Detail of AMK_20220511_AMK4_0003
Project
SSBD:Repository
Title
Cell morphology of AMK-resistant E. coli cells from the fourth strain in the first dataset under phase contrast light microscopy at position 3
Description
In this study, the authors isolated four lines of Amikacin (AMK)-resistant Escherichia coli (E. coli). This dataset includes one of the image of the E. coli in the fourth line. In the first dataset, single colonies were isolated from AMK-resistant E. coli and cultured without antibiotics. Parts of AMK-resistant cells from the fourth line of the resistant strain showed slightly elongated morphology under phase contrast light microscopy, compared to the parental strain. The cell morphology showed difference among strains of four lines.
Release, Updated
2026-07-08
License
CC BY 4.0
Kind
Image data
File Formats
tif
Data size
28.8 MB
Datatype
-
Molecular Function (MF)
Biological Process (BP)
response to antibiotic regulation of cell shape cell growth
Cellular Component (CC)
cell envelope cell wall
Biological Imaging Method
phase contrast microscopy
(
Fbbi:00000247
)
X scale
0.046 micrometer
Y scale
0.046 micrometer
Z scale
-
T scale
-
Image Acquisition
Experiment type
-
Microscope type
-
Acquisition mode
-
Contrast method
-
Microscope model
-
Detector model
-
Objective model
-
Filter set
-
Summary of Methods
Ikebe M, Aoki K, Hayashi-Nishino M, Furusawa C, Nishino K. Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning. Front Microbiol. 2024 Sep 19;15:1450804. doi: 10.3389/fmicb.2024.1450804. Erratum in: Front Microbiol. 2024 Nov 08;15:1516320.
Related paper(s)
Miki Ikebe, Kota Aoki, Mitsuko Hayashi-Nishino, Chikara Furusawa, Kunihiko Nishino (2024) Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning., Frontiers in microbiology, Volume 15, pp. 1450804
Published in 2024 (Electronic publication in Sept. 19, 2024, midnight )
- doi: 10.3389/fmicb.2024.1450804
-
pmid: 39364166
(Abstract) Although it is well known that the morphology of Gram-negative rods changes on exposure to antibiotics, the morphology of antibiotic-resistant bacteria in the absence of antibiotics has not been widely investigated. Here, we studied the morphologies of 10 antibiotic-resistant strains of Escherichia coli and used bioinformatics tools to classify the resistant cells under light microscopy in the absence of antibiotics. The antibiotic-resistant strains showed differences in morphology from the sensitive parental strain, and the differences were most prominent in the quinolone-and beta-lactam-resistant bacteria. A cluster analysis revealed increased proportions of fatter or shorter cells in the antibiotic-resistant strains. A correlation analysis of morphological features and gene expression suggested that genes related to energy metabolism and antibiotic resistance were highly correlated with the morphological characteristics of the resistant strains. Our newly proposed deep learning method for single-cell classification achieved a high level of performance in classifying quinolone-and beta-lactam-resistant strains.
Contact
Kota Aoki, Mitsuko Hayashi-Nishino, Kunihiko Nishino
, Osaka University, Osaka University, Osaka University
, SANKEN (Institute of Scientific and Industrial Research), SANKEN (Institute of Scientific and Industrial Research), SANKEN (Institute of Scientific and Industrial Research)
, SANKEN (Institute of Scientific and Industrial Research), SANKEN (Institute of Scientific and Industrial Research), SANKEN (Institute of Scientific and Industrial Research)
Contributors
OMERO Project