Joao A Paulo1, Joseph D Mancias, Steven P Gygi. 1. From the *Department of Cell Biology, Harvard Medical School; and †Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA.
Abstract
OBJECTIVES: Mass spectrometry-based proteomics enables near-comprehensive protein expression profiling. We aimed to compare quantitatively the relative expression levels of thousands of proteins across 5 pancreatic cell lines. METHODS: Using tandem mass tags (TMT10-plex), we profiled the global proteomes of 5 cell lines in duplicate in a single multiplexed experiment. We selected cell lines commonly used in pancreatic research: CAPAN-1, HPAC, HPNE, PANC1, and PaSCs. In addition, we examined the effects of different proteases (Lys-C and Lys-C plus trypsin) on the dataset depth. RESULTS: We quantified over 8000 proteins across the 5 cell lines. Analysis of variance testing of cell lines within each data set resulted in over 1400 statistically significant differences in protein expression levels. Comparing the data sets, 10% more proteins and 30% more peptides were identified in the Lys-C/trypsin data set than in the Lys-C-only data set. The correlation coefficient of quantified proteins common between the data sets was greater than 0.85. CONCLUSIONS: We illustrate protein level differences across pancreatic cell lines. In addition, we highlight the advantages of Lys-C/trypsin over Lys-C-only digests for discovery proteomics. These data sets provide a valuable resource of cell line-dependent peptide and protein differences for future targeted analyses, including those investigating on- or off-target drug effects across cell lines.
OBJECTIVES: Mass spectrometry-based proteomics enables near-comprehensive protein expression profiling. We aimed to compare quantitatively the relative expression levels of thousands of proteins across 5 pancreatic cell lines. METHODS: Using tandem mass tags (TMT10-plex), we profiled the global proteomes of 5 cell lines in duplicate in a single multiplexed experiment. We selected cell lines commonly used in pancreatic research: CAPAN-1, HPAC, HPNE, PANC1, and PaSCs. In addition, we examined the effects of different proteases (Lys-C and Lys-C plus trypsin) on the dataset depth. RESULTS: We quantified over 8000 proteins across the 5 cell lines. Analysis of variance testing of cell lines within each data set resulted in over 1400 statistically significant differences in protein expression levels. Comparing the data sets, 10% more proteins and 30% more peptides were identified in the Lys-C/trypsin data set than in the Lys-C-only data set. The correlation coefficient of quantified proteins common between the data sets was greater than 0.85. CONCLUSIONS: We illustrate protein level differences across pancreatic cell lines. In addition, we highlight the advantages of Lys-C/trypsin over Lys-C-only digests for discovery proteomics. These data sets provide a valuable resource of cell line-dependent peptide and protein differences for future targeted analyses, including those investigating on- or off-target drug effects across cell lines.
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