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

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

Genomic Medicine

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English (US)
Journal	Nature biotechnology
DOIs	https://doi.org/10.1038/s41587-023-02019-9
State	Accepted/In press - 2024

ASJC Scopus subject areas

Biotechnology
Bioengineering
Applied Microbiology and Biotechnology
Molecular Medicine
Biomedical Engineering

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10.1038/s41587-023-02019-9

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title = "Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology",

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.",

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

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive licence to Springer Nature America, Inc.",

year = "2024",

doi = "10.1038/s41587-023-02019-9",

language = "English (US)",

journal = "Nature biotechnology",

issn = "1087-0156",

publisher = "Nature Publishing Group",

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TY - JOUR

T1 - Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology

AU - Zhang, Daiwei

AU - Schroeder, Amelia

AU - Yan, Hanying

AU - Yang, Haochen

AU - Hu, Jian

AU - Lee, Michelle Y.Y.

AU - Cho, Kyung S.

AU - Susztak, Katalin

AU - Xu, George X.

AU - Feldman, Michael D.

AU - Lee, Edward B.

AU - Furth, Emma E.

AU - Wang, Linghua

AU - Li, Mingyao

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85181195459&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85181195459&partnerID=8YFLogxK

U2 - 10.1038/s41587-023-02019-9

DO - 10.1038/s41587-023-02019-9

M3 - Article

C2 - 38168986

AN - SCOPUS:85181195459

SN - 1087-0156

JO - Nature biotechnology

JF - Nature biotechnology

ER -

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

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