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Liu Z, Liu J, Geng J, Wu E, Zhu J, Cong B, Wu R, Sun H. Metatranscriptomic characterization of six types of forensic samples and its potential application to body fluid/tissue identification: A pilot study. Forensic Sci Int Genet 2024; 68:102978. [PMID: 37995518 DOI: 10.1016/j.fsigen.2023.102978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/21/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023]
Abstract
Microorganisms are potential markers for identifying body fluids (venous and menstrual blood, semen, saliva, and vaginal secretion) and skin tissue in forensic genetics. Existing published studies have mainly focused on investigating microbial DNA by 16 S rRNA gene sequencing or metagenome shotgun sequencing. We rarely find microbial RNA level investigations on common forensic body fluid/tissue. Therefore, the use of metatranscriptomics to characterize common forensic body fluids/tissue has not been explored in detail, and the potential application of metatranscriptomics in forensic science remains unknown. Here, we performed 30 metatranscriptome analyses on six types of common forensic sample from healthy volunteers by massively parallel sequencing. After quality control and host RNA filtering, a total of 345,300 unigenes were assembled from clean reads. Four kingdoms, 137 phyla, 267 classes, 488 orders, 985 families, 2052 genera, and 4690 species were annotated across all samples. Alpha- and beta-diversity and differential analysis were also performed. As a result, the saliva and skin groups demonstrated high alpha diversity (Simpson index), while the venous blood group exhibited the lowest diversity despite a high Chao1 index. Specifically, we discussed potential microorganism contamination and the "core microbiome," which may be of special interest to forensic researchers. In addition, we implemented and evaluated artificial neural network (ANN), random forest (RF), and support vector machine (SVM) models for forensic body fluid/tissue identification (BFID) using genus- and species-level metatranscriptome profiles. The ANN and RF prediction models discriminated six forensic body fluids/tissue, demonstrating that the microbial RNA-based method could be applied to BFID. Unlike metagenomic research, metatranscriptomic analysis can provide information about active microbial communities; thus, it may have greater potential to become a powerful tool in forensic science for microbial-based individual identification. This study represents the first attempt to explore the application potential of metatranscriptome profiles in forensic science. Our findings help deepen our understanding of the microorganism community structure at the RNA level and are beneficial for other forensic applications of metatranscriptomics.
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Affiliation(s)
- Zhiyong Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Jiajun Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Jiaojiao Geng
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Enlin Wu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Jianzhang Zhu
- Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou 510080, China
| | - Bin Cong
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Shijiazhuang 050017, China.
| | - Riga Wu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China.
| | - Hongyu Sun
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China.
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Marcos-Fernández R, Sánchez B, Ruiz L, Margolles A. Convergence of flow cytometry and bacteriology. Current and future applications: a focus on food and clinical microbiology. Crit Rev Microbiol 2023; 49:556-577. [PMID: 35749433 DOI: 10.1080/1040841x.2022.2086035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 05/12/2022] [Accepted: 05/31/2022] [Indexed: 11/03/2022]
Abstract
Since its development in the 1960s, flow cytometry (FCM) was quickly revealed a powerful tool to analyse cell populations in medical studies, yet, for many years, was almost exclusively used to analyse eukaryotic cells. Instrument and methodological limitations to distinguish genuine bacterial signals from the background, among other limitations, have hampered FCM applications in bacteriology. In recent years, thanks to the continuous development of FCM instruments and methods with a higher discriminatory capacity to detect low-size particles, FCM has emerged as an appealing technique to advance the study of microbes, with important applications in research, clinical and industrial settings. The capacity to rapidly enumerate and classify individual bacterial cells based on viability facilitates the monitoring of bacterial presence in foodstuffs or clinical samples, reducing the time needed to detect contamination or infectious processes. Besides, FCM has stood out as a valuable tool to advance the study of complex microbial communities, or microbiomes, that are very relevant in the context of human health, as well as to understand the interaction of bacterial and host cells. This review highlights current developments in, and future applications of, FCM in bacteriology, with a focus on those related to food and clinical microbiology.
