通过球形自组织图谱识别细胞表面标记的独特程序应用于DNA微阵列分析

Yuh Sugii, T. Kasai, Masashi Ikeda, Arun Vaidyanath, Kazuki Kumon, Akifumi Mizutani, Akimasa Seno, H. Tokutaka, Takayuki Kudoh, M. Seno
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引用次数: 5

摘要

为了鉴定细胞特异性标记,我们设计了一个DNA微阵列平台,其中包含针对人膜锚定蛋白的寡核苷酸探针。用微阵列技术对人胶质瘤细胞系进行了分析,并与正常脑组织和胎儿脑组织进行了比较。对于微阵列分析,我们采用了球形自组织图,这是一种适合于将多维数据转换为二维数据并在球面上显示关系的聚类方法。基于基因表达谱,成功地将细胞表面特征镜像到球面上,从而根据基因表达强度区分正常脑组织和疾病模型。通过聚合酶链反应和胶质瘤细胞免疫细胞化学染色进一步分析聚集性胶质瘤特异性基因。我们的平台和下面的程序成功地证明了对胶质瘤细胞特异性细胞表面蛋白编码基因的分类。我们的评估表明,球形自组织图谱是区分细胞表面标记物的一种有价值的工具,可以用于癌症治疗的标记物发现研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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