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Gene expression atlas of a developing tissue by single cell expression correlation analysis

Nature Methods volume 16, pages 750–756 (2019)Cite this article

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Abstract

The Drosophila wing disc has been a fundamental model system for the discovery of key signaling pathways and for our understanding of developmental processes. However, a complete map of gene expression in this tissue is lacking. To obtain a gene expression atlas in the wing disc, we employed single cell RNA sequencing (scRNA-seq) and developed a method for analyzing scRNA-seq data based on gene expression correlations rather than cell mapping. This enables us to compute expression maps for all detected genes in the wing disc and to discover 824 genes with spatially restricted expression patterns. This approach identifies clusters of genes with similar expression patterns and functional relevance. As proof of concept, we characterize the previously unstudied gene CG5151 and show that it regulates Wnt signaling. Our method will enable the leveraging of scRNA-seq data for generating expression atlases of undifferentiated tissues during development.

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Fig. 1: Single cell sequencing of wing disc cells identifies SRGs.
Fig. 2: Generation of gene expression maps based on gene expression correlations.
Fig. 3: Comparison of computed maps for genes of unknown expression patterns with their actual expression detected by in situ hybridization.
Fig. 4: Discovery of genes in processes of interest based on their expression pattern.

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Data availability

Data generated or analyzed during this study are included in this published article (and its Supplementary Information files). Raw sequencing data have been deposited to NCBI GEO with accession number GSE127832.

Code availability

All custom code, together with sample data, are freely available on the Boutros laboratory repository of Github (https://github.com/boutroslab/Supplemental-Material/tree/master/Bageritz_2019). A markdown document describing how to identify SRGs using R is provided. The software package is composed of four pieces of software: (1) 1_cross_correlation_all_genes calculates the cross-correlation of all genes against all genes provided as input in an expression matrix. (2) 2_identify_best_mapping_genes recursively identifies the gene with the highest correlation score in the cross-correlation matrix generated by software no. 1, and pulls it out as a mapping gene. See Supplementary Fig. 12b for a schematic diagram. (3) 3_cross_correlation_to_mapping_genes calculates the cross-correlation between all genes and the SRGs, taking as input an expression matrix. (4) 4_calculate_expression_maps calculates the expression maps following the algorithm described above in the section Generation of computed wing disc maps.

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Acknowledgements

We thank the High Throughput Sequencing group of the DKFZ Genomics and Proteomics Core Facility for providing excellent next-generation sequencing services. We thank J.-P. Mallm and the DKFZ Single-Cell Open Laboratory (scOpenLab) for assistance with the 10X Genomics experiment. We thank E. Rempel for implementing the DropSeq computational cookbook on our Galaxy Server. J.B. was supported by a research stipend from the Fritz Thyssen Foundation. P.W. was funded by a fellowship from CellNetworks—Cluster of Excellence (EXC81). Research in the laboratories of M.B. and A.A.T. is supported by ERC Grants of the European Commission.

Author information

Authors and Affiliations

  1. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Josephine Bageritz, Philipp Willnow, Erica Valentini, Svenja Leible, Michael Boutros & Aurelio A. Teleman

  2. Heidelberg University, Heidelberg, Germany

    Josephine Bageritz, Philipp Willnow, Erica Valentini, Svenja Leible, Michael Boutros & Aurelio A. Teleman

  3. CellNetworks—Cluster of Excellence, Heidelberg University, Heidelberg, Germany

    Philipp Willnow

Authors
  1. Josephine Bageritz
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Contributions

J.B., P.W., S.L. and A.A.T. performed experiments. J.B., P.W., E.V., M.B. and A.A.T. analyzed data. J.B., P.W., M.B. and A.A.T. wrote the manuscript.

Corresponding authors

Correspondence to Michael Boutros or Aurelio A. Teleman.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nina Vogt and Tal Nawy were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figs 1–15 and Supplementary Table 1.

Reporting Summary

Supplementary Table 2

Spatially Regulated Genes (SRGs) in the wing disc. List of 824 SRGs identified by scRNA-seq of wing disc cells.

Supplementary Table 3

‘Benchmark’ genes with spatially restricted expression domains in the wing disc. 68 genes known from literature to have spatially restricted expression patterns in the wing disc.

Supplementary Table 4

Mapping genes. List of 58 mapping genes used for generating computed expression maps of the Drosophila wing disc.

Supplementary Table 5

Genes correlating to Senseless. n=948 wing disc cells. Correlation was calculated using Pearson’s correlation coefficient with one outlier removed. See Methods for details.

Supplementary Table 6

Genes correlating to Wingless. n=948 wing disc cells. Correlation was calculated using Pearson’s correlation coefficient with one outlier removed. See Methods for details.

Supplementary Table 7

Genes correlating to Dpp. n=948 wing disc cells. Correlation was calculated using Pearson’s correlation coefficient with one outlier removed. See Methods for details.

Supplementary Table 8

Best mapping genes for the wing disc identified de novo from scRNA-seq data. n=948 wing disc cells. Correlation was calculated using Pearson’s correlation coefficient with one outlier removed. See Methods for details.

Supplementary Table 9

Best mapping genes for Drosophila embryo (stage 5) identified de novo from scRNA-seq data.

Supplementary Table 10

Oligo sequences. Sequences of oligos used to generate probes for in situs.

Supplementary Data Set 1

High-resolution version of the dendogram presented in Fig. 3a. This version allows the names of all the genes to be read by zooming in.

Supplementary Data Set 2

Computed expression maps for all SRGs. This compressed file contains a folder with computed expression maps for all SRGs in the wing disc as TIFF image files.

Supplementary Software

Compressed ZIP file containing executable software (as MacOS binaries), source code (in C) and sample data.

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Bageritz, J., Willnow, P., Valentini, E. et al. Gene expression atlas of a developing tissue by single cell expression correlation analysis. Nat Methods 16, 750–756 (2019). https://doi.org/10.1038/s41592-019-0492-x

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