2D and 3D in vitro assays to quantify the invasive behavior of glioblastoma stem cells in response to SDF-1α.

Invasion is a hallmark of cancer and therefore in vitro invasion assays are important tools in cancer research. We aimed to describe in vitro 2D transwell assays and 3D spheroid assays to quantitatively determine the invasive behavior of glioblastoma stem cells in response to the chemoattractant SDF-1α. Matrigel was used as a matrix in both assays. We demonstrated quantitatively that SDF-1α increased invasive behavior of glioblastoma stem cells in both assays. We conclude that the 2D transwell invasion assay is easy to perform, fast and less complex whereas the more time-consuming 3D spheroid invasion assay is physiologically closer to the in vivo situation.

A simple in silico approach to generate gene-expression profiles from subsets of cancer genomics data.

In biomedical research, large-scale profiling of gene expression has become routine and offers a valuable means to evaluate changes in onset and progression of diseases, in particular cancer. An overwhelming amount of cancer genomics data has become publicly available, and the complexity of these data makes it a challenge to perform in silico data exploration, integration and analysis, in particular for scientists lacking a background in computational programming or informatics. Many web interface tools make these large datasets accessible but are limited to process large datasets. To accelerate the translation of genomic data into new insights, we provide a simple method to explore and select data from cancer genomic datasets to generate gene-expression profiles of subsets that are of specific genetic, biological or clinical interest.