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Image-based transcriptomics in thousands of single human cells at single-molecule resolution

Nature Methods volume 10, pages 1127–1133 (2013)Cite this article

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Abstract

Fluorescence in situ hybridization (FISH) is widely used to obtain information about transcript copy number and subcellular localization in single cells. However, current approaches do not readily scale to the analysis of whole transcriptomes. Here we show that branched DNA technology combined with automated liquid handling, high-content imaging and quantitative image analysis allows highly reproducible quantification of transcript abundance in thousands of single cells at single-molecule resolution. In addition, it allows extraction of a multivariate feature set quantifying subcellular patterning and spatial properties of transcripts and their cell-to-cell variability. This has multiple implications for the functional interpretation of cell-to-cell variability in gene expression and enables the unbiased identification of functionally relevant in situ signatures of the transcriptome without the need for perturbations. Because this method can be incorporated in a wide variety of high-throughput image-based approaches, we expect it to be broadly applicable.

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Figure 1: bDNA FISH results in bright spots with high signal-to-noise ratio (SNR).
Figure 2: Image-based transcriptomics pipeline.
Figure 3: Image-based transcriptomics is reproducible, sensitive and comparable to RNA-seq.
Figure 4: Minimum number of cells required for reproducible single-cell distributions of transcript abundance.
Figure 5: Quantitative signatures of the in situ transcriptome.

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Acknowledgements

We would like to acknowledge B. Snijder and Y. Yakimovich for help with computational analysis and infrastructure, J. Patterson for assistance, Q. Nguyen and S. Lai from Affymetrix for helpful comments on experimental procedures, J. Ellenberg (European Molecular Biology Laboratory) for reagents, and all members of the lab for useful comments on the manuscript. L.P. acknowledges financial support for this project from SystemsX.ch, the European Union, University of Zurich and University of Zurich Research Priority Program in Systems Biology and Functional Genomics.

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Author notes

  1. Nico Battich and Thomas Stoeger: These authors contributed equally to this work.

Authors and Affiliations

  1. Faculty of Sciences, Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    Nico Battich, Thomas Stoeger & Lucas Pelkmans

  2. Systems Biology PhD program, Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland

    Nico Battich & Thomas Stoeger

Authors
  1. Nico Battich
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Contributions

L.P. initiated the study. N.B., T.S. and L.P. designed and analyzed the experiments and wrote the manuscript. N.B. and T.S. performed the experiments.

Corresponding author

Correspondence to Lucas Pelkmans.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15, Supplementary Tables 1 and 2, Supplementary Protocol and Supplementary Notes 1–6 (PDF 26058 kb)

Supplementary Table 3

High-throughput bDNA sm-FISH comparison to RNA-seq (XLSX 198 kb)

Supplementary Table 4

Features used for gene clustering (XLSX 7511 kb)

Supplementary Table 5

Equipment and settings (XLSX 11 kb)

Supplementary Table 6

Full RNA-seq dataset (XLSX 881 kb)

Supplementary Software

Spots detection and pattern recognition (ZIP 7358 kb)

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Battich, N., Stoeger, T. & Pelkmans, L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat Methods 10, 1127–1133 (2013). https://doi.org/10.1038/nmeth.2657

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