Large-scale estimates of cellular origins of mRNAs: enhancing the yield of transcriptome analyses. Sibille, E., Arango, V., Joeyen-Waldorf, J., Wang, Y., Leman, S., Surget, A., Belzung, C., Mann, J. J., & Lewis, D. A. Journal of Neuroscience Methods, 167(2):198--206, January, 2008. doi abstract bibtex Gene expression profiling holds great promise for identifying molecular pathologies of central nervous system disorders. However, the analysis of brain tissue poses unique analytical challenges, as typical microarray signals represent averaged transcript levels across neuronal and glial cell populations. Here we have generated ratios of gene transcript levels between gray and adjacent white matter samples to estimate the relative cellular origins of expression. We show that incorporating these ratios into transcriptome analysis (i) provides new analytical perspectives, (ii) increases the potential for biological insight obtained from postmortem transcriptome studies, (iii) expands knowledge about glial and neuronal cellular programs and (iv) facilitates the generation of cell-type specific hypotheses. This approach represents a robust and cost-effective "add-on" to transcriptome analyses of the mammalian brain. As this approach can be applied post hoc, we provide tables of ratios for analysis of existing mouse and human brain datasets.
@article{sibille_large-scale_2008,
title = {Large-scale estimates of cellular origins of {mRNAs}: enhancing the yield of transcriptome analyses},
volume = {167},
issn = {0165-0270},
shorttitle = {Large-scale estimates of cellular origins of {mRNAs}},
doi = {10.1016/j.jneumeth.2007.08.009},
abstract = {Gene expression profiling holds great promise for identifying molecular pathologies of central nervous system disorders. However, the analysis of brain tissue poses unique analytical challenges, as typical microarray signals represent averaged transcript levels across neuronal and glial cell populations. Here we have generated ratios of gene transcript levels between gray and adjacent white matter samples to estimate the relative cellular origins of expression. We show that incorporating these ratios into transcriptome analysis (i) provides new analytical perspectives, (ii) increases the potential for biological insight obtained from postmortem transcriptome studies, (iii) expands knowledge about glial and neuronal cellular programs and (iv) facilitates the generation of cell-type specific hypotheses. This approach represents a robust and cost-effective "add-on" to transcriptome analyses of the mammalian brain. As this approach can be applied post hoc, we provide tables of ratios for analysis of existing mouse and human brain datasets.},
language = {eng},
number = {2},
journal = {Journal of Neuroscience Methods},
author = {Sibille, Etienne and Arango, Victoria and Joeyen-Waldorf, Jennifer and Wang, Yingjie and Leman, Samuel and Surget, Alexandre and Belzung, Catherine and Mann, J. John and Lewis, David A.},
month = jan,
year = {2008},
pmid = {17889939},
pmcid = {PMC2262176},
keywords = {Animals, Brain, Cluster Analysis, Cohort Studies, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Humans, Mice, Microarray Analysis, Nerve Tissue Proteins, Neuroglia, Neurons, Postmortem Changes, RNA, Messenger, Transcription, Genetic},
pages = {198--206}
}
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