Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry

Abstract

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.

Figure 1.

Overall experimental design for single shot “2D-DIA”.

Figure 2.

Protein and peptide characterization of NCI-7 cell through 96 DDA runs. (A) The number of PSMs, peptides, and proteins in each fraction, respectively. The density bar (bottom of graph) indicates the peptide uniqueness (no. unique peptide/no. total peptides) of each fraction. (B) The number of peptides identified with an increasing number of fractions. (C) Same as (B), at the protein level.

Figure 3.

Comparison of 2D-DIA with 1D-DIA. (A) Coefficient variation (CV) was calculated from 3 replicates at the peptide (line) and protein (dot) levels, respectively. Asterisk (*) indicates the median CV (%). (B) The number of peptides and protein identified in 1D-DDA, 1D-DIA, and 2D-DIA. Asterisk (**) indicates the average number of peptides per protein. (C) Distribution of intensity-based absolute abundances of proteins (iBAQ) identified using 1D-DDA, 1D-DIA, and 2D-DIA methods.

Figure 4.

Quantitative proteomic analysis of PDX models (basal and luminal subtypes) using 2D-DIA. (A) The total number of identified protein groups that were human-only (green), from either human or mouse (yellow) or mouse-only (orange). The number of proteins with a quantification CV < 20% (dashed bars). (B) Volcano plot showing the Fold Change and the p-values (t test) between the basal and luminal subtypes. The known breast cancer markers were highlighted in red. (C) Hierarchical clustering analysis for protein expression between the basal and luminal subtypes. (D) Gene ontology analysis of up- and down-regulated proteins in the basal vs luminal subtype.