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Affiliation(s)
- Raquel Marcos-Fernández
- Department of Microbiology and Biochemistry of Dairy Products, Dairy Research Institute of Asturias, Spanish National Research Council (IPLA-CSIC), Asturias, Spain
- Functionality and Ecology of Beneficial Microbes (MicroHealth) Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Asturias, Spain
| | - Borja Sánchez
- Department of Microbiology and Biochemistry of Dairy Products, Dairy Research Institute of Asturias, Spanish National Research Council (IPLA-CSIC), Asturias, Spain
- Functionality and Ecology of Beneficial Microbes (MicroHealth) Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Asturias, Spain
| | - Lorena Ruiz
- Department of Microbiology and Biochemistry of Dairy Products, Dairy Research Institute of Asturias, Spanish National Research Council (IPLA-CSIC), Asturias, Spain
- Functionality and Ecology of Beneficial Microbes (MicroHealth) Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Asturias, Spain
| | - Abelardo Margolles
- Department of Microbiology and Biochemistry of Dairy Products, Dairy Research Institute of Asturias, Spanish National Research Council (IPLA-CSIC), Asturias, Spain
- Functionality and Ecology of Beneficial Microbes (MicroHealth) Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Asturias, Spain
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Schmiester M, Maier R, Riedel R, Durek P, Frentsch M, Kolling S, Mashreghi MF, Jenq R, Zhang L, Peterson CB, Bullinger L, Chang HD, Na IK. Flow cytometry can reliably capture gut microbial composition in healthy adults as well as dysbiosis dynamics in patients with aggressive B-cell non-Hodgkin lymphoma. Gut Microbes 2022; 14:2081475. [PMID: 35634713 PMCID: PMC9154785 DOI: 10.1080/19490976.2022.2081475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Modulation of commensal gut microbiota is increasingly recognized as a promising strategy to reduce mortality in patients with malignant diseases, but monitoring for dysbiosis is generally not routine clinical practice due to equipment, expertise and funding required for sequencing analysis. A low-threshold alternative is microbial diversity profiling by single-cell flow cytometry (FCM), which we compared to 16S rRNA sequencing in human fecal samples and employed to characterize longitudinal changes in the microbiome composition of patients with aggressive B-cell non-Hodgkin lymphoma undergoing chemoimmunotherapy. Diversity measures obtained from both methods were correlated and captured identical trends in microbial community structures, finding no difference in patients' pretreatment alpha or beta diversity compared to healthy controls and a significant and progressive loss of alpha diversity during chemoimmunotherapy. Our results highlight the potential of FCM-based microbiome profiling as a reliable and accessible diagnostic tool that can provide novel insights into cancer therapy-associated dysbiosis dynamics.
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Affiliation(s)
- Maren Schmiester
- Department of Hematology, Oncology and Tumor Immunology, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany,CONTACT Maren Schmiester Department of Hematology, Oncology and Tumor Immunology, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin', Berlin, Germany
| | - René Maier
- Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - René Riedel
- Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Pawel Durek
- Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Marco Frentsch
- BIH Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan Kolling
- BIH Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany,Berlin School of Integrative Oncology, Berlin, Germany
| | - Mir-Farzin Mashreghi
- BIH Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany,(DRFZ), an Institute of the Leibniz AssociationTherapeutic Gene Regulation, Deutsches Rheuma-Forschungszentrum, Berlin, Germany
| | - Robert Jenq
- Department of Genomic Medicine, the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Liangliang Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Christine B. Peterson
- Department of Biostatistics, the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lars Bullinger
- Department of Hematology, Oncology and Tumor Immunology, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,German Cancer Research Center (DKFZ), Heidelberg, Germany,German Cancer Consortium (DKTK), Berlin, Germany
| | - Hyun-Dong Chang
- Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), an Institute of the Leibniz Association, Berlin, Germany,Institute of Biotechnology, Technische Universität Berlin, Germany
| | - Il-Kang Na
- Department of Hematology, Oncology and Tumor Immunology, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,BIH Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany,German Cancer Consortium (DKTK), Berlin, Germany,ECRC Experimental and Clinical Research Center, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
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4
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Pereira AC, Tenreiro A, Cunha MV. When FLOW-FISH met FACS: Combining multiparametric, dynamic approaches for microbial single-cell research in the total environment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:150682. [PMID: 34600998 DOI: 10.1016/j.scitotenv.2021.150682] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 09/22/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
In environmental microbiology, the ability to assess, in a high-throughput way, single-cells within microbial communities is key to understand their heterogeneity. Fluorescence in situ hybridization (FISH) uses fluorescently labeled oligonucleotide probes to detect, identify, and quantify single cells of specific taxonomic groups. The combination of Flow Cytometry (FLOW) with FISH (FLOW-FISH) enables high-throughput quantification of complex whole cell populations, which when associated with fluorescence-activated cell sorting (FACS) enables sorting of target microorganisms. These sorted cells may be investigated in many ways, for instance opening new avenues for cytomics at a single-cell scale. In this review, an overview of FISH and FLOW methodologies is provided, addressing conventional methods, signal amplification approaches, common fluorophores for cell physiology parameters evaluation, and model variation techniques as well. The coupling of FLOW-FISH-FACS is explored in the context of different downstream applications of sorted cells. Current and emerging applications in environmental microbiology to outline the interactions and processes of complex microbial communities within soil, water, animal microbiota, polymicrobial biofilms, and food samples, are described.
