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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns
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Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns

机译：通过自动分解亚细胞模式来确定不同亚细胞位置之间的探针分布

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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 colocaliza-tion 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.
机译：许多蛋白质或其他生物大分子定位于一个以上的亚细胞结构。通常通过与细胞器特异性荧光标记共定位来测量不同细胞区室中蛋白质的比例，这要求每个区室都需要荧光探针，并需要与感兴趣的大分子结合获取每个区室的图像。另外，量身定制的算法可以找到图像中的特定区域并量化其中包含的荧光量。不幸的是，这种方法需要对算法进行大量的手动调整，并且通常取决于单元类型。在这里，我们描述了一种无需手动调整即可估算不同亚细胞区室中荧光信号量的机器学习方法，仅需要获取每个区室标记的单独训练图像即可。在用不同细胞器探针混合物染色的细胞图像测试中，我们在每个隔室中的探针估计量和预期量之间实现了93％的相关性。我们还证明了该方法可用于量化药物依赖性蛋白易位。该方法能够自动，无偏地确定跨细胞区室的蛋白质分布，并将显着改善基于成像的高通量检测并促进蛋白质组规模的定位工作。

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  • 作者

    Tao Peng; Ghislain M. C. Bonamy; Estelle Glory-Afshar; Daniel R. Rines; Sumit K. Chanda; Robert F. Murphy;

  • 作者单位

    Center for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213;

    rnGenomics Institute of the Novartis Research Foundation, San Diego, CA 92121 Hudson-Alpha Institute for Biotechnology, Huntsville, AL 35806;

    rnCenter for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213;

    rnGenomics Institute of the Novartis Research Foundation, San Diego, CA 92121;

    rnGenomics Institute of the Novartis Research Foundation, San Diego, CA 92121 Program of Inflammatory Disease Research, Burnham Institute for Medical Research,San Diego, CA 92037;

    rnCenter for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 Lane Center for Computational Biology and Departments of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213 Freiburg Institute for Advanced Studies, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    automated microscopy; fluorescence microscopy; location proteomics; pattern recognition; high content analysis;

    机译：自动显微镜荧光显微镜定位蛋白质组学模式识别;高含量分析;

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