Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

DestVI identifies continuums of cell types in spatial transcriptomics data

Nature Biotechnology volume 40, pages 1360–1369 (2022)Cite this article

Subjects

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).

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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/.

References

  1. Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

  3. Asp, M. et al. Spatial detection of fetal marker genes expressed at low level in adult human heart tissue. Sci. Rep. 7, 12941 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Hunter, M. V., Moncada, R., Weiss, J. M., Yanai I. & White, R. M. Spatial transcriptomics reveals the architecture of the tumor/microenvironment interface. Nat. Commun. 12, 6278 (2021).

  5. Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 1661–1662 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

  8. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  CAS  Google Scholar 

  11. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00830-w (2021).

  12. Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).

  13. Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Song, Q. & Su, J. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence. Brief. Bioinform. 22, bbaa414 (2021).

  15. Kleshchevnikov, V. et al. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01139-4 (2022).

  16. Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lähnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lopez, R., Gayoso, A. & Yosef, N. Enhancing scientific discoveries in molecular biology with deep generative models. Mol. Syst. Biol. 16, e9198 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Blecher-Gonen, R. et al. Single-cell analysis of diverse pathogen responses defines a molecular roadmap for generating antigen-specific immunity. Cell Syst. 8, 109–121 (2019).

  20. Kumar, S. et al. Intra-tumoral metabolic zonation and resultant phenotypic diversification are dictated by blood vessel proximity. Cell Metab. 30, 201–211 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Gayoso, A. et al. scvi-tools: a library for deep probabilistic analysis of single-cell omics data. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40, 163–166 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Blei, D. M. Build, compute, critique, repeat: data analysis with latent variable models. http://www.cs.columbia.edu/~blei/papers/Blei2014b.pdf (2014).

  23. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

    Article  PubMed  CAS  Google Scholar 

  24. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Int. Conf. Learning Representations (2014).

  26. Tomczak, J. M. & Welling, M. VAE with a VampPrior. Int. Conf. on Artificial Int. and Stat. (2018).

  27. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373, 111–117 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gayoso, A. et al. Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nat. Methods 18, 272–282 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17, e9620 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Garraud, O. et al. Revisiting the B-cell compartment in mouse and humans: more than one B-cell subset exists in the marginal zone and beyond. BMC Immunol. 13, 63 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hampton, H. R. & Chtanova, T. The lymph node neutrophil. Semin. Immunol. 28, 129–136 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Kastenmüller, W., Torabi-Parizi, P., Subramanian, N., Lämmermann, T. & Germain, R. N. A spatially-organized multicellular innate immune response in lymph nodes limits systemic pathogen spread. Cell 150, 1235–1248 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Hatfield, S. M. et al. Immunological mechanisms of the antitumor effects of supplemental oxygenation. Sci. Transl. Med. 7, 277ra30 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Katzenelenbogen, Y. et al. Coupled scRNA-seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer. Cell 182, 872–885 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Massi, D. et al. Arginine metabolism in tumor-associated macrophages in cutaneous malignant melanoma: evidence from human and experimental tumors. Hum. Pathol. 38, 1516–1525 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Yosef, N. & Regev, A. Writ large: genomic dissection of the effect of cellular environment on immune response. Science 354, 64–68 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Steinberg, J. D. et al. Negative contrast Cerenkov luminescence imaging of blood vessels in a tumor mouse model using [68Ga]gallium chloride. EJNMMI Res 4, 15 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Wippold, F. J. 2nd, Lämmle, M., Anatelli, F., Lennerz, J. & Perry, A. Neuropathology for the neuroradiologist: palisades and pseudopalisades. AJNR Am. J. Neuroradiol. 27, 2037–2041 (2006).

