Ferreira Marcio Soares, Stricker Sebastian, Fitzgerald Tomas, Monahan Jack, Defranoux Fanny, Watson Philip, Welz Bettina, Hammouda Omar, Wittbrodt Joachim, Birney Ewan
European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge CB10 1SD, United Kingdom.
Centre for Organismal Studies, Heidelberg University, Heidelberg 69120, Germany.
Bioinformatics. 2024 Nov 28;40(12). doi: 10.1093/bioinformatics/btae664.
High-resolution imaging of model organisms allows the quantification of important physiological measurements. In the case of fish with transparent embryos, these videos can visualize key physiological processes, such as heartbeat. High throughput systems can provide enough measurements for the robust investigation of developmental processes as well as the impact of system perturbations on physiological state. However, few analytical schemes have been designed to handle thousands of high-resolution videos without the need for some level of human intervention. We developed a software package, named FEHAT, to provide a fully automated solution for the analytics of large numbers of heart rate imaging datasets obtained from developing Medaka fish embryos in 96-well plate format imaged on an Acquifer machine. FEHAT uses image segmentation to define regions of the embryo showing changes in pixel intensity over time, followed by the classification of the most likely position of the heart and Fourier Transformations to estimate the heart rate. Here, we describe some important features of the FEHAT software, showcasing its performance across a large set of medaka fish embryos and compare its performance to established, less automated solutions. FEHAT provides reliable heart rate estimates across a range of temperature-based perturbations and can be applied to tens of thousands of embryos without the need for any human intervention.
Data used in this manuscript will be made available on request.
对模式生物进行高分辨率成像能够对重要的生理测量进行量化。对于具有透明胚胎的鱼类而言，这些视频可以可视化关键的生理过程，比如心跳。高通量系统能够提供足够的测量数据，用于对发育过程以及系统扰动对生理状态的影响进行稳健的研究。然而，几乎没有设计出能够在无需一定程度人工干预的情况下处理数千个高分辨率视频的分析方案。我们开发了一个名为FEHAT的软件包，为分析以96孔板形式培养的日本青鳉胚胎在含水层机器上成像获得的大量心率成像数据集提供全自动解决方案。FEHAT使用图像分割来定义胚胎中随时间像素强度发生变化的区域，随后对心脏最可能的位置进行分类，并通过傅里叶变换来估计心率。在此，我们描述了FEHAT软件的一些重要特性，展示了其在大量日本青鳉胚胎上的性能，并将其性能与已有的自动化程度较低的解决方案进行了比较。FEHAT能够在一系列基于温度的扰动下提供可靠的心率估计，并且无需任何人工干预即可应用于数万个胚胎。
本手稿中使用的数据可根据要求提供。
