Regulatory networks define phenotypic classes of human stem cell lines
Open accessAbstract
Stem cells are defined as self-renewing cell populations that can differentiate into multiple distinct cell types. However, hundreds of different human cell lines from embryonic, fetal and adult sources have been called stem cells, even though they range from pluripotent cells-typified by embryonic stem cells, which are capable of virtually unlimited proliferation and differentiation-to adult stem cell lines, which can generate a far more limited repertoire of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine has highlighted the need for a general, reproducible method for classification of these cells. We report here the creation and analysis of a database of global gene expression profiles (which we call the 'stem cell matrix') that enables the classification of cultured human stem cells in the context of a wide variety of pluripotent, multipotent and differentiated cell types. Using an unsupervised clustering method to categorize a collection of approximately 150 cell samples, we discovered that pluripotent stem cell lines group together, whereas other cell types, including brain-derived neural stem cell lines, are very diverse. Using further bioinformatic analysis we uncovered a protein-protein network (PluriNet) that is shared by the pluripotent cells (embryonic stem cells, embryonal carcinomas and induced pluripotent cells). Analysis of published data showed that the PluriNet seems to be a common characteristic of pluripotent cells, including mouse embryonic stem and induced pluripotent cells and human oocytes. Our results offer a new strategy for classifying stem cells and support the idea that pluripotency and self-renewal are under tight control by specific molecular networks.
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The utility of gene expression-based models (e.g., transcriptional entropy, pluripotency-associated gene sets, and machine learning strategies) across diverse developmental systems and single-cell sequencing technologies is still unclear.
“While gene expression-based models can potentially overcome these limitations (e.g., transcriptional entropy (11–13), pluripotency-associated gene sets (14), and machine learning strategies (15)), their utility across diverse developmental systems and single-cell sequencing technologies is still unclear.”
From Single-cell transcriptional diversity is a hallmark of developmental potential (2020) · cites this paper as [14]
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- Authors
- Müller, F.-J., Laurent, L. C., Kostka, D., Ulitsky, I., Williams, R., Lu, C., Park, I.-H., Rao, M. S., Shamir, R., Schwartz, P. H., Schmidt, N. O., & Loring, J. F.
- Affiliations
- Scripps Research Institute (US)
- Max Planck Society (DE)
- Tel Aviv University (IL)
- Sanford Burnham Prebys Medical Discovery Institute (US)
- Boston Children's Hospital (US)
- Buck Institute for Research on Aging (US)
- Children's Hospital of Orange County (US)
- University Medical Center Hamburg-Eppendorf (DE)
- Corresponding author
- Müller, Franz-Josef
- Loring, Jeanne F.
- ORCID
- Venue
- Nature
- Published
- 2008-08-24
- Volume
- 455
- Issue
- 7211
- Pages
- 401-405
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC2637443
- OpenAlex
- W2014426141
- Cited by
- 343
- References
- 40
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 13 · 2023: 8 · 2022: 5 · 2021: 16 · 2020: 12 · 2019: 7 · 2018: 12 · 2017: 11 · 2016: 18 · 2015: 16 · 2014: 28 · 2013: 36 · 2012: 30
- FWCI
- 16.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (P20GM075059)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K12 5K12HD000849-20, K12HD000849)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- pluripotency, protein-protein interaction network, gene expression profiling, embryonic stem cells, induced pluripotent stem cells, embryonal carcinoma, neural stem cells, self-renewal
- MeSH terms
- Algorithms, Animals, Artificial Intelligence, Cell Differentiation, Cell Line, Humans, Oocytes, Phenotype, Protein Binding, Stem Cells, Databases, Factual, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Multipotent Stem Cells, Pluripotent Stem Cells, Mice, Embryonic Stem Cells
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