The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Detection and identification of specific bacteria in wound biofilms using peptide nucleic acid fluorescent in situ hybridization (PNA FISH)
Abstract
Biofilms provide a reservoir of potentially infectious micro-organisms that are resistant to antimicrobial agents, and their importance in the failure of medical devices and chronic inflammatory conditions is increasingly being recognized. Particular research interest exists in the association of biofilms with wound infection and non-healing, i.e. chronic wounds. In this study, fluorescent in situ hybridization (FISH) was used in combination with confocal laser scanning microscopy (CLSM) to detect and characterize the spatial distribution of biofilm-forming bacteria which predominate within human chronic skin wounds ( Pseudomonas aeruginosa , Staphylococcus aureus , Streptococcus sp. and Micrococcus sp.). In vitro biofilms were prepared using a constant-depth film fermenter and a reconstituted human epidermis model. In vivo biofilms were also studied using biopsy samples from non-infected chronic venous leg ulcers. The specificity of peptide nucleic acid (PNA) probes for the target organisms was confirmed using mixed preparations of planktonic bacteria and multiplex PNA probing. Identification and location of individual bacterial species within multi-species biofilms demonstrated that P. aeruginosa was predominant. CLSM revealed clustering of individual species within mixed-species biofilms. FISH analysis of archive chronic wound biopsy sections showed bacterial presence and allowed bacterial load to be determined. The application of this standardized procedure makes available an assay for identification of single- or multi-species bacterial populations in tissue biopsies. The technique provides a reliable tool to study bacterial biofilm formation and offers an approach to assess targeted biofilm disruption strategies in vivo .
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“PNA-FISH has been established as a productive approach to detect and characterize wound biofilm in human tissues.76-78”
From SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning (2025) · cites this paper as [78]
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Pastar, I., Nusbaum, A. G., Gil, J., Patel, S. B., Chen, J., Valdes, J., Stojadinovic, O., Plano, L. R., Tomic-Canic, M., & Davis, S. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056846 · Cited by 391
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Self-citationSingh, S., Muniz De Oliveira, F., Wang, C., Kumar, M., Xuan, Y., DeMazumder, D., Sen, C. K., & Roy, S. (2025) · Advances in Wound Care · doi:10.1089/wound.2024.0291 · Cited by 6
Metadata
- Authors
- Malic, S., Hill, K. E., Hayes, A., Percival, S. L., Thomas, D. W., & Williams, D. W.
- Venue
- Microbiology
- Publisher
- Microbiology Society
- Published
- 2009-08-01
- Volume
- 155
- Issue
- 8
- Pages
- 2603-2611
- ISSN
- 1350-0872
- 1465-2080
- Cited by
- 182
- References
- 3
- Language
- en
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