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Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data

Nature Biotechnology volume 36, pages 1183–1190 (2018)Cite this article

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Abstract

How intrinsic gene-regulatory networks interact with a cell's spatial environment to define its identity remains poorly understood. We developed an approach to distinguish between intrinsic and extrinsic effects on global gene expression by integrating analysis of sequencing-based and imaging-based single-cell transcriptomic profiles, using cross-platform cell type mapping combined with a hidden Markov random field model. We applied this approach to dissect the cell-type- and spatial-domain-associated heterogeneity in the mouse visual cortex region. Our analysis identified distinct spatially associated, cell-type-independent signatures in the glutamatergic and astrocyte cell compartments. Using these signatures to analyze single-cell RNA sequencing data, we identified previously unknown spatially associated subpopulations, which were validated by comparison with anatomical structures and Allen Brain Atlas images.

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Figure 1: Overall goal of the project and cell type prediction in seqFISH data.
Figure 2: Spatial domain dissection in seqFISH data using HMRF.
Figure 3: HMRF analysis identified domain-associated heterogeneity in glutamatergic cells.
Figure 4: Reanalysis of single-cell RNAseq data (from ref. 27) with domain signatures summarized into metagenes.
Figure 5: Spatially dependent astrocyte variation revealed by HMRF.

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Gene Expression Omnibus

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Acknowledgements

This research was supported by a Claudia Barr Award, a Chan Zuckerberg Initiative Award, and NIH grant R01HL119099 to G.-C.Y., and by grants from the Paul G. Allen Foundation Discovery Center, NIH HD075605 and TR01 OD024686 to L.C.

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Authors and Affiliations

  1. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Qian Zhu, Ruben Dries & Guo-Cheng Yuan

  2. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA

    Sheel Shah & Long Cai

  3. UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA

    Sheel Shah

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  1. Qian Zhu
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Contributions

G.-C.Y. and L.C. conceived and supervised the project. Q.Z. and G.-C.Y. conceived the HMRF and SVM models. Q.Z. and G.-C.Y. conducted and supervised the computational analyses. S.S. and L.C. conducted and supervised the seqFISH experiments. Q.Z., S.S., R.D., G.-C.Y. and L.C. wrote the manuscript. All of the authors contributed ideas for this work. All of the authors reviewed and approved the manuscript.

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Correspondence to Long Cai or Guo-Cheng Yuan.

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Supplementary Tables 1–5 and Supplementary Figures 1–27 (PDF 20175 kb)

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Zhu, Q., Shah, S., Dries, R. et al. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat Biotechnol 36, 1183–1190 (2018). https://doi.org/10.1038/nbt.4260

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