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Integrating spatial transcriptomics data across different conditions, technologies and developmental stages

Nature Computational Science volume 3, pages 894–906 (2023)Cite this article

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Abstract

With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple ST datasets from different conditions, technologies and developmental stages is becoming increasingly important. Here we present a graph attention neural network called STAligner for integrating and aligning ST datasets, enabling spatially aware data integration, simultaneous spatial domain identification and downstream comparative analysis. We apply STAligner to ST datasets of the human cortex slices from different samples, the mouse olfactory bulb slices generated by two profiling technologies, the mouse hippocampus tissue slices under normal and Alzheimer’s disease conditions, and the spatiotemporal atlases of mouse organogenesis. STAligner efficiently captures the shared tissue structures across different slices, the disease-related substructures and the dynamical changes during mouse embryonic development. In addition, the shared spatial domain and nearest-neighbor pairs identified by STAligner can be further considered as corresponding pairs to guide the three-dimensional reconstruction of consecutive slices, achieving more accurate local structure-guided registration than the existing method.

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Fig. 1: Overview of STAligner.
Fig. 2: Benchmarking on the human DLPFC slices from the same and different samples.
Fig. 3: Identifying common and unique tissue structures in the mouse olfactory bulb data across two different sequencing platforms (Stereo-seq and Slide-seqV2).
Fig. 4: Revealing developmental dynamics of anatomical tissue substructures (spatial domains) in the early mouse embryo data.
Fig. 5: Integrating two mouse hippocampus ST slices of normal and AD conditions.
Fig. 6: Stacked 3D alignment of adjacent mouse hippocampus slices.

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Data availability

Source data for Figs. 2–6 is available with this paper. The datasets analyzed in this study are all from publicly available datasets (Supplementary Table 1). Specifically, the human DLPFC dataset can be accessed in the spatialLIBD package (http://spatial.libd.org/spatialLIBD). The mouse olfactory bulb tissue data generated by Stereo-seq and Slide-seqV2 platforms can be accessed from https://github.com/JinmiaoChenLab/SEDR_analyses and https://singlecell.broadinstitute.org/single_cell/study/SCP815, respectively. The mouse sagittal posterior and anterior brain data can be accessed at https://support.10xgenomics.com/spatial-gene-expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Posterior and https://support.10xgenomics.com/spatial-gene-expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Anterior, respectively. The mouse embryo data can be accessed at https://db.cngb.org/stomics/mosta/. The human embryo data can be accessed at https://heoa.shinyapps.io/code/. The human breast cancer data can be accessed at https://cf.10xgenomics.com/samples/spatial-exp/1.0.0/V1_Breast_Cancer_Block_A_Section_1/V1_Breast_Cancer_Block_A_Section_1_web_summary.html, https://cf.10xgenomics.com/samples/spatial-exp/1.2.0/V1_Human_Invasive_Ductal_Carcinoma/V1_Human_Invasive_Ductal_Carcinoma_web_summary.html and https://cf.10xgenomics.com/samples/spatial-exp/2.0.0/CytAssist_FFPE_Human_Breast_Cancer/CytAssist_FFPE_Human_Breast_Cancer_web_summary.html. The normal and Alzheimer’s disease mouse hippocampus data can be accessed at https://singlecell.broadinstitute.org/single_cell/study/SCP815 and https://singlecell.broadinstitute.org/single_cell/study/SCP1663, respectively. The Slide-seq 3D mouse hippocampus slice can be accessed at https://singlecell.broadinstitute.org/single_cell/study/SCP354/slide-seq-study. The annotation images from the Allen Mouse Brain Atlas can be accessed at https://mouse.brain-map.org/static/atlas (Supplementary Table2).

Code availability

An open-source Python implementation of the STAligner package is available at https://github.com/zhanglabtools/STAligner and https://doi.org/10.5281/zenodo.8315415 ref. 56.

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Acknowledgements

This work has been supported by the National Key Research and Development Program of China (no. 2019YFA0709501 to S.Z.), the Strategic Priority Research Program of the Chinese Academy of Sciences (no. XDPB17), the National Natural Science Foundation of China (no. 12126605), the Key-Area Research and Development of Guangdong Province (no. 2020B1111190001) and the CAS Project for Young Scientists in Basic Research (no. YSBR-034 to S.Z.).

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Author notes

  1. These authors contributed equally: Xiang Zhou, Kangning Dong.

Authors and Affiliations

  1. NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Xiang Zhou, Kangning Dong & Shihua Zhang

  2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China

    Shihua Zhang

  3. Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China

    Shihua Zhang

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Contributions

S.Z. conceived and supervised the project. X.Z. developed and implemented the STAligner algorithm. X.Z., K.D. and S.Z. validated the methods and wrote the paper. All authors read and approved the final paper.

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Correspondence to Shihua Zhang.

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Nature Computational Science thanks Qin Ma and Juexin Wang for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Supplementary Information

Supplementary Notes, Figs. 1–15, and Tables 1 and 2.

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Source data

Source Data Fig. 2

Raw numerical data and cell labels behind the UMAP plots, all of the data points behind box plots.

Source Data Fig. 3

Raw numerical data and cell labels behind the UMAP plots.

Source Data Fig. 5

Raw numerical data and cell labels behind the UMAP plots.

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Zhou, X., Dong, K. & Zhang, S. Integrating spatial transcriptomics data across different conditions, technologies and developmental stages. Nat Comput Sci 3, 894–906 (2023). https://doi.org/10.1038/s43588-023-00528-w

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