QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues
| Autor: | Jonathon J. O’Brien, Shunqiang Li, Xian Chen, Sherri R. Davies, Matthew J. Ellis, John A. Wrobel, Ling Xie, Harsha P. Gunawardena, Bahjat F. Qaqish |
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| Rok vydání: | 2016 |
| Předmět: |
Proteomics
0301 basic medicine Breast Neoplasms Peptide Computational biology Biology Bioinformatics Tandem mass spectrometry Biochemistry Analytical Chemistry Mice 03 medical and health sciences 0302 clinical medicine Protein sequencing Tandem Mass Spectrometry Cell Line Tumor Stable isotope labeling by amino acids in cell culture Protein biosynthesis Animals Humans Amino Acid Sequence Amino Acids Molecular Biology Peptide sequence chemistry.chemical_classification Technological Innovation and Resources Xenograft Model Antitumor Assays Gene Expression Regulation Neoplastic 030104 developmental biology chemistry Protein Biosynthesis 030220 oncology & carcinogenesis Proteome Female Peptides Chromatography Liquid |
| Zdroj: | Molecular & Cellular Proteomics. 15:740-751 |
| ISSN: | 1535-9476 |
| Popis: | Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to "rescue" the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes. |
| Databáze: | OpenAIRE |
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