Inferring Super-resolution Tissue Architecture by Integrating Spatial Transcriptomics with Histology

Overview

Journal Nat Biotechnol

Specialty Biotechnology

Date 2024 Jan 3

PMID 38168986

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

Affiliations

Soon will be listed here.

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.

Citing Articles

Spotiphy enables single-cell spatial whole transcriptomics across an entire section.

Yang J, Zheng Z, Jiao Y, Yu K, Bhatara S, Yang X Nat Methods. 2025; .

PMID: 40074951 DOI: 10.1038/s41592-025-02622-5.

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE.

Schroeder A, Loth M, Luo C, Yao S, Yan H, Zhang D bioRxiv. 2025; .

PMID: 40060412 PMC: 11888418. DOI: 10.1101/2025.02.25.640190.

Single-cell genomics and spatial transcriptomics in islet transplantation for diabetes treatment: advancing towards personalized therapies.

Mou L, Wang T, Chen Y, Luo Z, Wang X, Pu Z Front Immunol. 2025; 16:1554876.

PMID: 40051625 PMC: 11882877. DOI: 10.3389/fimmu.2025.1554876.

Systematic inference of super-resolution cell spatial profiles from histology images.

Zhang P, Gao C, Zhang Z, Yuan Z, Zhang Q, Zhang P Nat Commun. 2025; 16(1):1838.

PMID: 39984438 PMC: 11845739. DOI: 10.1038/s41467-025-57072-6.

Benchmarking the translational potential of spatial gene expression prediction from histology.

Wang C, Chan A, Fu X, Ghazanfar S, Kim J, Patrick E Nat Commun. 2025; 16(1):1544.

PMID: 39934114 PMC: 11814321. DOI: 10.1038/s41467-025-56618-y.

References

Burgess D . Spatial transcriptomics coming of age. Nat Rev Genet. 2019; 20(6):317. DOI: 10.1038/s41576-019-0129-z. View

Asp M, Bergenstrahle J, Lundeberg J . Spatially Resolved Transcriptomes-Next Generation Tools for Tissue Exploration. Bioessays. 2020; 42(10):e1900221. DOI: 10.1002/bies.201900221. View

Crosetto N, Bienko M, van Oudenaarden A . Spatially resolved transcriptomics and beyond. Nat Rev Genet. 2014; 16(1):57-66. DOI: 10.1038/nrg3832. View

Moor A, Itzkovitz S . Spatial transcriptomics: paving the way for tissue-level systems biology. Curr Opin Biotechnol. 2017; 46:126-133. DOI: 10.1016/j.copbio.2017.02.004. View

Hu J, Li X, Coleman K, Schroeder A, Ma N, Irwin D . SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat Methods. 2021; 18(11):1342-1351. DOI: 10.1038/s41592-021-01255-8. View

Sun S, Zhu J, Zhou X . Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat Methods. 2020; 17(2):193-200. PMC: 7233129. DOI: 10.1038/s41592-019-0701-7. View

Svensson V, Teichmann S, Stegle O . SpatialDE: identification of spatially variable genes. Nat Methods. 2018; 15(5):343-346. PMC: 6350895. DOI: 10.1038/nmeth.4636. View

Dries R, Zhu Q, Dong R, Eng C, Li H, Liu K . Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 2021; 22(1):78. PMC: 7938609. DOI: 10.1186/s13059-021-02286-2. View

Asp M, Giacomello S, Larsson L, Wu C, Furth D, Qian X . A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart. Cell. 2019; 179(7):1647-1660.e19. DOI: 10.1016/j.cell.2019.11.025. View

10.

Wang X, Allen W, Wright M, Sylwestrak E, Samusik N, Vesuna S . Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018; 361(6400). PMC: 6339868. DOI: 10.1126/science.aat5691. View

11.

Lubeck E, Coskun A, Zhiyentayev T, Ahmad M, Cai L . Single-cell in situ RNA profiling by sequential hybridization. Nat Methods. 2014; 11(4):360-1. PMC: 4085791. DOI: 10.1038/nmeth.2892. View

12.

Shah S, Lubeck E, Zhou W, Cai L . In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus. Neuron. 2016; 92(2):342-357. PMC: 5087994. DOI: 10.1016/j.neuron.2016.10.001. View

13.

Eng C, Lawson M, Zhu Q, Dries R, Koulena N, Takei Y . Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature. 2019; 568(7751):235-239. PMC: 6544023. DOI: 10.1038/s41586-019-1049-y. View

14.

Moffitt J, Bambah-Mukku D, Eichhorn S, Vaughn E, Shekhar K, Perez J . Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science. 2018; 362(6416). PMC: 6482113. DOI: 10.1126/science.aau5324. View

15.

Chen K, Boettiger A, Moffitt J, Wang S, Zhuang X . RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015; 348(6233):aaa6090. PMC: 4662681. DOI: 10.1126/science.aaa6090. View

16.

Stickels R, Murray E, Kumar P, Li J, Marshall J, Di Bella D . Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol. 2020; 39(3):313-319. PMC: 8606189. DOI: 10.1038/s41587-020-0739-1. View

17.

Chen A, Liao S, Cheng M, Ma K, Wu L, Lai Y . Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022; 185(10):1777-1792.e21. DOI: 10.1016/j.cell.2022.04.003. View

18.

Badea L, Stanescu E . Identifying transcriptomic correlates of histology using deep learning. PLoS One. 2020; 15(11):e0242858. PMC: 7688140. DOI: 10.1371/journal.pone.0242858. View

19.

Ash J, Darnell G, Munro D, Engelhardt B . Joint analysis of expression levels and histological images identifies genes associated with tissue morphology. Nat Commun. 2021; 12(1):1609. PMC: 7952575. DOI: 10.1038/s41467-021-21727-x. View

20.

Schmauch B, Romagnoni A, Pronier E, Saillard C, Maille P, Calderaro J . A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat Commun. 2020; 11(1):3877. PMC: 7400514. DOI: 10.1038/s41467-020-17678-4. View