Leonard C Rorrer, Liulin Deng, Lauren Royer, Isabel Uribe, Benjamin C Orsburn, Oliver Bernhardt, Tejas Gandhi, Lukas Reiter, Daniel DeBord
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引用次数: 0
Abstract
Here we present a novel data-independent acquisition (DIA) mass spectrometry (MS) operating mode termed parallel accumulation-mobility aligned fragmentation (PAMAF) that offers enhanced speed and sensitivity of ion fragmentation analysis for discovery workflows such as bottom-up proteomics. This mode of operation leverages high-resolution ion mobility (HRIM) separation capabilities of the structures for lossless ion manipulation technology to achieve HRIM-based precursor isolation in place of traditional quadrupole filtering approaches. PAMAF mode increases the number of features that can be identified per MS1/MS2 acquisition cycle by employing mobility-based time alignment to associate fragment ions with their corresponding precursor ions. By using a high-speed, lossless separation technique for precursor isolation instead of the comparatively slow and wasteful quadrupole filtering, ion losses are avoided while simultaneously increasing the rate at which precursor ions are sequentially fragmented and detected. In addition, by accumulating ions while the previous packet of ions is being analyzed, the PAMAF mode achieves ∼100% ion utilization efficiency. Benchmarking results of LC-PAMAF-MS analysis of a whole cell protein digest showed ∼6× more protein group identifications compared to a standard data-dependent acquisition analysis without HRIM on the same QTOF instrument, and >100 x improvement for low-load workflows. Quantitative evaluations demonstrated that PAMAF mode could quantify low abundance peptides, including those undetectable by data-dependent acquisition. In addition, since precursor isolation in PAMAF mode is size-based rather than m/z-based, coeluting isobars and isomers can be resolved prior to fragmentation, eliminating chimeric spectra that compromise identification accuracy. We also explored the benefits of combining HRIM and quadrupole isolation to achieve improved specificity termed DIA-PAMAF mode, which enabled the detection of over 8000 protein groups from a HeLa digest analysis. PAMAF mode brings a powerful new technique to the field of proteomics with the potential to improve the sensitivity and selectivity of mass spectrometry-based proteomics.
期刊介绍:
The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action.
The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data.
Scope:
-Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights
-Novel experimental and computational technologies
-Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes
-Pathway and network analyses of signaling that focus on the roles of post-translational modifications
-Studies of proteome dynamics and quality controls, and their roles in disease
-Studies of evolutionary processes effecting proteome dynamics, quality and regulation
-Chemical proteomics, including mechanisms of drug action
-Proteomics of the immune system and antigen presentation/recognition
-Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease
-Clinical and translational studies of human diseases
-Metabolomics to understand functional connections between genes, proteins and phenotypes