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...

Embryonic stem cells are unique among cultured cells in their ability to self-renew and differentiate into a wide diversity of cell types, suggesting that a specific molecular control network underlies these features. Human embryonic stem cells (hESCs) are known to have distinct mRNA expression, global DNA methylation, and chromatin profiles, but the involvement of high-level regulators, such as microRNAs (miRNA), in the hESC-specific molecular network is poorly understood. We report that global miRNA expression profiling of hESCs and a variety of stem cell and differentiated cell types using a novel microarray platform revealed a unique set of miRNAs differentially regulated in hESCs, including numerous miRNAs not previously linked to hESCs. These hESC-associated miRNAs were more likely to be located in large genomic clusters, and less likely to be located in introns of coding genes. hESCs had higher expression of oncogenic miRNAs and lower expression of tumor suppressor miRNAs than the other cell types. Many miRNAs upregulated in hESCs share a common consensus seed sequence, suggesting that there is cooperative regulation of a critical set of target miRNAs. We propose that miRNAs are coordinately controlled in hESCs, and are key regulators of pluripotence and differentiation. STEM CELLS

No abstract

Bone marrow (BM) chimeras (BMC) generated from mice carrying a null (-/-) mutation in the relB gene of the NF-kappaB family represent an ideal model for in vivo studies on the role of dendritic cells (DC) in the adaptive immune response. The spleen and lymph nodes (LN) of relB(-/-) BMC contain a small number of residual DC, mainly CD8alpha(+), that fail to up-regulate MHC class II and co-stimulatory molecules after stimulation in vitro. Moreover, residual spleen DC of relB(-/-) BMC have a 4-fold decrease in the ability to uptake and process soluble model antigen, ovalbumin (OVA), and failed to prime CD4 and CD8 T cells in vitro and in vivo. In addition, they also failed to present OVA peptide to OT-II transgenic T lymphocytes at a normal 1:10 (stimulator:responder) cell ratio. In spite of these multiple DC defects, relB(-/-) BMC immunized with plasmid DNA targeted to the spleen as the site of immune induction develop a specific CD4(+) T cell response comparable to that of relB competent mice. These data demonstrate that CD4( +) T cells can be primed in the absence of functional DC and suggest that relB may gauge the T cell response in vivo.