Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, UK.
Host-Toxoplasma Interaction Laboratory, The Francis Crick Institute, London, UK.
Cell Microbiol. 2021 Jul;23(7):e13349. doi: 10.1111/cmi.13349. Epub 2021 May 16.
To study the dynamics of infection processes, it is common to manually enumerate imaging-based infection assays. However, manual counting of events from imaging data is biased, error-prone and a laborious task. We recently presented HRMAn (Host Response to Microbe Analysis), an automated image analysis program using state-of-the-art machine learning and artificial intelligence algorithms to analyse pathogen growth and host defence behaviour. With HRMAn, we can quantify intracellular infection by pathogens such as Toxoplasma gondii and Salmonella in a variety of cell types in an unbiased and highly reproducible manner, measuring multiple parameters including pathogen growth, pathogen killing and activation of host cell defences. Since HRMAn is based on the KNIME Analytics platform, it can easily be adapted to work with other pathogens and produce more readouts from quantitative imaging data. Here we showcase improvements to HRMAn resulting in the release of HRMAn 2.0 and new applications of HRMAn 2.0 for the analysis of host-pathogen interactions using the established pathogen T. gondii and further extend it for use with the bacterial pathogen Chlamydia trachomatis and the fungal pathogen Cryptococcus neoformans.
为了研究感染过程的动态变化，人们通常需要手动对基于成像的感染检测进行计数。然而，从成像数据中手动计数事件既存在偏差，又容易出错，且非常繁琐。我们最近介绍了 HRMAn（微生物分析中的宿主反应），这是一个使用最先进的机器学习和人工智能算法来分析病原体生长和宿主防御行为的自动化图像分析程序。使用 HRMAn，我们可以以无偏且高度可重复的方式定量分析各种细胞类型中弓形虫和沙门氏菌等病原体的细胞内感染，测量包括病原体生长、病原体杀灭和宿主细胞防御激活在内的多个参数。由于 HRMAn 基于 KNIME 分析平台，因此可以轻松适应其他病原体，并从定量成像数据中生成更多结果。在这里，我们展示了 HRMAn 的改进，发布了 HRMAn 2.0，并将 HRMAn 2.0 用于分析已建立的病原体弓形虫的宿主-病原体相互作用，进一步将其扩展用于细菌病原体沙眼衣原体和真菌病原体新型隐球菌。
