4.2 Article

Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes

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MOLECULAR OMICS
卷 17, 期 3, 页码 413-425

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ROYAL SOC CHEMISTRY
DOI: 10.1039/d0mo00188k

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This study used a DIA-MS method to measure proteome and phosphoproteome of melanoma cell lines with different metastatic properties, demonstrating its potential for improving research reproducibility. The data confirmed mRNA-protein and protein-phosphosite correlations and identified altered phosphoprotein modules in metastatic cells. DIA-MS was established as a competitive proteotyping approach and provided valuable insights into metastatic proteotypes in melanoma cells.
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.

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