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PROFILING THE CHROMOSOME 16 BY HIGH-RESOLUTION DATA DEPENDENT MS: EXTRACTION/FRACTIONATION METHOD EVALUATION IN JURKAT CELLS. Spanish Human Proteome Project Consortium Chromosome 16. III Workshop of the Chromosome 16 Consortium
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PROFILING THE CHROMOSOME 16 BY HIGH-RESOLUTION DATA DEPENDENT MS: EXTRACTION/FRACTIONATION METHOD EVALUATION IN JURKAT CELLS. SpanishHumanProteome Project ConsortiumChromosome 16 III Workshop of theChromosome 16 Consortium December 2012, La Cristalera, Miraflores de la Sierra, Madrid
METHODOLOGY RIPA 4% SDS CHAPS/UREA Methanol/Chloroform precipitation In-solution digestion Off-line HPLC-RP Basic pH 1D-SDS PAGE 30 fractions / pooledinto 10 6 Experiments x 2 biologicalreplicates 15 bands Cut bands Automatic In-gel digestion Digestor (Bruker) HPLC-RP Acid pH 5600-Ttof (CID)
TOTAL PROTEOME COVERAGE TOTAL PROTEINS / PEPTIDES IDENTIFIED FDR 1% Proteinlevel 92278 peptides 12159 proteins
TOTAL PROTEOME COVERAGE PROTEIN OVERLAPPING BETWEEN REPLICATES Good overlapping between replicate experiments for the Gel or off-line basicRPprefractionation methods.
COMPARING FRACTIONATION METHODS PROTEIN OVERLAPPING BETWEEN FRACTIONATION METHODS For all the cell-lysing conditions tested, the in-solution digestion-basicRP-LC/MS workflow was by far the most compatible (between 20-30% more proteins identified).
COMPARING FRACTIONATION METHODS NUMBER OF EXCLUSIVE PROTEINS PER EXPERIMENT More exclusive proteins identified by HPLC-RP
COMPARING CELL-LYSING CONDITIONS
COMPARING PROTEIN EXTRACTION METHODS TOTAL PROTEINS / PEPTIDES 2 Replicates 1D SDS-PAGE HPLC-RP • Different cell-lysis conditions gave similar proteome coverage in the Gel-LC-MS workflow. • CHAPS lysis enabled greater protein identification in the basic-RP-LC/MS workflow. • The excess of detergents that would alter protein precipitation and digestion.
COMPARING PROTEIN EXTRACTION METHODS PROTEIN OVERLAPPING BETWEEN EXTRACTION METHODS 2 Replicates 1D SDS-PAGE HPLC-RP More exclusive proteins identified by CHAPS lysis combined with in-solution digestion/off-line HPLC separation.
CHROMOSOME 16 COVERAGE CHROMOSOME 16 PROTEINS/PEPTIDES 4000 péptidos 447 proteínas 351 genes
CHROMOSOME 16 COVERAGE Chr 16 PROTEINS: SUBCELLULAR LOCALIZATION PIKE • CHAPS lysis was better in recovering most sub-cellular compartments even for membraneproteins. • However, our ability to identify membrane proteins is low and we shouldconsiderate using plasma membrane enrichment methods (subcellular fractionation, cell-surface biotinylation…)
MAPPING POST-TRANSLATIONAL MODIFICATIONS OF THE CHR 16
MAPPING POST-TRANSLATIONAL MODIFICATIONS OF THE CHR 16 Jurkat CHAPS HPLC-RP Replicate 2 PHOSPHORYLATION ACETYLATION TOTAL: 1317 P-Peptides / 865 P-Proteins Chr 16: 66 P-Peptides / 42 P-Proteins Chr 16: 56 peptides N-term acetylated Can we use alternative strategies to complement this coverage of Chr16 PTMs? Average of 3% of peptides found acetylated
ISSUE OF ISOFORMS • Test of ProteinGrouping
TEST OF PROTEIN GROUPING TOTAL/Chr 16 PROTEINS AND PEPTIDES Replicate 2 Jurkat TOTAL Chr 16 IMPORTANTE: Exportar datos siempre de la misma forma FDR 1% Proteinlevel
COMPARING MASS SPECTROMETERS
MASS SPECTROMETER: Triple ToF 5600 (CNB) vs. Q-Exactive (UPV) 10 Fractions CHAPS/UREA
MASS SPECTROMETER: Triple ToF 5600 (CNB) vs. Q-Exactive (UPV) TOTAL/Chr 16 PROTEINS AND PEPTIDES Jurkat CHAPS HPLC-RP TOTAL Chr 16 Bothmassspectrometers are comparable FDR 1% Proteinlevel
MASS SPECTROMETER: Triple ToF 5600 (CNB) vs. Q-Exactive (UPV) PROTEIN OVERLAPPING Jurkat CHAPS HPLC-RP TOTAL Chr 16 Reproducibility FDR 1% Proteinlevel
SUMMARY • We have tested various workflows to increase our coverage of the Chr16. • We have used strong detergents to better solubilize and resolve membrane proteins and show that due to the low compatibility with in-solution digestion, proteins were not so efficiently recovered. • Our observation was that CHAPS cell-lysis coupled to basic-RP-LC/MS provided the best results. • We are combining various approaches to gain more insight and coverage of proteins of low solubility and their post-translational modification profiles: • Cell-surface biotinylation • Phosphopeptide enrichment • We are generating an increasing number of mass spectras and we will build an MS/MS library that will hopefully be used for MRM validations.
ACKNOWLEDGEMENTS RosanaNavajas Severine Gharbi Miguel Marcilla Alberto Paradela Carmen González Gonzalo Martínez AdánAlpízar Silvia Juárez Sergio Ciordia Marisol Fernández Alberto Medina Salvador Martínez Antonio Ramos Miguel ÁngelLópez Mª Carmen Mena Fernando Roncal Manuel Lombardía Virginia Pavón Lola Segura