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Affiliation(s)
- André C Pereira
- Centre for Ecology, Evolution and Environmental Changes (cE3c), Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal; Biosystems & Integrative Sciences Institute (BioISI), Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
| | - Ana Tenreiro
- Biosystems & Integrative Sciences Institute (BioISI), Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
| | - Mónica V Cunha
- Centre for Ecology, Evolution and Environmental Changes (cE3c), Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal; Biosystems & Integrative Sciences Institute (BioISI), Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal.
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5
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Złoch M, Rodzik A, Pauter K, Szultka-Młyńska M, Rogowska A, Kupczyk W, Pomastowski P, Buszewski B. Problems with identifying and distinguishing salivary streptococci: a multi-instrumental approach. Future Microbiol 2021; 15:1157-1171. [PMID: 32954849 DOI: 10.2217/fmb-2020-0036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Aim: The purpose of this study was to create an alternative protocol for the DNA-based identification of salivary microbiota focused on the distinguishing of Streptococcus species. Materials & methods: Salivary bacteria were identified using 16S rDNA sequencing and proteins and lipids profiling using MALDI-TOF/MS as well as FTIR analysis. Results: Most of the isolates belonged to streptococci - mostly the salivarious group indistinguishable by the molecular technique. In turn, MALDI analysis allowed for their fast and reliable classification. Although FTIR spectroscopy demonstrated the correct species classification, the spectra interpretation was time consuming and complicated. Conclusion: MALDI-TOF/MS demonstrated the biggest effectiveness in the identification and discrimination between the salivary streptococci, which could be easily incorporated in the workflow of routine microbiological laboratories.
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Affiliation(s)
- Michał Złoch
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Torun, Wileńska 4, 87-100 Torun, Poland
| | - Agnieszka Rodzik
- Department of Environmental Chemistry & Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
| | - Katarzyna Pauter
- Department of Environmental Chemistry & Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
| | - Małgorzata Szultka-Młyńska
- Department of Environmental Chemistry & Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
| | - Agnieszka Rogowska
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Torun, Wileńska 4, 87-100 Torun, Poland.,Department of Environmental Chemistry & Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
| | - Wojciech Kupczyk
- Department of General, Gastroenterological & Oncological Surgery, Faculty of Medicine, Collegium Medicum, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
| | - Paweł Pomastowski
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Torun, Wileńska 4, 87-100 Torun, Poland
| | - Bogusław Buszewski
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Torun, Wileńska 4, 87-100 Torun, Poland.,Department of Environmental Chemistry & Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
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6
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PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure. mSphere 2021; 6:6/1/e00530-20. [PMID: 33536320 PMCID: PMC7860985 DOI: 10.1128/msphere.00530-20] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics. IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.
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Abstract
Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.
