GenomicScape: An Easy-to-Use Web Tool for Gene Expression Data Analysis. Application to Investigate the Molecular Events in the Differentiation of B Cells into Plasma Cells

DNA microarrays have considerably helped to improve the understanding of biological processes and diseases. Large amounts of publicly available microarray data are accumulating, but are poorly exploited due to a lack of easy-to-use bioinformatics resources. The aim of this study is to build a free and convenient data-mining web site (www.genomicscape.com). GenomicScape allows mining dataset from various microarray platforms, identifying genes differentially expressed between populations, clustering populations, visualizing expression profiles of large sets of genes, and exporting results and figures. We show how easily GenomicScape makes it possible to construct a molecular atlas of the B cell differentiation using publicly available transcriptome data of naïve B cells, centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells and bone marrow plasma cells. Genes overexpressed in each population and the pathways encoded by these genes are provided as well as how the populations cluster together. All the analyses, tables and figures can be easily done and exported using GenomicScape and this B cell to plasma cell atlas is freely available online. Beyond this B cell to plasma cell atlas, the molecular characteristics of any biological process can be easily and freely investigated by uploading the corresponding transcriptome files into GenomicScape.

[1]  K. Tarte,et al.  Characterization of a Transitional Preplasmablast Population in the Process of Human B Cell to Plasma Cell Differentiation , 2011, The Journal of Immunology.

[2]  M. Jourdan,et al.  An in vitro model of differentiation of memory B cells into plasmablasts and plasma cells including detailed phenotypic and molecular characterization. , 2009, Blood.

[3]  V. Pantesco,et al.  The level of TACI gene expression in myeloma cells is associated with a signature of microenvironment dependence versus a plasmablastic signature. , 2005, Blood.

[4]  M. Care,et al.  In Vitro Generation of Long-lived Human Plasma Cells , 2012, The Journal of Immunology.

[5]  Hartmut Goldschmidt,et al.  A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments , 2008, BMC Bioinformatics.

[6]  Bernard Klein,et al.  Circulating human B and plasma cells. Age-associated changes in counts and detailed characterization of circulating normal CD138− and CD138+ plasma cells , 2010, Haematologica.

[7]  M. Shlomchik,et al.  Germinal center selection and the development of memory B and plasma cells , 2012, Immunological reviews.

[8]  K. Tarte,et al.  IL-6 supports the generation of human long-lived plasma cells in combination with either APRIL or stromal cell-soluble factors , 2014, Leukemia.

[9]  K. Tarte,et al.  Gene expression profiling of plasma cells and plasmablasts: toward a better understanding of the late stages of B-cell differentiation. , 2003, Blood.

[10]  P. Bierling,et al.  B cell depletion in immune thrombocytopenia reveals splenic long-lived plasma cells. , 2013, The Journal of clinical investigation.

[11]  K. Tarte,et al.  CXCR4 Expression Functionally Discriminates Centroblasts versus Centrocytes within Human Germinal Center B Cells1 , 2009, The Journal of Immunology.