DNA & PROTEIN MICRO-ARRAY FACILITY (SHARED RESOURCE)

UCI DMA & Protein MicroArray Facility最初成立于1999年，是一个综合癌症中心共享资源核心设施，旨在协助北校区和南校区的研究人员进行微阵列表达分析。在过去五年中，南校区内外约有200名调查人员接受了服务。我们主要利用Affyscore基因芯片阵列系统来监测基因表达并进行SNP/基因型作图。我们的使命是以一致、及时的方式为我们设施的所有用户提供最高质量的RNA表达和基因组DMA分析。除了提供基因表达和SNP/基因型定位方案的实验专业知识外，工作人员还提供实验设计的建议和咨询，以及基于微阵列研究的数据分析方法和统计数据。目前，该技术的主要用途是确定细胞或组织内基因的差异表达模式。这项技术使研究人员能够比较患病和正常细胞、癌细胞和非癌细胞以及不同组织类型中的表达模式。这些类型的研究的目标是更好地了解可能导致疾病状态的过程，并找到新的或更好的方法来控制或阻止疾病状态的进展。这项技术的一个快速增长的用途是分析基因型变异（SNP）以定位致病基因。这些类型的方法将提供对多因素疾病状态的洞察。与个体基因信息不同，全基因组信息可用于了解由多个基因或环境和遗传因素共同引起的疾病的更微妙模式。 由于全基因组阵列提供了大量详细的信息，微阵列很可能不仅在理解疾病过程中，而且在疾病诊断和个性化治疗设计中成为一种更加重要的工具。

The UCI DMA & Protein MicroArray Facility was initially established in 1999 as a Comprehensive Cancer Center Shared Resource Core Facility, directed at assisting both North and South Campus investigators performing microarray expression analysis. About 200 investigators inside and outside the South Campus have been provided services in the last five year period. We primarily utilize the Affymetrix GeneChip array system to monitor gene expression and perform SNP/genotype mapping. Our mission is to provide RNA expression and genomic DMA analysis in a consistent and timely manner that is of the highest quality for all of our Facility's users. In addition to providing experimental expertise for performing gene expression and SNP/genotype mapping protocols, the staff provides advice and consultation on experimental design, and data analysis approaches and statistics for microarray based research. Currently, the primary use of this technology is to determine the differential expression patterns of genes, either within cells or tissues. This technology enables investigators to compare patterns of expression in diseased and normal cells, cancer cells and non-cancerous cells and in different tissue types. The goal of these types of studies is to better understand the processes that potentially lead to diseased states and to find new or better ways of either controlling or stopping the progression of diseased states. A rapidly increasing use of this technology is analysis of genotypic variants (SNPs) to map disease-causing genes. These types of approaches will provide insight into multifactorial disease states. Genomewide information unlike individual gene information can be used to understand the more subtle patterns of diseases caused by multiple genes or by the confluence of environmental and genetic factors. Because of the great amount of detailed information provided from genomewide arrays, it is likely that microarrays will become an even more important tool not only in understanding disease processes, but also in disease diagnosis and the design of individualized therapies.