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Mapping cellular interactions from spatially resolved transcriptomics data

Nature Methods (2024)Cite this article

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Abstract

Cell–cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia’s power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand–receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

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Fig. 1: The Spacia model.
Fig. 2: Validating Spacia in real data.
Fig. 3: Applying Spacia to reveal EMT and lineage plasticity induction signals from the prostate cancer TME.
Fig. 4: Spacia reveals PD-L1 downstream target genes.
Fig. 5: The CD8-PD-L1 signature is prognostic and predictive.
Fig. 6: Spacia infers differential roles of γδ T cells in healthy livers and liver cancers.

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Data availability

The MERSCOPE datasets were downloaded from https://vizgen.com/data-release-program/ (the ‘MERSCOPE FFPE Human Immuno-oncology’ datasets). The breast cancer Xenium dataset was downloaded from www.10xgenomics.com/resources/datasets (the ‘xenium-ffpe-human-breast-with-custom-add-on-panel-1-standard’ dataset). The TCGA data were downloaded from https://gdac.broadinstitute.org/ (cohorts: BRCA, COAD, LIHC, LUSC, OV, PRAD, SKCM and UCEC). The scRNA-seq datasets by Zhang et al.49 and Sade-Feldman et al.48 were accessed via the Gene Expression Omnibus under accession numbers GSE169246 and GSE120575, respectively. The scRNA-seq datasets by Bassez et al.42 were accessed from https://biokey.lambrechtslab.org/. The CosMx datasets are available from https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/human-liver-rna-ffpe-dataset/. The prostate cancer scRNA-seq data that we generated are archived in Zenodo at https://doi.org/10.5281/zenodo.8270765 (ref. 70). The breast cancer GeoMx data that we generated are archived at https://github.com/yunguan-wang/Spacia/tree/main/geomx/. Basic clinical characteristics of the individuals with prostate cancer and those with breast cancer, from whom we generated the scRNA-seq and GeoMX data, respectively, are provided in Supplementary Table 4. Source data are provided with this paper.

Code availability

The Spacia software is available at the Database for Actionable Immunology47,71,72 (https://dbai.biohpc.swmed.edu/tools/) and at https://github.com/yunguan-wang/Spacia/tree/main/geomx/. Runtime and memory usage information is provided in Supplementary Fig. 30.

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Acknowledgements

This study was supported by the National Institutes of Health (R01CA258584 to T.W.; RC2DK129994, R01DK115477 and R01DK135535 to D.S.; R01CA222405 and R01CA255064 to S.Z.), Cancer Prevention Research Institute of Texas (RP230363 and RP190208 to T.W.; RR170061 to C.A.; RR220024 to S.Z.) and Dedman Family Scholars in Clinical Care (to N.D.).

Author information

Author notes

  1. These authors contributed equally: James Zhu, Yunguan Wang, Woo Yong Chang.

  2. These authors jointly supervised this work: Xinlei Wang, Yang Xie, Tao Wang.

Authors and Affiliations

  1. Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA

    James Zhu, Yunguan Wang, Woo Yong Chang, Rongqing Yuan, Fangjiang Wu, Lauren Yue, Lei Guo, Guanghua Xiao, Yang Xie & Tao Wang

  2. Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Yunguan Wang

  3. Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA

    Yunguan Wang

  4. Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Alicia Malewska, Jeffrey C. Gahan & Douglas Strand

  5. Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Fabiana Napolitano, Ariella B. Hanker & Carlos L. Arteaga

  6. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Nisha Unni

  7. Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Min Zhao & Siyuan Zhang

  8. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

    Zhuo Zhao & Danny Z. Chen

  9. Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Raquibul Hannan & Neil Desai

  10. Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Guanghua Xiao & Yang Xie

  11. Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, USA

    Ping Mu

  12. Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, USA

    Ping Mu

  13. Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA

    Xinlei Wang

  14. Division of Data Science, College of Science, University of Texas at Arlington, Arlington, TX, USA

    Xinlei Wang

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  1. James Zhu
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Contributions

J.Z., Y.W. and W.C. contributed to all bioinformatics analyses and implemented the software. A.M., J.G., R.H., P.M., D.S. and N.D. generated the prostate cancer scRNA-seq data and/or provided critical insights in analyses. M.Z., F.N., L.G., N.U., A.H. and C.A, generated the breast cancer GeoMX datasets. M.Z., S.Z., Z.Z. and D.C. performed in situ sequencing analyses on breast cancer mouse models. F.W. created the Read the Docs website. X.W., G.X., Y.X. and T.W. developed the initial concept and provided resources for the study. All authors wrote the paper.

