Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry

• Published in: Analytical chemistry (Washington) Vol. 92; no. 6; pp. 4217 - 4225
• Main Authors: Cho, Kyung-Cho, Clark, David J, Schnaubelt, Michael, Teo, Guo Ci, Leprevost, Felipe da Veiga, Bocik, William, Boja, Emily S, Hiltke, Tara, Nesvizhskii, Alexey I, Zhang, Hui
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 17.03.2020
• Subjects: Breast cancer; Chemistry; Data acquisition; Fractionation; Humans; Mass Spectrometry; Mass spectroscopy; Optimization; Peptides; Peptides - analysis; Proteins; Proteins - analysis; Proteomes; Proteomics; Quantitative analysis; Scientific imaging; Spectroscopy; Tumors; Two dimensional analysis; Uniqueness; Xenografts; Xenotransplantation
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.9b04418

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.