Defining developmental potency and cell lineage trajectories by expression profiling of differentiating mouse embryonic stem cells

The transcription factor E2A drives neural differentiation in pluripotent cells

The intrinsic mechanisms that link extracellular signalling to the onset of neural differentiation are not well understood. In pluripotent mouse cells, BMP blocks entry into the neural lineage via transcriptional upregulation of inhibitor of differentiation (Id) factors. We have previously identified the major binding partner of Id proteins in pluripotent cells as the basic helix-loop-helix (bHLH) transcription factor (TF) E2A. Id1 can prevent E2A from forming heterodimers with bHLH TFs or from forming homodimers. Here, we show that overexpression of a forced E2A homodimer is sufficient to drive robust neural commitment in pluripotent cells, even under non-permissive conditions. Conversely, we find that E2A null cells display a defect in their neural differentiation capacity. E2A acts as an upstream activator of neural lineage genes, including Sox1 and Foxd4, and as a repressor of Nodal signalling. Our results suggest a crucial role for E2A in establishing neural lineage commitment in pluripotent cells.

Integrative and perturbation-based analysis of the transcriptional dynamics of TGFβ/BMP system components in transition from embryonic stem cells to neural progenitors

Cooperative actions of extrinsic signals and cell‐intrinsic transcription factors alter gene regulatory networks enabling cells to respond appropriately to environmental cues. Signaling by transforming growth factor type β (TGFβ) family ligands (eg, bone morphogenetic proteins [BMPs] and Activin/Nodal) exerts cell‐type specific and context‐dependent transcriptional changes, thereby steering cellular transitions throughout embryogenesis. Little is known about coordinated regulation and transcriptional interplay of the TGFβ system. To understand intrafamily transcriptional regulation as part of this system's actions during development, we selected 95 of its components and investigated their mRNA‐expression dynamics, gene‐gene interactions, and single‐cell expression heterogeneity in mouse embryonic stem cells transiting to neural progenitors. Interrogation at 24 hour intervals identified four types of temporal gene transcription profiles that capture all stages, that is, pluripotency, epiblast formation, and neural commitment. Then, between each stage we performed esiRNA‐based perturbation of each individual component and documented the effect on steady‐state mRNA levels of the remaining 94 components. This exposed an intricate system of multilevel regulation whereby the majority of gene‐gene interactions display a marked cell‐stage specific behavior. Furthermore, single‐cell RNA‐profiling at individual stages demonstrated the presence of detailed co‐expression modules and subpopulations showing stable co‐expression modules such as that of the core pluripotency genes at all stages. Our combinatorial experimental approach demonstrates how intrinsically complex transcriptional regulation within a given pathway is during cell fate/state transitions.

Reconstructing the Temporal Progression of Biological Data Using Cluster Spanning Trees

Identifying the temporal progression of a set of biological samples is crucial for comprehending the dynamics of the underlying molecular interactions. It is often also a basic step in data denoising and synchronization. Finally, identifying the progression order is crucial for problems like cell lineage identification, disease progression, tumor classification, and epidemiology and thus impacts the spectrum of disciplines spanning basic biology, drug discovery, and public health. Current methods that attempt solving this problem, face difficulty when it is necessary to factor-in complex relationships within the data, such as grouping, partial ordering or bifurcating or multifurcating progressions. We propose the notion of cluster spanning trees (CST) that can model both linear as well as the aforementioned complex progression relationships in temporally evolving data. Through a number of experimental investigations involving synthetic data sets as well as data sets from the cell cycle, cellular differentiation, phenotypic screening, and genetic variation, we show that the proposed CST approach outperforms existing methods in reconstructing the temporal progression of the data.

Gene selection for the reconstruction of stem cell differentiation trees: a linear programming approach

MOTIVATION

Stem cell differentiation is largely guided by master transcriptional regulators, but it also depends on the expression of other types of genes, such as cell cycle genes, signaling genes, metabolic genes, trafficking genes, etc. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering can organize cell types into a tree, but in general this tree is different from the differentiation hierarchy itself.

METHODS

Given the differentiation hierarchy and gene expression data at each node, we construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming approach to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree.

