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Regulatory networks define phenotypic classes of human stem cell lines

Nature volume 455, pages 401–405 (2008)Cite this article

Abstract

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 medicine1,2 has highlighted the need for a general, reproducible method for classification of these cells3. 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 method4,5 to categorize a collection of ∼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 analysis6 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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Figure 1: Sample collection and analysis for the stem cell matrix.
Figure 2: Clusters of samples based on machine learning algorithm.
Figure 3: Pluripotent stem-cell-specific protein–protein interaction network detected by MATISSE.

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Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

The microarray data have been deposited at NCBI GEO (accession number GSE11508) and can also be accessed, processed and downloaded at http://www.stemcellmesa.org.

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Acknowledgements

We thank C. Stubban, H. Dittmer, S. Zapf and H. Meissner for their work with various cell cultures. We are grateful to D. Wakeman, R. Gonzalez, S. McKercher, J. P. Lee, H.-S. Park and S. Y. Moon for sharing their cell preparations for the type collection. We are also grateful to R. Wesselschmidt and M. Pera for their unique GCT lines and G. Daley for providing human iPSCs. A. M. Kocabas and J. Cibelli shared their human oocyte expression data with us. A. Barsky let us use the Cerebral 2.0 plug-in before its publication. M. Rosentraeger helped to compile the cell culture metadata. We thank J. Aldenhoff, D. Hinze-Selch, M. Westphal, K. Lamszus, U. Kehler, D. Barker and A. Fritz for their support and discussions of this project. This study has been supported by the following grants and awards: Christian-Abrechts University Young Investigator Award (F.-J.M.), SFB-654/C5 Sleep and Plasticity (F.-J.M. and D. Hinze-Selch), Hamburger Krebsgesellschaft Grant (N.O.S.), Edmond J. Safra Bioinformatics program fellowship at Tel-Aviv University (I.U.), Converging Technologies Program of The Israel Science Foundation Grant No 1767.07 (R.S.), Raymond and Beverly Sackler Chair in Bioinformatics (R.S.), Reproductive Scientist Development Program Scholar Award K12 5K12HD000849-20 (L.C.L.), California Institute for Regenerative Medicine Clinical Scholar Award (L.C.L.), NIH P20 GM075059-01 (J.F.L.), the Alzheimer’s Association (J.F.L.), and anonymous donations in support of stem cell research.

Author Contributions J.F.L. and F.-J.M. designed the study and wrote the manuscript; I.U., R.W., D.K., R.S., L.C.L. and F.-J.M. designed and conducted the bioinformatics analysis; L.C.L., C.L., P.H.S., M.S.R., I.-H.P., F.-J.M. and N.O.S. conducted experiments and provided essential materials for this study.

Author information

Author notes

  1. Dennis Kostka

    Present address: Present address: Genome and Biomedical Sciences Facility and Department of Statistics, University of California, Davis 451 Health Sciences Drive, Davis, California 95616, USA.,

Authors and Affiliations

  1. Center for Regenerative Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, USA ,

    Franz-Josef Müller, Louise C. Laurent, Christina Lu & Jeanne F. Loring

  2. Center for Psychiatry, ZIP-Kiel, University Hospital Schleswig Holstein, Niemannsweg 147, D-24105 Kiel, Germany ,

    Franz-Josef Müller

  3. Department of Reproductive Medicine, University of California, San Diego, 200 West Arbor Drive, San Diego, California 92035, USA,

    Louise C. Laurent

  4. Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany,

    Dennis Kostka

  5. School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel

    Igor Ulitsky & Ron Shamir

  6. The Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, California 92037, USA ,

    Roy Williams & Jeanne F. Loring

  7. Division of Pediatric Hematology/Oncology, Children’s Hospital Boston and Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA,

    In-Hyun Park

  8. Invitrogen Co, 3705 Executive Way, Frederick, Maryland 21704, USA ,

    Mahendra S. Rao

  9. Center for Stem Cell Biology, Buck Institute on Aging, 8001 Redwood Boulevard, Novato, California 94945, USA ,

    Mahendra S. Rao

  10. Center for Neuroscience Research, Children’s Hospital of Orange County Research Institute, 455 South Main Street, Orange, California 92868, USA ,

    Philip H. Schwartz

  11. Developmental Biology Center, University of California, Irvine, 4205 McGaugh Hall, Irvine, California 92697, USA ,

    Philip H. Schwartz

  12. Department for Neurosurgery University Medical Center Hamburg-Eppendorf, Martinistrasse 52, D-20246 Hamburg, Germany,

    Nils O. Schmidt

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Corresponding authors

Correspondence to Franz-Josef Müller or Jeanne F. Loring.

Supplementary information

Supplementary Information 1

This files contains Supplementary Method 1 (In vitro Culture of Adult Neural Progenitors (HANSE)) and Supplementary Method 1 References. (PDF 107 kb)

Supplementary Information 2

This files contains Supplementary Method 2 (Consensus Clustering of Stem Cell Transcriptional Profiles), Supplementary Method 2 Figure 1 and Supplementary Method 2 References. (PDF 157 kb)

Supplementary Information 3

This files contains Supplementary Discussion 1 (Rationale for Clustering Algorithm Selection), Supplementary Discussion 1 Figures 1-10 and Supplementary Discussion 1 References. (PDF 1109 kb)

Supplementary Information 4

This files contains Supplementary Discussion 2 (PluriNet and Cell Cycle Regulation), Supplementary Discussion Figures 1-4, Supplementary Discussion Tables 1-4 and Supplementary Discussion References. (PDF 669 kb)

Supplementary Information 5

This files contains Supplementary Tables 1-13 (+References to Supplementary Tables) (PDF 386 kb)

Supplementary Information 6

This files contains Supplementary Figures 1-13 (+References to Supplementary Figures) (PDF 4386 kb)

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Müller, FJ., Laurent, L., Kostka, D. et al. Regulatory networks define phenotypic classes of human stem cell lines. Nature 455, 401–405 (2008). https://doi.org/10.1038/nature07213

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