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

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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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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.

Author information

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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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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Long Cai or Guo-Cheng Yuan.

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DOI

https://doi.org/10.1038/nbt.4260