RESULTS

We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a weighted Euclidean metric that uses just 175 genes. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. We then report on the selected genes and their biological functions. Our approach offers a new way to identify genes that may have important roles in stem cell differentiation.

CONTACT

tperkins@ohri.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparative computational analysis of pluripotency in human and mouse stem cells

Pluripotent cells can be subdivided into two distinct states, the naïve and the primed state, the latter being further advanced on the path of differentiation. There are substantial differences in the regulation of pluripotency between human and mouse, and in humans only stem cells that resemble the primed state in mouse are readily available. Reprogramming of human stem cells into a more naïve-like state is an important research focus. Here, we developed a pipeline to reanalyze transcriptomics data sets that describe both states, naïve and primed pluripotency, in human and mouse. The pipeline consists of identifying regulated start-ups/shut-downs in terms of molecular interactions, followed by functional annotation of the genes involved and aggregation of results across conditions, yielding sets of mechanisms that are consistently regulated in transitions towards similar states of pluripotency. Our results suggest that one published protocol for naïve human cells gave rise to human cells that indeed share putative mechanisms with the prototypical naïve mouse pluripotent cells, such as DNA damage response and histone acetylation. However, cellular response and differentiation-related mechanisms are similar between the naïvehuman state and the primedmouse state, so the naïvehuman state did not fully reflect the naïvemouse state.

Epigenetic memory of the first cell fate decision prevents complete ES cell reprogramming into trophoblast

Embryonic (ES) and trophoblast (TS) stem cells reflect the first, irrevocable cell fate decision in development that is reinforced by distinct epigenetic lineage barriers. Nonetheless, ES cells can seemingly acquire TS-like characteristics upon manipulation of lineage-determining transcription factors or activation of the extracellular signal-regulated kinase 1/2 (Erk1/2) pathway. Here we have interrogated the progression of reprogramming in ES cell models with regulatable Oct4 and Cdx2 transgenes or conditional Erk1/2 activation. Although trans-differentiation into TS-like cells is initiated, lineage conversion remains incomplete in all models, underpinned by the failure to demethylate a small group of TS cell genes. Forced expression of these non-reprogrammed genes improves trans-differentiation efficiency, but still fails to confer a stable TS cell phenotype. Thus, even ES cells in ground-state pluripotency cannot fully overcome the boundaries that separate the first cell lineages but retain an epigenetic memory of their ES cell origin.

Chromatin properties of regulatory DNA probed by manipulation of transcription factors

Transcription factors (TFs) bind to DNA and regulate the transcription of nearby genes. However, only a small fraction of TF binding sites have such regulatory effects. Here we search for the predictors of functional binding sites by carrying out a systematic computational screening of a variety of contextual factors (histone modifications, nuclear lamin-bindings, and cofactor bindings). We used regression analysis to test if contextual factors are associated with upregulation or downregulation of neighboring genes following the induction or knockdown of the 9 TFs in mouse embryonic stem (ES) cells. Functional TF binding sites appeared to be either active (i.e., bound by P300, CHD7, mediator, cohesin, and SWI/SNF) or repressed (i.e., with H3K27me3 histone marks and bound by Polycomb factors). Active binding sites mediated the downregulation of nearby genes upon knocking down the activating TFs or inducing repressors. Repressed TF binding sites mediated the upregulation of nearby genes (e.g., poised developmental regulators) upon inducing TFs. In addition, repressed binding sites mediated repressive effects of TFs, identified by the downregulation of target genes after the induction of TFs or by the upregulation of target genes after the knockdown of TFs. The contextual factors associated with functions of DNA-bound TFs were used to improve the identification of candidate target genes regulated by TFs.