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Affiliation(s)
| | - Ruben Props
- Center for Microbial Ecology & Technology (CMET), Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
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Onyango SO, De Clercq N, Beerens K, Van Camp J, Desmet T, Van de Wiele T. Oral Microbiota Display Profound Differential Metabolic Kinetics and Community Shifts upon Incubation with Sucrose, Trehalose, Kojibiose, and Xylitol. Appl Environ Microbiol 2020; 86:e01170-20. [PMID: 32561577 PMCID: PMC7414948 DOI: 10.1128/aem.01170-20] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 06/05/2020] [Indexed: 01/19/2023] Open
Abstract
This study compares the metabolic properties of kojibiose, trehalose, sucrose, and xylitol upon incubation with representative oral bacteria as monocultures or synthetic communities or with human salivary bacteria in a defined medium. Compared to sucrose and trehalose, kojibiose resisted metabolism during a 48-h incubation with monocultures, except for Actinomyces viscosus Incubations with Lactobacillus-based communities, as well as salivary bacteria, displayed kojibiose metabolism, yet to a lesser extent than sucrose and trehalose. Concurring with our in vitro findings, screening for carbohydrate-active enzymes revealed that only Lactobacillus spp. and A. viscosus possess enzymes from glycohydrolase (GH) families GH65 and GH15, respectively, which are associated with kojibiose metabolism. Donor-dependent differences in salivary microbiome composition were noted, and differences in pH drop during incubation indicated different rates of sugar metabolism. However, functional analysis indicated that lactate, acetate, and formate evenly dominated the metabolic profile for all sugars except for xylitol. 16S rRNA gene sequencing analysis and α-diversity markers revealed that a significant shift of the microbiome community by sugars was more pronounced in sucrose and trehalose than in kojibiose and xylitol. In Streptococcus spp., a taxon linked to cariogenesis dominated in sucrose (mean ± standard deviation, 91.8 ± 6.4%) and trehalose (55.9 ± 38.6%), representing a high diversity loss. In contrast, Streptococcus (5.1 ± 3.7%) was less abundant in kojibiose, which instead was dominated by Veillonella (26.8 ± 19.6%), while for xylitol, Neisseria (29.4 ± 19.1%) was most abundant. Overall, kojibiose and xylitol incubations stimulated cariogenic species less yet closely maintained an abundance of key phyla and genera of the salivary microbiome, suggesting that kojibiose has low cariogenic properties.IMPORTANCE This study provides a detailed scientific insight on the metabolism of a rare disaccharide, kojibiose, whose mass production has recently been made possible. While the resistance of kojibiose was established with monocultures, delayed utilization of kojibiose was observed with communities containing lactobacilli and A. viscosus as well as with complex communities of bacteria from human saliva. Kojibiose is, therefore, less metabolizable than sucrose and trehalose. Moreover, although conventional sugars cause distinct shifts in salivary microbial communities, our study has revealed that kojibiose is able to closely maintain the salivary microbiome composition, suggesting its low cariogenic properties. This study furthermore underscores the importance and relevance of microbial culture and ex vivo mixed cultures to study cariogenicity and substrate utilization; this is in sharp contrast with tests that solely rely on monocultures such as Streptococcus mutans, which clearly fail to capture complex interactions between oral microbiota.
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Affiliation(s)
- Stanley O Onyango
- Center for Microbial Ecology and Technology (CMET), Ghent University, Ghent, Belgium
| | - Nele De Clercq
- Center for Microbial Ecology and Technology (CMET), Ghent University, Ghent, Belgium
| | - Koen Beerens
- Center for Synthetic Biology, Department of Biochemical and Microbial Technology, Ghent University, Ghent, Belgium
| | - John Van Camp
- Laboratory of Nutrition and Food Chemistry, Ghent University, Ghent, Belgium
| | - Tom Desmet
- Center for Synthetic Biology, Department of Biochemical and Microbial Technology, Ghent University, Ghent, Belgium
| | - Tom Van de Wiele
- Center for Microbial Ecology and Technology (CMET), Ghent University, Ghent, Belgium
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9
