Systematic Data Acquisition and Analysis Strategies for Quantitative Proteomics

dc.contributor.advisorMacCoss, Michael J
dc.contributor.authorHeil, Lilian
dc.date.accessioned2023-09-27T17:20:12Z
dc.date.available2023-09-27T17:20:12Z
dc.date.issued2023-09-27
dc.date.submitted2023
dc.descriptionThesis (Ph.D.)--University of Washington, 2023
dc.description.abstractMass spectrometry-based proteomics has emerged as a powerful tool to gain insight into biological systems. As biological studies get more complex, there is a pressing need to increase throughput without sacrificing quality or completeness of the measurements. One limitation of many traditional mass spectrometry methods has been stochastic sampling causing poor reproducibility between runs. Systematic data acquisition methods promise to eliminate these missing values while improving sensitivity but are inherently inefficient and may be limited by instrumentation. Therefore, the field of proteomics could benefit from innovations in systematic data acquisition strategies. Here, we present the fundamentals of quantitative mass spectrometry and systematic data acquisition and analysis methods. We describe a head-to-head comparison of the linear ion trap and Orbitrap for parallel reaction monitoring, followed by a similar comparison between the Astral analyzer and the Orbitrap analyzer for data independent acquisition (DIA). We then present a novel method for improving the efficiency and therefore sensitivity of DIA by shifting acquisition windows across time. Finally, we propose a new search algorithm to build spectral libraries for DIA analysis without prior knowledge of the samples.
dc.embargo.termsOpen Access
dc.format.mimetypeapplication/pdf
dc.identifier.otherHeil_washington_0250E_26020.pdf
dc.identifier.urihttp://hdl.handle.net/1773/50834
dc.language.isoen_US
dc.rightsCC BY-NC-SA
dc.subjectMass Spectrometry
dc.subjectProteomics
dc.subjectAnalytical chemistry
dc.subject.otherGenetics
dc.titleSystematic Data Acquisition and Analysis Strategies for Quantitative Proteomics
dc.typeThesis

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