Unprecedented cell-selection using ultra-quick freezing combined with aquaporin expression

Freezing is usually used for preservation and storage of biological samples; however, this process may have some adverse effects such as cell membrane damage. Aquaporin (AQP), a water channel protein, has been suggested to play some roles for cryopreservation although its molecular mechanism remains unclear. Here we show that membrane damage caused by ultra-quick freezing is rescued by the expression of AQP4. We next examine if the expression of AQP combined with ultra-quick freezing can be used to select cells efficiently under freezing conditions where most cells are died. CHO cells stably expressing AQP4 were exclusively selected from mixed cell cultures. Having identified the increased expression of AQP4 during ES cell differentiation into neuro-ectoderm using bioinformatics, we confirmed the improved survival of differentiated ES cells with AQP4 expression. Finally we show that CHO cells transiently transfected with Endothelin receptor A and Aqp4 were also selected and concentrated by multiple cycles of freezing/thawing, which was confirmed with calcium imaging in response to endothelin. Furthermore, we found that the expression of AQP enables a reduction in the amount of cryoprotectants for freezing, thereby decreasing osmotic stress and cellular toxicity. Taken together, we propose that this simple but efficient and safe method may be applicable to the selection of mammalian cells for applications in regenerative medicine as well as cell-based functional assays or drug screening protocols.

Zscan4 restores the developmental potency of embryonic stem cells

The developmental potency of mouse embryonic stem (ES) cells, which is the ability to contribute to a whole embryo, is known to deteriorate during long-term cell culture. Previously, we have shown that ES cells oscillate between Zscan4(-) and Zscan4(+) states, and the transient activation of Zscan4 is required for the maintenance of telomeres and genome stability of ES cells. Here we show that increasing the frequency of Zscan4 activation in mouse ES cells restores and maintains their developmental potency in long-term cell culture. Injection of a single ES cell with such increased potency into a tetraploid blastocyst gives rise to an entire embryo with a higher success rate. These results not only provide a means to rejuvenate ES cells by manipulating Zscan4 expression, but also indicate the active roles of Zscan4 in the long-term maintenance of ES cell potency.

Identification of transcription factors for lineage-specific ESC differentiation

A network of transcription factors (TFs) determines cell identity, but identity can be altered by overexpressing a combination of TFs. However, choosing and verifying combinations of TFs for specific cell differentiation have been daunting due to the large number of possible combinations of ∼2,000 TFs. Here, we report the identification of individual TFs for lineage-specific cell differentiation based on the correlation matrix of global gene expression profiles. The overexpression of identified TFs—Myod1, Mef2c, Esx1, Foxa1, Hnf4a, Gata2, Gata3, Myc, Elf5, Irf2, Elf1, Sfpi1, Ets1, Smad7, Nr2f1, Sox11, Dmrt1, Sox9, Foxg1, Sox2, or Ascl1—can direct efficient, specific, and rapid differentiation into myocytes, hepatocytes, blood cells, and neurons. Furthermore, transfection of synthetic mRNAs of TFs generates their appropriate target cells. These results demonstrate both the utility of this approach to identify potent TFs for cell differentiation, and the unanticipated capacity of single TFs directly guides differentiation to specific lineage fates.

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Lineage-determining single TFs are identified based on the correlation matrix

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A proof of concept is demonstrated for ESC differentiation by 21 TFs

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TFs orchestrate global gene expression changes via direct binding to target genes

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Transfections of synthetic TF mRNAs generate desired differentiated cells

Nanog-dependent feedback loops regulate murine embryonic stem cell heterogeneity

A number of key regulators of mouse embryonic stem (ES) cell identity, including the transcription factor Nanog, show strong expression fluctuations at the single cell level. The molecular basis for these fluctuations is unknown. Here we used a genetic complementation strategy to investigate expression changes during transient periods of Nanog downregulation. Employing an integrated approach, that includes high-throughput single cell transcriptional profiling and mathematical modelling, we found that early molecular changes subsequent to Nanog loss are stochastic and reversible. However, analysis also revealed that Nanog loss severely compromises the self-sustaining feedback structure of the ES cell regulatory network. Consequently, these nascent changes soon become consolidated to committed fate decisions in the prolonged absence of Nanog. Consistent with this, we found that exogenous regulation of Nanog-dependent feedback control mechanisms produced more a homogeneous ES cell population. Taken together our results indicate that Nanog-dependent feedback loops have a role in controlling both ES cell fate decisions and population variability.

