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DestVI identifies continuums of cell types in spatial transcriptomics data

Abstract

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools (https://scvi-tools.org).

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Fig. 1: Schematic representation of the ST analysis pipeline with DestVI.
Fig. 2: Evaluating the performance on DestVI on simulations.
Fig. 3: Application of DestVI to the murine lymph nodes.
Fig. 4: Application of DestVI to a MCA205 tumor sample.
Fig. 5: DestVI identifies a hypoxic population of macrophages in the tumor core.

Data availability

The raw data discussed in this manuscript have been deposited in the National Center for Biotechnology Information’s Gene Expression Omnibus under accession number GSE173778 (murine lymph node and tumor; spatial transcriptomics and scRNA-seq data). Processed sequencing data are available on our reproducibility repository (https://github.com/romain-lopez/DestVI-reproducibility).

Code availability

The code to reproduce the results in this manuscript is available on the GitHub repository (https://github.com/romain-lopez/DestVI-reproducibility) and has been deposited to Zenodo (https://doi.org/10.5281/zenodo.4685952). The reference implementation of DestVI, along with accompanying tutorials, is available via the scvi-tools codebase at https://scvi-tools.org/.

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Acknowledgements

We would like to acknowledge A. Gayoso, G. Xing and J. Hong for their help integrating DestVI in the scvi-tools codebase. Thanks to Z. Steier for providing guidance on the annotation of the lymph node single-cell data. We acknowledge S. Itzkovitz for guidance on interpreting the liver results. We thank E. Davidson for the artwork. We are grateful for insightful conversations with A. Regev, D. Pe’er, Q. Morris, A. Battle, A. Weiner, E. Rahmani and M. Jones. Funding: N.Y. and R.L. were supported by the Chan Zuckerberg Biohub, the Chan-Zuckerberg Foundation Network under grant number 2019–02452 (N.Y.) and the National Institute of Mental Health under grant number U19MH114821 (N.Y.). I.A. is an Eden and Steven Romick Professorial Chair, supported by Merck, the Chan Zuckerberg Initiative, the Howard Hughes Medical Institute International Scholar Award, European Research Council Consolidator Grant 724471-HemTree2.0, an SCA award of the Wolfson Foundation and Family Charitable Trust, the Thompson Family Foundation, a Melanoma Research Alliance Established Investigator Award (509044), the Israel Science Foundation (703/15), the Ernest and Bonnie Beutler Research Program for Excellence in Genomic Medicine, the Helen and Martin Kimmel award for innovative investigation, the NeuroMac DFG/Transregional Collaborative Research Center Grant, International Progressive MS Alliance/NMSS PA-1604 08459, the ISF Israel Precision Medicine Program (IPMP) 607/20 grant and an Adelis Foundation grant.

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Contributions

R.L., B.L, H.K.-S., I.A. and N.Y. designed the study and the experiments. B.L. performed the experimental procedures, with input from I.Y., O.G., F.R. and H.K.-S. M.K. and D.P prepared Visium and scRNA-seq libraries. A.J. processed scRNA-seq of the tumor data. B.L. and Y.A. contributed to microscopy analysis. E.D. assisted with RNA sequencing data processing and data upload. R.L. conceived the statistical model with input from B.L., H.K.-S. and M.I.J. R.L implemented the DestVI software and applied the software to analyze the data, with input from A.W. and C.E. P.B. proposed the spatially aware extension of DestVI. I.A. and N.Y. supervised the work.

Corresponding authors

Correspondence to Ido Amit or Nir Yosef.

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N.Y. is an advisor and/or has equity in Cellarity, Celsius Therapeutics and Rheos Medicine. The other authors declare no competing interests.

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Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary Figs. 1–31, Supplementary Methods, Supplementary Notes 1–9 and Supplementary Reports 1 and 2

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Supplementary Tables

Table 1 - Abundance of cell types in lymph node scRNA-seq data. Table 2 - Differential expression of monocyte expression only, in spatial transcriptomics across conditions. Table 3 - Differential expression of B cell expression only, in spatial transcriptomics across conditions. Table 4 - Differential expression of B cell expression only, in MS lymph nodes (active area versus rest). Table 5 - Abundance of cell types in scRNA-seq tumor data. Table 6 - Description of all MCA205 tumor sections used in the study. Table 7 - Differential expression of Mon-Mac expression only in spatial transcriptomics across Mreg-abundant area against the rest of the tissue.

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Lopez, R., Li, B., Keren-Shaul, H. et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01272-8

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