biostudies-literatureHuang QU.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)NIAID NIH HHSSilicon Valley Community Foundation (SVCF)U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)NIGMS NIH HHS1863-1872https://www.ebi.ac.uk/biostudies/studies/S-EPMC11468773biostudies-literatureUnknown21(10)Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proNature methodsMiLoPYP: self-supervised molecular pattern mining and particle localization in situ.PMC11468773R01 GM141223U54 AI170752DAF2021-234602R01GM141223U54-AI170752Bartesaghi AHuang QZhou YfalseMiLoPYP: self-supervised molecular pattern mining and particle localization in situ.Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of pro2024-01-01T00:00:00Z2024 Oct2026-06-01T07:06:26.011Z2025-04-04T23:50:13.265ZS-EPMC114687733925179810.1038/s41592-024-02403-6