Dataset Information

Label-Free Absolute Protein Quantification with Data-Independent Acquisition

ABSTRACT: Label-free absolute quantitative proteomics is commonly used for absolute quantification of the proteome or specific proteins of interest in various biological samples. Current label-free absolute protein quantification (APQ) methods determine MS1 intensities, MS2 spectral counts or intensities to absolutely quantify protein concentrations from data obtained from data-dependent acquisition (DDA). In recent years, label-free data-independent acquisition (DIA) has seen increasing use as a powerful tool for relative protein quantification. Here we present a novel label-free DIA-based absolute protein quantification (DIA-APQ) method for the absolute quantification of protein expressions from DIA data. To validate this method, both DDA and DIA experiments were performed on 36 individual human liver microsome and S9 samples. The DIA-APQ assay was able to quantify approximately twice as many proteins as the DDA MS1-based APQ method whereas protein concentrations determined by the two methods were comparable. To evaluate the accuracy of the DIA-APQ method, we absolutely quantified carboxylesterase 1 concentrations in human liver samples using an established SILAC internal standard-based proteomic assay; the SILAC results were consistent with those obtained from DIA-APQ analysis. Finally, we employed a unique algorithm in DIA-APQ to distribute the MS signals from shared peptides to different protein isoforms and successfully applied the DIA-APQ method to the absolute quantification of several drug-metabolizing enzyme isoforms in human liver microsomes. This novel DIA-based APQ method not only provides a powerful approach for absolutely quantifying entire proteomes and specific candidate proteins, but also has with the capacity differentiating protein isoforms.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Liver

SUBMITTER: Bing He

LAB HEAD: Hao-Jie Zhu

PROVIDER: PXD010912 | Pride | 2019-03-20

REPOSITORIES: Pride

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Publications

Label-free absolute protein quantification with data-independent acquisition.

He Bing B Shi Jian J Wang Xinwen X Jiang Hui H Zhu Hao-Jie HJ

Journal of proteomics 20190314

Despite data-independent acquisition (DIA) has been increasingly used for relative protein quantification, DIA-based label-free absolute quantification method has not been fully established. Here we present a novel DIA method using the TPA algorithm (DIA-TPA) for the absolute quantification of protein expressions in human liver microsomal and S9 samples. To validate this method, both data-dependent acquisition (DDA) and DIA experiments were conducted on 36 individual human liver microsome and S9 ...[more]

PMID: 30880166

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Dataset Information

Label-Free Absolute Protein Quantification with Data-Independent Acquisition

Publications

Label-free absolute protein quantification with data-independent acquisition.

Similar Datasets

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