Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, to reconstruct single sections back to their in situ three-dimensional morphology, existing approaches either neglect experiment-induced section distortions, or fail to account for structural consistency during reconstruction. This leads to significant discrepancies between reconstruction results and the actual in vivo locations of cells, imposing unreliable spatial profiles to downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS), which solves optimized "anchors" between in situ closest spots utilizing expressional and structural similarity across sections and recovers in vivo spatial information under the guidance of anchors. By employing innovative Distributive Constraints into the Optimization scheme, it retrieves more precise anchors compared to existing methods. Taking these anchors as reference points, ST-GEARS first rigidly aligns sections, then introduces and infers Elastic Fields to counteract distortions. ST-GEARS denoises the fields using context information by Gaussian Denoising. Utilizing the denoised fields, it eliminates distortions and eventually recovers original spatial profile through innovative and mathematically proved Bi-sectional Fields Application. Studying ST-GEARS on both bi-sectional registration and complete tissue reconstruction across sectional distances and sequencing platforms, we observed its outstanding performance in spatial information recovery across tissue, cell, and gene levels compared to current approaches. Through this recovery, ST-GEARS provides a precise and well-explainable bridge between in vitro analysis and 3D in vivo situations, powerfully fueling the potential of biological discoveries.

ST-GEARS: Advancing 3D Downstream Research through Accurate Spatial Information Recovery