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NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

Nature Protocols volume 16, pages 4177–4200 (2021)Cite this article

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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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Fig. 1: Schematic representation of the novoSpaRc algorithm.
Fig. 2: NovoSpaRc successfully reconstructs de novo the organ of Corti.
Fig. 3: Spatial reconstruction of osteosarcoma cultured cells using marker gene information.
Fig. 4: Computing times of the setup and reconstruction steps.
Fig. 5: Configure target space.
Fig. 6: Spatial expression based on reference atlas and novoSpaRc reconstruction.
Fig. 7: Structural correspondence and Moran’s I score for the Drosophila reference atlas.
Fig. 8: Cross-validation and self-consistency for selecting alpha-linear.
Fig. 9: Inspecting cell-to-location OT values.
Fig. 10: Analysis of spatially informative genes.
Fig. 11: Extracting spatial archetypes.

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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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Author notes

  1. These authors contributed equally: Noa Moriel, Enes Senel.

Authors and Affiliations

  1. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Noa Moriel, Nir Friedman & Mor Nitzan

  2. Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Enes Senel, Nikolaus Rajewsky & Nikos Karaiskos

  3. Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

    Nir Friedman

  4. Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel

    Mor Nitzan

  5. Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel

    Mor Nitzan

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Contributions

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.

Corresponding authors

Correspondence to Nikos Karaiskos or Mor Nitzan.

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The authors declare no competing interests.

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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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Related links

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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