Lee Cody A, Sánchez Moreno Carmen, Badyaev Alexander V
Department of Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ 85721, United States.
College of Human Medicine, Michigan State University, Grand Rapids, Michigan 49503, United States.
MethodsX. 2024 Jul 9;13:102855. doi: 10.1016/j.mex.2024.102855. eCollection 2024 Dec.
Study of morphogenesis and its regulation requires analytical tools that enable simultaneous assessment of processes operating at cellular level, such as synthesis of transcription factors (TF), with their effects at the tissue scale. Most current studies conduct histological, cellular and immunochemical (IHC) analyses in separate steps, introducing inevitable biases in finding and alignment of areas of interest at vastly distinct scales of organization, as well as image distortion associated with image repositioning or file modifications. These problems are particularly severe for longitudinal analyses of growing structures that change size and shape. Here we introduce a python-based application for automated and complete whole-slide measurement of expression of multiple TFs and associated cellular morphology. The plugin collects data at customizable scale from the cell-level to the entire structure, records each data point with positional information, accounts for ontogenetic transformation of structures and variation in slide positioning with scalable grid, and includes a customizable file manager that outputs collected data in association with full details of image classification (e.g., ontogenetic stage, population, IHC assay). We demonstrate the utility and accuracy of this application by automated measurement of morphology and associated expression of eight TFs for more than six million cells recorded with full positional information in beak tissues across 12 developmental stages and 25 study populations of a wild passerine bird. Our script is freely available as an open-source Fiji plugin and can be applied to IHC slides from any imaging platforms and transcriptional factors.
形态发生及其调控的研究需要分析工具，以便能够同时评估在细胞水平上发生的过程，例如转录因子（TF）的合成，以及它们在组织尺度上的作用。目前大多数研究在不同步骤中进行组织学、细胞和免疫化学（IHC）分析，这在不同组织尺度上寻找和对齐感兴趣区域时不可避免地引入偏差，以及与图像重新定位或文件修改相关的图像失真。对于生长过程中大小和形状发生变化的结构进行纵向分析时，这些问题尤为严重。在此，我们介绍一种基于Python的应用程序，用于自动完整地对多个TF的表达和相关细胞形态进行全玻片测量。该插件从细胞水平到整个结构以可定制的尺度收集数据，记录每个带有位置信息的数据点，考虑结构的个体发育转变以及玻片定位随可扩展网格的变化，并包括一个可定制的文件管理器，其输出收集的数据并关联图像分类的全部详细信息（例如，个体发育阶段、群体、IHC检测）。我们通过自动测量12个发育阶段和25个野生雀形目鸟类研究群体的喙组织中超过600万个记录了完整位置信息的细胞的形态以及8种TF的相关表达，证明了该应用程序的实用性和准确性。我们的脚本作为开源的Fiji插件免费提供，可应用于来自任何成像平台的IHC玻片和转录因子。
