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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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.).
The authors declare no competing financial interests.
Supplementary Figures 1–13, Supplementary Tables 4 and 6 and Supplementary Results (PDF 2503 kb)
Cell type and tissue ontology terms (XLS 50 kb)
Microarray samples mapped to ontology terms (XLS 247 kb)
The order of cell types as it appears in the heat maps presented (XLS 29 kb)
Functional evidence for a role in transcription regulation found in the gene-set curation (XLS 306 kb)
Identification of candidate toggle pairs (XLS 69 kb)
Rank-based differential expression analysis comparison using RCoS (XLS 710 kb)
Rank-based differential expression analysis comparison using RDAM (XLS 245 kb)
Public ChIP-seq data sets used (XLS 23 kb)
Genomic region enrichment results for GATA1 ChIP-seq data (XLS 1413 kb)
Genomic region enrichment results for TAL1 ChIP-seq data (XLS 713 kb)
Genomic region enrichment results for SPI1 ChIP-seq data (XLS 1936 kb)
Genomic region enrichment results for EBF1 ChIP-seq data (XLS 969 kb)
Genomic region enrichment results for GATA3 ChIP-seq data (XLS 3041 kb)
Mouse knockout phenotypes of Gata1, Tal1, Sfpi1, Ebf1 and Gata3 (XLS 114 kb)
Additional microarray data used for validation. (XLS 97 kb)
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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