A focused microarray for screening rat embryonic stem cell lines

Here, we describe a focused microarray for screening rat embryonic stem cells (ESCs) and provide validation data that this array can distinguish undifferentiated rat ESCs from rat trophoblast stem (TS) cells, rat extraembryonic endoderm cells, mouse embryonic fibroblast feeder cells, and differentiated rat ESCs. Using this tool, genuine rat ESC lines, which have been expanded in a conventional rat ESC medium containing two inhibitors (2i), for example, glycogen synthase kinase 3 (GSK3) and mitogen-activated protein kinase (MEK) inhibitors, and leukemia inhibitory factor, and genuine rat ESCs, which have been expanded in rat ESC medium containing four inhibitors (4i), for example, GSK3, MEK, Alk5, and Rho-associated kinase inhibitors were compared; as were genuine rat ESCs from 4 different strains of rats. Expression of Cdx2, a gene associated with trophoblast determination, was observed in genuine, undifferentiated rat ESCs from 4 strains and from both 2i and 4i ESC derivation medium. This finding is in contrast to undifferentiated mouse ESCs that do not express Cdx2. The rat ESC focused microarray described in this report has utility for rapid screening of rat ESCs. This tool will enable optimization of culture conditions in the future.

Supporting regenerative medicine by integrative dimensionality reduction

OBJECTIVE

The assessment of the developmental potential of stem cells is a crucial step towards their clinical application in regenerative medicine. It has been demonstrated that genome-wide expression profiles can predict the cellular differentiation stage by means of dimensionality reduction methods. Here we show that these techniques can be further strengthened to support decision making with i) a novel strategy for gene selection; ii) methods for combining the evidence from multiple data sets.

METHODS

We propose to exploit dimensionality reduction methods for the selection of genes specifically activated in different stages of differentiation. To obtain an integrated predictive model, the expression values of the selected genes from multiple data sets are combined. We investigated distinct approaches that either aggregate data sets or use learning ensembles.

RESULTS

We analyzed the performance of the proposed methods on six publicly available data sets. The selection procedure identified a reduced subset of genes whose expression values gave rise to an accurate stage prediction. The assessment of predictive accuracy demonstrated a high quality of predictions for most of the data integration methods presented.

CONCLUSION

The experimental results highlighted the main potentials of proposed approaches. These include the ability to predict the true staging by combining multiple training data sets when this could not be inferred from a single data source, and to focus the analysis on a reduced list of genes of similar predictive performance.

An optimized gene set for transcriptomics based neurodevelopmental toxicity prediction in the neural embryonic stem cell test

The murine neural embryonic stem cell test (ESTn) is an in vitro model for neurodevelopmental toxicity testing. Recent studies have shown that application of transcriptomics analyses in the ESTn is useful for obtaining more accurate predictions as well as mechanistic insights. Gene expression responses due to stem cell neural differentiation versus toxicant exposure could be distinguished using the Principal Component Analysis based differentiation track algorithm. In this study, we performed a de novo analysis on combined raw data (10 compounds, 19 exposures) from three previous transcriptomics studies to identify an optimized gene set for neurodevelopmental toxicity prediction in the ESTn. By evaluating predictions of 200,000 randomly selected gene sets, we identified genes which significantly contributed to the prediction reliability. A set of 100 genes was obtained, predominantly involved in (neural) development. Further stringency restrictions resulted in a set of 29 genes that allowed for 84% prediction accuracy (area under the curve 94%). We anticipate these gene sets will contribute to further improve ESTn transcriptomics studies aimed at compound risk assessment.

Cnot1, Cnot2, and Cnot3 maintain mouse and human ESC identity and inhibit extraembryonic differentiation

Embryonic stem cell (ESC) identity and self-renewal is maintained by extrinsic signaling pathways and intrinsic gene regulatory networks. Here, we show that three members of the Ccr4-Not complex, Cnot1, Cnot2, and Cnot3, play critical roles in maintaining mouse and human ESC identity as a protein complex and inhibit differentiation into the extraembryonic lineages. Enriched in the inner cell mass of blastocysts, these Cnot genes are highly expressed in ESC and downregulated during differentiation. In mouse ESCs, Cnot1, Cnot2, and Cnot3 are important for maintenance in both normal conditions and the 2i/LIF medium that supports the ground state pluripotency. Genetic analysis indicated that they do not act through known self-renewal pathways or core transcription factors. Instead, they repress the expression of early trophectoderm (TE) transcription factors such as Cdx2. Importantly, these Cnot genes are also necessary for the maintenance of human ESCs, and silencing them mainly lead to TE and primitive endoderm differentiation. Together, our results indicate that Cnot1, Cnot2, and Cnot3 represent a novel component of the core self-renewal and pluripotency circuitry conserved in mouse and human ESCs.

