Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry

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.