Artikel
Single cell-based bacterial phenotyping for microbiota-based diagnosis and monitoring of rheumatic diseases
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Autoren
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Hyun-Dong Chang
- Deutsches Rheuma-Forscnhungszentrum Berlin, ein Leibniz Institut, Berlin
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Lisa Budzinski
- Deutsches Rheuma-Forscnhungszentrum Berlin, ein Leibniz Institut, Berlin
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Gi-Ung Kang
- Deutsches Rheuma-Forscnhungszentrum Berlin, ein Leibniz Institut, Berlin
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Toni Sempert
- Deutsches Rheuma-Forscnhungszentrum Berlin, ein Leibniz Institut, Berlin
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Marcell Toth
- Charité-Universitätsmedizin Berlin, Berlin
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Anne Benken
- Charité-Universitätsmedizin Berlin, Berlin
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Robert Biesen
- Charité-Universitätsmedizin Berlin, Berlin
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Bosse Jessen
- Charité-Universitätsmedizin Berlin, Berlin
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Carl Weidinger
- Charité-Universitätsmedizin Berlin, Berlin
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Britta Siegmund
- Charité-Universitätsmedizin Berlin, Berlin
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Andreas Radbruch
- Deutsches Rheuma-Forscnhungszentrum Berlin, ein Leibniz Institut, Berlin
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Tobias Alexander
- Charité-Universitätsmedizin Berlin, Berlin
| Veröffentlicht: | 18. September 2024 |
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Gliederung
Text
Introduction: The intestinal microbiota is considered highly relevant in the pathogenesis of rheumatic diseases. Alterations in the composition of microbial communities, e.g. within the intestine, also termed dysbiosis, have been associated with almost every rheumatic disease. However, so far, the individual’s microbiota has not found its way into personalized medicine. We hypothesize that bacteria are sensitive to alterations in the intestinal microenvironment and that their adaptations and immunological context are reflected in their surface phenotypes.
Methods: We have developed a protocol with which we phenotype the microbiota on the single-cell level using multi-parametric flow cytometry. We fluorescently stain bacteria from stool samples of patients and healthy donors to assess their coating with host immunoglobulins and the expression of specific surface sugars. For analysis the bacterial cells are grouped into clusters of phenotypically similar cells. The cluster information is then used by machine-learning algorithms to identify relevant signatures allowing disease classification and patient stratification.
Results: We have analyzed bacterial communities derived from stool samples of patients with various chronic inflammatory diseases. We can extract phenotypic signatures which are specific and selective for the different rheumatic diseases, but also signatures which can be used for improved, microbiota-based patient stratification. In a proof-of-concept study, we demonstrate the utility of single-cell microbiota phenotyping for monitoring of patients undergoing anti-TNF treatment but also the ability to predict treatment success before start of the treatment in a cohort of patients with Crohn’s disease.
Conclusion: We think the phenotypic monitoring of the microbiota from easily accessible and non-invasive stool samples has significant potential for patient diagnosis and stratification, paving the way for microbiota-based personalized medicine.
Disclosures: The authors have no conflicts of interest.
