HRMAn 2.0: Next‐generation artificial intelligence–driven analysis for broad host–pathogen interactions
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 analy...
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| Veröffentlicht in: | Cellular microbiology 2021-07, Vol.23 (7), p.e13349-n/a, Article 13349 |
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| creator | Fisch, Daniel Evans, Robert Clough, Barbara Byrne, Sophie K. Channell, Will M. Dockterman, Jacob Frickel, Eva‐Maria |
| description | 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.
HRMAn 2.0 is an artificial intelligence‐driven, high‐throughput image analysis tool to study host‐pathogen interactions. It uses AI for object detection and phenotypical classification. HRMAn 2.0 has been validated for several different intracellular pathogens and is easily adaptable for new pathogens and experimental questions. |
| doi_str_mv | 10.1111/cmi.13349 |
| format | Article |
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HRMAn 2.0 is an artificial intelligence‐driven, high‐throughput image analysis tool to study host‐pathogen interactions. It uses AI for object detection and phenotypical classification. HRMAn 2.0 has been validated for several different intracellular pathogens and is easily adaptable for new pathogens and experimental questions.</description><identifier>ISSN: 1462-5814</identifier><identifier>EISSN: 1462-5822</identifier><identifier>DOI: 10.1111/cmi.13349</identifier><identifier>PMID: 33930228</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Artificial Intelligence ; Cell activation ; Cell Biology ; Cell Line, Tumor ; Chlamydia Infections - diagnostic imaging ; Cryptococcosis - diagnostic imaging ; Fungi ; Host-Pathogen Interactions ; host‐pathogen interaction ; Humans ; Image analysis ; Image processing ; Image Processing, Computer-Assisted - methods ; Infections ; Learning algorithms ; Life Sciences & Biomedicine ; Machine learning ; Microbiology ; Pathogens ; Salmonella ; Science & Technology ; Sexually transmitted diseases ; STD ; Toxoplasmosis - diagnostic imaging</subject><ispartof>Cellular microbiology, 2021-07, Vol.23 (7), p.e13349-n/a, Article 13349</ispartof><rights>2021 The Authors. published by John Wiley & Sons Ltd.</rights><rights>2021 The Authors. Cellular Microbiology published by John Wiley & Sons Ltd.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>14</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000651003200001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c3889-221cfcdb3da80b015d96643e15c0d3e1dfdf396f58cc4ad5d5293f3164ddd1a93</citedby><cites>FETCH-LOGICAL-c3889-221cfcdb3da80b015d96643e15c0d3e1dfdf396f58cc4ad5d5293f3164ddd1a93</cites><orcidid>0000-0002-8155-0367 ; 0000-0002-9515-3442 ; 0000-0002-3235-6170</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fcmi.13349$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fcmi.13349$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>315,782,786,1419,1435,27931,27932,39265,45581,45582,46416,46840</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33930228$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fisch, Daniel</creatorcontrib><creatorcontrib>Evans, Robert</creatorcontrib><creatorcontrib>Clough, Barbara</creatorcontrib><creatorcontrib>Byrne, Sophie K.</creatorcontrib><creatorcontrib>Channell, Will M.</creatorcontrib><creatorcontrib>Dockterman, Jacob</creatorcontrib><creatorcontrib>Frickel, Eva‐Maria</creatorcontrib><title>HRMAn 2.0: Next‐generation artificial intelligence–driven analysis for broad host–pathogen interactions</title><title>Cellular microbiology</title><addtitle>CELL MICROBIOL</addtitle><addtitle>Cell Microbiol</addtitle><description>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.
HRMAn 2.0 is an artificial intelligence‐driven, high‐throughput image analysis tool to study host‐pathogen interactions. It uses AI for object detection and phenotypical classification. 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Evans, Robert ; Clough, Barbara ; Byrne, Sophie K. ; Channell, Will M. ; Dockterman, Jacob ; Frickel, Eva‐Maria</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3889-221cfcdb3da80b015d96643e15c0d3e1dfdf396f58cc4ad5d5293f3164ddd1a93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Cell activation</topic><topic>Cell Biology</topic><topic>Cell Line, Tumor</topic><topic>Chlamydia Infections - diagnostic imaging</topic><topic>Cryptococcosis - diagnostic imaging</topic><topic>Fungi</topic><topic>Host-Pathogen Interactions</topic><topic>host‐pathogen interaction</topic><topic>Humans</topic><topic>Image analysis</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Infections</topic><topic>Learning algorithms</topic><topic>Life Sciences & Biomedicine</topic><topic>Machine learning</topic><topic>Microbiology</topic><topic>Pathogens</topic><topic>Salmonella</topic><topic>Science & Technology</topic><topic>Sexually transmitted diseases</topic><topic>STD</topic><topic>Toxoplasmosis - diagnostic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fisch, Daniel</creatorcontrib><creatorcontrib>Evans, Robert</creatorcontrib><creatorcontrib>Clough, Barbara</creatorcontrib><creatorcontrib>Byrne, Sophie K.</creatorcontrib><creatorcontrib>Channell, Will M.</creatorcontrib><creatorcontrib>Dockterman, Jacob</creatorcontrib><creatorcontrib>Frickel, Eva‐Maria</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Cellular microbiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fisch, Daniel</au><au>Evans, Robert</au><au>Clough, Barbara</au><au>Byrne, Sophie K.</au><au>Channell, Will M.</au><au>Dockterman, Jacob</au><au>Frickel, Eva‐Maria</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>HRMAn 2.0: Next‐generation artificial intelligence–driven analysis for broad host–pathogen interactions</atitle><jtitle>Cellular microbiology</jtitle><stitle>CELL MICROBIOL</stitle><addtitle>Cell Microbiol</addtitle><date>2021-07</date><risdate>2021</risdate><volume>23</volume><issue>7</issue><spage>e13349</spage><epage>n/a</epage><pages>e13349-n/a</pages><artnum>13349</artnum><issn>1462-5814</issn><eissn>1462-5822</eissn><abstract>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.
HRMAn 2.0 is an artificial intelligence‐driven, high‐throughput image analysis tool to study host‐pathogen interactions. It uses AI for object detection and phenotypical classification. HRMAn 2.0 has been validated for several different intracellular pathogens and is easily adaptable for new pathogens and experimental questions.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Inc</pub><pmid>33930228</pmid><doi>10.1111/cmi.13349</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-8155-0367</orcidid><orcidid>https://orcid.org/0000-0002-9515-3442</orcidid><orcidid>https://orcid.org/0000-0002-3235-6170</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Artificial Intelligence Cell activation Cell Biology Cell Line, Tumor Chlamydia Infections - diagnostic imaging Cryptococcosis - diagnostic imaging Fungi Host-Pathogen Interactions host‐pathogen interaction Humans Image analysis Image processing Image Processing, Computer-Assisted - methods Infections Learning algorithms Life Sciences & Biomedicine Machine learning Microbiology Pathogens Salmonella Science & Technology Sexually transmitted diseases STD Toxoplasmosis - diagnostic imaging |
| title | HRMAn 2.0: Next‐generation artificial intelligence–driven analysis for broad host–pathogen interactions |
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