Dataset Information

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition.

ABSTRACT: We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data-independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high-resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high-quality quantitative measurements across a wide dynamic range. We also use a newly developed extracellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5000 plasma proteins in a 60 min gradient with the Orbitrap Astral mass spectrometer.

SUBMITTER: Heil LR

PROVIDER: S-EPMC10563156 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition.

Heil Lilian R LR Damoc Eugen E Arrey Tabiwang N TN Pashkova Anna A Denisov Eduard E Petzoldt Johannes J Peterson Amelia C AC Hsu Chris C Searle Brian C BC Shulman Nicholas N Riffle Michael M Connolly Brian B MacLean Brendan X BX Remes Philip M PM Senko Michael W MW Stewart Hamish I HI Hock Christian C Makarov Alexander A AA Hermanson Daniel D Zabrouskov Vlad V Wu Christine C CC MacCoss Michael J MJ

Journal of proteome research 20230908 10

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data-independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high-resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high-quality quantita ...[more]

PMID: 37683181

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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

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition.

Publications

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition.

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