Dataset Information

Mass Defect-Based DiLeu Tagging for Multiplexed Data-Independent Acquisition.

ABSTRACT: The unbiased selection of peptide precursors makes data-independent acquisition (DIA) an advantageous alternative to data-dependent acquisition (DDA) for discovery proteomics, but traditional multiplexed quantification approaches employing mass difference labeling or isobaric tagging are incompatible with DIA. Here, we describe a strategy that permits multiplexed quantification by DIA using mass defect-based N,N-dimethyl leucine (mdDiLeu) tags and high-resolution tandem mass spectrometry (MS²) analysis. Millidalton mass differences between mdDiLeu isotopologues produce fragment ion multiplet peaks separated in mass by as little as 5.8 mDa, enabling up to 4-plex quantification in DIA MS² spectra. Quantitative analysis of yeast samples displayed comparable accuracy and precision for MS²-based DIA and MS¹-based DDA methods. Multiplexed DIA analysis of cerebrospinal fluid revealed the dynamic proteome changes in Alzheimer's disease, demonstrating its utility for discovery of potential clinical biomarkers. We show that the mdDiLeu tagging approach for multiplexed DIA is a viable methodology for investigating proteome changes, particularly for low-abundance proteins, in different biological matrices.

SUBMITTER: Zhong X

PROVIDER: S-EPMC7438256 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Mass Defect-Based DiLeu Tagging for Multiplexed Data-Independent Acquisition.

Zhong Xiaofang X Frost Dustin C DC Yu Qinying Q Li Miyang M Gu Ting-Jia TJ Li Lingjun L

Analytical chemistry 20200730 16

The unbiased selection of peptide precursors makes data-independent acquisition (DIA) an advantageous alternative to data-dependent acquisition (DDA) for discovery proteomics, but traditional multiplexed quantification approaches employing mass difference labeling or isobaric tagging are incompatible with DIA. Here, we describe a strategy that permits multiplexed quantification by DIA using mass defect-based <i>N</i>,<i>N</i>-dimethyl leucine (mdDiLeu) tags and high-resolution tandem mass spectr ...[more]

PMID: 32649829

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Project description: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.

Dataset Information

Mass Defect-Based DiLeu Tagging for Multiplexed Data-Independent Acquisition.

Publications

Mass Defect-Based DiLeu Tagging for Multiplexed Data-Independent Acquisition.

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