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Nature Machine Intelligence
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Construction of a 3D whole organism spatial atlas by joint modelling of multiple slices with deep neural networks

Nature Machine Intelligencevolume 5, pages1200–1213 (2023)Cite this article

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Apreprint version of the article is available at bioRxiv.

Abstract

Spatial transcriptomics (ST) technologies are revolutionizing the way to explore the spatial architecture of tissues. Currently, ST data analysis is often restricted to a single two-dimensional (2D) tissue slice, limiting our capacity to understand biological processes that take place in 3D space. Here we present STitch3D, a unified framework that integrates multiple ST slices to reconstruct 3D cellular structures. By jointly modelling multiple slices and integrating them with single-cell RNA-sequencing data, STitch3D simultaneously identifies 3D spatial regions with coherent gene-expression levels and reveals 3D cell-type distributions. STitch3D distinguishes biological variation among slices from batch effects, and effectively borrows information across slices to assemble powerful 3D models. Through comprehensive experiments, we demonstrate STitch3D’s performance in building comprehensive 3D architectures, which allow 3D analysis in the entire tissue region or even the whole organism. The outputs of STitch3D can be used for multiple downstream tasks, enabling a comprehensive understanding of biological systems.

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Fig. 1: Overview of STitch3D.
Fig. 2: Benchmarking of STitch3D and other state-of-the-art methods.
Fig. 3: 3D reconstruction of the adult mouse brain.
Fig. 4: 3D reconstruction of the developing human heart.
Fig. 5: Application of STitch3D to the breast cancer data.
Fig. 6: STitch3D constructed the 3D atlas of theDrosophila embryo.

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Data availability

All data used in this work are publicly available through online sources: human dorsolateral prefrontal cortex dataset profiled by Visium platform6 (http://spatial.libd.org/spatialLIBD/); human dorsolateral prefrontal cortex dataset profiled by 10x Genomics Chromium platform23 (GSE144136); mouse cortex dataset profiled by seqFISH+ (ref.25) (https://github.com/CaiGroup/seqFISH-PLUS); mouse primary visual cortex dataset profiled by SMART-seq26 (https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq); mouse visual cortex dataset profiled by STARmap27 (https://kangaroo-goby.squarespace.com/data); mouse hypothalamic preoptic dataset profiled by MERFISH28 (Dryad); mouse hypothalamic preoptic dataset profiled by Illumina NextSeq 500 (ref.28) (GSE113576); mouse whole brain dataset profiled by ST platform5 (GSE147747); mouse brain dataset profiled by 10x Genomics Chromium platform16 (E-MTAB-11115); human embryonic heart dataset profiled by ST platform7 (https://data.mendeley.com/datasets/dgnysc3zn5/1); human embryonic heart dataset profiled by 10x Genomics Chromium platform7 (https://data.mendeley.com/datasets/mbvhhf8m62/2); murine lymph node spatial dataset profiled by Visium platform and scRNA-seq dataset profiled by 10x Genomics Chromium platform18 (GSE173778); mouse skin sections profiled by Visium platform34 (GSE178758); mouse skin dataset profiled by 10x Genomics Chromium platform35 (GSE142471); HER2-positive breast tumour dataset profiled by ST platform36 (https://doi.org/10.5281/zenodo.4751624); HER2-positive breast tumour dataset profiled by 10x Genomics Chromium platform37 (GSE176078);Drosophila embryo dataset profiled by Stereo-seq8 (https://db.cngb.org/stomics/datasets/STDS0000060);Drosophila embryo dataset profiled by sci-RNA-seq39 (GSE190149); mouse olfactory bulb dataset profiled by Visium platform (https://www.10xgenomics.com/resources/datasets/adult-mouse-olfactory-bulb-1-standard-1); mouse olfactory bulb scRNA-seq dataset profiled by 10x Genomics Chromium platform61 (GSE121891); mouse primary cortex 3D dataset profiled by STARmap27 (https://kangaroo-goby.squarespace.com/data).Source data are provided with this paper.

Code availability

STitch3D software is available athttps://github.com/YangLabHKUST/STitch3D. All codes are deposited in the Zenodo repository62.

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Acknowledgements

We acknowledge the following grants: Hong Kong Research Grant Council grants nos. 16301419, 16308120, 16307221 and 16307322, Hong Kong University of Science and Technology Startup Grants R9405 and Z0428 from the Big Data Institute, Guangdong-Hong Kong-Macao Joint Laboratory grant no. 2020B1212030001 and the RGC Collaborative Research Fund grant no. C6021-19EF to C.Y.; Hong Kong Research Grant Council grant no. 16209820, Lo Ka Chung Foundation through the Hong Kong Epigenomics Project, Chau Hoi Shuen Foundation, the SpatioTemporal Omics Consortium (STOC) and the STOmics Grant Program to A.R.W.; Hong Kong Research Grant Council grant no. 16103620, the Shenzhen Science and Technology Innovation Commission JCYJ20180223181229868 and JCYJ20200109140201722 to Y.Y.

Author information

Author notes

  1. These authors contributed equally: Gefei Wang, Jia Zhao.

Authors and Affiliations

  1. Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Gefei Wang, Jia Zhao, Yang Wang & Can Yang

  2. Shenzhen PKU-HKUST Medical Center, Shenzhen, China

    Yan Yan

  3. Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Yan Yan & Angela Ruohao Wu

  4. Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Yang Wang & Can Yang

  5. Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Angela Ruohao Wu

  6. State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Angela Ruohao Wu

  7. Center for Aging Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Angela Ruohao Wu

Authors
  1. Gefei Wang

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  4. Yang Wang

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  5. Angela Ruohao Wu

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  6. Can Yang

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Contributions

G.W., J.Z., A.R.W. and C.Y. conceived the idea. G.W. and J.Z. developed the method. A.R.W. and C.Y. supervised the project. G.W., J.Z., Y.Y., A.R.W. and C.Y. designed the experiments, performed the analyses and wrote the paper. Y.W. provided critical feedback during the study and helped revise the manuscript.

Corresponding authors

Correspondence toAngela Ruohao Wu orCan Yang.

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Nature Machine Intelligence thanks Mengjie Chen, Miguel Esteban and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Mirko Pieropan, in collaboration with theNature Machine Intelligence team.

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Supplementary information

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Wang, G., Zhao, J., Yan, Y.et al. Construction of a 3D whole organism spatial atlas by joint modelling of multiple slices with deep neural networks.Nat Mach Intell5, 1200–1213 (2023). https://doi.org/10.1038/s42256-023-00734-1

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