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


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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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.

Author information




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.

Corresponding authors

Correspondence to Long Cai or Guo-Cheng Yuan.

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The authors declare no competing financial interests.

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Supplementary Figures and Tables

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

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Supplementary Note 1 (PDF 849 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).

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