AI-driven 3D Spatial Transcriptomics
Journal:
arXiv
Published Date:
Feb 25, 2025
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 tissue-related and sample-specific morphological correlates
of gene expression. This approach enables dense, high-throughput, and fast 3D
ST, scaling seamlessly to large tissue volumes far beyond the reach of existing
3D ST techniques. By offering a cost-effective and minimally destructive route
to obtaining volumetric molecular insights, we anticipate that VORTEX will
accelerate biomarker discovery and our understanding of morphomolecular
associations and cell states in complex tissues. Interactive 3D ST volumes can
be viewed at https://vortex-demo.github.io/
