Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes. (17th March 2021)
- Record Type:
- Journal Article
- Title:
- Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes. (17th March 2021)
- Main Title:
- Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes
- Authors:
- Gao, Erli
Li, Wenxue
Wu, Chongde
Shao, Wenguang
Di, Yi
Liu, Yansheng - Abstract:
- Abstract : We present a high-quality data-independent acquisition dataset, profiling the abundance and variation of both proteomes and phosphoproteomes across melanoma cells. Abstract : Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA–protein and protein–phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complexAbstract : We present a high-quality data-independent acquisition dataset, profiling the abundance and variation of both proteomes and phosphoproteomes across melanoma cells. Abstract : Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA–protein and protein–phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells. … (more)
- Is Part Of:
- Molecular omics. Volume 17:Number 3(2021)
- Journal:
- Molecular omics
- Issue:
- Volume 17:Number 3(2021)
- Issue Display:
- Volume 17, Issue 3 (2021)
- Year:
- 2021
- Volume:
- 17
- Issue:
- 3
- Issue Sort Value:
- 2021-0017-0003-0000
- Page Start:
- 413
- Page End:
- 425
- Publication Date:
- 2021-03-17
- Subjects:
- Molecular biology -- Periodicals
Biochemistry -- Periodicals
Biological systems -- Periodicals
Molecular Biology
Computational Biology
Biochemistry
Biological systems
Molecular biology
Periodicals
Electronic journals
Periodicals
Fulltext
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Periodicals - Journal URLs:
- http://www.rsc.org/journals-books-databases/about-journals/molecular-omics/ ↗
http://pubs.rsc.org/en/journals/journalissues/mo#!recentarticles&adv ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/d0mo00188k ↗
- Languages:
- English
- ISSNs:
- 2515-4184
- Deposit Type:
- Legaldeposit
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