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

摘要

Representative stained cell and bead standards with known volume and mass (a) are analyzed by FCM to capture multidimensional optical and shape characteristics (b). Note that FITC here represents the channel to capture the SYBR Green I fluorescence of cell staining. Multiparametric data of each of the strain and bead standards, separated where they consist of recognizable subpopulations, are used as input for training, testing and validating the ANN, producing the classifiers (c). FCM data from stained target untrained known or unknown microbial communities (d) are assigned to the strain and bead output classes using the ANN classifiers (e). The diversity attribution can subsequently be used to estimate individual population densities and their biomass, and, in the case of unknown communities, to calculate similarities to the used standards (f).