ST-DAI: Single-shot 2.5D Spatial Transcriptomics with Intra-Sample Domain Adaptive Imputation for Cost-efficient 3D Reconstruction

TL;DR

ST-DAI introduces a cost-effective 3D spatial transcriptomics method combining sparse sampling with intra-sample domain adaptation, enabling accurate gene expression imputation with reduced experimental costs.

Contribution

This work presents a novel single-shot framework that integrates 2.5D sampling and intra-sample domain adaptive imputation for efficient 3D spatial transcriptomics.

Findings

Achieves comparable gene expression prediction to fully sampled methods.

Reduces experimental cost significantly.

Demonstrates effective intra-sample domain adaptation.

Abstract

For 3D spatial transcriptomics (ST), the high per-section acquisition cost of fully sampling every tissue section remains a significant challenge. Although recent approaches predict gene expression from histology images, these methods require large external datasets, which leads to high-cost and suffers from substantial domain discrepancies that lead to poor generalization on new samples. In this work, we introduce ST-DAI, a single-shot framework for 3D ST that couples a cost-efficient 2.5D sampling scheme with an intra-sample domain-adaptive imputation framework. First, in the cost-efficient 2.5D sampling stage, one reference section (central section) is fully sampled while other sections (adjacent sections) is sparsely sampled, thereby capturing volumetric context at significantly reduced experimental cost. Second, we propose a single-shot 3D imputation learning method that allows us to generate fully sampled 3D ST from this cost-efficient 2.5D ST scheme, using only sample-specific training. We observe position misalignment and domain discrepancy between sections. To address those issues, we adopt a pipeline that first aligns the central section to the adjacent section, thereafter generates dense pseudo-supervision on the central section, and then performs Fast Multi-Domain Refinement (FMDR), which adapts the network to the domain of the adjacent section while fine-tuning only a few parameters through the use of Parameter-Efficient Domain-Alignment Layers (PDLs). During this refinement, a Confidence Score Generator (CSG) reweights the pseudo-labels according to their estimated reliability, thereby directing imputation toward trustworthy regions. Our experimental results demonstrate that ST-DAI achieves gene expression prediction performance comparable to fully sampled approaches while substantially reducing the measurement burden.