Corresponding authors

Correspondence to Xinlei Wang, Yang Xie or Tao Wang.

Ethics declarations

Competing interests

T.W. receives personal consulting fees from Merck for projects unrelated to this study. A.B.H. receives or has received research grants from Takeda and Lilly and non-financial support from Puma Biotechnology and Tempus. C.L.A. receives or has received research grants from Pfizer, Lilly and Takeda; holds minor stock options in Provista; serves or has served in an advisory role to Novartis, Merck, Lilly, Daiichi Sankyo, Taiho Oncology, OrigiMed, Puma Biotechnology, Immunomedics, AstraZeneca, Arvinas and Sanofi; and reports scientific advisory board remuneration from the Susan G. Komen Foundation. The other authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Xiuwei Zhang, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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

Extended data

Extended Data Fig. 1 Traceplots and autocorrelation plots prove convergence and stability of the MCMC estimation process in Spacia.

Only MCMC iterations after the burn-in period are shown.

Source data

Extended Data Fig. 2 Visualizing the CCCs predicted by (a) Spacia, CellPhoneDB, CellChat, SpatialDM, SpaTalk, and (b) COMMOT in their spatial context and at the single cell level.

To reduce cluttering, for each sender-receiver cell type pair, 10 connections were selected at random and visualized for CellPhoneDB’s results, and 500 connections were selected at random and visualized for CellChat’s results. The interactions refer to the overall potential for a given pair of cells to interact, taken from the output statistics of each CCC inference software. COMMOT was separately listed in panel (b) since the internal data used by COMMOT for the spatial plot were not readily accessible, and the plot was generated by COMMOT’s own plotting function instead. For SpatialDM, the software did not output specific interactions between individual cells, but rather, only gave a score for each cell that participated in the CCCs without knowing the interaction partners. The black dots refer to this score.

Source data

Extended Data Fig. 3

The sharing of sending-receiving genes by each pair of sending cell types, in the results of CellPhoneDB, COMMOT, SpatialDM, and SpaTalk. The colors represent the ratio of sending-receiving gene pairs shared between corresponding cell types, and the dendrograms represent the results of unsupervised clustering.

Source data

Extended Data Fig. 4

EMT activation potentials of each sending cell type by patient groups, dichotomized according to their lineage plasticity levels, in the prostate cancer cells. Colors refer to the status of the lineages (high or low).

Source data

Extended Data Fig. 5 Tumor cell PD-L1 up-regulates PDGFRA expression in B cells.

(a) The expression of PDGFRA in B cells before and after anti-PD1 treatment, in the Bassez cohort (n = 31). (b) The spatial distribution of the different types of cells in the breast cancer Xenium dataset. (c) The spatial distribution of the CCCs that Spacia inferred in this dataset. We zoomed into one area to more clearly show the interactions (in black). (d) The distributions of the inferred βs by Spacia, across MCMC iterations, for the interactions between tumor PD-L1 and B cell PDGFRA, indicating the direction and the strength of the interaction between these two genes. (e) Scatterplot showing the βs from the Spacia analyses on both the MERSCOPE and Xenium breast cancer datasets for B cells. The fitted curves between the X axis and the Y axis are shown as solid lines, with the shading denoting 95% CI.

Source data

Extended Data Fig. 6 Higher tumor PD-L1 expression is associated with better overall survival in TCGA patients of all eight cancer types when combined.

Patients were dichotomized by bulk tumor PD-L1 expression. P values are derived from log-rank tests of the dichotomized patient populations.

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Extended Data Fig. 7

The spatial distribution of stromal/immune cells in the liver cancer and healthy liver CosMx datasets. Each dot represent a cell, and different colors refer to the different cell types.

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Supplementary information

Supplementary Information

Supplementary File 1 Additional bioinformatics analyses associated with this study. Supplementary File 2 Details of the Spacia model.

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Supplementary Table

Supplementary Table 1 Comparing Spacia against other related tools previously published. Supplementary Table 2 Correlations between EMT activation potentials of fibroblasts, endothelial cells and B cells and the EMT levels and lineage plasticity levels of the prostate cancer cells. Supplementary Table 3 CD8-PD-L1 signature genes in all eight cancer types. Supplementary Table 4 Basic clinical characteristics of the individuals with prostate cancer and the individuals with breast cancer, from whom we generated the scRNA-seq and GeoMX data, respectively.

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Zhu, J., Wang, Y., Chang, W.Y. et al. Mapping cellular interactions from spatially resolved transcriptomics data. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02408-1

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