and DIA software comparison

• Spectronaut 17 directDIA+ increased precursors by ~ 30% and protein groups by ~ 5% versus Spectronaut 16; DIA-NN showed comparable performance.

Workflow robustness

• Over 500 h of operation and ~ 130 µg peptide load, intermittent DDA QC runs maintained protein and peptide identifications within a 5% range, demonstrating column and system stability.

Benefits and Practical Applications

This HR-DIA workflow offers high-throughput, robust LFQ with reliable accuracy and precision, making it well-suited for large-cohort clinical and biomarker discovery studies without the need for spectral libraries, thereby reducing time and costs.

Future Trends and Applications

Advances in machine-learning-driven spectral prediction and shorter gradients promise further throughput gains. Continued improvements in MS acquisition speed, resolution, and data analysis algorithms will enhance sensitivity and depth, enabling broader application in clinical proteomics and translational research.

Conclusion

The described high-throughput HR-DIA workflow on Orbitrap Exploris 240 and Vanquish Neo UHPLC with µPAC Neo columns achieves excellent LFQ accuracy, precision, proteome coverage, and long-term reproducibility, supporting its use in large-scale proteomic investigations.

References

1. Stadlmann J, Hudecz O, Krššáková G, et al. Improved Sensitivity in Low-Input Proteomics Using Micropillar Array-Based Chromatography. Anal Chem. 2019;91(22):14203–14207.
2. Demichev V, Messner CB, Vernardis SI, et al. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods. 2020;17:41–44.