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Proteomic analysis/profiling of human dendritic cells

proteomics dataset

A comprehensive protein inventory of clinical grade immature and cytokine cocktail matured (Il-6, IL-1 beta, TNF alpha, PGE2; 48 hours) monocyte derived human dendritic cells (DC) from a healthy donor has been established by using high accuracy, high sensitivity protein identification technology. We have identified 2794 proteins in DCs by liquid chromatography tandem mass spectrometry. Prior to MS analysis, DC were lysed and divided into a soluble fraction containing cytosolic proteins and into an insoluble fraction enriched for membrane containing proteins. Highly complex protein mixtures of DC cellular fractions were separated on 1 D SDS-PAGE into 20 gel slices (fractions), followed by in-gel digestion with trypsin. Purified tryptic peptides were analysed in triplicate by LC-MS/MS using a linear ion trap/ Fourier Transform (LTQ-FT) hybrid mass spectrometer.

Iterative calibration algorithms were used to achieve a final, absolute mass accuracy of better than 10 parts per million (p.p.m.) for all peptide ions. These high-accuracy spectra were searched against a human protein database, using MASCOT probability-based scoring in which the fragment ions are matched against the calculated fragments of all tryptic peptides from the human sequences. Additional constraints were set by searches against a reversed human database to minimize false positive protein identifications. These criteria resulted in a list of 2794 unique proteins that were identified with high confidence, comprising of 2623 proteins detected in immature DCs, 1896 proteins in mature DCs, and 1725 proteins in both cell types. Differences were quantified by the exponentially modified Protein Abundance Index (emPAI) method, a label free spectral counting procedure for determining relative protein amounts of LC-MS/MS data.

Protein expression profiles were compared with gene expression profiles generated by microarray technology (M2.2). In total, we could map 96 percent of the detected proteins to expressed genes for the immature DCs and 90 percent for the mature DCs. A Pearson correlation analysis revealed for both cell types, good correlations, respectively 0.57 and 0.50. These correlations are in good agreement with similar correlation studies, where correlation coefficients approximately range from 0.2-0.7 for large scale datasets. These studies include quantitative highly accurate proteomic data generated by chemically-or metabolically-labeled approaches. Therefore, we conclude that our data contains sufficient quantitative information to define subsets of up- and down-regulated proteins for functional analysis. We find 202 proteins upregulated by more than 2 fold in mature DCs and 463 proteins upregulated in immature DCs.

Functional annotation by gene ontology classification and pathway analysis by several bioinformatics approaches have been performed. Preliminary results obtained from querying DAVID (http://david.abcc.ncifcrf.gov/) and Ontologizer (http://www.charite.de/ch/medgen/ontologizer/) show statistically significant enrichment for immunological-relevant signaling pathways and immunology-related GO terms of the upregulated proteins in mature DCs.





created over 8 years ago (2 March 2009)    last modified over 6 years ago (28 September 2011)   [ RDF Rdf ]   [ RelFinder Relfinder ]