Decoding gut microbiota by imaging analysis of fecal samples
Chikara Furusawa,
Kumi Tanabe,
Chiharu Ishii,
Noriko Kagata,
Masaru Tomita,
Shinji Fukuda
Affiliations
Chikara Furusawa
Center for Biosystem Dynamics Research, RIKEN, Suita, Japan; Universal Biology Institute, The University of Tokyo, Tokyo, Japan; Corresponding author
Kumi Tanabe
Center for Biosystem Dynamics Research, RIKEN, Suita, Japan
Chiharu Ishii
Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
Noriko Kagata
Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
Masaru Tomita
Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
Shinji Fukuda
Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan; Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan; Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan; Metabologenomics, Inc., Tsuruoka, Japan; Corresponding author
Summary: 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.
