Abstract
Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detailed protocol for using novoSpaRc, a computational framework that probabilistically assigns cells to tissue locations. At the core of this framework lies a structural correspondence hypothesis, that cells in physical proximity share similar gene expression profiles. Given scRNA-seq data, novoSpaRc spatially reconstructs tissues based on this hypothesis, and optionally, by including a reference atlas of marker genes to improve reconstruction. We describe the novoSpaRc algorithm, and its implementation in an open-source Python package (https://pypi.org/project/novosparc). NovoSpaRc maps a scRNA-seq dataset of 10,000 cells onto 1,000 locations in <5 min. We describe results obtained using novoSpaRc to reconstruct the mouse organ of Corti de novo based on the structural correspondence assumption and human osteosarcoma cultured cells based on marker gene information, and provide a step-by-step guide to Drosophila embryo reconstruction in the Procedure to demonstrate how these two strategies can be combined.
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Data availability
All data analyzed within this protocol are publicly available. The osteosarcoma dataset14 (Fig. 3) and the organ of Corti data42 (Fig. 2) can be downloaded from the accompanying supplementary files of their corresponding papers. The Drosophila embryo scRNA-seq data27 used in the Procedure was acquired from the GEO database with accession number GSE95025, and the reference Berkeley Drosophila Transcription Network Project (BDTNP) dataset can be downloaded directly from the BDTNP webpage (ref. 46 and FlyBase, http://flybase.org/reports/FBlc0003350.html). The whole-kidney dataset47 used for benchmarking runtimes (Fig. 4) is available in the GEO database with accession number GSE107585.
Code availability
NovoSpaRc is available as a Python package at https://pypi.org/project/novosparc/, and its source code is available on GitHub (https://github.com/rajewsky-lab/novosparc) and on Zenodo49.
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Acknowledgements
N.M. is supported by the Center for Interdisciplinary Data Science Research at the Hebrew University of Jerusalem. N.K. and E.S were supported by the DFG grant KA 5006/1-1. M.N. is supported by an Early Career Faculty Fellowship by the Azrieli Foundation. N.F. is supported in part by Israel Science Foundation grants 1064/19 and 2612/18 and an Alexander von Humboldt Foundation Research award.
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This protocol is based on a paper by M.N., N.K., N.F. and N.R. Here, N.M., E.S., N.K. and M.N. implemented the method and performed computational and data analyses. N.M., E.S., N.F., N.R., N.K. and M.N. wrote the manuscript.
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Peer review information Nature Protocols thanks Jean Yee Hwa Yang and the other, anonymous reviewer(s) for their contribution to the peer review of this work.
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Key references using this protocol
Nitzan, M. et al. Nature 576, 132–137 (2019): https://doi.org/10.1038/s41586-019-1773-3
Key data used in this protocol
Waldhaus, J. et al. Cell Rep. 11, 1385–1399 (2015): https://doi.org/10.1016/j.celrep.2015.04.062
Xia, C. et al. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019): https://doi.org/10.1073/pnas.1912459116
Park, J. et al. Science 360, 758–763 (2018): https://doi.org/10.1126/science.aar2131
Karaiskos, N. et al. Science 358, 194–199 (2017): https://doi.org/10.1126/science.aan3235
Larkin, A. et al. Nucleic Acids Res. 49, D899–D907 (2021): https://doi.org/10.1093/nar/gkaa1026
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Moriel, N., Senel, E., Friedman, N. et al. NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport. Nat Protoc 16, 4177–4200 (2021). https://doi.org/10.1038/s41596-021-00573-7
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DOI: https://doi.org/10.1038/s41596-021-00573-7
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