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

Abstract

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.