Work

Segmenting cryo-electron tomography data: Extracting models from cellular landscapes

Year: 2025

Type: review

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.

Source: Current Opinion in Structural Biology

DOI: 10.1016/j.sbi.2025.103114

Author: Danielle A. Grotjahn

Institution: Scripps Research Institute

Language: English

Topic: Advanced Electron Microscopy Techniques and Applications

Subfield: Structural Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $4,360