TY - JOUR
T1 - Decoding gut microbiota by imaging analysis of fecal samples
AU - Furusawa, Chikara
AU - Tanabe, Kumi
AU - Ishii, Chiharu
AU - Kagata, Noriko
AU - Tomita, Masaru
AU - Fukuda, Shinji
N1 - Funding Information:
We thank Dr. Yasushi Okada for fruitful discussion; Ms. Yuka Ohara, Mrs. Mitsuko Komatsu, and Noriko Fukuda for technical support; and Dr. Natsumi Saito for conducting the animal experiment. This work was supported in part by the RIKEN Aging Project and the interdisciplinary research program Integrated Symbiology (iSYM), JSPS KAKENHI (17H06389 and 19H05626 to C. F.; 18H04805 to S.F.), JST PRESTO (JPMJPR1537 to S.F.),JST ERATO (JPMJER1902 to S.F. and C.F.), AMED-CREST (JP20gm1010009 to S.F.), the Takeda Science Foundation (to S.F.), the Food Science Institute Foundation (to S.F.), the Yamagata Prefectural Government, and the City of Tsuruoka. C.F. and S.F. conceived and designed the study. C.I. N.K. and S.F. performed the experiment. C.F. K.T. C.I. M.T. and S.F. analyzed the data. C.F. K.T. C.I. and S.F. wrote the paper. S.F. is founder and CEO of Metabologenomics, Inc. which is focused on the design and control of the gut environment for human health.
Funding Information:
We thank Dr. Yasushi Okada for fruitful discussion; Ms. Yuka Ohara, Mrs. Mitsuko Komatsu, and Noriko Fukuda for technical support; and Dr. Natsumi Saito for conducting the animal experiment. This work was supported in part by the RIKEN Aging Project and the interdisciplinary research program Integrated Symbiology (iSYM), JSPS KAKENHI ( 17H06389 and 19H05626 to C. F.; 18H04805 to S.F.), JST PRESTO ( JPMJPR1537 to S.F.), JST ERATO ( JPMJER1902 to S.F. and C.F.), AMED -CREST ( JP20gm1010009 to S.F.), the Takeda Science Foundation (to S.F.), the Food Science Institute Foundation (to S.F.), the Yamagata Prefectural Government, and the City of Tsuruoka.
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/12/17
Y1 - 2021/12/17
N2 - 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.
AB - 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.
KW - Biochemistry methods
KW - Microbiome
UR - http://www.scopus.com/inward/record.url?scp=85120467482&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120467482&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2021.103481
DO - 10.1016/j.isci.2021.103481
M3 - Article
AN - SCOPUS:85120467482
SN - 2589-0042
VL - 24
JO - iScience
JF - iScience
IS - 12
M1 - 103481
ER -