    PubMed  PubMed Central  Google Scholar 

  42. Godet, I. et al. Fate-mapping post-hypoxic tumor cells reveals a ROS-resistant phenotype that promotes metastasis. Nat. Commun. 10, 4862 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Carmona-Fontaine, C. et al. Metabolic origins of spatial organization in the tumor microenvironment. Proc. Natl Acad. Sci. USA 114, 2934–2939 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Duque-Correa, M. A. et al. Macrophage arginase-1 controls bacterial growth and pathology in hypoxic tuberculosis granulomas. Proc. Natl Acad. Sci. USA 111, E4024–E4032 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lopez, R., Boyeau, P., Yosef, N., Jordan, M. I. & Regier, J. Decision-making with auto-encoding variational Bayes. 34th Conference on Neural Information Processing Systems. https://papers.nips.cc/paper/2020/file/357a6fdf7642bf815a88822c447d9dc4-Paper.pdf (2020).

  46. Boyeau, P. et al. Deep generative models for detecting differential expression in single cells. Preprint at https://www.biorxiv.org/content/10.1101/794289v1?rss=1 (2019).

  47. Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22, 627–644 (2021).

    Article  CAS  PubMed  Google Scholar 

  48. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cho, C.-S. et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184, 3559–3572 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).

    Google Scholar 

  52. Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning (2015).

  53. Ba, J. L., Kiros, J. R. & Hinton, G. E. Layer normalization. Preprint at https://arxiv.org/abs/1607.06450 (2016).

  54. Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J.-P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Zhang, X., Xu, C. & Yosef, N. Simulating multiple faceted variability in single cell RNA sequencing. Nat. Commun. 10, 2611 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 18, 27 (2022).

  57. Zou, H., Hastie, T. & Tibshirani, R. Sparse principal component analysis. J. Comput. Graph. Stat. 15, 265–286 (2006).

    Article  Google Scholar 

  58. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  60. Geary. R. C. The contiguity ratio and statistical mapping. The Incorporated Statistician. 5, 115–127 (1954).

  61. DeTomaso, D. & Yosef, N. Hotspot identifies informative gene modules across modalities of single-cell genomics. Cell Syst. (2021).

  62. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Koren, Y. & Carmel, L. Robust linear dimensionality reduction. IEEE Transactions on Visualization and Computer Graphics 10, 459–470 (2004).

  64. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinf. 14, 128 (2013).

    Article  Google Scholar 

  65. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).

    Google Scholar 

  66. Tabula Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).

    Article  CAS  Google Scholar 

Download references

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.

Author information

Author notes

  1. These authors contributed equally: Romain Lopez, Baoguo Li, Hadas Keren-Shaul.

  2. These authors jointly supervised this work: Ido Amit, Nir Yosef.

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley CA, USA

    Romain Lopez, Pierre Boyeau, Allon Wagner, Can Ergen & Nir Yosef

  2. Department of Immunology, Weizmann Institute of Science, Rehovot, Israel

    Baoguo Li, Adam Jelinski, Ido Yofe, Eyal David, Michael I. Jordan & Ido Amit

  3. Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel

    Hadas Keren-Shaul, Merav Kedmi, David Pilzer, Yoseph Addadi & Ofra Golani

  4. Malaghan Institute of Medical Research, Wellington, New Zealand

    Franca Ronchese

  5. Department of Statistics, University of California, Berkeley, Berkeley CA, USA

    Michael I. Jordan

  6. Center for Computational Biology, University of California, Berkeley, Berkeley CA, USA

    Nir Yosef

  7. Chan Zuckerberg Biohub, San Francisco CA, USA

    Nir Yosef

  8. Ragon Institute of MGH, MIT and Harvard, Cambridge MA, USA

    Nir Yosef

Authors
  1. Romain Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baoguo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hadas Keren-Shaul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pierre Boyeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Merav Kedmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David Pilzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Adam Jelinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ido Yofe
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eyal David
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Allon Wagner
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Can Ergen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yoseph Addadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ofra Golani
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Franca Ronchese
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Michael I. Jordan
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ido Amit
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Nir Yosef
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

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

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

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

Reporting Summary

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.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lopez, R., Li, B., Keren-Shaul, H. et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat Biotechnol 40, 1360–1369 (2022). https://doi.org/10.1038/s41587-022-01272-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-022-01272-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing