Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology

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

doi:10.1038/s41587-023-02019-9

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology

Nat Biotechnol. 2024 Jan 2. doi: 10.1038/s41587-023-02019-9. Online ahead of print.

Authors

Daiwei Zhang¹, Amelia Schroeder², Hanying Yan², Haochen Yang², Jian Hu³, Michelle Y Y Lee^{2

4}, Kyung S Cho⁵, Katalin Susztak⁶, George X Xu⁷, Michael D Feldman⁸, Edward B Lee⁷, Emma E Furth⁷, Linghua Wang⁵, Mingyao Li^{9

10}

Affiliations

¹ Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Daiwei.Zhang@Pennmedicine.upenn.edu.
² Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
³ Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA.
⁴ Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁵ Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
⁶ Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁷ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁸ Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA.
⁹ Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. mingyao@pennmedicine.upenn.edu.
¹⁰ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. mingyao@pennmedicine.upenn.edu.

PMID: 38168986
DOI: 10.1038/s41587-023-02019-9

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.

Grants and funding

P01 AG066597/AG/NIA NIH HHS/United States