Center for Bioimage Informatics, and Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, Pennsylvania 15213, USA.
Cytometry A. 2011 May;79(5):383-91. doi: 10.1002/cyto.a.21066. Epub 2011 Apr 6.
Given the importance of subcellular location to protein function, computational simulations of cell behaviors will ultimately require the ability to model the distributions of proteins within organelles and other structures. Toward this end, statistical learning methods have previously been used to build models of sets of two-dimensional microscope images, where each set contains multiple images for a single subcellular location pattern. The model learned from each set of images not only represents the pattern but also captures the variation in that pattern from cell to cell. The models consist of sub-models for nuclear shape, cell shape, organelle size and shape, and organelle distribution relative to nuclear and cell boundaries, and allow synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. Here we extend this generative models approach to three dimensions using a similar framework, permitting protein subcellular locations to be described more accurately. Models of different patterns can be combined to yield a synthetic multi-channel image containing as many proteins as desired, something that is difficult to obtain by direct microscope imaging for more than a few proteins. In addition, the model parameters represent a more compact and interpretable way of communicating subcellular patterns than descriptive image features and may be particularly effective for automated identification of changes in subcellular organization caused by perturbagens.
鉴于亚细胞定位对蛋白质功能的重要性，细胞行为的计算模拟最终需要能够对细胞器和其他结构内的蛋白质分布进行建模。为此，统计学习方法之前已被用于构建一组二维显微镜图像的模型，其中每组包含单个亚细胞位置模式的多个图像。从每组图像中学习到的模型不仅代表了模式，还捕获了细胞间该模式的变化。这些模型由核形状、细胞形状、细胞器大小和形状以及细胞器相对于核和细胞边界的分布的子模型组成，并允许合成图像，期望它们与用于训练它们的图像具有相同的基础统计分布。在这里，我们使用类似的框架将这种生成模型方法扩展到三维，从而可以更准确地描述蛋白质的亚细胞位置。不同模式的模型可以组合以生成包含所需数量蛋白质的合成多通道图像，这对于通过直接显微镜成像获得超过几个蛋白质的图像来说是困难的。此外，模型参数代表了比描述性图像特征更紧凑和可解释的方式来传达亚细胞模式，并且对于自动识别由扰动剂引起的亚细胞组织变化可能特别有效。
