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 Toxoplasma gondii and Salmonella enterica 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.Editorial note: 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).

IFN-stimulated gene (ISG) 15 is a ubiquitin-like protein induced after type I IFN stimulation. There is a dearth of in vivo models to study free unconjugated ISG15 function. We found that free ISG15 enhances the production of IFN-γ and IL-1β during murine infection with Toxoplasma gondii. In our model, ISG15 is induced in a type I IFN–dependent fashion and released into the serum. Increased ISG15 levels are dependent on an actively invading and replicating parasite. Two cysteine residues in the hinge domain are necessary determinants for ISG15 to induce increased cytokine levels during infection. Increased ISG15 is concurrent with an influx of IL-1β–producing CD8α+ dendritic cells to the site of infection. In this article, we present Toxoplasma infection as a novel in vivo murine model to study the immunomodulatory properties of free ISG15 and uniquely link it to IL-1β production by CD8α+ dendritic cells driven by two cysteines in the hinge region of the protein.

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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 capability to learn phenotypes from the data, without relying on researcher-based assumptions. Using Toxoplasma gondii and Salmonella typhimurium we demonstrate HRMAn’s capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses.

SummaryPathogenic infections and the diseases they cause are defined by invasion and colonization of distinct host cell types and tissue niches. In the case of viruses and bacteria, molecular and cellular barcoding has shaped our understanding of within-host pathogen population dynamics, and informed therapeutic intervention strategies. Host brain colonization is a clinically untreatable feature of persistent infection by the eukaryotic pathogen Toxoplasma gondii, and the process remains poorly understood. The host blood-brain barrier is expected to physically restrict parasite colonization of this tissue niche and force the infection through a selection bottleneck, however tools and technologies to test this hypothesis have not been available. Here, we have developed a simple CRISPR-based method to barcode Toxoplasma parasites, and then used complex libraries of barcoded parasites to define how the different phases of an infection shape the pathogen population structure. Unexpectedly, we have discovered that the murine host brain does not restrict parasite colonization, with the population structure predominantly shaped by a bottleneck experienced during the acute phase of infection. These data support an evolutionary strategy to maximize genetic diversity of parasite persister cells within the intermediate host brain for subsequent transmission into the definitive feline host.