Application of BACarray for comparative analysis of CNS cell types
By J. Doyle, J. Dougherty, M. Heiman, E. F. Schmidt, Tanya R. Stevens, Guojun Ma, Sujata Bupp, Prerana Shrestha, Rajiv D. E. Shah, M. Doughty and 3 more. Cell, 2008
896 citations recorded by the source corpus.
Summary Comparative analysis of BACarray data can provide important insights into complex biological systems. As demonstrated in the accompanying paper, BACarray translational profiling permits comprehensive studies of translated mRNAs in genetically defined cell populations following physiological perturbations. To establish the generality of this approach, we present BACarray translational profiles for twenty four CNS cell populations, and identify known cell-specific and enriched transcripts for each population. We report thousands of cell-specific mRNAs that were not detected in whole tissue microarray studies, and provide examples that demonstrate the benefits deriving from comparative analysis. To provide a foundation for further biological and in silico studies, we provide a resource of sixteen transgenic mouse lines, their corresponding anatomic characterization, and BACarray translational profiles for cell types from a variety of CNS structures. This resource will enable a wide spectrum of molecular and mechanistic studies of both well known and previously uncharacterized neural cell populations.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/53764a9480969d81b62eae459325918d5ea386f5
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In Molecular Biology
Citations in the corpus
- Cell-type-specific isolation of ribosome-associated mRNA from complex tissues (2009, methodology): “The Heintz laboratory has recently reported a similar approach (18, 19).”
- An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014, methodology): “Previous groups have performed transcriptome studies of one or more purified cell types in the brain (Dugas et al., 2006; Rossner et al., 2006; Lovatt et al., 2007; Cahoy et al., 2008; Doyle et al., 2008; Lau et al., 2008; Daneman et al., 2010; Wylie et al., 2010; Bracko et al., 2012; Friedrich et…”
- Astrocytes and disease: a neurodevelopmental perspective. (2012, background): “Recently, the application of gene expression profiling to purified astrocyte populations (Cahoy et al. 2008; Doyle et al. 2008; Fu et al. 2009; Rowitch and Kriegstein 2010) has revealed several potential new markers, such as the gene encoding the folate metabolic enzyme Aldh1L1 (Anthony and Heintz 2”
- Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain (2015, background): “Genetically directed strategies that isolate RNA from specific cell populations in mice (Doyle et al., 2008; Gay et al., 2013; Heiman et al., 2008; Sanz et al., 2009) have begun to chart transcriptional diversity across cell types but cannot profile epigenomic features unless combined with FACS…”
- Astrocyte reactivity and reactive astrogliosis: costs and benefits. (2014, background): “Extensive work in this area is currently ongoing in many laboratories, and a shift from assessing individually selected potential molecular targets to high throughput screening strategies, when molecular candidates can be evaluated in the context of the whole transcriptome or proteome, ensures that ”
- The Aging Astrocyte Transcriptome from Multiple Regions of the Mouse Brain (2018, methodology): “Ribo-tag restricts analysis to ribosome-associated mRNAs, which are more likely to be actively being made into proteins, providing a representation of the translational state of the cells, a similar approach to that used by others (Chai et al., 2017; Doyle et al., 2008; Morel et al., 2017).”
- Normal aging induces A1-like astrocyte reactivity (2018, methodology): “RNAwas collected from hippocampi, striata, and cortex of Aldh1l1-eGFP-L10a mice based on published protocols (21, 22).”