Decoding gut microbiota by imaging analysis of fecal samples

Chikara Furusawa; Kumi Tanabe; Chiharu Ishii; Noriko Kagata; Masaru Tomita; Shinji Fukuda

doi:10.1016/j.isci.2021.103481

Decoding gut microbiota by imaging analysis of fecal samples

iScience. 2021 Nov 22;24(12):103481. doi: 10.1016/j.isci.2021.103481. eCollection 2021 Dec 17.

Authors

Chikara Furusawa^{1

2}, Kumi Tanabe¹, Chiharu Ishii³, Noriko Kagata³, Masaru Tomita³, Shinji Fukuda^{3

4

5

6}

Affiliations

¹ Center for Biosystem Dynamics Research, RIKEN, Suita, Japan.
² Universal Biology Institute, The University of Tokyo, Tokyo, Japan.
³ Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.
⁴ Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan.
⁵ Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan.
⁶ Metabologenomics, Inc., Tsuruoka, Japan.

Abstract

The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis.

Keywords: Biochemistry methods; Microbiome.