The statistical geometry of transcriptome divergence in cell-type evolution and cancer
Open accessAbstract
In evolution, body plan complexity increases due to an increase in the number of individualized cell types. Yet, there is very little understanding of the mechanisms that produce this form of organismal complexity. One model for the origin of novel cell types is the sister cell-type model. According to this model, each cell type arises together with a sister cell type through specialization from an ancestral cell type. A key prediction of the sister cell-type model is that gene expression profiles of cell types exhibit tree structure. Here we present a statistical model for detecting tree structure in transcriptomic data and apply it to transcriptomes from ENCODE and FANTOM5. We show that transcriptomes of normal cells harbour substantial amounts of hierarchical structure. In contrast, cancer cell lines have less tree structure, suggesting that the emergence of cancer cells follows different principles from that of evolutionary cell-type origination. Body plan complexity is associated with the number of different cell types, yet the processes that create this diversity are unclear. Here the authors use transcriptomics to test the hypothesis that unlike cancer cells, novel normal cell types arise through sub-specialization of an ancestral cell type.
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A recent study comparing cellular transcriptomes indicates that a hierarchical tree structure dominates cell-type interrelationships.
“a recent study comparing cellular transcriptomes indicates that a hierarchical tree structure indeed dominates cell-type interrelationships [6]”
From Gastric pouches and the mucociliary sole: setting the stage for nervous system evolution (2015) · cites this paper as [6]
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- Authors
- Liang, C., the FANTOM Consortium, Forrest, A. R. R., & Wagner, G. P.
- Affiliations
- Yale University (US)
- RIKEN (JP)
- Corresponding author
- Wagner, Günter P.
- Venue
- Nature Communications
- Published
- 2015-01-14
- Volume
- 6
- Issue
- 1
- Pages
- 6066-6066
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2103682580
- Cited by
- 59
- References
- 25
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 6 · 2023: 14 · 2022: 1 · 2021: 6 · 2019: 3 · 2018: 2 · 2017: 6 · 2016: 8 · 2015: 6
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion)
- PubMed (publishedVersion)
- KNAW research portal (Royal Academy of Art and Sciences (KNAW)) (publishedVersion, other-oa)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- UWA Profiles and Research Repository (UWA) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- SDL (submittedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion)
- Funders
- John Templeton Foundation (54860)
- Yale University
- Ministry of Education, Culture, Sports, Science and Technology
- China Scholarship Council
- Japan Society for the Promotion of Science (25330333)
- RIKEN
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- transcriptome divergence, cell type evolution, cancer cell lines, ENCODE, FANTOM5
- MeSH terms
- Biological Evolution, Humans, Neoplasms, Models, Statistical, Transcriptome
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