Gruber Michael P, Coldren Christopher D, Woolum Malcolm D, Cosgrove Gregory P, Zeng Chan, Barón Anna E, Moore Mark D, Cool Carlyne D, Worthen G Scott, Brown Kevin K, Geraci Mark W
Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Health Sciences Center, 4200 East Ninth Ave, Denver, CO 80262, USA.
Am J Respir Cell Mol Biol. 2006 Jul;35(1):65-71. doi: 10.1165/rcmb.2004-0261OC. Epub 2006 Feb 23.
Nondiseased tissue is an important reference for microarray studies of pulmonary disease. We obtained 23 single lungs from multiorgan donors at time of procurement. Donors varied in age, sex, smoking history, and ethnicity. Lungs were dissected into upper and lower lobe peripheral sections for RNA extraction. Microarray analysis was performed using Affymetrix Hu-133 Plus 2.0 arrays. We observed that the relative variability of gene expression increased rapidly from technical (lowest), to regional, to population (highest). In addition, age and sex have measurable effects on gene expression. Gene expression variability is heterogeneously distributed among biologic categories. We conclude that gene expression variability is greater between individuals than within individuals and that population variability is the most important factor in the study design of microarray experiments of the human lung. Classes of genes with high population variability are biologically important and provide a novel perspective into lung physiology and pathobiology. Our study represents the first comprehensive analysis of nondiseased lung tissue. The generation of this robust dataset has important implications for the design and implementation of future comparative expression analysis with pulmonary disease states.
正常组织是肺部疾病微阵列研究的重要参考。我们在获取器官时从多器官供体处获得了23个单肺。供体在年龄、性别、吸烟史和种族方面存在差异。将肺解剖成上叶和下叶外周部分用于RNA提取。使用Affymetrix Hu-133 Plus 2.0阵列进行微阵列分析。我们观察到基因表达的相对变异性从技术层面（最低）到区域层面再到群体层面（最高）迅速增加。此外，年龄和性别对基因表达有可测量的影响。基因表达变异性在生物学类别中呈异质性分布。我们得出结论，个体间的基因表达变异性大于个体内的变异性，并且群体变异性是人类肺部微阵列实验研究设计中最重要的因素。具有高群体变异性的基因类别具有生物学重要性，并为肺生理学和病理生物学提供了新的视角。我们的研究是对正常肺组织的首次全面分析。这个强大数据集的生成对未来与肺部疾病状态进行比较表达分析的设计和实施具有重要意义。
