Proteomics

Dataset Information

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Trace sample analysis using Data-Dependent Acquisition Without Dynamic Exclusion


ABSTRACT: In this study, we report the application of turboDDA in improving trace sample analysis. Using different amount of K562 samples, we detected increases in identified protein number of more than 20% compared to all samples analyzed by standard DDA with dynamic exclusion.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Ci Wu  

LAB HEAD: Shen Zhang

PROVIDER: PXD042385 | Pride | 2024-01-26

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
90133_20201212_100ng_DE_01.wiff Wiff
90133_20201212_100ng_DE_01.wiff.scan Wiff
90135_20201212_100ng_noDE_01.wiff Wiff
90135_20201212_100ng_noDE_01.wiff.scan Wiff
90137_20201212_100ng_noDE_02.wiff Wiff
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Publications

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]

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