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Gene-pair expression signatures reveal lineage control

Nature Methods volume 10, pages 577–583 (2013)Cite this article

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Abstract

The distinct cell types of multicellular organisms arise owing to constraints imposed by gene regulatory networks on the collective change of gene expression across the genome, creating self-stabilizing expression states, or attractors. We curated human expression data comprising 166 cell types and 2,602 transcription-regulating genes and developed a data-driven method for identifying putative determinants of cell fate built around the concept of expression reversal of gene pairs, such as those participating in toggle-switch circuits. This approach allows us to organize the cell types into their ontogenic lineage relationships. Our method identifies genes in regulatory circuits that control neuronal fate, pluripotency and blood cell differentiation, and it may be useful for prioritizing candidate factors for direct conversion of cell fate.

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Figure 1: Gene-pair expression-reversal analysis.
Figure 2: Reversal participation analysis in ESCs.
Figure 3: Reversal participation analysis of a candidate gene set for neuronal specification.
Figure 4: Identification of reversal pairs in lineage splits of the blood system.
Figure 5: Lineage relationships based on gene-pair expression reversals.

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Acknowledgements

We thank R. Bressler (Institute for Systems Biology) for providing the interactive landscape visualization for the web page, T. Sauter and T. Schilling (University of Luxembourg) for the use of their computational resource, D. Galas and C. Carlberg for useful discussions and suggestions and E. Friederich and N. Vlassis for reading the manuscript; and we gratefully acknowledge these sources of funding: the Academy of Finland, project no. 132877 (M.N.), the University of Luxembourg, Tekes FiDiPro Program (S.A.K.), Alberta Innovates the Future (S.H.) and US National Institutes of Health–National Institute of General Medical Sciences grants R01GM072855 and P50GMO76547 (I.S.).

Author information

Author notes

  1. Anke Wienecke-Baldacchino

    Present address: Present address: Department of Immunology, Laboratoire National de Santé, Centre de Recherche Public de la Santé, Luxembourg, Luxembourg.,

Authors and Affiliations

  1. Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg

    Merja Heinäniemi, Anke Wienecke-Baldacchino & Lasse Sinkkonen

  2. Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg

    Merja Heinäniemi

  3. Department of Biotechnology and Molecular Medicine, A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

    Merja Heinäniemi

  4. Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland

    Merja Heinäniemi

  5. Department of Signal Processing, Tampere University of Technology, Tampere, Finland

    Matti Nykter & Stuart A Kauffman

  6. Institute of Biomedical Technology, University of Tampere, Tampere, Finland

    Matti Nykter

  7. Institute for Systems Biology, Seattle, Washington, USA

    Roger Kramer, Joseph Xu Zhou, Richard Kreisberg, Stuart A Kauffman, Sui Huang & Ilya Shmulevich

  8. Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta, Canada

    Joseph Xu Zhou, Stuart A Kauffman & Sui Huang

  9. Complex Systems Center, University of Vermont, Burlington, Vermont, USA

    Stuart A Kauffman

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Contributions

M.H., M.N., R. Kramer and I.S. designed the gene-pair analysis, and M.H. and R. Kramer performed the analysis. M.H. and A.W.-B. designed the gene curation pipeline, and M.H., A.W.-B. and L.S. curated the genes. M.N., M.H., J.X.Z., S.A.K., S.H. and I.S. designed the clustering experiments and visualization of cell type dissimilarities. M.N. designed the branch-point placement algorithm. M.H. and M.N. compiled the ChIP-seq validations. M.H. and S.H. designed the reversal participation analysis. R. Kreisberg, M.H., M.N. and I.S. designed the content of the online resource. M.H., M.N., R. Kramer, S.H. and I.S. wrote the manuscript. All authors commented on the manuscript.

Corresponding author

Correspondence to Ilya Shmulevich.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 4 and 6 and Supplementary Results (PDF 2503 kb)

Supplementary Table 1

Cell type and tissue ontology terms (XLS 50 kb)

Supplementary Table 2

Microarray samples mapped to ontology terms (XLS 247 kb)

Supplementary Table 3

The order of cell types as it appears in the heat maps presented (XLS 29 kb)

Supplementary Table 5

Functional evidence for a role in transcription regulation found in the gene-set curation (XLS 306 kb)

Supplementary Table 7

Identification of candidate toggle pairs (XLS 69 kb)

Supplementary Table 8

Rank-based differential expression analysis comparison using RCoS (XLS 710 kb)

Supplementary Table 9

Rank-based differential expression analysis comparison using RDAM (XLS 245 kb)

Supplementary Table 10

Public ChIP-seq data sets used (XLS 23 kb)

Supplementary Table 11

Genomic region enrichment results for GATA1 ChIP-seq data (XLS 1413 kb)

Supplementary Table 12

Genomic region enrichment results for TAL1 ChIP-seq data (XLS 713 kb)

Supplementary Table 13

Genomic region enrichment results for SPI1 ChIP-seq data (XLS 1936 kb)

Supplementary Table 14

Genomic region enrichment results for EBF1 ChIP-seq data (XLS 969 kb)

Supplementary Table 15

Genomic region enrichment results for GATA3 ChIP-seq data (XLS 3041 kb)

Supplementary Table 16

Mouse knockout phenotypes of Gata1, Tal1, Sfpi1, Ebf1 and Gata3 (XLS 114 kb)

Supplementary Table 17

Additional microarray data used for validation. (XLS 97 kb)

Supplementary Software

Online data resource and tool TREL. The online data resource and interactive tool (http://trel.systemsbiology.net/) encompassing pairwise comparisons of the genes and cell types presented in this article is available to explore transcriptome diversity in metazoa; this resource accompanied by a user guide and video tutorial. (ZIP 4901 kb)

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Heinäniemi, M., Nykter, M., Kramer, R. et al. Gene-pair expression signatures reveal lineage control. Nat Methods 10, 577–583 (2013). https://doi.org/10.1038/nmeth.2445

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