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Duquenoy A, Bellais S, Gasc C, Schwintner C, Dore J, Thomas V. Assessment of Gram- and Viability-Staining Methods for Quantifying Bacterial Community Dynamics Using Flow Cytometry. Front Microbiol 2020; 11:1469. [PMID: 32676069 PMCID: PMC7333439 DOI: 10.3389/fmicb.2020.01469] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 06/04/2020] [Indexed: 01/06/2023] Open
Abstract
Over the past years, gut microbiota became a major field of interest with increasing reports suggesting its association with a large number of human diseases. In this context, there is a major interest to develop analysis tools allowing simple and cost-effective population pattern analysis of these complex ecosystems to follow changes over time. Whereas sequence-based metagenomics profiling is widely used for microbial ecosystems characterization, it still requires time and specific expertise for analysis. Flow cytometry overcomes these disadvantages, providing key information on communities within hours. In addition, it can potentially be used to select, isolate and cultivate specific bacteria of interest. In this study, we evaluated the culturability of strictly anaerobic bacteria that were stained with a classical Live/Dead staining, and then sorted using flow cytometry under anaerobic conditions. This sorting of “viable” fraction demonstrated that 10–80% of identified “viable” cells of pure cultures of strictly anaerobic bacteria were culturable. In addition, we tested the use of a combination of labeled vancomycin and Wheat Germ Agglutinin (WGA) lectin to discriminate Gram-positive from Gram-negative bacteria in complex ecosystems. After validation on both aerobic/anaerobic facultative and strictly anaerobic bacteria, the staining methods were applied on complex ecosystems, revealing differences between culture conditions and demonstrating that minor pH variations have strong impacts on microbial community structure, which was confirmed by 16S rRNA gene sequencing. This combination of staining methods makes it possible to follow-up evolutions of complex microbial communities, supporting its future use as a rapid analysis tool in various applications. The flow cytometry staining method that was developed has the potential to facilitate the analysis of complex ecosystems by highlighting changes in bacterial communities’ dynamics. It is assumed to be applicable as an efficient and fast approach to improve the control of processes linked to a wide range of ecosystems or known communities of bacterial species in both research and industrial contexts.
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Affiliation(s)
| | - Samuel Bellais
- Bioaster, Institut de Recherche Technologique, Paris, France
| | | | | | - Joël Dore
- Université Paris-Saclay, INRAE, MetaGenoPolis, AgroParisTech, MICALIS, Jouy-en-Josas, France
| | - Vincent Thomas
- Bioaster, Institut de Recherche Technologique, Paris, France
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10
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Carlson-Jones JAP, Kontos A, Kennedy D, Martin J, Lushington K, McKerral J, Paterson JS, Smith RJ, Dann LM, Speck P, Mitchell JG. The microbial abundance dynamics of the paediatric oral cavity before and after sleep. J Oral Microbiol 2020; 12:1741254. [PMID: 32341758 PMCID: PMC7170375 DOI: 10.1080/20002297.2020.1741254] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 02/12/2020] [Accepted: 02/16/2020] [Indexed: 01/12/2023] Open
Abstract
Objective: Microhabitats in the oral cavity differ in microbial taxonomy. However, abundance variations of bacterial and viral communities within these microhabitats are not fully understood. Aims and Hypothesis: To assess the spatial distribution and dynamics of the microbial abundances within 6 microhabitats of the oral cavity before and after sleep. We hypothesise that the abundance distributions of these microbial communities will differ among oral sites. Methods: Using flow cytometry, bacterial and virus-like particle (VLP) abundances were enumerated for 6 oral microhabitats before and after sleep in 10 healthy paediatric sleepers. Results: Bacterial counts ranged from 7.2 ± 2.8 × 105 at the palate before sleep to 1.3 ± 0.2 × 108 at the back of the tongue after sleep, a difference of 187 times. VLPs ranged from 1.9 ± 1.0 × 106 at the palate before sleep to 9.2 ± 5.0 × 107 at the back of the tongue after sleep, a difference of 48 times. Conclusion: The oral cavity is a dynamic numerically heterogeneous environment where microbial communities can increase by a count of 100 million during sleep. Quantification of the paediatric oral microbiome complements taxonomic diversity information to show how biomass varies and shifts in space and time.
