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Cell2location maps fine-grained cell types in spatial transcriptomics

Nature Biotechnology volume 40, pages 661–671 (2022)Cite this article

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Abstract

Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present сell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. We assessed cell2location in three different tissues and show improved mapping of fine-grained cell types. In the mouse brain, we discovered fine regional astrocyte subtypes across the thalamus and hypothalamus. In the human lymph node, we spatially mapped a rare pre-germinal center B cell population. In the human gut, we resolved fine immune cell populations in lymphoid follicles. Collectively, our results present сell2location as a versatile analysis tool for mapping tissue architectures in a comprehensive manner.

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Fig. 1: Cell2location overview and validation using simulated data.
Fig. 2: Comprehensive and accurate spatial mapping of cell types in the mouse brain.
Fig. 3: Spatial discovery of regional astrocyte subtypes.
Fig. 4: Mapping the cellular compartments of the human lymph node.
Fig. 5: Cell2location outperforms alternative methods in mapping fine immune subtypes in the human gut.

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Data availability

Data generated for this manuscript (snRNA-seq and Visium from adjacent sections in the mouse brain (Fig. 2a), sequencing reads as well as Cell Ranger and Space Ranger output) were submitted to ArrayExpress under accession numbers E-MTAB-11114 (Visium) and E-MTAB-11115 (snRNA-seq). Annotated snRNA-seq data are publicly available via cellxgene portals64: full dataset (annotation_1_print column denotes cell types) and astrocyte subclusters. The integrated secondary lymphoid organ scRNA-seq data are publicly available for download through S3 bucket. Image data generated in this manuscript were deposited to the BioImage Archive (accession no. S-BIAD207).

Ground truth annotations of germinal center zones in lymph node Visium data can be found on GitHub. Ground truth annotations of the gut lymphoid follicles can be found on GitHub.

Published datasets. snRNA-seq data from Yao et al. were downloaded from the Allen Brain Institute data portal. Slide-seq V2 data from Stickels et al. were downloaded from the Broad Institute data portal (subject to use agreement). Visium data of human lymph nodes can be downloaded from the 10x website (via a function in the scanpy package).

Code availability

The cell2location package is available at https://github.com/BayraktarLab/cell2location/. Documentation and tutorials are available at https://cell2location.readthedocs.io/.

Code used to generate synthetic data is available at https://github.com/vitkl/cell2location_paper/blob/master/notebooks/benchmarking/synthetic_data_construction_improved_real_mg.ipynb. Code used to segment nuclei in histology images is available at https://github.com/yozhikoff/segmentation. Jupyter notebooks covering the analysis in this paper are available at https://github.com/vitkl/cell2location_paper/ and upon reasonable request.

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Acknowledgements

We thank B. Velten, Y. Huang and L. Marconato for feedback on the cell2location model; M. Prete and V. Kiselev for dockerizing the cell2location tool and creating the web portals for sharing our data; N. Kumasaka for helpful comments on single-cell analysis; S. Leonard and K. Polanski for help with spatial and single-nucleus data processing; K. James for advice on gut immune cell types; K. Roberts for advice on smFISH; J. E. Kwa for advice on snRNA-seq; J. Eliasova for illustrations and logo design; K. James advice on human gut immune cells; A. Antanaviciute, H. Koohy and A. Simmons for sharing gut Visium data; F. Obermeyer and M. Jankowiak for help with advanced use of pyro code base; and D. Rowitch and S. Teichmann for comments on the manuscript. H.W.K. was funded by a Sir Henry Wellcome Postdoctoral Fellowship (213555/Z/18/Z). This study was supported by Wellcome Trust Core Funding to O.A.B.

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Authors and Affiliations

  1. Wellcome Sanger Institute, Hinxton, Cambridge, UK

    Vitalii Kleshchevnikov, Artem Shmatko, Emma Dann, Alexander Aivazidis, Hamish W. King, Tong Li, Rasa Elmentaite, Veronika Kedlian, Mika Sarkin Jain, Jun Sung Park, Lauma Ramona, Elizabeth Tuck, Anna Arutyunyan, Roser Vento-Tormo, Oliver Stegle & Omer Ali Bayraktar

  2. Moscow State University, Leninskie Gory, Moscow, Russia

    Artem Shmatko

  3. Centre for Immunobiology, Blizard Institute, Queen Mary University of London, London, UK

    Hamish W. King & Louisa James

  4. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK

    Artem Lomakin, Jun Sung Park & Moritz Gerstung

  5. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany

    Artem Lomakin, Moritz Gerstung & Oliver Stegle

  6. Center for Computational Biology, University of California, Berkeley, Berkeley CA, USA

    Adam Gayoso

  7. Theory of Condensed Matter, Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Mika Sarkin Jain

  8. Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Oliver Stegle

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  1. Vitalii Kleshchevnikov
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Contributions

V.K., O.S. and O.A.B. conceived the study. V.K. developed and implemented the cell2location model with feedback from O.S. and worked on validation, mouse brain, human lymph node, gut and benchmarking analyses. A.S. performed Slide-seq and hyperparameter sensitivity analysis, performed histology image segmentation, wrote data visualization modules and contributed to implementation of cell2location in pyro and benchmarking analyses. E.D. worked on generating synthetic data, mouse brain cell annotation and validation of cell type mapping and contributed to lymph node analysis. V.KE. and A.S. contributed to the development and interpretation of the model for estimating cell type signatures. A.L., M.G., A.A.I. and M.S.J. contributed to the developing of model architecture, writing the model and downstream analysis code, interpretation of the cell2location results as well as downstream clustering and NMF. A.A.R. performed benchmarking of stereoscope, supervised by R.V.-T. and O.S. A.G. contributed to implementation of cell2location using scvi-tools and pyro. H.W.K. contributed to lymph node data analysis and interpretation, supervised by L.J. R.E. contributed to gut data analysis. L.R. performed snRNA-seq experiments. L.T. performed Visium experiments. J.S.P. performed smFISH experiments and imaging. T.L. performed smFISH image processing and astrocyte segmentation analysis. V.K., O.S. and O.A.B. wrote the manuscript, with feedback from all authors.

Corresponding authors

Correspondence to Oliver Stegle or Omer Ali Bayraktar.

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Peer review information Nature Biotechnology thanks Steven Sloan, Xiaoqun Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–27 and Supplementary Methods

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Supplementary Files 1–13

Supplementary Files 1–13 show spatial cell and mRNA abundance (color) of all cell types (panels) estimated by cell2location in 10x Visium data for the respective tissues and samples.

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Kleshchevnikov, V., Shmatko, A., Dann, E. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol 40, 661–671 (2022). https://doi.org/10.1038/s41587-021-01139-4

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