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

Details

Serval ID
serval:BIB_2AAB8F56D9C5
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.
Journal
Communications biology
Author(s)
Özel Duygan B.D., Hadadi N., Babu A.F., Seyfried M., van der Meer J.R.
ISSN
2399-3642 (Electronic)
ISSN-L
2399-3642
Publication state
Published
Issued date
15/07/2020
Peer-reviewed
Oui
Volume
3
Number
1
Pages
379
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
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 <sup>14</sup> C-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.
Pubmed
Open Access
Yes
Create date
24/07/2020 14:13
Last modification date
20/01/2021 6:26
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