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有读书笔记有附件Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns

4 dechang 添加于 2010-4-6 22:41 | 1260 次阅读 | 2 个评论
  •  作 者

    Peng T, Bonamy GMC, Glory-Afshar E, Rines DR, Chanda SK, Murphy RF
  •  摘 要

    Many proteins or other biological macromolecules are localized to more than one subcellular structure. The fraction of a protein in different cellular compartments is often measured by colocalization with organelle-specific fluorescent markers, requiring availability of fluorescent probes for each compartment and acquisition of images for each in conjunction with the macromolecule of interest. Alternatively, tailored algorithms allow finding particular regions in images and quantifying the amount of fluorescence they contain. Unfortunately, this approach requires extensive hand-tuning of algorithms and is often cell type-dependent. Here we describe a machine-learning approach for estimating the amount of fluorescent signal in different subcellular compartments without hand tuning, requiring only the acquisition of separate training images of markers for each compartment. In testing on images of cells stained with mixtures of probes for different organelles, we achieved a 93% correlation between estimated and expected amounts of probes in each compartment. We also demonstrated that the method can be used to quantify drug-dependent protein translocations. The method enables automated and unbiased determination of the distributions of protein across cellular compartments, and will significantly improve imaging-based high-throughput assays and facilitate proteome-scale localization efforts.
  •  详细资料

    • 文献种类:期刊
    • 期刊名称: Proceedings of the National Academy of Sciences of the United States of America
    • 期刊缩写: Proc Natl Acad Sci U S A
    • 期卷页: 2010  107 7 2944-2949
    • 地址: Center for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    • ISBN: 0027-8424
    • 备注:PMID:20133616
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  • 相关链接 DOI URL 

  •  附 件

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  •  dechang 的文献笔记  订阅

    许多蛋白或其他生物大分子定位于一个以上亚细胞结构,不同细胞区室中蛋白质的分布通常采用与细胞器特异的荧光标记共定位的方式测量,需要获得各区室的探针并获取各个研究大分子及探针的图像,或者,精心设计的算法可以发现图像中的特定区域并进行荧光定量,然而这种方法需要大量的人工算法调整并经常依赖于细胞类型,本文介绍了一种估计各亚细胞区室荧光信号定量的机器学习方法,无须手工调谐,仅需获取各区室标记的分离训练图像。

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