Computational analysis of microbial flow cytometry data

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

Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities

Flow cytometry is able measure up to 50.000 cells in various dimensions in seconds of time. This large amount of data gives rise to the possibility of making predictions at the single-cell level, however, applied to bacterial populations a systemic investigation lacks. In order to combat this deficiency, we cultivated twenty individual bacterial populations and measured them through flow cytometry. By creating in silico communities we are able to use supervised machine learning techniques in order to examine to what extent single-cell predictions can be made; this can be used to identify the community composition. We show that for more than half of the communities consisting out of two bacterial populations we can identify single cells with an accuracy >90%. Furthermore we prove that in silico communities can be used to identify their in vitro counterpart communities. This result leads to the conclusion that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of bacterial flow cytometric data and for the experimental study of low-complexity communities

Identifying synthetic microbial communities by learning in silico communities using flow cytometry

Single cells can be characterized in terms of their phenotypic properties using flow cytometry. However, up to our knowledge there has not yet been a thorough survey concerning the classification of bacterial species based on flow cytometric data. This paper aims to perform a thorough investigation concerning the identification of bacterial communities of various complexities in species richness. We do this by creating so-called in silico communities, communities created by aggregating the data coming from individual cultures; moreover we show that it is possible to use in silico communities to identify in vitro created communities as well, proving the biological relevance and usability of bacterial in silico communities

Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods

In diagnostics of infectious diseases, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) can be applied for the identification of pathogenic microorganisms. However, to achieve a trustworthy identification from MALDI-TOF MS data, a significant amount of biomass should be considered. The bacterial load that potentially occurs in a sample is therefore routinely amplified by culturing, which is a time-consuming procedure. In this paper, we show that culturing can be avoided by conducting MALDI-TOF MS on individual bacterial cells. This results in a more rapid identification of species with an acceptable accuracy. We propose a deep learning architecture to analyze the data and compare its performance with traditional supervised machine learning algorithms. We illustrate our workflow on a large data set that contains bacterial species related to urinary tract infections. Overall we obtain accuracies up to 85% in discriminating five different species

Clustering environmental flow cytometry data by searching density peaks

Microbial single cells can be characterized by their phenotypic properties using flow cytometry. Therefore flow cytometry can be used to analyze various aspects of environmental microbial communities. In recent years, researchers have focused on fully exploiting the multivariate data that such analyses generate. As they are interested in the diversity of an environmental sample, we need a proper estimation of the number of species and their abundances. We modified a recently published algorithm to estimate the microbial diversity based on flow cytometry data. After giving a brief sketch of the problem setup, we will review this algorithm alongside its various implementations. Moreover we will present our current implementation combined with future challenges we foresee

Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry

High-nucleic-acid (HNA) and low-nucleic-acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. A number of reports have shown that HNA cell density correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R-2 = 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA bacteria could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels. Selected operational taxonomic units (OTUs) ranged from low to high relative abundance and were mostly lake system specific (89.5% to 99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5% to 33.3%), suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of system-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships. IMPORTANCE A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system specific, regularly rare members of the community, and that some could possibly switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production

Detecting phenotypes with Raman spectroscopy

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