Inferring Super-Resolution Tissue Architecture by Integrating Spatial Transcriptomics With Histology

Authors

Daiwei Zhang
Amelia Schroeder
Hanying Yan
Haochen Yang
Jian Hu
Michelle Y Y Lee
Kyung S Cho
Katalin Susztak
George X Xu
Michael D Feldman
Edward B Lee
Emma E Furth
Linghua Wang
Mingyao Li

Publication Date

9-1-2024

Journal

Nature Biotechnology

DOI

10.1038/s41587-023-02019-9

PMID

38168986

PMCID

PMC11260191

PubMedCentral® Posted Date

3-1-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.

Keywords

Transcriptome, Gene Expression Profiling, Humans, Algorithms, Image Processing, Computer-Assisted, Single-Cell Analysis, Animals

Published Open-Access

yes

Recommended Citation

Zhang, Daiwei; Schroeder, Amelia; Yan, Hanying; et al., "Inferring Super-Resolution Tissue Architecture by Integrating Spatial Transcriptomics With Histology" (2024). Faculty, Staff and Student Publications. 1934.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/1934