Multi-draw: Enabling large volume injections for lyophilization-free LC-MS proteomics workflows on the Vanquish Neo UHPLC system

Significance of the Topic

Bottom-up proteomics by LC–MS frequently relies on lyophilization and reconstitution to concentrate peptides for injection, adding labor and risking peptide loss. Introducing a dilute-and-inject workflow that skips lyophilization can streamline sample preparation, preserve peptides with limited solubility, and increase throughput in proteomics laboratories.

Objectives and Overview of the Study

This study evaluates the multi-draw functionality of the Thermo Scientific Vanquish Neo UHPLC system to enable large volume injections (up to 500 µL) in a trap-and-elute bottom-up proteomics workflow. Performance is compared between conventional small-volume injections (5 µL of 200 ng/µL HeLa digest) and large-volume injections (500 µL of 2 ng/µL) without lyophilization.

Methodology and Instrumentation

Sample Preparation:

• Thermo Scientific Pierce HeLa digest with PRTC standard at defined concentrations.
• Dilution in 0.1% (v/v) TFA in water for sample solvent and loading buffer.

Chromatography and MS Conditions:

• Vanquish Neo UHPLC system in trap-and-elute mode with a 300 µm × 5 mm trap cartridge and 75 µm × 150 mm separation column.
• Multi-draw injection using a 100 µL sample loop and iterative aspiration/dispense cycles at 20 µL/s and 200 µL/min loading flow (800 bar).
• Orbitrap Exploris 480 mass spectrometer in data-dependent acquisition mode.
• Data processing with Proteome Discoverer 2.5, SEQUEST HT, INFERYS rescoring, and 1% FDR at peptide and protein levels.

Instrumentation Used

• Thermo Scientific Vanquish Neo UHPLC system (Binary Pump N, Split Sampler NT, Solvent Rack).
• Thermo Scientific Orbitrap Exploris 480 mass spectrometer.
• Thermo Scientific EASY-Spray PepMap Neo column (75 µm × 150 mm, 2 µm).
• PepMap Neo trap cartridge (300 µm × 5 mm, 5 µm) with 100 µL sample loop.

Main Results and Discussion

• Chromatographic profiles and total ion chromatograms of 5 µL and 500 µL injections were nearly identical, with consistent PRTC peptide retention times.
• Proteome coverage—number of peptide groups and proteins identified—was equivalent across injection volumes, confirming full sample loading.
• Quantitative reproducibility showed high correlation of peptide peak areas between replicates of small and large volume injections, with no evidence of hydrophilic peptide breakthrough when using 0.1% TFA.

Benefits and Practical Applications

• Eliminates the need for lyophilization and reconstitution, reducing hands-on time and potential peptide losses.
• Supports dilute-and-inject workflows with final aqueous conditions (<2% ACN), facilitating direct sample loading.
• Automated multi-draw injection up to 500 µL requires only a 100 µL loop and no additional user programming.
• Enables high-throughput proteome profiling with robust chromatographic performance.

Future Trends and Potential Applications

The multi-draw approach may extend to diverse sample types, including clinical and environmental matrices, and can be integrated into automated platforms for higher throughput. Further optimization of ion-pairing reagents and loop sizes could expand the range of compatible solvents and injection volumes, enhancing versatility in quantitative proteomics.

Conclusion

The Vanquish Neo UHPLC multi-draw functionality effectively supports large-volume injections in bottom-up proteomics without lyophilization. It delivers equivalent chromatographic performance, identification depth, and quantitative reproducibility as conventional small-volume methods, streamlining workflows and reducing sample preparation burdens.

References

1. Zheng R.; Arrey T. N.; Hakimi A.; et al. Fast, sensitive, and reproducible nano- and capillary-flow LC-MS methods for high-throughput proteome profiling using the Vanquish Neo UHPLC system hyphenated with the Orbitrap Exploris 480 MS. Thermo Scientific Technical Note 000138.
2. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2020.