HRMAn 2.0: Next-generation artificial intelligence-driven analysis for broad host-pathogen interactions

Daniel Fisch, Robert Evans, Barbara Clough, Sophie K Byrne, Will M Channell, Jacob Dockterman, Eva-Maria Frickel

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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.

Original languageEnglish
Article numbere13349
Pages (from-to)e13349
JournalCellular Microbiology
Issue number7
Early online date30 Apr 2021
Publication statusPublished - Jul 2021

Bibliographical note

© 2021 The Authors. Cellular Microbiology published by John Wiley & Sons Ltd.


  • artificial intelligence
  • host-pathogen interaction
  • image analysis


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