Browsing by Author "Payne, Samuel H."

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    A proteomic meta-analysis refinement of plasma extracellular vesicles
    (Springer Nature, 2023-11-28) Vallejo, Milene C.; Sarkar, Soumyadeep; Elliott, Emily C.; Henry, Hayden R.; Powell, Samantha M.; Diaz Ludovico, Ivo; You, Youngki; Huang, Fei; Payne, Samuel H.; Ramanadham, Sasanka; Sims, Emily K.; Metz, Thomas O.; Mirmira, Raghavendra G.; Nakayasu, Ernesto S.; Pediatrics, School of Medicine
    Extracellular vesicles play major roles in cell-to-cell communication and are excellent biomarker candidates. However, studying plasma extracellular vesicles is challenging due to contaminants. Here, we performed a proteomics meta-analysis of public data to refine the plasma EV composition by separating EV proteins and contaminants into different clusters. We obtained two clusters with a total of 1717 proteins that were depleted of known contaminants and enriched in EV markers with independently validated 71% true-positive. These clusters had 133 clusters of differentiation (CD) antigens and were enriched with proteins from cell-to-cell communication and signaling. We compared our data with the proteins deposited in PeptideAtlas, making our refined EV protein list a resource for mechanistic and biomarker studies. As a use case example for this resource, we validated the type 1 diabetes biomarker proplatelet basic protein in EVs and showed that it regulates apoptosis of β cells and macrophages, two key players in the disease development. Our approach provides a refinement of the EV composition and a resource for the scientific community.
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    AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry-mass spectrometry
    (Oxford University Press, 2021) Lee, Joon-Yong; Bilbao, Aivett; Conant, Christopher R.; Bloodsworth, Kent J.; Orton, Daniel J.; Zhou, Mowei; Wilson, Jesse W.; Zheng, Xueyun; Webb, Ian K.; Li, Ailin; Hixson, Kim K.; Fjeldsted, John C.; Ibrahim, Yehia M.; Payne, Samuel H.; Jansson, Christer; Smith, Richard D.; Metz, Thomas O.; Chemistry and Chemical Biology, School of Science
    Motivation: Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion's collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. Results: We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. Availability and implementation: https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. Supplementary information: Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).
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    Identification and Quantification of Proteoforms by Mass Spectrometry
    (Wiley, 2019-05) Schaffer, Leah V.; Millikin, Robert J.; Miller, Rachel M.; Anderson, Lissa C.; Fellers, Ryan T.; Ge, Ying; Kelleher, Neil L.; LeDuc, Richard D.; Liu, Xiaowen; Payne, Samuel H.; Sun, Liangliang; Thomas, Paul M.; Tucholski, Trisha; Wang, Zhe; Wu, Si; Wu, Zhijie; Yu, Dahang; Shortreed, Michael R.; Smith, Lloyd M.; BioHealth Informatics, School of Informatics and Computing
    A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post-translational modifications. In top-down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top-down proteomic workflows. In this review, we outline some recent advances and discuss current challenges and future directions for the field.