Pluripotent stem cells (PSCSCs) are defined by their potential to generate all cell types of an organism. The standard assay for pluripotency of mouse PSCSCs is cell transmission through the germline, but for human PSCSCs researchers depend on indirect methods such as differentiation into teratomas in immunodeficient mice. Here we report PluriTest, a robust open-access bioinformatic assay of pluripotency in human cells based on their gene expression profiles

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...

The tumor microenvironment plays an important role in modulating tumor progression. Earlier, we showed that S100A8/A9 proteins secreted by myeloid-derived suppressor cells (MDSC) present within tumors and metastatic sites promote an autocrine pathway for accumulation of MDSC. In a mouse model of colitis-associated colon cancer, we also showed that S100A8/A9-positive cells accumulate in all regions of dysplasia and adenoma. Here we present evidence that S100A8/A9 interact with RAGE and carboxylated glycans on colon tumor cells and promote activation of MAPK and NF-kB signaling pathways. Comparison of gene expression profiles of S100A8/A9-activated colon tumor cells versus unactivated cells led us to identify a small cohort of genes upregulated in activated cells, including Cxcl1, Ccl5 and Ccl7, Slc39a10, Lcn2, Zc3h12a, Enpp2, and other genes, whose products promote leukocyte recruitment, angiogenesis, tumor migration, wound healing, and formation of premetastatic niches in distal metastatic organs. Consistent with this observation, in murine colon tumor models we found that chemokines were upregulated in tumors, and elevated in sera of tumor-bearing wild-type mice. Mice lacking S100A9 showed significantly reduced tumor incidence, growth and metastasis, reduced chemokine levels, and reduced infiltration of CD11bþ cells within tumors and premetastatic organs. Studies using bone marrow chimeric mice revealed that S100A8/A9 expression on myeloid cells is essential for development of colon tumors. Our results thus reveal a novel role for myeloid-derived S100A8/A9 in activating specific downstream genes associated with tumorigenesis and in promoting tumor growth and metastasis. Mol Cancer Res; 9(2); 133-48. Ó2011 AACR.

Tissue-specific transcriptional activators initiate differentiation towards specialized cell types by inducing chromatin modifications permissive for transcription at target loci, through the recruitment of SWItch/Sucrose NonFermentable (SWI/SNF) chromatin-remodelling complex. However, the molecular mechanism that regulates SWI/ SNF nuclear distribution in response to differentiation signals is unknown. We show that the muscle determination factor MyoD and the SWI/SNF subunit BAF60c interact on the regulatory elements of MyoD-target genes in myoblasts, prior to activation of transcription. BAF60c facilitates MyoD binding to target genes and marks the chromatin for signal-dependent recruitment of the SWI/ SNF core to muscle genes. BAF60c phosphorylation on a conserved threonine by differentiation-activated p38a kinase is the signal that promotes incorporation of MyoD-BAF60c into a Brg1-based SWI/SNF complex, which remodels the chromatin and activates transcription of MyoD-target genes. Our data support an unprecedented two-step model by which pre-assembled BAF60c-MyoD complex directs recruitment of SWI/SNF to muscle loci in response to differentiation cues.

Summary Neuroendocrine (NE) phenotype, seen in >30% of prostate adenocarcinomas (PCa), and NE prostate tumors are implicated in aggressive prostate cancer. Formation of NE prostate tumors in the TRAMP mouse model was suppressed in mice lacking the ubiquitin ligase Siah2, which regulates HIF-1α availability. Cooperation between HIF-1α and FoxA2, a transcription factor expressed in NE tissue, promotes recruitment of p300 to transactivate select HIF-regulated genes, Hes6, Sox9 and Jmjd1a. These HIF-regulated genes are highly expressed in metastatic PCa and required for hypoxia-mediated NE phenotype, metastasis in PCa and the formation of NE tumors. Tissue-specific expression of FoxA2 combined with Siah2-dependent HIF-1α availability enables a transcriptional program required for NE prostate tumor development and NE phenotype in PCa.

Undifferentiated cells have been identified in the prenatal blastocyst, inner cell mass, and gonadal ridges of rodents and primates, including humans. After isolation these cells express molecular and immunological markers for embryonic cells, capabilities for extended self‐renewal, and telomerase activity. When allowed to differentiate, embryonic stem cells express phenotypic markers for tissues of ectodermal, mesodermal, and endodermal origin. When implanted in vivo, undifferentiated noninduced embryonic stem cells formed teratomas. In this report we describe a cell clone isolated from postnatal rat skeletal muscle and derived by repetitive single‐cell clonogenic analysis. In the undifferentiated state it consists of very small cells having a high ratio of nucleus to cytoplasm. The clone expresses molecular and immunological markers for embryonic stem cells. It exhibits telomerase activity, which is consistent with its extended capability for self‐renewal. When induced to differentiate, it expressed phenotypic markers for tissues of ectodermal, mesodermal, and endodermal origin. The clone was designated as a postnatal pluripotent epiblastic‐like stem cell (PPELSC). The undifferentiated clone was transfected with a genomic marker and assayed for alterations in stem cell characteristics. No alterations were noted. The labeled clone, when implanted into heart after injury, incorporated into myocardial tissues undergoing repair. The labeled clone was subjected to directed lineage induction in vitro, resulting in the formation of islet‐like structures (ILSs) that secreted insulin in response to a glucose challenge. This study suggests that embryonic‐like stem cells are retained within postnatal mammals and have the potential for use in gene therapy and tissue engineering. Anat Rec Part A 277A:178–203, 2004. © 2004 Wiley‐Liss, Inc.

The Ets2 transcription factor is essential for the development of the mouse placenta and for generating signals for embryonic mesoderm and axis formation. Using a conditional targeted Ets2 allele, we show that Ets2 is essential for trophoblast stem (TS) cells self-renewal. Inactivation of Ets2 results in TS cell slower growth, increased expression of a subset of differentiation-associated genes and decreased expression of several genes implicated in TS self-renewal. Among the direct TS targets of Ets2 is Cdx2, a key master regulator of TS cell state. Thus Ets2 contributes to the regulation of multiple genes important for maintaining the undifferentiated state of TS cells and as candidate signals for embryonic development.

SummaryReprogramming somatic cells to induced pluripotent stem cells (iPSCs) offers the possibility of studying the molecular mechanisms underlying human diseases in cell types difficult to extract from living patients, such as neurons and cardiomyocytes. To date, studies have been published that use small panels of iPSC-derived cell lines to study monogenic diseases. However, to study complex diseases, where the genetic variation underlying the disorder is unknown, a sizable number of patient-specific iPSC lines and controls need to be generated. Currently the methods for deriving and characterizing iPSCs are time consuming, expensive, and, in some cases, descriptive but not quantitative. Here we set out to develop a set of simple methods that reduce cost and increase throughput in the characterization of iPSC lines. Specifically, we outline methods for high-throughput quantification of surface markers, gene expression analysis of in vitro differentiation potential, and evaluation of karyotype with markedly reduced cost.