The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography.
Front Mol Biosci
; 11: 1390858, 2024.
Article
en En
| MEDLINE
| ID: mdl-38868297
ABSTRACT
Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.
artificial intelligence (AI); cryogenic correlative light and electron microscopy (cryoCLEM); cryogenic electron microscopy (cryoEM); cryogenic electron tomography (cryoET); cryogenic focused ion beam milling scanning electron microscopy (cryoFIB-SEM); cryogenic volume electron microscopy (cryoVEM); machine learning (ML); structural digital and computational cellular pathology
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Front Mol Biosci
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Suiza