Infrared Photoactivation Enables nano-DESI MS of Protein Complexes in Tissue on a Linear Ion Trap Mass Spectrometer
Native mass spectrometry analysis of proteins directly from tissues can be performed by using nanospray-desorption electrospray ionization (nano-DESI). Typically, supplementary collisional activation is essential to decluster protein complex ions from solvent, salt, detergent, and lipid clusters that comprise the ion beam. As an alternative, we have implemented declustering by infrared (IR) photoactivation on a linear ion trap mass spectrometer equipped with a CO<sub>2</sub> laser (λ = 10.6 μm). The prototype system demonstrates declustering of intact protein complex ions up to approximately 50 kDa in molecular weight that were sampled directly from brain and eye lens tissues by nano-DESI. For example, signals for different metal binding states of hSOD1<sup>G93A</sup> homodimers (approximately 32 kDa) separated by only approximately 6 Th (10+ ions) were resolved with IR declustering, but not with collisional activation. We found IR declustering to outperform collisional activation in its ability to reduce chemical background attributable to nonspecific clusters in the nano-DESI ion beam. The prototype system also demonstrates <i>in situ</i> native MS on a low-cost mass spectrometer and the potential of linear ion trap mass spectrometers for this type of analysis.
Authors
- Helen J. Cooper (ORCID: https://orcid.org/0000-0003-4590-9384)
- Todd H. Mize (ORCID: https://orcid.org/0000-0002-0261-1580)
- Oliver J. Hale (ORCID: https://orcid.org/0000-0002-2286-5780)
Institutions
- University of Birmingham (GB)
Publication Details
- Journal
- Journal of the American Society for Mass Spectrometry
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1021/jasms.4c00377
- Citations
- 8
- Primary Topic
- Mass Spectrometry Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.93
Funders
- Advantage West Midlands
- Engineering and Physical Sciences Research Council
- Biotechnology and Biological Sciences Research Council