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Regulatory networks define phenotypic classes of human stem cell lines

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.

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.

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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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