Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin, Madison, WI, United States of America.
UCL Cancer Institute, Paul O'Gorman Building, University College London, London, United Kingdom.
PLoS One. 2020 Dec 30;15(12):e0238327. doi: 10.1371/journal.pone.0238327. eCollection 2020.
In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ's routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.
在荧光显微镜领域，人们对能够利用图像中每个像素的完整信息的动态技术一直有需求。一种具有从荧光成像中获得额外信息的能力的成像技术是荧光寿命成像显微镜（FLIM）。FLIM 允许测量荧光团在激发态下停留的时间，而这种测量会受到其化学微环境变化的影响，例如与其他荧光团的接近程度、pH 值和疏水区。这种提供有关微环境信息的能力使 FLIM 成为从代谢测量到测量蛋白质之间距离的细胞成像研究的强大工具。FLIM 的广泛使用使得必须开发用于将 FLIM 分析与图像和数据处理集成的计算工具。为了满足这一需求，我们创建了 FLIMJ，这是一个 ImageJ 插件和工具包，允许轻松使用和开发具有 FLIM 数据的可扩展图像分析工作流程。FLIMJ 构建在 FLIMLib 衰减曲线拟合库和 ImageJ Ops 框架之上，提供了具有与许多其他 ImageJ 组件无缝集成的 FLIM 拟合例程，并且能够扩展以创建复杂的 FLIM 分析工作流程。基于 ImageJ Ops 的构建还使 FLIMJ 的例程能够与 Jupyter 笔记本一起使用，并自然地与 Python 和 Groovy 等科学友好的编程集成。我们在两个分析场景中展示了 FLIMJ 的可扩展性：基于寿命的图像分割和图像共定位。我们还通过将它们与行业 FLIM 分析标准进行比较来验证拟合例程的准确性。
