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

Nature Biotechnology volume 42, pages 1372–1377 (2024)Cite this article

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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.

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Fig. 1: Workflow and super-resolution gene expression prediction accuracy of iStar.
Fig. 2: Tissue annotation using iStar.

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Data availability

We analyzed the following publicly available ST datasets: (1) 10x Xenium human breast cancer data (https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast); (2) 10x Xenium mouse brain data (https://www.10xgenomics.com/resources/datasets/fresh-frozen-mouse-brain-replicates-1-standard); (3) human HER2-positive breast cancer ST data reported in Anderson et al. (https://github.com/almaan/her2st); (4) 10x Visium human breast cancer data (https://www.10xgenomics.com/resources/datasets/human-breast-cancer-visium-fresh-frozen-whole-transcriptome-1-standard); (5) 10x Visium human colorectal cancer data (https://www.10xgenomics.com/resources/datasets/human-colorectal-cancer-whole-transcriptome-analysis-1-standard-1-2-0); (6) 10x Visium human prostate cancer data (https://www.10xgenomics.com/resources/datasets/human-prostate-cancer-adenocarcinoma-with-invasive-carcinoma-ffpe-1-standard-1-3-0); (7) human prostate cancer data reported in Erickson et al. (https://doi.org/10.17632/svw96g68dv.1); (8) human clear cell renal cell carcinoma primary tumors reported in Meylan et al. (GSE175540); (9) 10x Visium mouse kidney data (https://www.10xgenomics.com/resources/datasets/adult-mouse-kidney-ffpe-1-standard-1-3-0); (10) 10x Visium mouse brain coronal cut data (https://www.10xgenomics.com/resources/datasets/mouse-brain-coronal-section-2-ffpe-2-standard); (11) 10x Visium mouse brain sagittal cut posterior data (https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-2-sagittal-posterior-1-standard); (12) 10x Visium mouse brain olfactory bulb data (https://www.10xgenomics.com/resources/datasets/adult-mouse-olfactory-bulb-1-standard-1). Details of the datasets analyzed in this paper are described in Supplementary Table 3. Gene expression visualizations for other spatial resolutions in the 10x Xenium breast cancer and mouse brain data are available at https://zenodo.org/doi/10.5281/zenodo.10071636. Source data are provided with this paper.

Code availability

The iStar algorithm was implemented in Python and is available on GitHub at https://github.com/daviddaiweizhang/istar.

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Acknowledgements

M.L. was supported by the following NIH grants: R01GM125301, R01EY030192, R01HL150359, R01HG013185 and P01AG066597. E.B.L. was supported by NIH grant P01AG066597. L.W. was supported in part by NIH grant R01CA266280, the Cancer Prevention and Research Institute of Texas (CPRIT) award RP200385, the University Cancer Foundation via the Institutional Research Grant Program at the University of Texas MD Anderson Cancer Center, the Andrew Sabin Family Foundation, and the Break Through Cancer Foundation. We thank M. Meylan and W. H. Fridman for sharing the kidney cancer histology image data. We also thank Erickson, Lamb and Lundberg for sharing the prostate cancer histology image and clone annotation data.

Author information

Authors and 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, Amelia Schroeder, Hanying Yan, Haochen Yang, Michelle Y. Y. Lee & Mingyao Li

  2. Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA

    Jian Hu

  3. Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Michelle Y. Y. Lee

  4. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Kyung S. Cho & Linghua Wang

  5. Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Katalin Susztak

  6. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    George X. Xu, Edward B. Lee, Emma E. Furth & Mingyao Li

  7. Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA

    Michael D. Feldman

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Contributions

This study was conceived of and led by M.L. D.Z. designed the model and algorithm, implemented the iStar software and led data analyses with input from M.L., E.E.F., L.W., K.S., G.X.X., M.D.F. and E.B.L. E.E.F. examined histology images. A.S., H.Y., M.Y.Y.L., K.S.C. and J.H. helped with data analyses. L.W. provided marker genes for TLS detection and interpreted results related to cancer. D.Z. and M.L. wrote the paper with feedback from the other co-authors.

Corresponding authors

Correspondence to Daiwei Zhang or Mingyao Li.

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Competing interests

M.L. receives research funding from Biogen Inc. unrelated to the current manuscript. The other authors declare no competing financial interests.

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Extended data

Extended Data Fig. 1 Correlation between super-resolution gene expression and histology.

Comparison of iStar’s super-resolution gene expression patterns with the paired histology image in the Xenium-derived pseudo-Visium data. Spot boundaries are highlighted.

Extended Data Fig. 2 Resolution enhancement at various scales.

Visualization of predicted super-resolution gene expressions by iStar at various scales of resolution enhancement for three breast cancer-related genes (ESR1, ERBB2, and PGR) in the Xenium-derived pseudo-Visium data.

Extended Data Fig. 3 Numerical evaluation of prediction accuracy.

