A Unique Procedure to Identify Cell Surface Markers Through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis.

To identify cell-specific markers, we designed a DNA microarray platform with oligonucleotide probes for human membrane-anchored proteins. Human glioma cell lines were analyzed using microarray and compared with normal and fetal brain tissues. For the microarray analysis, we employed a spherical self-organizing map, which is a clustering method suitable for the conversion of multidimensional data into two-dimensional data and displays the relationship on a spherical surface. Based on the gene expression profile, the cell surface characteristics were successfully mirrored onto the spherical surface, thereby distinguishing normal brain tissue from the disease model based on the strength of gene expression. The clustered glioma-specific genes were further analyzed by polymerase chain reaction procedure and immunocytochemical staining of glioma cells. Our platform and the following procedure were successfully demonstrated to categorize the genes coding for cell surface proteins that are specific to glioma cells. Our assessment demonstrates that a spherical self-organizing map is a valuable tool for distinguishing cell surface markers and can be employed in marker discovery studies for the treatment of cancer.

Spherical self-organizing map as a helpful tool to identify category-specific cell surface markers.

We analyzed gene expression profiles of five tumor cell lines (NB2a, NB41A3, C1300N18, BC3H1, and Neuro2a) derived from a category of nervous system using our originally developed cell surface marker DNA microarray in order to search for tumor-specific cell surface markers common to these cells. To visualize the expression patterns and to extract candidate genes of interest based on the expression profiles of several cell lines, we employed the clustering procedure of spherical self-organizing-map. As the result, three candidates of tumor-specific cell surface markers were picked up when the expression profiles were compared with that from normal brain tissue. RT-qPCR showed the expression of these genes was higher in tumor cells than in normal brain. Here we demonstrated the spherical self-organizing-map analysis should be useful to identify the candidates of cell surface markers common and specific to the group of cells or tissues of interest.