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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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 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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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.
The authors declare no competing interests.
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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Kleshchevnikov, V., Shmatko, A., Dann, E. et al. Cell2location maps fine-grained cell types in spatial transcriptomics.
Nat Biotechnol (2022). https://doi.org/10.1038/s41587-021-01139-4