Published ahead of print on February 23, 2006, doi:10.1165/rcmb.2004-0261OC Am. J. Respir. Cell Mol. Biol., Volume 35, Number 1, July 2006, 65-71 A more recent version of this article appeared on July 1, 2006
Submitted on August 12, 2004 Human Lung Project: Evaluating Variance of Gene Expression in the Human LungMichael P Gruber1,1 Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Health Sciences Center, Denver, CO, USA, 2 Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Health Sciences Center, Denver, CO, USA; National Jewish Medical and Research Center, Denver, CO, USA, 3 Section of Biometrics and Informatics, University of Colorado Health Sciences Center, Denver, CO, USA * To whom correspondence should be addressed. E-mail: mark.geraci{at}uchsc.edu.
Non-diseased tissue is an important reference for microarray studies of pulmonary disease. We obtained twenty-three single lungs from multi-organ 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 AffymetrixTM 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 gender have measurable effects on gene expression. Gene expression variability is heterogeneously distributed amongst 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 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 non-diseased 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.
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