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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- Supplementary Text and Figures (3 MB)
Supplementary Figures 1–13, Supplementary Tables 4 and 6 and Supplementary Results
- Supplementary Table 1 (53 KB)
Cell type and tissue ontology terms
- Supplementary Table 2 (254 KB)
Microarray samples mapped to ontology terms
- Supplementary Table 3 (33 KB)
The order of cell types as it appears in the heat maps presented
- Supplementary Table 5 (315 KB)
Functional evidence for a role in transcription regulation found in the gene-set curation
- Supplementary Table 7 (74 KB)
Identification of candidate toggle pairs
- Supplementary Table 8 (729 KB)
Rank-based differential expression analysis comparison using RCoS
- Supplementary Table 9 (254 KB)
Rank-based differential expression analysis comparison using RDAM
- Supplementary Table 10 (25 KB)
Public ChIP-seq data sets used
- Supplementary Table 11 (1 MB)
Genomic region enrichment results for GATA1 ChIP-seq data
- Supplementary Table 12 (733 KB)
Genomic region enrichment results for TAL1 ChIP-seq data
- Supplementary Table 13 (2 MB)
Genomic region enrichment results for SPI1 ChIP-seq data
- Supplementary Table 14 (995 KB)
Genomic region enrichment results for EBF1 ChIP-seq data
- Supplementary Table 15 (3 MB)
Genomic region enrichment results for GATA3 ChIP-seq data
- Supplementary Table 16 (119 KB)
Mouse knockout phenotypes of Gata1, Tal1, Sfpi1, Ebf1 and Gata3
- Supplementary Table 17 (102 KB)
Additional microarray data used for validation.
- Supplementary Software (5 MB)
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.