Morphomics via next-generation electron microscopy.

3D bioimaging comprehensive morphological analysis deep learning imaging database next-generation electron microscopy

Journal

Journal of molecular cell biology
ISSN: 1759-4685
Titre abrégé: J Mol Cell Biol
Pays: United States
ID NLM: 101503669

Informations de publication

Date de publication:
26 Dec 2023
Historique:
medline: 27 12 2023
pubmed: 27 12 2023
entrez: 26 12 2023
Statut: aheadofprint

Résumé

The living body is composed of innumerable fine and complex structures. Although these structures have been studied in the past, a vast amount of information pertaining to them still remains unknown. When attempting to observe these ultra-structures, the use of electron microscopy (EM) has become indispensable. However, conventional EM settings are limited to a narrow tissue area, which can bias observations. Recently, new trends in EM research have emerged that provide coverage of far broader, nano-scale fields of view for two-dimensional wide areas and three-dimensional large volumes. Moreover, cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological bioimages. Taken together, these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology, which now arises as a new omics science termed 'morphomics'.

Identifiants

pubmed: 38148118
pii: 7499729
doi: 10.1093/jmcb/mjad081
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences.

Auteurs

Raku Son (R)

RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.

Kenji Yamazawa (K)

Advanced Manufacturing Support Team, RIKEN Center for Advanced Photonics, Wako 351-0198, Japan.

Akiko Oguchi (A)

RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.

Mitsuo Suga (M)

Multimodal Microstructure Analysis Unit, RIKEN-JEOL Collaboration Center, Kobe 650-0047, Japan.

Masaru Tamura (M)

Technology and Development Team for Mouse Phenotype Analysis, RIKEN BioResource Research Center, Tsukuba 305-0074, Japan.

Motoko Yanagita (M)

Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan.

Yasuhiro Murakawa (Y)

RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan.
IFOM-the FIRC Institute of Molecular Oncology, Milan 20139, Italy.

Satoshi Kume (S)

Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan.
Center for Health Science Innovation, Osaka City University, Osaka 530-0011, Japan.
Osaka Electro-Communication University, Neyagawa 572-8530, Japan.

Classifications MeSH