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Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry.

Kyung-Cho Cho1, David J Clark1, Michael Schnaubelt1

  • 1Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States.

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This study introduces a novel two-dimensional Data Independent Acquisition (2D-DIA) method for faster, deeper proteomic analysis. The optimized 2D-DIA enhances protein identification and quantification, particularly for low-abundance proteins, advancing clinical translation.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Biotechnology

Background:

  • High-throughput proteomic analysis is crucial for clinical translation.
  • Data Independent Acquisition (DIA) offers high-throughput quantitative analysis but faces limitations in sensitivity and coverage for low-abundance proteins.
  • Two-dimensional fractionation (2D) is used in mass spectrometry to reduce sample complexity and improve detection depth.

Purpose of the Study:

  • To develop and validate a novel 2D-DIA method for rapid and deeper proteome analysis.
  • To improve the identification and quantification of low-abundance proteins compared to conventional 1D-DIA.
  • To apply the 2D-DIA method for proteomic profiling of breast cancer patient-derived xenograft (PDX) models.

Main Methods:

  • Characterization of 96 fractions from NCI-7 protein standard using Data Dependent Acquisition (DDA).
  • Optimization to identify six key fractions for combining into a single sample for 2D-DIA.
  • Development of a hybrid spectral library using DIA-Umpire for enhanced DIA quantification.
  • Application of the optimized 2D-DIA method to basal- and luminal- breast cancer PDX models.

Main Results:

  • Identification of 151,366 unique peptides from 11,273 protein groups in the NCI-7 standard.
  • Determination that six selected fractions captured 80% of identified proteins.
  • Improved identification and quantification of low-abundance proteins using 2D-DIA compared to 1D-DIA.
  • Quantification of 6,217 and 6,167 unique proteins in basal- and luminal- breast cancer PDX models, respectively.

Conclusions:

  • The developed 2D-DIA method enables rapid and deeper proteome analysis.
  • This novel approach enhances the identification and quantification of low-abundance proteins.
  • The 2D-DIA method shows significant potential for high-throughput quantitative proteomics in clinical applications.