Fault-tolerant 3D reconstruction from 2D spatial proteomics sections

Abstract excerpt

Reconstructing 3D molecular volumes from sparsely sampled 2D tissue sections is limited by per-section marker dropout and tissue loss. We present 3D-Omics-Flow, a generative pipeline that jointly repairs damaged sections and interpolates between them at single-cell resolution. Across datasets spanning health and disease, 3D-Omics-Flow expands 3D spatial proteomics to practical sampling regimes, enabling atlas cons...

Topics

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• AI in cancer detection
• Cell Line
• Image Processing, Computer-Assisted
• Machine Learning
• Palatine Tonsil
• Phenotype
• Single-Cell Analysis
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

0baa261c-2b6d-5b5b-ad9a-8b5736589bad

DOI

10.64898/2026.06.23.733649