Host-Toxoplasma Interaction Laboratory, The Francis Crick Institute, London, United Kingdom.
MRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.
Elife. 2019 Feb 12;8:e40560. doi: 10.7554/eLife.40560.
For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capacity to learn phenotypes from the data, without relying on researcher-based assumptions. Using and Typhimurium we demonstrate HRMAn's capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses. HRMAn thus presents the only intelligent solution operating at human capacity suitable for both single image and high content image analysis.
This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
对于基于图像的感染生物学，准确、无偏的宿主-病原体相互作用定量分析至关重要，但通常是手动完成的，或者使用基于图像分割的简单图像分析算法进行有限的计数。由于其异质性，宿主蛋白对病原体的募集往往难以进行准确的自动评估。缺乏一种直观的智能图像分析程序来评估一般细胞病原体防御中的宿主蛋白募集。我们提出了 HRMAn（微生物分析的宿主反应），这是一个基于机器学习算法和深度学习的开源图像分析平台。我们表明，HRMAn 有能力从数据中学习表型，而无需依赖于基于研究人员的假设。我们使用 和 沙门氏菌表明，HRMAn 能够识别、分类和量化病原体杀伤、复制和细胞防御反应。因此，HRMAn 是唯一适合单图像和高内涵图像分析的具有人类能力的智能解决方案。
本文经过编辑处理，作者在其中决定如何应对同行评审中提出的问题。审稿编辑的评估是所有问题都已得到解决（见评审意见）。
