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.