THiRST: Computer Vision Empowered Three-dimensional High-Resolution Spatial Transcriptomics Analytics
- 1Vanderbilt University
Abstract
High-resolution spatial transcriptomics (ST) preserves gene expression within tissue architecture but remains limited to individual two-dimensional (2D) sections, restricting analysis of continuous three-dimensional (3D) structures and cross-section consistency. We present THiRST, a computational framework for reconstructing multi-resolution 3D spatial transcriptomic representations from serial sections. THiRST combines coarse-to-fine morphology-guided registration, spatial matching of ST units, cross-section association of segmented structures, and integration of Visium V2 and high-definition ST. In serial kidney sections, patch-level refinement increased the same-cluster proportions of matched units at adjacent offsets from 63.8% and 65.6% to 70.7% and 70.0%, respectively, while improving expression concordance. The reconstructed correspondences enabled 3D glomerular and object-level expression analysis, with 54.1% of differential genes detected only in 3D. Cross-platform integration further aligned V2 and high-definition measurements in a shared expression space. THiRST demonstrates the feasibility of object-level 3D ST analysis from serial 2D sections and underscores the importance of accurate local registration.
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BibTeX
@misc{zhu2026thirst,
title = {{THiRST: Computer Vision Empowered Three-dimensional High-Resolution Spatial Transcriptomics Analytics}},
author = {J. Zhu and Y. Wang and Z. Lu and C. Qu and Y. Zhu and Y. Yang and S. Zhao and H. Yang and Y. Huo and R. Deng},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.29.762926},
url = {https://www.biorxiv.org/content/10.64898/2026.10.29.762926v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - THiRST: Computer Vision Empowered Three-dimensional High-Resolution Spatial Transcriptomics Analytics
AU - J. Zhu
AU - Y. Wang
AU - Z. Lu
AU - C. Qu
AU - Y. Zhu
AU - Y. Yang
AU - S. Zhao
AU - H. Yang
AU - Y. Huo
AU - R. Deng
PY - 2026
DA - 2026/10/29
PB - bioRxiv
DO - 10.64898/2026.10.29.762926
UR - https://www.biorxiv.org/content/10.64898/2026.10.29.762926v1
AB - High-resolution spatial transcriptomics (ST) preserves gene expression within tissue architecture but remains limited to individual two-dimensional (2D) sections, restricting analysis of continuous three-dimensional (3D) structures and cross-section consistency. We present THiRST, a computational framework for reconstructing multi-resolution 3D spatial transcriptomic representations from serial sections. THiRST combines coarse-to-fine morphology-guided registration, spatial matching of ST units, cross-section association of segmented structures, and integration of Visium V2 and high-definition ST. In serial kidney sections, patch-level refinement increased the same-cluster proportions of matched units at adjacent offsets from 63.8% and 65.6% to 70.7% and 70.0%, respectively, while improving expression concordance. The reconstructed correspondences enabled 3D glomerular and object-level expression analysis, with 54.1% of differential genes detected only in 3D. Cross-platform integration further aligned V2 and high-definition measurements in a shared expression space. THiRST demonstrates the feasibility of object-level 3D ST analysis from serial 2D sections and underscores the importance of accurate local registration.
ER -