High-resolution rendering of the AI attention map superimposed on Toxoplasma...
High-resolution rendering of the AI attention map superimposed on Toxoplasma Gondii.
Source: Artur Yakimovich

AI-driven platform “HRMAn” never sleeps

Researchers have developed a new AI-driven platform that can analyse how pathogens infect our cells with the precision of a trained biologist. The platform, HRMAn (‘Herman’), which stands for Host Response to Microbe Analysis, is open-source, easy-to-use and can be tailored for different pathogens including Salmonella enterica.

Pioneered by scientists at the Francis Crick Institute and University College London, HRMAn uses deep neural networks to analyse complex patterns in images of pathogen and human (‘host’) cell interactions, pulling out the same detailed characteristics that scientists do by-hand. “What used to be a manual, time-consuming task for biologists now takes us a matter of minutes on a computer, enabling us to learn more about infectious pathogens and how our bodies respond to them, more quickly and more precisely,” says Eva Frickel, Group Leader at the Crick, who led the project. “HRMAn can actually see host-pathogen interactions like a biologist, but unlike us, it doesn’t get tired and need to sleep!”

To demonstrate the power of HRMAn – which runs on the KNIME platform – the team used it to analyse the body’s response to Toxoplasma gondii, a parasite that replicates in cats and is thought to be carried by more than a third of the world’s population.

Researchers in the Crick’s High Throughput Screening facility collected over 30,000 microscope images of five different types of Toxoplasma-infected human cells and loaded them into HRMAn for analysis. HRMAn detected and analysed over 175,000 pathogen-containing cellular compartments, providing detailed information about the number of parasites per cell, the location of the parasites within the cells, and how many cell proteins interacted with the parasites, among other variables. “Previous attempts at automating host-pathogen image analysis failed to capture this level of detail,” says Artur Yakimovich, Research Associate in Jason Mercer’s lab at the MRC LMCB at UCL and co-first author of the study. “Using the same sorts of algorithms that run self-driving cars, we’ve created a platform that boosts the precision of high volume biological data analysis, which has revolutionised what we can do in the lab. AI algorithms come in handy when the platform evaluates the image-based data in a way a trained specialist would. It’s also really easy to use, even for scientists with little to no knowledge of coding.”

The team also used HRMAn to analyse Salmonella enterica – a bacterial pathogen 16 times smaller than Toxoplasma, demonstrating its versatility for studying different pathogens. “Our team uses HRMAn to answer specific questions about host-pathogen interactions, but it has far-reaching implications outside the field too,” says Daniel Fisch, Crick PhD student and co-first author of the study. “HRMAn can analyse any fluorescence image, making it relevant for lots of different areas of biology, including cancer research.”

Subscribe to our newsletter

Related articles

AI method can detect precursors to cervical cancer

AI method can detect precursors to cervical cancer

Using AI and mobile digital microscopy, researchers hope to create screening tools that can detect precursors to cervical cancer in women in resource-limited settings.

AI tool to predict accurate flu forecasts

AI tool to predict accurate flu forecasts

Researchers have developed an AI-powered forecasting tool for predicting influenza outbreaks.

AI finds patterns of mutations in tumour images

AI finds patterns of mutations in tumour images

Researchers have developed an AI algorithm that uses computer vision to analyze tissue samples from cancer patients.

AI model accurately classifies colorectal polyps

AI model accurately classifies colorectal polyps

An AI model for automated classification of colorectal polyps could benefit cancer screening programs by improving efficiency, reproducibility, and accuracy.

Smart microscope adapts to diagnose infectious diseases

Smart microscope adapts to diagnose infectious diseases

Using machine learning, a prototype microscope teaches itself the best illumination settings for diagnosing malaria.

Tracking cholera outbreaks with AI

Tracking cholera outbreaks with AI

Algorithms using data from antibody signatures in peoples’ blood may enable scientists to assess the size of cholera outbreaks and identify hotspots of cholera transmission more accurately than ever.

Smart biosensor to explore the biomolecular world

Smart biosensor to explore the biomolecular world

Scientists have developed AI-powered nanosensors that let researchers track various kinds of biological molecules without disturbing them.

AI platform to assess blood vessel anomalies

AI platform to assess blood vessel anomalies

Researchers have developed an AI platform that could one day be used in a system to assess vascular and eye diseases.

Smart system detects errors when medication is self-administered

Smart system detects errors when medication is self-administered

Many patients use their inhalers and insulin pens wrong. Researchers have developed a system to reduce those numbers for some types of medications.

Popular articles

Subscribe to Newsletter