Dataset Information

Phosphoproteomics data classify haematological cancer cell lines according to tumour type and sensitivity to kinase inhibitors

ABSTRACT: Acute myeloid leukemia, lymphoma and multiple myeloma cell lines were grown for 24h in RPMI medium and cells were harvested by centrifugation. Samples were digested with trypsin and subjected to phosphoenrichment using IMAC. Phosphopeptides were run in a LTQ-Orbitrap-XL. Peaks lists were generated with Mascot Distiller (version 2.3) in MGF format and Database searches were with Mascot Server (version 2.3) against the SwissProt database restricted to human sequences (release December 2011) and trypsin cleavage. Restrictions were 7ppm for parent ions and 600 mmu for fragment masses. Allowed modifications were phosphorylation of Ser/Thr/Tyr, pyro-Glu (N-term) and methionine oxidation and one miss-cleavage allowed. Quantification was by label-free using peak heights of extracted ion chromatograms (XICs) constructed with narrow mass windows (7ppm) and time windows (1.5 minutes). Pescal, a computer program written in house, was used to automate the generation of XICs and to calculate peak heights.

INSTRUMENT(S): LTQ Orbitrap, instrument model

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): B Cell, Blood Cell

SUBMITTER: Pedro Casado-Izquierdo

PROVIDER: PXD000217 | Pride | 2013-04-26

REPOSITORIES: Pride

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Publications

Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors.

Casado Pedro P Alcolea Maria P MP Iorio Francesco F Rodríguez-Prados Juan-Carlos JC Rodríguez-Prados Juan-Carlos JC Vanhaesebroeck Bart B Saez-Rodriguez Julio J Joel Simon S Cutillas Pedro R PR

Genome biology 20130429 4

<h4>Background</h4>Tumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.<h4>Results</h4>Mass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple ...[more]

PMID: 23628362

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Project description:Shotgun and positional proteomics study of a mouse embryonic stem cell line. We devised a proteogenomic approach constructing a custom protein sequence search space, built from both SwissProt and RIBO-seq derived translation products, applicable for LC-MSMS spectrum identification. To record the impact of using the constructed deep proteome database we performed two alternative MS-based proteomic strategies: (I) a regular shotgun proteomic and (II) an N-terminal COFRADIC approach. The obtained fragmentation spectra were searched against the custom database (combination of UniProtKB-SwissProt and RIBO-seq derived translation sequences) using three different search engines: OMSSA (version 2.1.9), X!Tandem (TORNADO, version 2010.01.01.04) and Mascot (version 2.3). The first two were run from the SearchGUI graphical user interface (version 1.10.4). A combination of X!Tandem and Mascot was used for the N-terminal COFRADIC analysis, a combination of all three search engines for the shotgun proteome analysis. Note that OMMSA cannot cope with the protease setting semi-ArgC/P needed to analyze N-terminal COFRADIC data.For the shotgun proteome data, trypsin was set as cleavage enzyme allowing for one missed cleavage, and singly to triply charged precursors or singly to quadruple charged precursors were taken into account respectively for the Mascot or X!Tandem/OMSSA search engines, and the precursor and fragment mass tolerance were set to respectively 10 ppm and 0.5 Da. Methionine oxidation to methionine-sulfoxide, pyroglutamate formation of N-terminal glutamine and acetylation (protein N-terminus) were set as variable modifications. For the N-terminal COFRADIC analysis the protease setting semi-ArgC/P (Arg-C specificity with arginine-proline cleavage allowed) was used. No missed cleavages were allowed and the precursor and fragment mass tolerance were also set to respectively 10 ppm and 0.5 Da. Carbamidomethylation of cysteine and methionine oxidation to methionine-sulfoxide and 13C3D2-acetylation of lysines were set as fixed modifications. Peptide N-terminal acetylation or 13C3D2-acetylation and pyroglutamate formation of N-terminal glutamine were set as variable modifications and instrument setting was put on ESI-TRAP. Protein and peptide identification in addition to data interpretation was done using the PeptideShaker algorithm (http://code.google.com/p/peptide-shaker, version 0.18.3), setting the false discovery rate to 1% at all levels (protein, peptide, and peptide to spectrum matching). Aforementioned tools and algorithms (SearchGui, X!Tandem, OMSSA, and PeptideShaker) are freely available as open source.

Dataset Information

Phosphoproteomics data classify haematological cancer cell lines according to tumour type and sensitivity to kinase inhibitors

Publications

Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors.

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