High-throughput high-resolution data-independent acquisition workflow on an Orbitrap Ascend Tribrid mass spectrometer for accurate label-free quantitation

Importance of the topic

The ability to accurately quantify protein expression in complex biological samples underpins biomarker discovery, systems biology and clinical research. Label-free quantitation (LFQ) using data-independent acquisition (DIA) addresses shortcomings of traditional data-dependent methods, such as missing values and run-to-run variability, by fragmenting all ions within defined m/z windows. Optimizing DIA parameters and chromatographic separation is critical to achieving the balance between throughput, depth of coverage and quantitative precision required for large-scale proteomic studies.

Objectives and overview of the study

This technical investigation aimed to develop and evaluate an end-to-end LFQ DIA workflow—termed Velocity LFQ DIA—on an Orbitrap Ascend Tribrid mass spectrometer. The study compared three active UHPLC gradient lengths (9, 30 and 60 minutes) for HeLa digest standard and a complex three-proteome mixture (HeLa, yeast, E. coli). Key performance metrics included proteome coverage, quantitation precision, accuracy across known abundance ratios and the benefits of FAIMS gas-phase fractionation.

Methodology and Instrumentation

Sample preparation was optionally automated using the Thermo Scientific AccelerOme platform to minimize handling variability. Peptides were separated on a 50 cm µPAC Neo HPLC column coupled to a Vanquish Neo UHPLC at 350 nL/min and temperatures of 50 °C (sample temperature 7 °C). Three active gradients (9, 30, 60 min) were paired with the Orbitrap Ascend Tribrid MS equipped with a FAIMS Pro interface. DIA settings included narrow isolation windows, high-resolution MS1/MS2 scans (60,000 at MS1; 15,000–30,000 at MS2), HCD fragmentation and optimized AGC targets.

Data analysis and processing employed three software approaches:

• Spectronaut 18 directDIA (1% FDR filtering)
• Proteome Discoverer 3.1 with CHIMERYS intelligent search (1% FDR)
• DIA-NN v1.8.1 (library-free and library-based search)

Main results and discussion

Inclusion of FAIMS increased protein identifications by ~5% while reducing background interference. The 30 min gradient yielded >7,000 proteins and ~47,000 peptides from 200 ng HeLa, with protein CV ~5%. Extending to 60 min increased coverage to ~7,800 proteins and ~76,000 peptides. A 9 min workflow was implemented in two modes: “MaxID” identified ~5,400 proteins, while “MaxQuan” prioritized quantitative precision (protein CV ~6%). A higher load (500 ng HeLa) on the 60 min gradient further increased identifications to ~8,100 proteins (CV ~4%).

Analysis of the three-proteome mix confirmed accurate quantitation across a >10-fold dynamic range, with median measured abundance ratios matching theoretical values and narrow distribution. Library-based DIA-NN searches provided an additional 3–10% gain in protein IDs, particularly boosting coverage in shorter gradients.

Benefits and practical application of the method

Velocity LFQ DIA combines high throughput and deep proteome coverage with excellent quantitative accuracy and precision. The workflow is robust for large cohorts, automatable to reduce technical variability, and flexible across gradient lengths and sample loads. It supports clinical proteomics, biomarker validation, QA/QC in pharmaceutical development and fundamental research requiring reproducible label-free quant.

Future trends and potential applications

Advances in in silico spectral library generation, machine learning for interference correction and real-time acquisition control will further enhance DIA. Integration with single-cell workflows, more compact and faster chromatography devices, and extended gas-phase fractionation modalities (e.g., stepped FAIMS) will push sensitivity and throughput. Automated end-to-end platforms can enable high-volume clinical studies and longitudinal biomarker monitoring.

Conclusion

The Velocity LFQ DIA workflow on an Orbitrap Ascend Tribrid mass spectrometer, combined with μPAC Neo separation and optional AccelerOme automation, delivers high-resolution, high-throughput label-free proteomics. It balances depth of coverage, quantitative precision and throughput across gradient lengths, providing a versatile solution for large-scale proteomic investigations.

Reference

1. Stadlmann J., Hudecz O., Krššáková G., et al. Improved Sensitivity in Low-Input Proteomics Using Micropillar Array-Based Chromatography. Analytical Chemistry. 2019;91(22):14203–14207.
2. Thermo Fisher Scientific. Technical Note 1251: High-throughput High-resolution Data-Independent Acquisition Workflow for Accurate Label-Free Quantitation. 2023.
3. Demichev V., Messner C.B., Vernardis S.I., et al. DIA-NN: Neural Networks and Interference Correction Enable Deep Proteome Coverage in High Throughput. Nature Methods. 2020;17(1):41–44.