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Affiliation(s)
- Jessica A P Carlson-Jones
- Department of Respiratory and Sleep Medicine, Women's and Children's Hospital, Adelaide, Australia.,Robinson Research Institute, School of Paediatrics and Reproductive Health, the University of Adelaide, Adelaide, Australia.,College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
| | - Anna Kontos
- Department of Respiratory and Sleep Medicine, Women's and Children's Hospital, Adelaide, Australia.,Robinson Research Institute, School of Paediatrics and Reproductive Health, the University of Adelaide, Adelaide, Australia
| | - Declan Kennedy
- Department of Respiratory and Sleep Medicine, Women's and Children's Hospital, Adelaide, Australia.,Robinson Research Institute, School of Paediatrics and Reproductive Health, the University of Adelaide, Adelaide, Australia
| | - James Martin
- Department of Respiratory and Sleep Medicine, Women's and Children's Hospital, Adelaide, Australia.,Robinson Research Institute, School of Paediatrics and Reproductive Health, the University of Adelaide, Adelaide, Australia
| | - Kurt Lushington
- School of Psychology, Social Work and Social Policy, University of South Australia, Adelaide, Australia
| | - Jody McKerral
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
| | - James S Paterson
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
| | - Renee J Smith
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Lisa M Dann
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
| | - Peter Speck
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
| | - James G Mitchell
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
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11
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Belstrøm D. The salivary microbiota in health and disease. J Oral Microbiol 2020; 12:1723975. [PMID: 32128039 PMCID: PMC7034443 DOI: 10.1080/20002297.2020.1723975] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 11/26/2019] [Accepted: 11/29/2019] [Indexed: 12/19/2022] Open
Abstract
The salivary microbiota (SM), comprising bacteria shed from oral surfaces, has been shown to be individualized, temporally stable and influenced by diet and lifestyle. SM reflects local bacterial alterations of the supragingival and subgingival microbiota, and periodontitis and dental-caries associated characteristics of SM have been reported. Also, data suggest an impact of systemic diseases on SM as demonstrated in patients with a wide variety of systemic diseases including diabetes, cancer, HIV and rheumatoid arthritis. The presence of systemic diseases seems to influence salivary levels of specific bacterial species, as well as α- and β-diversity of SM. The composition of SM might thereby potentially mirror oral and general health status. The contentious development of advanced molecular techniques such as metagenomics, metatranscriptomics and metabolomics has enabled the possibility to address bacterial functions rather than presence in microbial samples. However, at present only a few studies have employed such techniques on SM to reveal functional and metabolic characteristics in oral health and disease. Future studies are therefore warranted to illuminate the possible impact of metabolic functions of SM on oral and general health status. Ultimately, such an approach has the possibility to reveal novel and personalized therapeutic avenues in oral and general medicine.
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Affiliation(s)
- Daniel Belstrøm
- Section for Periodontology and Microbiology, Department of Odontology, University of Copenhagen, Copenhagen, Denmark
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12
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Ludwig J, Zu Siederdissen CH, Liu Z, Stadler PF, Müller S. flowEMMi: an automated model-based clustering tool for microbial cytometric data. BMC Bioinformatics 2019; 20:643. [PMID: 31815609 PMCID: PMC6902487 DOI: 10.1186/s12859-019-3152-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 10/10/2019] [Indexed: 12/17/2022] Open
Abstract
Background Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM. Results We bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further. Conclusions flowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.
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Affiliation(s)
- Joachim Ludwig
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
| | | | - Zishu Liu
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
| | - Peter F Stadler
- Department of Computer Science, University Leipzig, Härtelstr. 16-18, Leipzig, 04107, Germany
| | - Susann Müller
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
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13
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Schäpe SS, Krause JL, Engelmann B, Fritz-Wallace K, Schattenberg F, Liu Z, Müller S, Jehmlich N, Rolle-Kampczyk U, Herberth G, von Bergen M. The Simplified Human Intestinal Microbiota (SIHUMIx) Shows High Structural and Functional Resistance against Changing Transit Times in In Vitro Bioreactors. Microorganisms 2019; 7:E641. [PMID: 31816881 PMCID: PMC6956075 DOI: 10.3390/microorganisms7120641] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 01/10/2023] Open
Abstract
Many functions in host-microbiota interactions are potentially influenced by intestinal transit times, but little is known about the effects of altered transition times on the composition and functionality of gut microbiota. To analyze these effects, we cultivated the model community SIHUMIx in bioreactors in order to determine the effects of varying transit times (TT) on the community structure and function. After five days of continuous cultivation, we investigated the influence of different medium TT of 12 h, 24 h, and 48 h. For profiling the microbial community, we applied flow cytometric fingerprinting and revealed changes in the community structure of SIHUMIx during the change of TT, which were not associated with changes in species abundances. For pinpointing metabolic alterations, we applied metaproteomics and metabolomics and found, along with shortening the TT, a slight decrease in glycan biosynthesis, carbohydrate, and amino acid metabolism and, furthermore, a reduction in butyrate, methyl butyrate, isobutyrate, valerate, and isovalerate concentrations. Specifically, B. thetaiotaomicron was identified to be affected in terms of butyrate metabolism. However, communities could recover to the original state afterward. This study shows that SIHUMIx showed high structural stability when TT changed-even four-fold. Resistance values remained high, which suggests that TTs did not interfere with the structure of the community to a certain degree.
