Soltisz Andrew M, Craigmile Peter F, Veeraraghavan Rengasayee
Department of Biomedical Engineering, College of Engineering, 2124 Fontana Labs,140 W. 19th Ave, The Ohio State University, Columbus, OH 43210, USA.
Department of Mathematics and Statistics, Hunter College, City University of New York, Hunter East 908,695 Park Avenue, New York, NY 10065, USA.
Microsc Microanal. 2024 Apr 29;30(2):306-317. doi: 10.1093/mam/ozae022.
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.
生物结构的定量描述在生命科学中是一项有价值但却困难的任务。这通常是通过使用荧光显微镜对样本进行成像，并使用皮尔逊相关性或基于曼德尔斯共现强度的共定位范式来分析所得图像来完成的。尽管这些方法在概念和计算上很简单，但由于它们依赖信号重叠、对粗略信号质量敏感且无法区分真正的和偶然的共定位，因此存在严重缺陷。点模式分析提供了一个框架，用于使用观测值之间的距离而非它们的重叠来定量表征空间模式之间的空间关系，从而克服了这些问题。在此，我们介绍一种名为“使用最近事件的空间模式分析”（SPACE）的图像分析工具，它利用基于最近邻的点模式分析来表征来自图像数据的荧光显微镜信号的空间关系。通过评估心肌细胞共聚焦图像中mRNA与细胞核之间的空间关联，证明了SPACE的实用性。此外，我们使用合成图像和实证图像来表征SPACE对图像分割参数以及诸如信号丰度和图像分辨率等粗略图像质量的敏感性。最终，SPACE的性能优于传统的共定位方法，并为显微镜工作者的工具箱增添了一项有价值的工具。
