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Automated sample introduction method for high-throughput intact native protein analysis using collision induced unfolding coupled with drift-tube ion mobility-mass spectrometry

Posters | 2022 | Agilent Technologies | ASMSInstrumentation
Ion Mobility, LC/TOF, LC/HRMS, LC/MS, LC/MS/MS
Industries
Proteomics
Manufacturer
Agilent Technologies, MOBILion Systems

Summary

Importance of the Topic


Native protein analysis by ion mobility-mass spectrometry delivers detailed insights into protein conformations and complexes. Collision induced unfolding coupled with drift-tube ion mobility provides unique structural fingerprints that go beyond mass and simple collision cross section measures. Automating high-throughput intact protein CIU workflows addresses critical needs in structural biology, biopharmaceutical characterization, QA/QC and proteomics research.

Objectives and Study Overview


This work aimed to develop and validate an automated sample introduction method for high-throughput CIU experiments using an Agilent 6560C IM-QTOF instrument. Key goals included minimizing sample consumption, ensuring reproducible CIU fingerprints across runs, and enabling rapid analysis of serum proteins (BSA, HSA) and monoclonal antibodies (IgG variants).

Methodology and Instrumentation


Proteins were prepared in ammonium acetate buffers and desalted via spin columns prior to analysis. The flow injection setup combined an Agilent 1290 Infinity II LC pump, a 40 µL metering device, a 40 µL sample loop and a 100 psi back-pressure regulator. Sample volumes from 1.5 to 15 µL were injected at flow rates between 5 and 35 µL/min using a time-segment method. In-source collision energy was ramped stepwise up to 400 V. Data were acquired on an Agilent 6560C IM-QTOF with updated in-source ion activation hardware. CIU data processing and classification employed CIUSuite 2 software.

Key Results and Discussion


  • BSA and HSA CIU fingerprints for charge states +16 to +19 showed consistent collision cross section transitions with RMSD values below 4%, demonstrating high repeatability over ten replicates.
  • Monoclonal antibodies (IgG1-λ, IgG1-κ, IgG2-κ, IgG4-κ) yielded distinct CIU profiles at charge states +28 and +29, enabling accurate classification with RMSD values under 6%.
  • The automated flow injection method completed CIU experiments in under four minutes for serum proteins and under two minutes for IgGs, with minimal carryover after water blanks.

Benefits and Practical Applications


The presented workflow reduces manual intervention, lowers sample requirements to microgram levels, and achieves rapid CIU analysis suitable for routine protein characterization. Its high reproducibility supports reliable fingerprinting in biopharmaceutical development and quality control environments.

Future Trends and Opportunities


Continued integration of automated CIU with machine learning classification promises enhanced discrimination of protein isoforms and post-translational variants. Combining high-throughput CIU with chromatographic separation or native top-down MS could further expand structural analyses of complex mixtures. Miniaturization and multiplexed injection strategies may increase throughput for large-scale proteomics applications.

Conclusion


An automated flow injection CIU method on the Agilent 6560C IM-QTOF delivers rapid, reproducible structural fingerprints of intact native proteins and antibodies. The approach streamlines sample introduction, reduces consumption, and facilitates high-throughput CIU, supporting diverse research and QC needs.

References


  1. Vallejo DD et al Anal Chem 2019 91 8137-8146
  2. Gadkari VV et al Anal Chem 2020 92 15489-15496
  3. Eschweiler JD et al Anal Chem 2015 87 11516-11522

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