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Pushing the Limits of Bottom-Up Proteomics with State-Of-The-Art Capillary UHPLC and Orbitrap Mass Spectrometry for Reproducible Quantitation of Proteomes

Applications | 2016 | Thermo Fisher ScientificInstrumentation
LC/HRMS, LC/MS, LC/MS/MS, LC/Orbitrap
Industries
Proteomics
Manufacturer
Thermo Fisher Scientific

Summary

Significance of the Topic

Bottom-up proteomics aims to comprehensively identify and quantify proteins in complex biological samples. High separation efficiency and fast, accurate mass spectrometry are essential to improve depth of coverage and reproducibility, especially when working with limited sample amounts or demanding high throughput analyses.

Study Objectives and Overview

This work evaluates a quantitative proteomics workflow combining the Thermo Scientific™ EASY-nLC™ 1200 UHPLC system and an Orbitrap Fusion Lumos™ Tribrid mass spectrometer. The performance of 50 cm and 75 cm C18 capillary columns (75 µm i.d.) was compared under 2 h and 4 h gradients, focusing on peptide/protein identifications, chromatographic reproducibility, and label-free quantitation.

Methodology and Instrumentation

  • EASY-nLC 1200 UHPLC (up to 1200 bar backpressure)
  • Orbitrap Fusion Lumos Tribrid MS (120 K resolution survey scan, top-speed DDA)
  • EASY-Spray PepMap C18 columns, 2 µm particles, 75 µm i.d. × 50 cm and 75 cm
  • Gradients: 120 min and 240 min at 300 nL/min with 0.1% formic acid in water/ACN
  • HeLa protein digest (1–2 µg load) with PRTC standards for QC
  • Data analysis: Proteome Discoverer 2.1, Percolator validation, DAnTE RDN, Skyline

Main Results and Discussion

  • 75 cm columns increased unique peptide identifications by >10% and protein IDs by ~7% versus 50 cm in 4 h gradients.
  • Peak capacity (Cp) exceeded 800 with the 75 cm column and 240 min gradient, nearly doubling prior reports.
  • Retention time shifts remained <1 min across replicates, indicating excellent chromatographic reproducibility.
  • Quantifiable peptides increased by 20% with the 75 cm column, achieving replicate correlation >85% and lower CVs.
  • Doubling sample load to 2 µg improved quantitation precision (proteins with CV <5% doubled) without compromising chromatography.
  • Workflow identified ~6 500 proteins in unfractionated HeLa digest and quantified >5 000 proteins reproducibly in three injections.

Benefits and Practical Applications

  • Deeper proteome coverage without offline fractionation, reducing analysis time and complexity.
  • High quantitative reproducibility supports pathway profiling, biomarker discovery, and QA/QC workflows.
  • Shorter gradients with longer columns maintain or improve identification/quantitation, enhancing sample throughput.
  • Untargeted DDA quantitation matches DIA depth and precision without requiring spectral libraries.

Future Trends and Applications

Integration of ultra-high pressure nano-LC with advanced Orbitrap platforms will continue to expand proteome depth and throughput. Emerging developments in microscale column formats, real-time data analytics, and machine learning–driven acquisition strategies may enable single-cell proteomics and clinical point-of-care assays.

Conclusion

The EASY-nLC 1200 coupled to Orbitrap Fusion Lumos defines a state-of-the-art platform for high-resolution, high-throughput bottom-up proteomics. Utilizing 75 cm columns with 2–4 h gradients delivers superior peptide separation, deeper coverage, and enhanced label-free quantitation, pushing the boundaries of large-scale proteome profiling.

References

  1. Wilhelm M. et al. Mass-spectrometry-based draft of the human proteome. Nature. 2014;509(7502):582–587.
  2. Hebert AS et al. The one hour yeast proteome. Mol Cell Proteomics. 2014;13(1):339–347.
  3. Wu X. et al. Global phosphotyrosine survey in triple-negative breast cancer. Oncotarget. 2015;Epub ahead of print.
  4. Livesay EA et al. Fully automated four-column capillary LC-MS system. Anal Chem. 2008;80(1):294–302.
  5. Cox J.; Mann M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem. 2011;80:273–299.
  6. Scigelova M. et al. Fourier transform mass spectrometry. Mol Cell Proteomics. 2011;10(7):M111.009431.
  7. Hsieh EJ et al. Effects of column and gradient lengths on peak capacity. J Am Soc Mass Spectrom. 2013;24(1):148–153.
  8. Käll L. et al. Semi-supervised learning for peptide identification from shotgun proteomics. Nat Methods. 2007;4(11):923–925.
  9. Polpitiya AD et al. DAnTE: a statistical tool for quantitative analysis of ‑omics data. Bioinformatics. 2008;24(13):1556–1558.
  10. MacLean B. et al. Skyline: an open source editor for targeted proteomics. Bioinformatics. 2010;26(7):966–968.

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