Department of Computer Science, University of California, Santa Barbara, CA 93106, USA.
Bioinformatics. 2013 Apr 1;29(7):940-6. doi: 10.1093/bioinformatics/btt052. Epub 2013 Feb 8.
Microscopy advances have enabled the acquisition of large-scale biological images that capture whole tissues in situ. This in turn has fostered the study of spatial relationships between cells and various biological structures, which has proved enormously beneficial toward understanding organ and organism function. However, the unique nature of biological images and tissues precludes the application of many existing spatial mining and quantification methods necessary to make inferences about the data. Especially difficult is attempting to quantify the spatial correlation between heterogeneous structures and point objects, which often occurs in many biological tissues.
We develop a method to quantify the spatial correlation between a continuous structure and point data in large (17 500 × 17 500 pixel) biological images. We use this method to study the spatial relationship between the vasculature and a type of cell in the retina called astrocytes. We use a geodesic feature space based on vascular structures and embed astrocytes into the space by spatial sampling. We then propose a quantification method in this feature space that enables us to empirically demonstrate that the spatial distribution of astrocytes is often correlated with vascular structure. Additionally, these patterns are conserved in the retina after injury. These results prove the long-assumed patterns of astrocyte spatial distribution and provide a novel methodology for conducting other spatial studies of similar tissue and structures.
The Matlab code for the method described in this article can be found at http://www.cs.ucsb.edu/∼dbl/software.php.
Supplementary data are available at Bioinformatics online.
显微镜技术的进步使得能够获取大规模的生物图像，从而原位捕获整个组织。这反过来又促进了对细胞和各种生物结构之间空间关系的研究，这对理解器官和机体功能有很大的帮助。然而，生物图像和组织的独特性质排除了应用许多现有的空间挖掘和量化方法的可能性，这些方法对于对数据进行推断是必要的。特别是，尝试量化异质结构和点对象之间的空间相关性是非常困难的，这种情况在许多生物组织中经常发生。
我们开发了一种方法来量化大（17500×17500 像素）生物图像中连续结构和点数据之间的空间相关性。我们使用这种方法来研究血管系统和视网膜中一种称为星形胶质细胞的细胞之间的空间关系。我们使用基于血管结构的测地线特征空间，并通过空间采样将星形胶质细胞嵌入到该空间中。然后，我们在这个特征空间中提出了一种量化方法，使我们能够通过经验证明星形胶质细胞的空间分布通常与血管结构相关。此外，这些模式在损伤后的视网膜中是保守的。这些结果证明了星形胶质细胞空间分布的长期假设模式，并为对类似组织和结构进行其他空间研究提供了一种新的方法。
本文中描述的方法的 Matlab 代码可在 http://www.cs.ucsb.edu/∼dbl/software.php 找到。
补充数据可在 Bioinformatics 在线获得。
