GNPS MassIVE data submission of Artemisia MSe data
Ontology highlight
ABSTRACT: LC-MSe data of 5 Artemisia species (whole plant body) extracts for analyzing their chemical diversity, including 7 QC samples (mixture of all the samples in same conc.)
Project description:LC-MSe data of 13 Taraxacum species (whole plant body) extracts for analyzing their chemical diversity, including 8 QC samples (mixture of all the samples in same conc.)
Project description:Label-free protein quantification has developed into an attractive alternative to isotopic labeling for the quantification of proteins by mass spectrometry. Recently, the suite of label-free quantification strategies was expanded by LC-MSE–based absolute and relative protein quantification. We report here a systematic evaluation of high definition (HD) MSE-based protein quantification and identification with the chloroplast stroma proteome. This proteome is of high complexity and comprises a wide dynamic range of protein concentrations. Our analysis identified many chloroplast proteins that were not previously identified in large-scale proteome analyses, suggesting HD-MSE as a suitable complementary tool for discovery proteomics. We find that HD-MSE tends to underestimate protein abundances at concentrations above 30 fmol, which is likely due to ion transmission loss and detector saturation. This limitation can be circumvented by combining HD-MSE and standard MSE scan types. The selection of peptides for protein quantification depends on sample characteristics; therefore different peptides may be used for the quantification of one protein in different replicates. This influences the robustness of protein quantification and requires critical scrutiny of quantification results. Based on the quantification of chloroplast stroma proteins we performed a meta-analysis and compared published quantitative data with our results, using a parts per million normalization scheme. Important pathways in the chloroplast stroma show quantitative stability against different experimental conditions and different quantification strategies.
Project description:The experiment was conducted to compare the MSE and IM-MSE acquisiton modes as well as to find the optimal on column loading. This was done using 1DLC and cytosolic e coli digest.Raw data were processed and searched using Proteinlynx Global Server version 3.0. The following parameters were used to generate peak lists: minimum intensity for precursors was set to 140, minimum intensity for fragment ions 30 and total bin-count was set to minimum of 400. Processed data was searched against the UniprotKB, Escherichia coli K12 strain, concatenated with 125 common laboratory contaminates. Fixed modification was set to carbamidomethylation of C and variable modification was set to oxidation of M. Searches were conducted against a froward and reverse sequence database. Minimum identification criteria included 3 fragment ions per peptide, 5 fragment ions per protein, minimum peptide identification score of 5.9 and minimum of 2 peptides per protein. Search results and spectra were imported into Scaffold and the global false discovery rate was less than 1%.