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SpiceMix enables integrative single-cell spatial modeling of cell identity

Nature Genetics volume 55, pages 78–88 (2023)Cite this article

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Abstract

Spatial transcriptomics can reveal spatially resolved gene expression of diverse cells in complex tissues. However, the development of computational methods that can use the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here we introduce SpiceMix, an interpretable method based on probabilistic, latent variable modeling for joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SpiceMix markedly improves on the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap and Visium, we show that SpiceMix can enhance the inference of complex cell identities, reveal interpretable spatial metagenes and uncover differentiation trajectories. SpiceMix is a generalizable analysis framework for spatial transcriptome data to investigate cell-type composition and spatial organization of cells in complex tissues.

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Fig. 1: Overview of SpiceMix.
Fig. 2: Performance evaluation based on simulated spatial transcriptome data.
Fig. 3: Application of SpiceMix to the seqFISH+ data from the mouse primary visual cortex.
Fig. 4: Metagenes and refined cell types discovered by SpiceMix from the STARmap data of the mouse primary visual cortex10.
Fig. 5: Spatial glial subtypes and the process of myelination in oligodendrocytes revealed by SpiceMix metagenes in STARmap data of the mouse primary visual cortex.
Fig. 6: Application to the Visium dataset of human dorsolateral prefrontal cortex.
Fig. 7: SpiceMix metagenes associated with finer anatomical structures in the human dorsolateral prefrontal cortex from Visium data.

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Data availability

The simulated data generated for this work are available at https://github.com/ma-compbio/SpiceMix. The spatial transcriptomic and single-cell datasets used in this study were obtained through publicly available repositories. The STARmap dataset is from https://www.starmapresources.org/data. The seqFISH+ dataset is from https://github.com/CaiGroup/seqFISH-PLUS. The Visium dataset is from https://research.libd.org/spatialLIBD, using the provided R commands. The snRNA-seq dataset of the human cortex is from https://portal.brain-map.org/atlases-and-data/rnaseq/human-multiple-cortical-areas-smart-seq. The scRNA-seq datasets of the mouse cortex are from the National Center for Biotechnology Information Gene Expression Omnibus (accession numbers GSE115746 and GSE71585).

Code availability

The source code of SpiceMix can be accessed at https://github.com/ma-compbio/SpiceMix and is downloadable from https://doi.org/10.5281/zenodo.725610759. For our comparisons against other methods, the following versions were used: Seurat v4.0.5, SpaGCN v1.0.0, BayesSpace v1.2.0, HMRF v1.3.3 and scHPF v0.5.0. The tool scDesign2 v0.1.0 for single-cell simulation was used as part of the process for generating the simulated data of approach II.

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Acknowledgements

This work was supported in part by the National Institutes of Health Common Fund 4D Nucleome Program grant UM1HG011593 (J.M.), National Institutes of Health Common Fund Cellular Senescence Network Program grant UG3CA268202 (J.M.), National Institutes of Health grants R01HG007352 (J.M.) and R01HG012303 (J.M.), and National Science Foundation grant 1717205 (J.M.). J.M. is additionally supported by a Guggenheim Fellowship from the John Simon Guggenheim Memorial Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

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  1. These authors contributed equally: Benjamin Chidester, Tianming Zhou.

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  1. Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Benjamin Chidester, Tianming Zhou, Shahul Alam & Jian Ma

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Contributions

Conceptualization: B.C. and J.M. Methodology: B.C., T.Z. and J.M. Software: T.Z. and B.C. Investigation: B.C., T.Z., S.A. and J.M. Writing—original draft: B.C., T.Z. and J.M. Writing—review and editing: B.C., T.Z., S.A. and J.M. Funding acquisition: J.M.

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Correspondence to Jian Ma.

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Supplementary Information

Supplementary Figs. 1–23 and Note.

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Supplementary Table 1

A table of the top 300 genes for each SpiceMix metagene for each dataset.

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Chidester, B., Zhou, T., Alam, S. et al. SpiceMix enables integrative single-cell spatial modeling of cell identity. Nat Genet 55, 78–88 (2023). https://doi.org/10.1038/s41588-022-01256-z

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