Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data

Duygan, Birge D. Özel; Hadadi, Noushin; Babu, Ambrin Farizah; Seyfried, Markus; Meer, van der Peter

doi:10.1038/s42003-020-1106-y

Cited by 29 publications

(44 citation statements)

References 41 publications

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“…Recently, flowEMMI ( 54 ), a clustering approach based on Gaussian mixture models and the expectation-maximization algorithm, has been proposed and compared to a number of additional algorithms to identify clusters in two-channel bacterial samples. Another option is to perform single-cell classification to identify known bacterial populations ( 55 – 58 ). These can be helpful in case it is known which populations are present in the data and one expects their properties to remain stable throughout the experiment; however, especially the latter is often difficult, due to the phenotypic heterogeneity of bacterial populations ( 59 ).…”

Section: Cell Population Identificationmentioning

confidence: 99%

Computational Analysis of Microbial Flow Cytometry Data

Rubbens

Props

2021

mSystems

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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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Section: Cell Population Identificationmentioning

confidence: 99%

Computational Analysis of Microbial Flow Cytometry Data

Rubbens

Props

2021

mSystems

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“…The abundance of these methanotrophs could subsequently be determined together with CH 4 oxidation rates in the samples which could allow for a quantitative link between rates and OTUs. It is also noted, that total abundance measurements via PCR-based methods are generally error-prone and further research could be improved by employing additional quantification assays like flow cytometry [83][84][85][86].…”

Section: Plos Onementioning

confidence: 99%

Shifts in methanogenic archaea communities and methane dynamics along a subtropical estuarine land use gradient

et al. 2020

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In coastal aquatic ecosystems, prokaryotic communities play an important role in regulating the cycling of nutrients and greenhouse gases. In the coastal zone, estuaries are complex and delicately balanced systems containing a multitude of specific ecological niches for resident microbes. Anthropogenic influences (i.e. urban, industrial and agricultural land uses) along the estuarine continuum can invoke physical and biochemical changes that impact these niches. In this study, we investigate the relative abundance of methanogenic archaea and other prokaryotic communities, distributed along a land use gradient in the subtropical Burnett River Estuary, situated within the Great Barrier Reef catchment, Australia. Microbiological assemblages were compared to physicochemical, nutrient and greenhouse gas distributions in both pore and surface water. Pore water samples from within the most urbanised site showed a high relative abundance of methanogenic Euryarchaeota (7.8% of all detected prokaryotes), which coincided with elevated methane concentrations in the water column, ranging from 0.51 to 0.68 μM at the urban and sewage treatment plant (STP) sites, respectively. These sites also featured elevated dissolved organic carbon (DOC) concentrations (0.66 to 1.16 mM), potentially fuelling methanogenesis. At the upstream freshwater site, both methane and DOC concentrations were considerably higher (2.68 μM and 1.8 mM respectively) than at the estuarine sites (0.02 to 0.66 μM and 0.39 to 1.16 mM respectively) and corresponded to the highest relative abundance of methanotrophic bacteria. The proportion of sulfate reducing bacteria in the prokaryotic community was elevated within the urban and STP sites (relative abundances of 8.0%– 10.5%), consistent with electron acceptors with higher redox potentials (e.g. O2, NO3-) being scarce. Overall, this study showed that ecological niches in anthropogenically altered environments appear to give an advantage to specialized prokaryotes invoking a potential change in the thermodynamic landscape of the ecosystem and in turn facilitating the generation of methane–a potent greenhouse gas.

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“…Flow cytometry (FCM) is also a revolutionary tool that can analyze a huge number of cells at a single cell level in a short period. Therefore, various studies have focused on applying FCM to a clinical setting 5 – 7 .…”

Section: Introductionmentioning

confidence: 99%

Early detection of drug-resistant Streptococcus pneumoniae and Haemophilus influenzae by quantitative flow cytometry

Sawada

Katayama

Takatani

et al. 2021

Sci Rep

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Early detection of drug resistance contributes to combating drug-resistant bacteria and improving patient outcomes. Microbial testing in the laboratory is essential for treating infectious diseases because it can provide critical information related to identifying pathogenic bacteria and their resistance profiles. Despite these clinical requirements, conventional phenotypic testing is time-consuming. Additionally, recent rapid drug resistance tests are not compatible with fastidious bacteria such as Streptococcus and Haemophilus species. In this study, we validated the feasibility of direct bacteria counting using highly sensitive quantitative flow cytometry. Furthermore, by combining flow cytometry and a nucleic acid intercalator, we constructed a highly sensitive method for counting viable fastidious bacteria. These are inherently difficult to measure due to interfering substances from nutrients contained in the medium. Based on the conventional broth microdilution method, our method acquired a few microliter samples in a time series from the same microplate well to exclude the growth curve inconsistency between the samples. Fluorescent staining and flow cytometry measurements were completed within 10 min. Therefore, this approach enabled us to determine antimicrobial resistance for these bacteria within a few hours. Highly sensitive quantitative flow cytometry presents a novel avenue for conducting rapid antimicrobial susceptibility tests.

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Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data

Cited by 29 publications

References 41 publications

Computational Analysis of Microbial Flow Cytometry Data

Computational Analysis of Microbial Flow Cytometry Data

Shifts in methanogenic archaea communities and methane dynamics along a subtropical estuarine land use gradient

Early detection of drug-resistant Streptococcus pneumoniae and Haemophilus influenzae by quantitative flow cytometry

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