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

Communications Biology
B D Özel DuyganJ R van der Meer

Abstract

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from 14C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.

References

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Citations

Oct 17, 2020·Environmental Microbiology Reports·Lucas Paoli, Shinichi Sunagawa
Jan 21, 2021·MSystems·Peter Rubbens, Ruben Props

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Methods Mentioned

BETA
flow cytometry
PCA
amplicon sequencing

Software Mentioned

ADONIS
Dividerand
QIIME2
phyloseq
Nanolive
Image J
R
CellCognize
STEWE
MatLab mdscale

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