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Integrating taxonomic and phenotypic information through FISH-enhanced flow cytometry for microbial community dynamics analysis

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Title: Integrating taxonomic and phenotypic information through FISH-enhanced flow cytometry for microbial community dynamics analysis
Authors: Valérie Mattelin, Josefien Van Landuyt, Frederiek-Maarten Kerkhof, Yorick Minnebo, Nico Boon
Source: MICROBIOLOGY SPECTRUM
Publisher Information: American Society for Microbiology, 2025.
Publication Year: 2025
Subject Terms: IDENTIFICATION, flow cytometry, BACTERIA, Biology and Life Sciences, FIXATION, phenotypic fingerprinting, IN-SITU HYBRIDIZATION, RIBOSOMAL-RNA, fluorescence in situ hybridization, microbial community dynamics
Description: Flow cytometry is a powerful tool to monitor microbial communities, as it allows tracking both changes in the subpopulations and cell numbers at high throughput and a low sample cost. This information can be combined in a phenotypic fingerprint that can be leveraged for diversity analysis. However, as isogenic individuals can manifest phenotypic diversity, for example, due to differing physiological state and phenotypic plasticity, combining the phenotypic information with taxonomic information adds an extra dimension for describing the dynamics of a microbial community. In this research, taxonomic information was incorporated in the microbial fingerprint through fluorescent in situ hybridization (FISH) at a single-cell level. To validate this concept and explore its versatility, two ecosystems with different micro-biodiversity were considered. In the first environment, marine bacteria were monitored for plastic biodegradation in a trickling filter, and in the second, an in vitro simulated human gut microbiome was followed over time. Samples were prepared using different (staining) methods, including FISH, and beta diversity analysis was used to evaluate the level of distinction between differently treated groups in both environments. As a reference to correlate increased distinction with the incorporation of taxonomic information, 16S rRNA gene sequencing was used. Finally, a predictive algorithm was trained to correctly classify samples in the differently treated groups. The results showed that the implementation of FISH in flow cytometry provides more information on a single-cell level to answer specific scientific questions, like distinguishing between phenotypically similar communities or following a specific taxonomic group over time. IMPORTANCE Understanding microbial communities is crucial for elucidating their role in maintaining ecosystem health and stability. Researchers are increasingly interested in studying microbial communities by looking at not just their genetic makeup but also their physical traits and functions. In our study, we used common techniques like fluorescence in situ hybridization and flow cytometry, along with advanced data analysis, to better understand these communities. This combination allowed us to gather and use data more effectively, demonstrating that these easy-to-use methods, when paired with proper analysis, can enhance our understanding of changing microbial ecosystems.
Document Type: Article
File Description: application/pdf
Language: English
ISSN: 2165-0497
DOI: 10.1128/spectrum.01973-24
Access URL: https://biblio.ugent.be/publication/01JZJ0MTX12VC7KV67NAKJDG4F
http://hdl.handle.net/1854/LU-01JZJ0MTX12VC7KV67NAKJDG4F
http://doi.org/10.1128/spectrum.01973-24
https://biblio.ugent.be/publication/01JZJ0MTX12VC7KV67NAKJDG4F/file/01K362N9EFNECBYE6XWGVXBWZJ
Rights: CC BY
Accession Number: edsair.doi.dedup.....80ce5f0f3496654fc8ab41c6d5c35aca
Database: OpenAIRE
Description
Abstract:Flow cytometry is a powerful tool to monitor microbial communities, as it allows tracking both changes in the subpopulations and cell numbers at high throughput and a low sample cost. This information can be combined in a phenotypic fingerprint that can be leveraged for diversity analysis. However, as isogenic individuals can manifest phenotypic diversity, for example, due to differing physiological state and phenotypic plasticity, combining the phenotypic information with taxonomic information adds an extra dimension for describing the dynamics of a microbial community. In this research, taxonomic information was incorporated in the microbial fingerprint through fluorescent in situ hybridization (FISH) at a single-cell level. To validate this concept and explore its versatility, two ecosystems with different micro-biodiversity were considered. In the first environment, marine bacteria were monitored for plastic biodegradation in a trickling filter, and in the second, an in vitro simulated human gut microbiome was followed over time. Samples were prepared using different (staining) methods, including FISH, and beta diversity analysis was used to evaluate the level of distinction between differently treated groups in both environments. As a reference to correlate increased distinction with the incorporation of taxonomic information, 16S rRNA gene sequencing was used. Finally, a predictive algorithm was trained to correctly classify samples in the differently treated groups. The results showed that the implementation of FISH in flow cytometry provides more information on a single-cell level to answer specific scientific questions, like distinguishing between phenotypically similar communities or following a specific taxonomic group over time. IMPORTANCE Understanding microbial communities is crucial for elucidating their role in maintaining ecosystem health and stability. Researchers are increasingly interested in studying microbial communities by looking at not just their genetic makeup but also their physical traits and functions. In our study, we used common techniques like fluorescence in situ hybridization and flow cytometry, along with advanced data analysis, to better understand these communities. This combination allowed us to gather and use data more effectively, demonstrating that these easy-to-use methods, when paired with proper analysis, can enhance our understanding of changing microbial ecosystems.
ISSN:21650497
DOI:10.1128/spectrum.01973-24