High-sensitivity low-nano flow LC-MS methods for high-throughput sample-limited proteomics

Significance of the Topic

The analysis of limited samples such as single cells demands both high sensitivity and high throughput in nano-flow LC-MS workflows. Precise gradient delivery, low internal diameter columns and reliable fluidic connections are critical to obtain reproducible proteome coverage from sub-nanogram quantities of material.

Objectives and Study Overview

This study aimed to develop and benchmark five nano-LC-MS methods using a 50 μm ID column at 100 nL/min on a Vanquish Neo UHPLC system coupled to an Orbitrap Exploris 480 mass spectrometer. The methods target sample-limited proteomics, optimizing balance between throughput and depth in both DDA and DIA modes for applications such as single-cell proteomics.

Methodology and Instrumentation

• Sample preparation: HeLa protein digest diluted to 1 ng/μL; injection volumes 1–2 μL.
• LC platform: Vanquish Neo UHPLC with Binary Pump N, Split Sampler NT; 50 μm×150 mm Acclaim PepMap 100 C18 column (2 μm); 10 μm emitter; gradient flows at 100 nL/min.
• Solvents: mobile phase A: water with 0.1% formic acid; B: 80/20 ACN/water with 0.1% FA.
• MS parameters: Orbitrap Exploris 480 with FAIMS Pro interface; DDA and DIA acquisition optimized for low sample amounts; variable isolation windows; resolution up to 120,000; AGC targets and injection times tuned for sensitivity.

Main Results and Discussion

• High-throughput methods: five gradient lengths (10, 14, 20, 30, 50 min) enabling 24–72 samples/day with up to 85% MS utilization.
• DDA mode: ~800 protein groups from 250 pg HeLa sample; ~1,500 proteins at 2 ng; INFERYS rescoring increased identifications.
• CHIMERYS processing: ~1,800 protein groups identified from 250 pg with wide window acquisition.
• DIA mode: >3,000 protein groups from 250 pg over four orders of magnitude dynamic range using LFQ-DIA.
• Reproducibility: retention time variation <6 seconds; protein abundance CV typically below 20%.

Benefits and Practical Applications

• Enables single-cell proteomics workflows at up to 72 cells per day without multiplexing.
• Flexible method selection to trade off depth and throughput according to project needs.
• Robust and reproducible performance suitable for quality control and large-scale studies.

Future Trends and Applications

• Integration of multiplexed labeling strategies (e.g., TMT) to further increase throughput.
• Advances in AI-driven deconvolution algorithms to boost proteome coverage from minimal input.
• Continued miniaturization and fluidic optimization for single-cell and subcellular proteomics.
• Expansion of applications in clinical proteomics and biopharmaceutical characterization.

Conclusion

The five optimized nano-LC-MS methods deliver exceptional sensitivity and throughput for sample-limited proteomics. High depth of coverage in both DDA and DIA modes, combined with robust reproducibility, positions this workflow as a powerful platform for single-cell proteomics and high-throughput studies.

References

1. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2020.
2. Stejskal K, Op de Beeck J et al. Ultrasensitive NanoLC-MS of Subnanogram Protein Samples Using Second Generation Micropillar Array LC Technology with Orbitrap Exploris 480 and FAIMS PRO. Anal. Chem. 2021;93(25):8704–8710.