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Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry

  • Kyung-Cho Cho
    Kyung-Cho Cho
    Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
  • David J. Clark
    David J. Clark
    Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
  • Michael Schnaubelt
    Michael Schnaubelt
    Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
  • Guo Ci Teo
    Guo Ci Teo
    Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
    More by Guo Ci Teo
  • Felipe da Veiga Leprevost
    Felipe da Veiga Leprevost
    Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
  • William Bocik
    William Bocik
    Antibody Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
  • Emily S. Boja
    Emily S. Boja
    Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, Maryland 20892, United States
  • Tara Hiltke
    Tara Hiltke
    Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, Maryland 20892, United States
    More by Tara Hiltke
  • Alexey I. Nesvizhskii*
    Alexey I. Nesvizhskii
    Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
    *Address: Departments of Pathology and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. E-mail: [email protected]. Tel.: 734.764.3516.
  • , and 
  • Hui Zhang*
    Hui Zhang
    Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
    *Address: Johns Hopkins School of Medicine, 400 N Broadway, Baltimore, MD 21231, USA. E-mail: [email protected]. Tel.: (410) 502-8149. Fax: (443) 287-6388.
    More by Hui Zhang
Cite this: Anal. Chem. 2020, 92, 6, 4217–4225
Publication Date (Web):February 14, 2020
https://doi.org/10.1021/acs.analchem.9b04418
Copyright © 2020 American Chemical Society
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Supporting Info (1)»

Abstract

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Methodologies that facilitate high-throughput proteomic analysis are a key step toward moving proteome investigations into clinical translation. Data independent acquisition (DIA) has potential as a high-throughput analytical method due to the reduced time needed for sample analysis, as well as its highly quantitative accuracy. However, a limiting feature of DIA methods is the sensitivity of detection of low abundant proteins and depth of coverage, which other mass spectrometry approaches address by two-dimensional fractionation (2D) to reduce sample complexity during data acquisition. In this study, we developed a 2D-DIA method intended for rapid- and deeper-proteome analysis compared to conventional 1D-DIA analysis. First, we characterized 96 individual fractions obtained from the protein standard, NCI-7, using a data-dependent approach (DDA), identifying a total of 151,366 unique peptides from 11,273 protein groups. We observed that the majority of the proteins can be identified from just a few selected fractions. By performing an optimization analysis, we identified six fractions with high peptide number and uniqueness that can account for 80% of the proteins identified in the entire experiment. These selected fractions were combined into a single sample which was then subjected to DIA (referred to as 2D-DIA) quantitative analysis. Furthermore, improved DIA quantification was achieved using a hybrid spectral library, obtained by combining peptides identified from DDA data with peptides identified directly from the DIA runs with the help of DIA-Umpire. The optimized 2D-DIA method allowed for improved identification and quantification of low abundant proteins compared to conventional unfractionated DIA analysis (1D-DIA). We then applied the 2D-DIA method to profile the proteomes of two breast cancer patient-derived xenograft (PDX) models, quantifying 6,217 and 6,167 unique proteins in basal- and luminal- tumors, respectively. Overall, this study demonstrates the potential of high-throughput quantitative proteomics using a novel 2D-DIA method.

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.9b04418.

  • Conditions for optimization of DIA method (Table S1), information of spectral libraries that used in this experiment (Table S2), comparison DIA results by both spectral libraries (DDA only and DDA/DIA combining, Table S3), and link to raw data: https://cptac-data-portal.georgetown.edu/cptac/s/S052 (PDF)

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This article is cited by 15 publications.

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