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Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics

Nature Methods volume 21, pages 1231–1244 (2024)Cite this article

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Abstract

Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities in mapping complex tissue structures and functions. Here we present integrative and reference-informed tissue segmentation (IRIS), a computational method designed to characterize tissue spatial organization in SRT studies through accurately and efficiently detecting spatial domains. IRIS uniquely leverages single-cell RNA sequencing data for reference-informed detection of biologically interpretable spatial domains, integrating multiple SRT slices while explicitly considering correlations both within and across slices. We demonstrate the advantages of IRIS through in-depth analysis of six SRT datasets encompassing diverse technologies, tissues, species and resolutions. In these applications, IRIS achieves substantial accuracy gains (39–1,083%) and speed improvements (4.6–666.0) in moderate-sized datasets, while representing the only method applicable for large datasets including Stereo-seq and 10x Xenium. As a result, IRIS reveals intricate brain structures, uncovers tumor microenvironment heterogeneity and detects structural changes in diabetes-affected testis, all with exceptional speed and accuracy.

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Fig. 1: Schematic overview of IRIS.
Fig. 2: Analyzing the human DLPFC 10x Visium data.
Fig. 3: Analyzing the human SCC ST data.
Fig. 4: Analyzing the mouse spermatogenesis Slide-seq data.
Fig. 5: Analyzing the mouse olfactory bulb Stereo-seq subcellular data.
Fig. 6: Analyzing the human BC 10x Xenium data.

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Data availability

The original public data used in this work can be accessed through the following links: human DLPFC data by 10x Visium available at http://spatial.libd.org/spatialLIBD/, with human post-mortem brain single-nucleus RNA-seq reference data available at Synapse (https://www.synapse.org/#!Synapse:syn18485175); human SCC data by ST are available at GEO accession GSE144240, with the human SCC scRNA-seq reference data available at GEO accession GSE144236; mouse spermatogenesis data by Slide-seq are available at https://www.dropbox.com/s/ygzpj0d0oh67br0/Testis_Slideseq_Data.zip?dl=0, with the mouse testis scRNA-seq reference data available at GEO accession GSE112393; mouse brain (coronal section) Vizgen MERFISH data are available at https://info.vizgen.com/mouse-brain-data, with the mouse brain scRNA-seq reference data available at http://mousebrain.org/adolescent/; mouse olfactory bulb by Stereo-seq data are available at https://db.cngb.org/stomics/mosta/download/, with the mouse olfactory bulb scRNA-seq data available at GEO accession GSE121891; human BC by 10x Xenium data are available at https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast, with the human BC scRNA-seq reference data available at GSE accession GSE176078; details about the data we used in this study are provided in Supplementary Tables 1 and 2.

Code availability

The IRIS software package and source code have been deposited at https://xiangzhou.github.io/software/ and https://github.com/YingMa0107/IRIS. All scripts used to reproduce all the analysis are also available at the same website.

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Acknowledgements

This study was supported by the National Institutes of Health (NIH) grants R01GM126553, R01HG011883 and R01GM144960, all to X.Z.

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Authors and Affiliations

  1. Department of Biostatistics, Brown University, Providence, RI, USA

    Ying Ma

  2. Center for Computational Molecular Biology, Brown University, Providence, RI, USA

    Ying Ma

  3. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Xiang Zhou

  4. Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

    Xiang Zhou

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Contributions

Y.M. and X.Z. conceived the idea and designed the study. Y.M. developed the method, implemented the software and analyzed real data. Y.M. and X.Z. wrote the manuscript, and all authors read and approved the final manuscript.

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Correspondence to Xiang Zhou.

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Nature Methods thanks Chenfei Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Rita Strack, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Analyzing the mouse brain Vizgen MERFISH single-cell data.

(a) The structure of the mouse brain with the main tissue regions annotated from the Allen Reference Atlas – Mouse Brain. (b) Spatial domains detected by IRIS, spaGCN, BayesSpace, BayesSpaceJoint, and SEDR in the ‘Sample 2’ analysis. Details of the tissue slices used in this setting are provided in Supplementary Table 2. (c) Boxplots display CHAOS values for different methods, which measure the spatial continuity and compactness of the detected spatial domains from different methods, on the tissue replicates of slice 2 (n = 2, left panel). Each boxplot ranges from the first and third quartiles with the median as the horizontal line while whiskers represent 1.5 times the interquartile range from the lower and upper bounds of the box. Compared spatial domain detection methods (x-axis) include spaGCN (yellow), BayesSpace (purple), BayesSpaceJoint (green), SEDR (blue), and IRIS (red). Line plots display CHAOS values when varying the pre-specified number of spatial domains. The median CHAOSs across all 3 tissue replicates of regional sample 2 was calculated. (d) Scatter plots display the spatial distribution of important brain tissue related marker genes. (e) Heatmap plot displays the estimated mean cell type proportion for representative cell types in each spatial domain detected by IRIS. Color scale was normalized to 0-1 range. (f) Spatial scatter plot displays the spatial distribution of IRIS estimated cell type proportion for representative cell types across spatial locations. For D – F, the results are shown for the example S2R3 slice in the main analysis.

Supplementary information

Supplementary Information

The file contains Supplementary Figs. 1–74, Tables 1–12 and Notes 1–17. The table of contents is displayed on page 1, and all the other contents are displayed on page 2 of the Supplementary Information file.

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Ma, Y., Zhou, X. Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics. Nat Methods 21, 1231–1244 (2024). https://doi.org/10.1038/s41592-024-02284-9

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