Effect of RNA quality on transcript intensity levels in microarray analysis of human post-mortem brain tissues

BackgroundLarge-scale gene expression analysis of post-mortem brain tissue offers unique opportunities for investigating genetic mechanisms of psychiatric and neurodegenerative disorders. On the other hand microarray data analysis associated with these studies is a challenging task. In this publication we address the issue of low RNA quality data and corresponding data analysis strategies.ResultsA detailed analysis of effects of post chip RNA quality on the measured abundance of transcripts is presented. Overall Affymetrix GeneChip data (HG-U133_AB and HG-U133_Plus_2.0) derived from ten different brain regions was investigated. Post chip RNA quality being assessed by 5'/3' ratio of housekeeping genes was found to introduce a well pronounced systematic noise into the measured transcript expression levels. According to this study RNA quality effects have: 1) a "random" component which is introduced by the technology and 2) a systematic component which depends on the features of the transcripts and probes. Random components mainly account for numerous negative correlations of low-abundant transcripts. These negative correlations are not reproducible and are mainly introduced by an increased relative level of noise. Three major contributors to the systematic noise component were identified: the first is the probe set distribution, the second is the length of mRNA species, and the third is the stability of mRNA species. Positive correlations reflect the 5'-end to 3'-end direction of mRNA degradation whereas negative correlations result from the compensatory increase in stable and 3'-end probed transcripts. Systematic components affect the expressed transcripts by introducing irrelevant gene correlations and can strongly influence the results of the main experiment. A linear model correcting the effect of RNA quality on measured intensities was introduced.In addition the contribution of a number of pre-mortem and post-mortem attributes to the overall detected RNA quality effect was investigated. Brain pH, duration of agonal stage, post-mortem interval before sampling and donor's age of death within considered limits were found to have no significant contribution.ConclusionBasic conclusions for data analysis in expression profiling study are as follows: 1) testing for RNA quality dependency should be included in the preprocessing of the data; 2) investigating inter-gene correlation without regard to RNA quality effects could be misleading; 3) data normalization procedures relying on housekeeping genes either do not influence the correlation structure (if 3'-end intensities are used) or increase it for negatively correlated transcripts (if 5'-end or median intensities are included in normalization procedure); 4) sample sets should be matched with regard to RNA quality; 5) RMA preprocessing is more sensitive to RNA quality effect, than MAS 5.0.

[1]  Huda Akil,et al.  Effect of agonal and postmortem factors on gene expression profile: quality control in microarray analyses of postmortem human brain , 2004, Biological Psychiatry.

[2]  J. DeFelipe,et al.  Correlation of transcriptome profile with electrical activity in temporal lobe epilepsy , 2006, Neurobiology of Disease.

[3]  Tala Bakheet,et al.  ARED 2.0: an update of AU-rich element mRNA database , 2003, Nucleic Acids Res..

[4]  Carol A. Tamminga,et al.  Human postmortem tissue: What quality markers matter? , 2006, Brain Research.

[5]  T. Foster,et al.  Gene Microarrays in Hippocampal Aging: Statistical Profiling Identifies Novel Processes Correlated with Cognitive Impairment , 2003, The Journal of Neuroscience.

[6]  Edmund S. Jackson,et al.  A microarray data analysis framework for postmortem tissues. , 2005, Methods.

[7]  F. Middleton,et al.  Gene Expression Profiling with DNA Microarrays: Advancing Our Understanding of Psychiatric Disorders , 2002, Neurochemical Research.

[8]  David J. Lockhart,et al.  Expressing what's on your mind: DNA arrays and the brain , 2001, Nature Reviews Neuroscience.

[9]  J. Kleinman,et al.  Critical Factors in Gene Expression in Postmortem Human Brain: Focus on Studies in Schizophrenia , 2006, Biological Psychiatry.

[10]  Paul J. Harrison,et al.  The relative importance of premortem acidosis and postmortem interval for human brain gene expression studies: selective mRNA vulnerability and comparison with their encoded proteins , 1995, Neuroscience Letters.

[11]  J. Pollock,et al.  Gene expression profiling: methodological challenges, results, and prospects for addiction research. , 2002, Chemistry and physics of lipids.

[12]  Albert Zlotnik,et al.  Effects of RNA degradation on gene expression analysis of human postmortem tissues , 2005, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[13]  J. Eberwine,et al.  Expression Profile Analysis of Neurodegenerative Disease: Advances in Specificity and Resolution , 2004, Neurochemical Research.

[14]  R. Myers,et al.  Mitochondrial-related gene expression changes are sensitive to agonal-pH state: implications for brain disorders , 2006, Molecular Psychiatry.

[15]  P. Mazière,et al.  Impact of RNA degradation on gene expression profiles: assessment of different methods to reliably determine RNA quality. , 2007, Journal of biotechnology.

[16]  R. Ross,et al.  Brain pH has a significant impact on human postmortem hippocampal gene expression profiles , 2006, Brain Research.

[17]  Henriette Franz,et al.  Systematic analysis of gene expression in human brains before and after death , 2005, Genome Biology.

[18]  P. Levitt,et al.  DNA microarray analysis of postmortem brain tissue. , 2004, International review of neurobiology.

[19]  D. Lockhart,et al.  Effects of environmental enrichment on gene expression in the brain. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Karl Kornacker,et al.  Chipping away at the chip bias: RNA degradation in microarray analysis , 2003, Nature Genetics.

[21]  Kellie J Archer,et al.  Assessing quality of hybridized RNA in Affymetrix GeneChip experiments using mixed-effects models. , 2005, Biostatistics.

[22]  P. Levitt,et al.  Critical Appraisal of DNA Microarrays in Psychiatric Genomics , 2006, Biological Psychiatry.

[23]  Stanley J. Watson,et al.  Methodological considerations for gene expression profiling of human brain , 2007, Journal of Neuroscience Methods.

[24]  C. Reilly,et al.  Genome-wide analysis of mRNA decay in resting and activated primary human T lymphocytes. , 2002, Nucleic acids research.

[25]  I. Ferrer,et al.  DNA Chip Technology in Brain Banks: Confronting a Degrading World , 2004, Journal of neuropathology and experimental neurology.

[26]  Jennifer M. Taylor,et al.  A microarray study of post-mortem mRNA degradation in mouse brain tissue. , 2005, Brain research. Molecular brain research.

[27]  D. Niu,et al.  Relationship Between mRNA Stability and Length: An Old Question with a New Twist , 2007, Biochemical Genetics.

[28]  Rafael A. Irizarry,et al.  A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database , 2006, BMC Bioinformatics.

[29]  Jonathan Pevsner,et al.  Progress in the use of microarray technology to study the neurobiology of disease , 2004, Nature Neuroscience.

[30]  Lance D. Miller,et al.  Correlation test to assess low-level processing of high-density oligonucleotide microarray data , 2005, BMC Bioinformatics.

[31]  David A Lewis,et al.  The Human Brain Revisited: Opportunities and Challenges in Postmortem Studies of Psychiatric Disorders , 2002, Neuropsychopharmacology.