Evaluating the performance of the Astral mass analyzer for quantitative proteomics using data independent acquisition
Heil, L. R.; Damoc, N. E.; Arrey, T. N.; Pashkova, A.; Denisov, E.; Petzoldt, J.; Peterson, A.; Hsu, C.; Searle, B. C.; Shulman, N.; Riffle, M.; Connolly, B.; MacLean, B. X.; Remes, P. M.; Senko, M.; Stewart, H.; Hock, C.; Makarov, A.; Hermanson, D.; Zabrouskov, V.; Wu, C. C.; MacCoss, M. J.
Show abstract
We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high quality quantitative measurements across a wide dynamic range. We also use a newly developed extra-cellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5,000 plasma proteins in a 60-minute gradient with the Orbitrap Astral mass spectrometer.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Hybrid Quadrupole Mass Filter Radial Ejection Linear Ion Trap and Intelligent Data Acquisition Enable Highly Multiplex Targeted Proteomics 98%
- High sensitivity limited material proteomics empowered by data-independent acquisition on linear ion traps 98%
- Data-Driven Optimization of DIA Mass Spectrometry by DO-MS 98%
Similar papers in this journal
- Dynamic Data Independent Acquisition Mass Spectrometry with Real-Time Retrospective Alignment 98%
- Super-resolution mass spectrometry enables rapid, accurate, and highly-multiplexed proteomics at the MS2-level 98%
- Expanding the depth and sensitivity of cross-link identification by differential ion mobility using FAIMS 98%
Similar papers in this journal
- IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection 98%
- MealTime-MS: A Machine Learning-Guided Real-Time Mass SpectrometryAnalysis for Protein Identification and Efficient DynamicExclusion 97%
- Development of a PNGase Rc column for online deglycosylation of complex glycoproteins during HDX-MS 96%
Similar papers in this journal
- Spatial top-down proteomics for the functional characterization of human kidney 96%
- A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19) 96%
- Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.