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Molecular & Cellular Proteomics 4:1297-1310, 2005.
© 2005 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Expression Clustering Reveals Detailed Co-expression Patterns of Functionally Related Proteins during B Cell Differentiation

A Proteomic Study Using a Combination of One-Dimensional Gel Electrophoresis, LC-MS/MS, and Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)^*,S

Edwin P. Romijn^,,¶, Chantal Christis^||,, Marnix Wieffer^**, Joost W. Gouw, Asier Fullaondo^,, Peter van der Sluijs^**, Ineke Braakman^|| and Albert J. R. Heck^,

From the Department of Biomolecular Mass Spectrometry, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CA Utrecht, The Netherlands, ^|| Department of Cellular Protein Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CH Utrecht, The Netherlands, ^** Department of Cell Biology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands, and Genetika, Antropologia Fisikoa eta Animalien Fisiologia Saila, Leioako Campusa, Leioa, 48940 Bizkaia, Spain

B cells play an essential role in the immune response. Upon activation they may differentiate into plasma cells that secrete specific antibodies against potentially pathogenic non-self antigens. To identify the cellular proteins that are important for efficient production of these antibodies we set out to study the B cell differentiation process at the proteome level. We performed an in-depth proteomic study to quantify dynamic relative protein expression patterns of several hundreds of proteins at five consecutive time points after lipopolysaccharide-induced activation of B lymphocytes. The proteome analysis was performed using a combination of stable isotope labeling using [¹³C₆]leucine added to the murine B cell cultures, one-dimensional gel electrophoresis, and LC-MS/MS. In this study we identified 1,001 B cell proteins. We were able to quantify the expression levels of a quarter of all identified proteins (i.e. 234) at each of the five different time points. Nearly all proteins revealed changes in expression patterns. The quantitative dataset was further analyzed using an unbiased clustering method. Based on their expression profiles, we grouped the entire set of 234 quantified proteins into a limited number of 12 distinct clusters. Functionally related proteins showed a strong correlation in their temporal expression profiles. The quality of the quantitative data allowed us to even identify subclusters within functionally related classes of proteins such as in the endoplasmic reticulum proteins that are involved in antibody production.

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