{"title":"Segmenting cryo-electron tomography data: Extracting models from cellular landscapes","authors":"Danielle A. Grotjahn","doi":"10.1016/j.sbi.2025.103114","DOIUrl":null,"url":null,"abstract":"<div><div>Cryo-electron tomography provides an unprecedented view of cellular architecture, yet extracting meaningful biological insights remains challenging. Segmentation is a crucial step in this process through its ability to identify structural relationships between subcellular components visible in cryo-electron tomography data. While segmentation pipelines were historically low throughput, recent advancements in deep learning have significantly improved their automation, accuracy, and scalability. This review explores how these innovations redefine best practices for segmentation and accelerate biological discovery. This article highlights the critical role of segmentation in unlocking the full potential of cryo-electron tomography—not only for resolving macromolecular structures but also for quantifying their impact on subcellular organization and function.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"93 ","pages":"Article 103114"},"PeriodicalIF":6.1000,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in structural biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959440X25001320","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Cryo-electron tomography provides an unprecedented view of cellular architecture, yet extracting meaningful biological insights remains challenging. Segmentation is a crucial step in this process through its ability to identify structural relationships between subcellular components visible in cryo-electron tomography data. While segmentation pipelines were historically low throughput, recent advancements in deep learning have significantly improved their automation, accuracy, and scalability. This review explores how these innovations redefine best practices for segmentation and accelerate biological discovery. This article highlights the critical role of segmentation in unlocking the full potential of cryo-electron tomography—not only for resolving macromolecular structures but also for quantifying their impact on subcellular organization and function.
期刊介绍:
Current Opinion in Structural Biology (COSB) aims to stimulate scientifically grounded, interdisciplinary, multi-scale debate and exchange of ideas. It contains polished, concise and timely reviews and opinions, with particular emphasis on those articles published in the past two years. In addition to describing recent trends, the authors are encouraged to give their subjective opinion of the topics discussed.
In COSB, we help the reader by providing in a systematic manner:
1. The views of experts on current advances in their field in a clear and readable form.
2. Evaluations of the most interesting papers, annotated by experts, from the great wealth of original publications.
[...]
The subject of Structural Biology is divided into twelve themed sections, each of which is reviewed once a year. Each issue contains two sections, and the amount of space devoted to each section is related to its importance.
-Folding and Binding-
Nucleic acids and their protein complexes-
Macromolecular Machines-
Theory and Simulation-
Sequences and Topology-
New constructs and expression of proteins-
Membranes-
Engineering and Design-
Carbohydrate-protein interactions and glycosylation-
Biophysical and molecular biological methods-
Multi-protein assemblies in signalling-
Catalysis and Regulation