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
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.