Dataset Information

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics.

ABSTRACT: Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.

SUBMITTER: Frankenfield AM

PROVIDER: S-EPMC10040255 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics.

Frankenfield Ashley M AM Ni Jiawei J Ahmed Mustafa M Hao Ling L

Journal of proteome research 20220706 9

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteom ...[more]

PMID: 35793413

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Project description:Data-independent acquisition (DIA) is a promising method for quantitative proteomics. Library-based DIA database searching against project-specific data-dependent acquisition (DDA) spectral libraries is the gold standard. These libraries are constructed using material-consuming pre-fractionation two dimensional DDA analysis. The alternative to this is library-free DIA analysis. Limited sample amounts restrict the use of fractionation to build spectral libraries for post-translational modifications (PTMs) DIA analysis. We present the use of gas-phase fractionation (GPF) DDA data to improve the depth of library-free DIA identification for the phosphoproteome, called GPF-DDA hybrid DIA. This method fully utilizes the remnants of samples post-DIA analysis and leverages both library-based and -free DIA database searching. GPF-DDA hybrid DIA analyzes phosphopeptides surplus sample after DIA analysis using a number of DDA injections with each scanning different mass-to-charge (m/z) windows, instead of preforming traditional off-line fractionation-based DDA. The GPF-DDA data is integrated into the library-free DIA database search to create a hybrid library, enhancing phosphopeptide identification. Two GPF-DDA injections proved to increase 18 % phosphopeptide and 13 % phosphosite identification in HEK293 cell lines, while five injections resulted in up to 28 % phosphopeptide and 21 % phosphosite increases compared to library-free DIA analysis alone. We used GPF-DDA hybrid DIA phosphoproteomics to characterize lung tissue upon direct (smoke induced) and indirect (sepsis induced) acute lung injury (ALI) in mice. The differentially expressed phosphosites (DEPsites) in direct ALI were found in proteins related to mRNA processing and RNA. DEPsites in indirect ALI were enriched in proteins related to microtubule polymerization, positive regulation of microtubule polymerization and fibroblast migration. This study demonstrates that GPF-DDA hybrid DIA analysis workflow can indeed promote depth of DIA analysis of phosphoproteome and could be extended to DIA analysis of other PTMs.

Dataset Information

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics.

Publications

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics.

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