AI-driven 3D Spatial Transcriptomics
Cristina Almagro-P\'erez, Andrew H. Song, Luca Weishaupt, Ahrong Kim,, Guillaume Jaume, Drew F.K. Williamson, Konstantin Hemker, Ming Y. Lu, Kritika, Singh, Bowen Chen, Long Phi Le, Alexander S. Baras, Sizun Jiang, Ali, Bashashati, Jonathan T.C. Liu, Faisal Mahmood

TL;DR
VORTEX is an AI framework that predicts 3D spatial transcriptomics from minimal 2D data, enabling scalable, high-throughput, and non-destructive volumetric tissue analysis for biomedical research.
Contribution
The paper introduces VORTEX, a novel AI method that combines 3D tissue morphology with minimal 2D data to generate comprehensive 3D gene expression maps, overcoming limitations of existing techniques.
Findings
Enables dense 3D transcriptomics with minimal tissue sectioning
Scales to large tissue volumes efficiently
Provides cost-effective, non-destructive volumetric insights
Abstract
A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology-transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic…
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Taxonomy
TopicsSingle-cell and spatial transcriptomics
