학술논문
Automated characterisation of neutrophil activation phenotypes in ex vivo human Candida blood infections.
Document Type
Academic Journal
Author
Belyaev I; Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.; Faculty of Biological Sciences, Friedrich-Schiller-University Jena, Germany.; Marolda A; Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology -Hans-Knöll-Institute, Jena, Germany.; Praetorius JP; Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.; Faculty of Biological Sciences, Friedrich-Schiller-University Jena, Germany.; Sarkar A; Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.; Faculty of Biological Sciences, Friedrich-Schiller-University Jena, Germany.; Medyukhina A; Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.; Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, TN.; Hünniger K; Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology -Hans-Knöll-Institute, Jena, Germany.; Institute for Hygiene and Microbiology, University of Würzburg, Würzburg, Germany.; Kurzai O; Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology -Hans-Knöll-Institute, Jena, Germany.; Institute for Hygiene and Microbiology, University of Würzburg, Würzburg, Germany.; Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany.; Figge MT; Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.; Institute of Microbiology, Faculty of Biological Sciences, Friedrich-Schiller-University Jena, Germany.
Source
Publisher: Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology Country of Publication: Netherlands NLM ID: 101585369 Publication Model: eCollection Cited Medium: Print ISSN: 2001-0370 (Print) Linking ISSN: 20010370 NLM ISO Abbreviation: Comput Struct Biotechnol J Subsets: PubMed not MEDLINE
Subject
Language
English
ISSN
2001-0370
Abstract
Rapid identification of pathogens is required for early diagnosis and treatment of life-threatening bloodstream infections in humans. This requirement is driving the current developments of molecular diagnostic tools identifying pathogens from human whole blood after successful isolation and cultivation. An alternative approach is to determine pathogen-specific signatures from human host immune cells that have been exposed to pathogens. We hypothesise that activated immune cells, such as neutrophils, may exhibit a characteristic behaviour - for instance in terms of their speed, dynamic cell morphology - that allows (i) identifying the type of pathogen indirectly and (ii) providing information on therapeutic efficacy. In this feasibility study, we propose a method for the quantitative assessment of static and morphodynamic features of neutrophils based on label-free time-lapse imaging data. We investigate neutrophil activation phenotypes after confrontation with fungal pathogens and isolation from a human whole-blood assay. In particular, we applied a machine learning supported approach to time-lapse microscopy data from different infection scenarios and were able to distinguish between Candida albicans and C. glabrata infection scenarios with test accuracies well above 75%, and to identify pathogen-free samples with accuracy reaching 100%. These results significantly exceed the test accuracies achieved using state-of-the-art deep neural networks to classify neutrophils by their morphodynamics.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2022 The Authors.)
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2022 The Authors.)