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

Détails

ID Serval
serval:BIB_2AAB8F56D9C5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.
Périodique
Communications biology
Auteur(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
Statut éditorial
Publié
Date de publication
15/07/2020
Peer-reviewed
Oui
Volume
3
Numéro
1
Pages
379
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
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
Oui
Création de la notice
24/07/2020 15:13
Dernière modification de la notice
20/01/2021 7:26
Données d'usage