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Multiplexed Quantitative Analyses Using Data-Dependent Acquisition Without Dynamic Exclusion for trace sample analysis

ABSTRACT: In this project, we applied our recently developed turboDDA method in the analysis of low amount of sample and iTRAQ labeled trace sample. Compared with standard data-ependent acquisition approach with dynamic exclusion, we detected improvment in spectral purity and quantification accuracy.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human) Escherichia Coli Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Ci Wu

LAB HEAD: Shen Zhang

PROVIDER: PXD042252 | Pride | 2024-01-26

REPOSITORIES: Pride

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Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion.

Wu Ci C Lei Jiao J Meng Fei F Wang Xingyao X Wong Cassandra J CJ Peng Jiaxi J Lin Ge G Gingras Anne-Claude AC Ma Junfeng J Zhang Shen S

Analytical chemistry 20231130 49

Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent ...[more]

PMID: 38032138

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