Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns
Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns
复制标题
DOI：
10.1073/pnas.0912090107
复制
发表时间：
2010-02-16
影响因子：
11.1
通讯作者：
Murphy, Robert F.
中科院分区：
文献类型：
--
作者：
Peng, Tao;Bonamy, Ghislain M. C.;Murphy, Robert F.
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。蛋白质在不同细胞区室中的比例通常是用细胞器特异性荧光标记共定位来测量的，这需要每个区室都有荧光探针，并且每个区室都需要与感兴趣的大分子一起获取图像。另外，定制的算法允许在图像中找到特定区域并量化它们包含的荧光量。不幸的是，这种方法需要大量的算法手动调整，并且通常依赖于细胞类型。在这里，我们描述了一种机器学习方法，用于估计不同亚细胞区室中荧光信号的数量，而无需手动调整，只需要获取每个区室的标记物的单独训练图像。在对不同细胞器的探针混合物染色的细胞图像进行测试时，我们在每个室中的估计探针数量和预期探针数量之间实现了93%的相关性。我们还证明，该方法可用于定量药物依赖性蛋白易位。该方法能够自动、无偏地测定蛋白质在细胞间的分布，并将显著改善基于成像的高通量测定，促进蛋白质组级定位工作。
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.