Department of Physiology and Cell Biology, University of South Alabama College of Medicine, Mobile, Alabama, United States of America.
Department of Pharmacology, University of South Alabama College of Medicine, Mobile, Alabama, United States of America.
PLoS One. 2023 May 11;18(5):e0284394. doi: 10.1371/journal.pone.0284394. eCollection 2023.
Physiological function is regulated through cellular communication that is facilitated by multiple signaling molecules such as second messengers. Analysis of signal dynamics obtained from cell and tissue imaging is difficult because of intricate spatially and temporally distinct signals. Signal analysis tools based on static region of interest analysis may under- or overestimate signals in relation to region of interest size and location. Therefore, we developed an algorithm for biological signal detection and analysis based on dynamic regions of interest, where time-dependent polygonal regions of interest are automatically assigned to the changing perimeter of detected and segmented signals. This approach allows signal profiles to be rigorously and precisely tracked over time, eliminating the signal distortion observed with static methods. Integration of our approach with state-of-the-art image processing and particle tracking pipelines enabled the isolation of dynamic cellular signaling events and characterization of biological signaling patterns with distinct combinations of parameters including amplitude, duration, and spatial spread. Our algorithm was validated using synthetically generated datasets and compared with other available methods. Application of the algorithm to volumetric time-lapse hyperspectral images of cyclic adenosine monophosphate measurements in rat microvascular endothelial cells revealed distinct signal heterogeneity with respect to cell depth, confirming the utility of our approach for analysis of 5-dimensional data. In human tibial arteries, our approach allowed the identification of distinct calcium signal patterns associated with atherosclerosis. Our algorithm for automated detection and analysis of second messenger signals enables the decoding of signaling patterns in diverse tissues and identification of pathologic cellular responses.
生理功能是通过细胞通讯来调节的，细胞通讯是由多种信号分子（如第二信使）介导的。由于信号在空间和时间上存在复杂的差异，因此从细胞和组织成像中分析信号动态非常困难。基于静态感兴趣区域分析的信号分析工具可能会低估或高估与感兴趣区域大小和位置有关的信号。因此，我们开发了一种基于动态感兴趣区域的生物信号检测和分析算法，其中时间相关的多边形感兴趣区域会自动分配给检测到的和分割的信号的变化周长。这种方法可以严格而精确地跟踪信号随时间的变化，消除了静态方法观察到的信号失真。我们的方法与最先进的图像处理和粒子跟踪管道的集成，实现了动态细胞信号事件的隔离，并通过包括幅度、持续时间和空间扩展在内的不同参数组合来描述生物信号模式。我们的算法使用合成数据集进行了验证，并与其他可用方法进行了比较。将该算法应用于大鼠微血管内皮细胞中环磷酸腺苷测量的体积时移高光谱图像，结果表明信号在细胞深度上存在明显的异质性，这证实了我们的方法在分析 5 维数据方面的实用性。在人类胫骨动脉中，我们的方法可以识别与动脉粥样硬化相关的独特钙信号模式。我们的第二信使信号自动检测和分析算法能够解码不同组织中的信号模式，并识别病理细胞反应。
