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 repertory of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine 1, 2 have highlighted the need for a general, reproducible method for classification of these To sort the cell types we used an unsupervised machine learning approach to cluster transcriptional profiles of the cell preparations into stable distinct groups. Sparse nonnegative matrix factorization (sNMF) was adjusted for this task by implementing a bootstrapping algorithm to find the most stable groupings (see also Supplementary Discussion 1). 4, 5 The stability of the clustering 9 indicated that the dataset most likely contained about twelve different types of samples ( . The HANSE cell group consisted of transcriptional profiles that were derived from neurosurgical specimens following published protocols for multipotent neural progenitor derivation and propagation. 10, 11 These cells expressed markers that are commonly used to identify neural stem cells 12 (see Supplementary Figure 4), but the clustering clearly separated them from the other samples that had been derived from postmortem brains of prematurely born infants (see Figure 2). 10,11 We used a combination of analysis tools to explore the basis of the unsupervised classification of the samples in the core dataset. Gene Set Analysis 3 (GSA) is a means to identify the underlying themes in transcriptional data in terms of their biological relevance.GSA uses lists of genes 5 that are related in some way; the common criterion is that the relationships among the genes in the lists are supported by empirical evidence. 20 GSA highlighted numerous significant differences among the computationally defined categories.(See Supplementary Figure 2, Supplementary Table 11 and Supplementary Online Materials).While GSA is valuable for discovering specific differences among sample groups, it is limited to curated gene lists and cannot be used to discover new regulatory networks. The MATISSE algorithm 6 (http://acgt.cs.tau.ac.il/matisse) takes predefined protein-protein interactions (e.g. from yeast-two-hybrid screens) and seeks connected subnetworks that manifest high similarity in sample subsets. The modified version used in this analysis is capable of extracting subnetworks that are co-expressed in many samples but also significantly up-or down-regulated in a specific sample cluster. Since the PSC preparations were consistently clustered together we used MATISSE to look for distinctive molecular networks that might be associated with the unique PSC qualities of pluri...

Taking stem cells to the clinic: Major challenges

“…ESC markers include the surface marker antigens (Tra series, SSEA series, GCT series, HLA, and CD markers), OCT4 and other genomic markers for the three primordial germ layers, confirmation of pluripotency by the production of teratomas in immunodeficient mice, stability of normal karyotype with serial culture, alkaline phosphatase positiveness, telomerase production, the ability to differentiate into tissues originating from all three primordial germ layers, confirmation of function, and a full transcriptome profiling to confirm the expression of ''stemness'' characteristics [Bongso et al, 2005]. Through such an analysis it is evident that genomic similarities and differences surface between stem cell lines, sources, and types [International Stem Cell Initiative, 2007;Muller et al, 2008]. Unlike ESCs, MSCs have less specific markers that define them.…”

TGFβ induces the formation of tumour-initiating cells in claudinlow breast cancer

“…S3). To explore these signatures further, we used a recently derived protein-protein interaction subnetwork characterizing pluripotency and stemness (the 'PluriNet') 35 and found it was preferentially upregulated only in TβSC-A (Fig. 3d).…”

REST and CoREST are transcriptional and epigenetic regulators of seminal neural fate decisions

“…We unexpectedly found that CoREST binds to a significantly greater number of genomic sites (1,820) compared to REST and encompassing a larger proportion of unique target genes in NSCs. 71 These included many loci encoding factors that are part of pluripotency networks 83 and individual and composite Oct4/Sox2/Nanog transcriptional networks; 84 those with critical roles in NSC maintenance, lineage restriction and neurogenesis; those involved in epigenetic regulatory networks, including genes targeted by brain-related miRNAs (e.g., miR-124a, miR-9 and miR-132); and those linked to specific disease states associated with deregulation of NSC functions (i.e., cancer stem cells), including glioblastoma multiforme. By contrast, we found that both REST and CoREST target genes included factors implicated in Huntington's disease, a disorder linked to deregulation of REST subcellular localization and function.…”