Dataset Information

Large-scale estimates of cellular origins of mRNAs: enhancing the yield of transcriptome analyses.

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.

SUBMITTER: Sibille E

PROVIDER: S-EPMC2262176 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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Large-scale estimates of cellular origins of mRNAs: enhancing the yield of transcriptome analyses.

Sibille Etienne E Arango Victoria V Joeyen-Waldorf Jennifer J Wang Yingjie Y Leman Samuel S Surget Alexandre A Belzung Catherine C Mann J John JJ Lewis David A DA

Journal of neuroscience methods 20070821 2

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 r ...[more]

PMID: 17889939

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Project description:Large-scale biodiversity studies can be more informative if observed diversity in a study site is accompanied by dark diversity, the set of absent although ecologically suitable species. Dark diversity methodology is still being developed and a comparison of different approaches is needed. We used plant data at two different scales (European and seven large regions) and compared dark diversity estimates from two mathematical methods: species co-occurrence (SCO) and species distribution modeling (SDM). We used plant distribution data from the Atlas Florae Europaeae (50 × 50 km grid cells) and seven different European regions (10 × 10 km grid cells). Dark diversity was estimated by SCO and SDM for both datasets. We examined the relationship between the dark diversity sizes (type II regression) and the overlap in species composition (overlap coefficient). We tested the overlap probability according to the hypergeometric distribution. We combined the estimates of the two methods to determine consensus dark diversity and composite dark diversity. We tested whether dark diversity and completeness of site diversity (log ratio of observed and dark diversity) are related to various natural and anthropogenic factors differently than simple observed diversity. Both methods provided similar dark diversity sizes and distribution patterns; dark diversity is greater in southern Europe. The regression line, however, deviated from a 1:1 relationship. The species composition overlap of two methods was about 75%, which is much greater than expected by chance. Both consensus and composite dark diversity estimates showed similar distribution patterns. Both dark diversity and completeness measures exhibit relationships to natural and anthropogenic factors different than those exhibited by observed richness. In summary, dark diversity revealed new biodiversity patterns which were not evident when only observed diversity was examined. A new perspective in dark diversity studies can incorporate a combination of methods.

Dataset Information

Large-scale estimates of cellular origins of mRNAs: enhancing the yield of transcriptome analyses.

Publications

Large-scale estimates of cellular origins of mRNAs: enhancing the yield of transcriptome analyses.

Similar Datasets

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