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Spatial charting of single-cell transcriptomes in tissues

Abstract

Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections, but do not have single-cell resolution. Here, we developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. We benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness. We then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. We performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data show that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization.

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Fig. 1: Overview of the CellTrek workflow.
Fig. 2: CellTrek reconstructs spatial organization in a mouse brain tissue.
Fig. 3: CellTrek reconstructs spatial organization in a mouse kidney tissue.
Fig. 4: CellTrek identifies the spatial subclone heterogeneity in DCIS1.
Fig. 5: CellTrek displays the spatial tumor-immune microenvironment in DCIS2.

Data availability

The scRNA-seq and ST data were submitted to the Gene Expression Omnibus (GEO): GSE181254.

Code availability

The CellTrek software toolkit is available at GitHub: https://github.com/navinlabcode/CellTrek.

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Acknowledgements

This work was supported by grants to N.E.N. from the NIH National Cancer Institute (RO1CA240526, RO1CA236864), the CPRIT Single Cell Genomics Center (RP180684), the Chan-Zuckerberg Initiative (CZI) SEED Network Grant (CZF2019-002432) and the PRECISION Cancer Grand Challenge Grant. N.E.N. is an AAAS Fellow, AAAS Wachtel Scholar, Damon-Runyon Rachleff Innovator, Andrew Sabin Fellow, and Jack & Beverly Randall Innovator. This study was supported by the MD Anderson Sequencing Core Facility Grant (CA016672). R.W. is a Damon Runyon Fellow supported by the Damon Runyon Cancer Research Foundation (DRQ-08-20). We thank J. Wang, X. He and J. Wei for their unwavering support. We also thank D. Minussi and Y. Lin for their valuable suggestions.

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Authors and Affiliations

Authors

Contributions

R.W. and S.H. developed the CellTrek method, analyzed data, prepared figures and wrote the manuscript. S.B. and E.S. performed single-cell and ST experiments. M.H. processed data. A.T. and S.K. collected the DCIS tissue samples, interpreted data and managed the IRB protocols. K.C. provided input on the CellTrek method and manuscript. N.E.N. managed the project and wrote the manuscript.

Corresponding author

Correspondence to Nicholas E. Navin.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Tables 1–4 and Data 1–4.

Reporting Summary

Supplementary Data 1

The CellTrek map for mouse brain.

Supplementary Data 2

The CellTrek map for mouse brain.

Supplementary Data 3

The CellTrek map for DCIS1.

Supplementary Data 4

The CellTrek map for DCIS2.

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Wei, R., He, S., Bai, S. et al. Spatial charting of single-cell transcriptomes in tissues. Nat Biotechnol 40, 1190–1199 (2022). https://doi.org/10.1038/s41587-022-01233-1

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  • DOI: https://doi.org/10.1038/s41587-022-01233-1

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