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3D computational reconstruction of tissues with hollow spherical morphologies using single-cell gene expression data

Nature Protocols volume 10, pages 459–474 (2015)Cite this article

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Abstract

Single-cell gene expression analysis has contributed to a better understanding of the transcriptional heterogeneity in a variety of model systems, including those used in research in developmental, cancer and stem cell biology. Nowadays, technological advances facilitate the generation of large gene expression data sets in high-throughput format. Strategies are needed to pertinently visualize this information in a tissue structure–related context, so as to improve data analysis and aid the drawing of meaningful conclusions. Here we describe an approach that uses spatial properties of the tissue source to enable the reconstruction of hollow sphere–shaped tissues and organs from single-cell gene expression data in 3D space. To demonstrate our method, we used cells of the mouse otocyst and the renal vesicle as examples. This protocol presents a straightforward computational expression analysis workflow, and it is implemented on the MATLAB and R statistical computing and graphics software platforms. Hands-on time for typical experiments can be <1 h using a standard desktop PC or Mac.

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Figure 1: Comparison of different technologies to study gene expression levels.
Figure 2: Overview of the protocol workflow.
Figure 3: Anchor gene correlation and principal component analysis.
Figure 4: Data projection onto the first three PCs.
Figure 5: Visualization and quantification of otocyst and renal vesicle models.
Figure 6: Sphere partitioning.

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Gene Expression Omnibus

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Acknowledgements

We thank all members of the Heller laboratory for comments on the manuscript. This work was supported by National Institutes of Health grants DC006167 and DC012250 to S.H., by P30 core support (DC010363), by the Stanford Initiative to Cure Hearing Loss and in part by FP7-Health-2013-Innovation, a cooperative grant by the European Commission.

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Author notes

  1. Robert Durruthy-Durruthy and Assaf Gottlieb: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Otolaryngology—Head and Neck Surgery, Stanford University School of Medicine, Stanford, California, USA

    Robert Durruthy-Durruthy & Stefan Heller

  2. Department of Genetics, Stanford University School of Medicine, Stanford, California, USA

    Assaf Gottlieb

Authors
  1. Robert Durruthy-Durruthy
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  2. Assaf Gottlieb
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  3. Stefan Heller
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Contributions

R.D.-D. and S.H. designed the experiments; R.D.-D. performed the experiments; R.D.-D. and A.G. performed data analysis; and R.D.-D., A.G. and S.H. wrote the manuscript.

Corresponding author

Correspondence to Stefan Heller.

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Competing interests

S.H. is affiliated with Inception 3, Inc.

Supplementary information

Supplementary Text and Figures

Supplementary Methods (PDF 818 kb)

Supplementary Software 1

Contains all function files for MATLAB. (ZIP 18 kb)

Supplementary Software 2

Contains script files for R (ZIP 13 kb)

Supplementary Data

Contains gene expression data (qRT-PCR for otocysts and RNA-seq for renal vesicles) in comma-separated values (CSV) format to be used in the protocol. (ZIP 2225 kb)

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Durruthy-Durruthy, R., Gottlieb, A. & Heller, S. 3D computational reconstruction of tissues with hollow spherical morphologies using single-cell gene expression data. Nat Protoc 10, 459–474 (2015). https://doi.org/10.1038/nprot.2015.022

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