Three-dimensional spatial transcriptomics at isotropic resolution enabled by generative deep learning

Abstract excerpt

Mapping the complete three-dimensional (3D), transcriptome-wide spatial architecture of tissues and organs remains a fundamental challenge in biology. Typically, studies approximate 3D structures by profiling serial two-dimensional (2D) tissue sections, resulting in incomplete and anisotropic reconstructions. Here, we demonstrate that organ-wide, isotropic-resolution 3D gene expression landscapes can be effectivel...

Topics

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• Cell Image Analysis Techniques
• Ferroptosis and cancer prognosis
• Gene expression and cancer classification
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

b437e449-bfa3-5b69-973a-a759617e9ef6

DOI

10.1101/2025.08.15.670472