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

van der Meer, J.R.

doi:10.1038/s42003-020-1106-y

Titre

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

Type

article

Institution

UNIL/CHUV/Unisanté + institutions partenaires

Périodique

Communications Biology

Auteur(s)

Özel Duygan, B.D.

Auteure/Auteur

Hadadi, N.

Auteure/Auteur

Babu, A.F.

Auteure/Auteur

Seyfried, M.

Auteure/Auteur

van der Meer, J.R.

Auteure/Auteur

Liens vers les personnes

van der Meer, Jan Roelof

Liens vers les unités

Dép. microbiologie fondamentale

ISSN

2399-3642

Statut éditorial

Publié

Date de publication

2020-07-15

Volume

3

Numéro

1

Première page

379

Peer-reviewed

Oui

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.

PID Serval

serval:BIB_2AAB8F56D9C5

DOI

10.1038/s42003-020-1106-y

PMID

32669688

Permalien

https://iris.unil.ch/handle/iris/42295

Open Access

Oui

Date de création

2020-07-24T13:13:21.478Z

Date de création dans IRIS

2025-05-20T14:05:13Z

Fichier(s)

Nom

32669688_BIB_2AAB8F56D9C5.pdf

Version du manuscrit

published

Licence

https://creativecommons.org/licenses/by/4.0

Taille

2 MB

Format

Adobe PDF

PID Serval

serval:BIB_2AAB8F56D9C5.P001

URN

urn:nbn:ch:serval-BIB_2AAB8F56D9C54

Somme de contrôle

(MD5):80c7ae31f0f8c83c2aa1ea2ab8a4bc53