Prediction accuracy of iStar and XFuse as measured by root mean squared error (RMSE) and structural similarity index measure (SSIM) for all the 313 genes in the Xenium-derived pseudo-Visium data obtained from a breast cancer patient. In each scatter plot, a dot represents a gene. In this analysis, Section 1 was first treated as the training sample in which in-sample prediction was perfromed for Section 1 and then out-of-sample prediction was perfromed for Section 2. We then repeated this analysis by treating Section 2 as the ‘in-sample’ and Section 1 as the ‘out-of-sample’. The evaluation metrics in’Average’ is the average of those in ‘Sections 1’ and ‘Section 2’.

Source data

Extended Data Fig. 4 Visualization of single-cell level gene expression prediction.

Visualization of single-cell level gene expression predicted by iStar for the pseudo-Visium breast cancer data derived from Xenium data. Shown on the left is the ground truth single-cell level gene expression directly measured by Xenium, and shown on the right is the single-cell level gene expression predicted by iStar. For each gene, the root mean squared error (RMSE) and Pearson’s correlation coefficient (PCC) between the prediction and the ground truth across the whole tissue and within the shown region are displayed.

Extended Data Fig. 5 Prediction accuracy evaluation of single-cell level gene expression prediction.

Prediction accuracy of iStar and XFuse for single-cell level gene expression prediction as measured by root mean squared error (RMSE) for all the 313 genes in the Xenium-derived pseudo-Visium data obtained from a breast cancer patient. In each scatter plot, a dot represents a gene. In this analysis, Section 1 was first treated as the training sample in which in-sample prediction was performed for Section 1 and then out-of-sample prediction was performed for Section 2. We then repeated this analysis by treating Section 2 as the ‘in-sample’ and Section 1 as the ‘out-of-sample’. The evaluation metrics in’Average’ is the average of those in ‘Sections 1’ and ‘Section 2’. We stratified cells by the quantiles of their cell size.

Source data

Extended Data Fig. 6 Super-resolution vs spot-level signature scoring of tertiary lymphoid structures (TLSs).

iStar detected tertiary lymphoid structures (TLSs) in the Anderson et al. (2021) HER2+ breast cancer dataset. Displayed are the predicted TLS scores by iStar and the original publication, along with the pathologist’s manual annotation reported in the original publication. Super-resolution was performed with 128x resolution enhancement.

Extended Data Fig. 7 Super-resolution gene expression prediction in the Xenium-derived pseudo-Visium mouse brain data.

Visualization of the spot-level training data, ground truth gene expression, and predicted super-resolution gene expressions by iStar and XFuse for 24 highly variable genes, whose variances are in the 80%-100% quantiles among all the 248 genes in the Xenium-derived pseudo-Visium data obtained from mouse brain. The variance quantiles of the genes in the order of top-left, top-right, bottom-left, bottom-right are equally spaced from 100% to 80% (in descending order). Super-resolution gene expressions are visualized at the scale of 8x resolution enhancement.

Extended Data Fig. 8 Prediction accuracy evaluation and gene-based segmentation comparison in the Xenium-derived pseudo-Visium mouse brain data.

a. Prediction accuracy of by iStar and XFuse as measured by root mean squared error (RMSE) and structural similarity index measure (SSIM) for all the 248 genes in the Xenium-derived pseudo-Visium data obtained from mouse brain. In each scatter plot, a dot represents one gene. b. Segmentation of the Xenium-derived pseudo-Visium data obtained from mouse brain by iStar and XFuse using all 248 genes available in this dataset. Super-resolution was performed with 128x resolution enhancement.

Extended Data Fig. 9 Gene-based segmentation of mouse brain datasets.

Analyses of a. mouse brain (coronal cut), b. mouse brain posterior (sagittal cut), and c. mouse brain ofactory bulb Visium datasets generated by 10x Genomics. For each dataset, iStar was applied to enhance the resolution of the top 1000 most highly variable genes. Super-resolution was performed with 128x resolution enhancement. Segmentations by iStar identified fine-grained tissue structures in the mouse brain and agreed with the Allen Brain Atlas annotations.

Extended Data Fig. 10 Gene-based tissue segmentation and tertiary lymphoid structure (TLS) signature scoring of cancer datasets and a mouse kidney dataset.

Comparison of gene-based segmentation by iStar with manual tissue annotation in a. mouse kidney and b. prostate cancer Visium datasets generated by 10x Genomics. c. Gene-based segmentation by iStar in a colorectal cancer Visium dataset by 10x Genomics. d. Comparison of tertiary lymphoid structure (TLS) signature score by iStar with manual TLS annotation in a kidney cancer Visium dataset generated by Meylan et al. (2022).

Supplementary information

Supplementary Information

Supplementary Tables 1–3 and Figs. 1–24.

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Source data

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

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Zhang, D., Schroeder, A., Yan, H. et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat Biotechnol 42, 1372–1377 (2024). https://doi.org/10.1038/s41587-023-02019-9

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