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Exploring tissue architecture using spatial transcriptomics

Nature volume 596, pages 211–220 (2021)Cite this article

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Abstract

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions—including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

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Fig. 1: The technologies of spatial transcriptomics provide a gene-expression matrix.
Fig. 2: Exploratory data analysis using spatial transcriptomic datasets.
Fig. 3: Hypothesis generation and testing using spatial transcriptomics.

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Acknowledgements

We thank F. Kuperwaser, A. Pountain, B. Xia and other members of the Yanai laboratory, as well as M. Phillips for critical reading and feedback. We thank the students of the exploratory data analysis course at NYU Langone Health. I.Y. was supported by grants from the NIH (R01AI143290 and R01LM013522) and the Lowenstein Foundation, and D.B. was supported by the National Institutes for Health (F30CA257400).

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  1. These authors contributed equally: Anjali Rao, Dalia Barkley

Authors and Affiliations

  1. Institute for Computational Medicine, NYU Langone Health, New York, NY, USA

    Anjali Rao, Dalia Barkley, Gustavo S. França & Itai Yanai

  2. Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY, USA

    Itai Yanai

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A.R., D.B. and I.Y. collectively wrote the review. A.R., D.B., G.S.F. and I.Y. edited and revised the manuscript and conceptualized the figures, which G.S.F. then designed and created.

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Rao, A., Barkley, D., França, G.S. et al. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021). https://doi.org/10.1038/s41586-021-03634-9

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