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Affiliation(s)
- Stephanie Serena Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (S.S.S.); (B.E.); (K.F.-W.); (N.J.); (U.R.-K.)
| | - Jannike Lea Krause
- Department of Environmental Immunology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (J.L.K.); (G.H.)
| | - Beatrice Engelmann
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (S.S.S.); (B.E.); (K.F.-W.); (N.J.); (U.R.-K.)
| | - Katarina Fritz-Wallace
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (S.S.S.); (B.E.); (K.F.-W.); (N.J.); (U.R.-K.)
| | - Florian Schattenberg
- Department of Environmental Microbiology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (F.S.); (Z.L.); (S.M.)
| | - Zishu Liu
- Department of Environmental Microbiology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (F.S.); (Z.L.); (S.M.)
| | - Susann Müller
- Department of Environmental Microbiology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (F.S.); (Z.L.); (S.M.)
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (S.S.S.); (B.E.); (K.F.-W.); (N.J.); (U.R.-K.)
| | - Ulrike Rolle-Kampczyk
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (S.S.S.); (B.E.); (K.F.-W.); (N.J.); (U.R.-K.)
| | - Gunda Herberth
- Department of Environmental Immunology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (J.L.K.); (G.H.)
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research―UFZ GmbH, 04316 Leipzig, Germany; (S.S.S.); (B.E.); (K.F.-W.); (N.J.); (U.R.-K.)
- Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, University of Leipzig, 04103 Leipzig, Germany
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14
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Shi Y, Xia W, Liu S, Guo J, Qi Z, Zou Y, Wang L, Duan SZ, Zhou Y, Lin C, Shi J, Wang L, Fan C, Lv M, Tang Z. Impact of Graphene Exposure on Microbial Activity and Community Ecosystem in Saliva. ACS APPLIED BIO MATERIALS 2019; 2:226-235. [DOI: 10.1021/acsabm.8b00566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yuting Shi
- National Clinical
Research Center of Oral Diseases, Shanghai 200011, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Wenjun Xia
- National Clinical
Research Center of Oral Diseases, Shanghai 200011, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Shima Liu
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
- College of Sciences, Shanghai University, Shanghai 200444, China
| | - Jingyang Guo
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
- College of Sciences, Shanghai University, Shanghai 200444, China
| | - Zhengnan Qi
- National Clinical
Research Center of Oral Diseases, Shanghai 200011, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Yan Zou
- National Clinical
Research Center of Oral Diseases, Shanghai 200011, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Liping Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200011, China
| | - Sheng-Zhong Duan
- National Clinical
Research Center of Oral Diseases, Shanghai 200011, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Yi Zhou
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Chenglie Lin
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Jiye Shi
- UCB Pharma, Slough, Berkshire SL1 3WE, U.K
| | - Lihua Wang
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Chunhai Fan
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Min Lv
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Zisheng Tang
- National Clinical
Research Center of Oral Diseases, Shanghai 200011, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China
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15
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Lambrecht J, Schattenberg F, Harms H, Mueller S. Characterizing Microbiome Dynamics - Flow Cytometry Based Workflows from Pure Cultures to Natural Communities. J Vis Exp 2018. [PMID: 30059034 DOI: 10.3791/58033] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.
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Affiliation(s)
- Johannes Lambrecht
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ
| | - Florian Schattenberg
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ
| | - Hauke Harms
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ
| | - Susann Mueller
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ;
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16
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McFarlin BK, Bowman EM. Advanced flow cytometry techniques for clinical detection. Methods 2018; 134-135:1-2. [DOI: 10.1016/j.ymeth.2018.01.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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