Spatial Pattern Analysis using Closest Events (SPACE)-A Nearest Neighbor Point Pattern Analysis Framework for Assessing Spatial Relationships from Digital Images.
Spatial Pattern Analysis using Closest Events (SPACE)-A Nearest Neighbor Point Pattern Analysis Framework for Assessing Spatial Relationships from Digital Images.
复制标题
使用最近事件 (SPACE) 的空间模式分析 - 用于评估数字图像空间关系的最近邻点模式分析框架。
DOI：
10.1093/mam/ozae022
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Veeraraghavan,Rengasayee
中科院分区：
文献类型：
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作者：
Soltisz,AndrewM;Craigmile,PeterF;Veeraraghavan,Rengasayee
生物结构的定量描述是生命科学中一项有价值又有难度的任务。这通常是通过使用荧光显微镜对样品成像并使用皮尔逊相关性或Manders的基于共生强度的共局部化范例来分析结果图像来实现的。虽然这些方法在概念上和计算上都很简单，但由于它们依赖于信号重叠、对粗略信号质量的敏感性以及无法区分真实和偶然的共址，因此存在严重缺陷。点模式分析为使用观测之间的距离而不是它们的重叠来定量描述空间模式之间的空间关系提供了一个框架，从而克服了这些问题。在这里，我们介绍了一种称为空间模式分析的图像分析工具，它使用最近事件(SPACE)，利用基于最近邻的点模式分析来表征图像数据中荧光显微镜信号的空间关系。通过从心肌细胞的共聚焦图像中评估mRNA和细胞核之间的空间关联，证明了空间的有效性。此外，我们使用合成图像和经验图像来表征空间对图像分割参数和粗略图像质量(如信号丰度和图像分辨率)的敏感性。归根结底，SPACE提供了优于传统共焦方法的性能，并为显微镜工作者的工具箱提供了宝贵的补充。
The quantitative description of biological structures is a valuable yet difficult task in the life sciences. This is commonly accomplished by imaging samples using fluorescence microscopy and analyzing resulting images using Pearson's correlation or Manders’ co-occurrence intensity-based colocalization paradigms. Though conceptually and computationally simple, these approaches are critically flawed due to their reliance on signal overlap, sensitivity to cursory signal qualities, and inability to differentiate true and incidental colocalization. Point pattern analysis provides a framework for quantitative characterization of spatial relationships between spatial patterns using the distances between observations rather than their overlap, thus overcoming these issues. Here we introduce an image analysis tool called Spatial Pattern Analysis using Closest Events (SPACE) that leverages nearest neighbor-based point pattern analysis to characterize the spatial relationship of fluorescence microscopy signals from image data. The utility of SPACE is demonstrated by assessing the spatial association between mRNA and cell nuclei from confocal images of cardiac myocytes. Additionally, we use synthetic and empirical images to characterize the sensitivity of SPACE to image segmentation parameters and cursory image qualities such as signal abundance and image resolution. Ultimately, SPACE delivers performance superior to traditional colocalization methods and offers a valuable addition to the microscopist's toolbox.