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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

References

  1. Quail, D.F.D. & Joyce, J.A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423–1437 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Riquelme, P.A., Drapeau, E. & Doetsch, F. Brain micro-ecologies: neural stem cell niches in the adult mammalian brain. Phil. Trans. R. Soc. Lond. B 363, 123–137 (2008).

    Google Scholar 

  3. Swain, P.S., Elowitz, M.B. & Siggia, E.D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. USA 99, 12795–12800 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhang, J. & Li, L. Stem cell niche: microenvironment and beyond. J. Biol. Chem. 283, 9499–9503 (2008).

    CAS  PubMed  Google Scholar 

  6. Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Raj, A., van den Bogaard, P., Rifkin, S.A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  PubMed  Google Scholar 

  10. Deng, Q., Ramsköld, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

    CAS  PubMed  Google Scholar 

  11. Jaitin, D.A. et al. Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Kolodziejczyk, A.A. et al. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17, 471–485 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    CAS  PubMed  Google Scholar 

  16. Chen, K.H., Boettiger, A.N., Moffitt, J.R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    PubMed  PubMed Central  Google Scholar 

  17. Lubeck, E. & Cai, L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9, 743–748 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Moffitt, J.R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl. Acad. Sci. USA 113, 14456–14461 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    CAS  PubMed  Google Scholar 

  21. Yuan, G.C. et al. Challenges and emerging directions in single-cell analysis. Genome Biol. 18, 84 (2017).

    PubMed  PubMed Central  Google Scholar 

  22. Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    CAS  PubMed  Google Scholar 

  23. Halpern, K.B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).

    CAS  PubMed  Google Scholar 

  25. Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Joost, S. et al. Single-cell transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity. Cell Syst. 3, 221–237 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).

    Google Scholar 

  29. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R. & Lin, C.-J. LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008).

    Google Scholar 

  30. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    CAS  PubMed  Google Scholar 

  31. Platt, J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10, 61–74 (1999).

    Google Scholar 

  32. Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation–maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001).

    CAS  PubMed  Google Scholar 

  33. Sunkin, S.M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2013).

    CAS  PubMed  Google Scholar 

  34. Andjelic, S. et al. Glutamatergic nonpyramidal neurons from neocortical layer VI and their comparison with pyramidal and spiny stellate neurons. J. Neurophysiol. 101, 641–654 (2009).

    CAS  PubMed  Google Scholar 

  35. Ben Haim, L. & Rowitch, D.H. Functional diversity of astrocytes in neural circuit regulation. Nat. Rev. Neurosci. 18, 31–41 (2017).

    PubMed  Google Scholar 

  36. Ståhl, P.L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    PubMed  Google Scholar 

  37. Svensson, V., Teichmann, S.A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. Caicedo, J.C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Edgar, R., Domrachev, M. & Lash, A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    PubMed  PubMed Central  Google Scholar 

  42. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  43. Li, S.Z. Modeling image analysis problems using Markov random fields. in Handbook of Statistics Vol. 20, 1–43 (Elsevier Science, 2003).

  44. Rousseeuw, P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).

    Google Scholar 

  45. Obayashi, T. & Kinoshita, K. COXPRESdb: a database to compare gene coexpression in seven model animals. Nucleic Acids Res. 39, D1016–D1022 (2011).

    CAS  PubMed  Google Scholar 

  46. Moffat, A. & Zobel, J. Rank-biased precision for measurement of retrieval effectiveness. ACM Trans. Inf. Syst. 27, 1–27 (2008).

    Google Scholar 

  47. Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B. 63, 411–423 (2001).

    Google Scholar 

  48. Dempster, A.P., Lamb, N.M. & Rubin, D.B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39, 1–38 (1977).

    Google Scholar 

  49. Feng, D., Tierney, L. & Magnotta, V. MRI tissue classification using high-resolution Bayesian hidden Markov normal mixture models. J. Am. Stat. Assoc. 107, 102–119 (2012).

    Google Scholar 

  50. Brélaz, D. New methods to color the vertices of a graph. Commun. ACM 22, 251–256 (1979).

    Google Scholar 

  51. Storey, J.D. & Tibshirani, R. Statistical significance for genome-wide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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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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The authors declare no competing financial interests.

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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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