Geometric compensation applied to image analysis of cell populations with morphological variability: a new role for a classical concept.
Geometric compensation applied to image analysis of cell populations with morphological variability: a new role for a classical concept.
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DOI：
10.1038/s41598-018-28570-z
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发表时间：
2018-07-06
影响因子：
4.6
通讯作者：
Sanches JM
中科院分区：
文献类型：
--
作者：
Figueiredo J;Rodrigues I;Ribeiro J;Fernandes MS;Melo S;Sousa B;Paredes J;Seruca R;Sanches JM
免疫荧光是确定荧光标记分子的水平和空间分布的金标准技术。然而，荧光显微镜图像的定量分析面临着关键的挑战，如细胞内的形态变异。在这项工作中，我们开发了一种分析策略，以处理细胞的形状和大小的变化，是基于弹性几何对齐算法。首先，在受控条件下，使用模拟具有形态变异性的细胞群体的合成图像来测试和优化算法。我们已经计算了表达谱，特别是评估细胞-细胞相互作用（IN谱）和侧重于标记物在单个细胞的整个细胞内空间中分布的谱（RD谱）。为了实验验证我们的分析管道，我们使用了E-钙粘蛋白，微管蛋白和线粒体染料染色的细胞培养物的真实的图像，选择作为膜，细胞质和细胞器特异性标志物的原型。结果表明，我们的算法能够生成一个详细的定量报告和一个忠实的代表性的一个大面板的分子，分布在不同的细胞室，独立于细胞的形态特征。这是一种简单的最终用户方法，可以在研究和诊断实验室中广泛探索，以揭示蛋白质调控机制或识别与疾病相关的蛋白质表达模式。
Immunofluorescence is the gold standard technique to determine the level and spatial distribution of fluorescent-tagged molecules. However, quantitative analysis of fluorescence microscopy images faces crucial challenges such as morphologic variability within cells. In this work, we developed an analytical strategy to deal with cell shape and size variability that is based on an elastic geometric alignment algorithm. Firstly, synthetic images mimicking cell populations with morphological variability were used to test and optimize the algorithm, under controlled conditions. We have computed expression profiles specifically assessing cell-cell interactions (IN profiles) and profiles focusing on the distribution of a marker throughout the intracellular space of single cells (RD profiles). To experimentally validate our analytical pipeline, we have used real images of cell cultures stained for E-cadherin, tubulin and a mitochondria dye, selected as prototypes of membrane, cytoplasmic and organelle-specific markers. The results demonstrated that our algorithm is able to generate a detailed quantitative report and a faithful representation of a large panel of molecules, distributed in distinct cellular compartments, independently of cell’s morphological features. This is a simple end-user method that can be widely explored in research and diagnostic labs to unravel protein regulation mechanisms or identify protein expression patterns associated with disease.
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影响因子：
4.5
作者：
Hamilton, Nicholas
通讯作者：
Hamilton, Nicholas
微管蛋白代码：分子成分、读出机制和功能。
DOI：
10.1083/jcb.201406055
发表时间：
2014-08-18
期刊：
The Journal of cell biology
影响因子：
--
作者：
Janke C
通讯作者：
Janke C
影响因子：
5.2
作者：
Figueiredo, Joana;Soederberg, Ola;Seruca, Raquel
通讯作者：
Seruca, Raquel
影响因子：
14.9
作者：
Ortiz-de-Solorzano, Carlos;Munoz-Barrutia, Arrate;Kozubek, Michal
通讯作者：
Kozubek, Michal
DOI：
10.1146/annurev.cellbio.042308.113408
发表时间：
2009
影响因子：
11.3
作者：
Muzzey D;van Oudenaarden A
通讯作者：
van Oudenaarden A