Laboratory of Molecular Biology, Howard Hughes Medical Institute, The Rockefeller University, New York, NY 10065, USA.
Nucleic Acids Res. 2010 Jul;38(13):4218-30. doi: 10.1093/nar/gkq130. Epub 2010 Mar 22.
We have recently developed a novel method for the affinity purification of the complete suite of translating mRNA from genetically labeled cell populations. This method permits comprehensive quantitative comparisons of the genes employed by each specific cell type. We provide a detailed description of tools for analysis of data generated with this and related methodologies. An essential question that arises from these data is how to identify those genes that are enriched in each cell type relative to all others. Genes relatively specifically employed by a cell type may contribute to the unique functions of that cell, and thus may become useful targets for development of pharmacological tools for cell-specific manipulations. We describe here a novel statistic, the specificity index, which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles. This measure correctly predicts in situ hybridization patterns for many cell types. We apply this measure to a large survey of CNS cell-specific microarray data to identify those genes that are significantly enriched in each population Data and algorithms are available online (www.bactrap.org).
我们最近开发了一种从遗传标记的细胞群体中亲和纯化完整翻译 mRNA 文库的新方法。这种方法允许对每种特定细胞类型使用的基因进行全面的定量比较。我们提供了详细的分析工具描述,这些工具用于分析使用这种方法和相关方法学生成的数据。这些数据提出的一个基本问题是如何识别相对于其他所有细胞类型在每种细胞类型中富集的那些基因。细胞类型相对特异性使用的基因可能有助于该细胞的独特功能,因此可能成为开发用于细胞特异性操作的药理学工具的有用靶标。我们在这里描述了一种新的统计量,即特异性指数,可用于比较定量分析,以识别在大量图谱中富集于特定细胞群体的基因。该度量标准可正确预测许多细胞类型的原位杂交模式。我们将该度量标准应用于对 CNS 细胞特异性微阵列数据的大量调查,以鉴定在每个群体中显著富集的那些基因。数据和算法可在线获得(www.bactrap.org)。