3D-Guided Scalable Flow Matching for Generating Volumetric Tissue Spatial Transcriptomics from Serial Histology

TL;DR

HoloTea is a 3D-aware flow-matching framework that accurately imputes gene expression in volumetric tissue data by leveraging adjacent histological sections and novel priors, improving 3D transcriptomics analysis.

Contribution

The paper introduces HoloTea, a scalable 3D flow-matching method that incorporates cross-section context and a new prior to enhance volumetric tissue transcriptomics from serial histology.

Findings

HoloTea outperforms 2D and 3D baselines in accuracy across multiple datasets.

The method scales linearly with the number of spots, enabling large dataset analysis.

Increases in 3D expression accuracy and generalization are demonstrated.

Abstract

A scalable and robust 3D tissue transcriptomics profile can enable a holistic understanding of tissue organization and provide deeper insights into human biology and disease. Most predictive algorithms that infer ST directly from histology treat each section independently and ignore 3D structure, while existing 3D-aware approaches are not generative and do not scale well. We present Holographic Tissue Expression Inpainting and Analysis (HoloTea), a 3D-aware flow-matching framework that imputes spot-level gene expression from H&E while explicitly using information from adjacent sections. Our key idea is to retrieve morphologically corresponding spots on neighboring slides in a shared feature space and fuse this cross section context into a lightweight ControlNet, allowing conditioning to follow anatomical continuity. To better capture the count nature of the data, we introduce a 3D-consistent prior for flow matching that combines a learned zero-inflated negative binomial (ZINB) prior with a spatial-empirical prior constructed from neighboring sections. A global attention block introduces 3D H&E scaling linearly with the number of spots in the slide, enabling training and inference on large 3D ST datasets. Across three spatial transcriptomics datasets spanning different tissue types and resolutions, HoloTea consistently improves 3D expression accuracy and generalization compared to 2D and 3D baselines. We envision HoloTea advancing the creation of accurate 3D virtual tissues, ultimately accelerating biomarker discovery and deepening our understanding of disease.