Inference of gene regulation via miRNAs during ES cell differentiation using MiRaGE method

MicroRNA (miRNA) is a critical regulator of cell growth, differentiation, and development. To identify important miRNAs in a biological process, many bioinformatical tools have been developed. We have developed MiRaGE (MiRNA Ranking by Gene Expression) method to infer the regulation of gene expression by miRNAs from changes of gene expression profiles. The method does not require precedent array normalization. We applied the method to elucidate possibly important miRNAs during embryonic stem (ES) cell differentiation to neuronal cells and we infer that certain miRNAs, including miR-200 family, miR-429, miR-302 family, and miR-17-92 cluster members may be important to the maintenance of undifferentiated status in ES cells.

Expression2Kinases: mRNA profiling linked to multiple upstream regulatory layers

MOTIVATION

Genome-wide mRNA profiling provides a snapshot of the global state of cells under different conditions. However, mRNA levels do not provide direct understanding of upstream regulatory mechanisms. Here, we present a new approach called Expression2Kinases (X2K) to identify upstream regulators likely responsible for observed patterns in genome-wide gene expression. By integrating chromatin immuno-precipitation (ChIP)-seq/chip and position weight matrices (PWMs) data, protein-protein interactions and kinase-substrate phosphorylation reactions, we can better identify regulatory mechanisms upstream of genome-wide differences in gene expression. We validated X2K by applying it to recover drug targets of food and drug administration (FDA)-approved drugs from drug perturbations followed by mRNA expression profiling; to map the regulatory landscape of 44 stem cells and their differentiating progeny; to profile upstream regulatory mechanisms of 327 breast cancer tumors; and to detect pathways from profiled hepatic stellate cells and hippocampal neurons. The X2K approach can advance our understanding of cell signaling and unravel drugs mechanisms of action.

AVAILABILITY

The software and source code are freely available at: http://www.maayanlab.net/X2K.

CONTACT

avi.maayan@mssm.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

lincRNAs act in the circuitry controlling pluripotency and differentiation

Although thousands of large intergenic non-coding RNAs (lincRNAs) have been identified in mammals, few have been functionally characterized, leading to debate about their biological role. To address this, we performed loss-of-function studies on most lincRNAs expressed in mouse embryonic stem (ES) cells and characterized the effects on gene expression. Here we show that knockdown of lincRNAs has major consequences on gene expression patterns, comparable to knockdown of well-known ES cell regulators. Notably, lincRNAs primarily affect gene expression in trans. Knockdown of dozens of lincRNAs causes either exit from the pluripotent state or upregulation of lineage commitment programs. We integrate lincRNAs into the molecular circuitry of ES cells and show that lincRNA genes are regulated by key transcription factors and that lincRNA transcripts bind to multiple chromatin regulatory proteins to affect shared gene expression programs. Together, the results demonstrate that lincRNAs have key roles in the circuitry controlling ES cell state.

Learning biomarkers of pluripotent stem cells in mouse

Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.

Stage prediction of embryonic stem cell differentiation from genome-wide expression data

MOTIVATION

The developmental stage of a cell can be determined by cellular morphology or various other observable indicators. Such classical markers could be complemented with modern surrogates, like whole-genome transcription profiles, that can encode the state of the entire organism and provide increased quantitative resolution. Recent findings suggest that such profiles provide sufficient information to reliably predict the cell's developmental stage.

RESULTS

We use whole-genome transcription data and several data projection methods to infer differentiation stage prediction models for embryonic cells. Given a transcription profile of an uncharacterized cell, these models can then predict its developmental stage. In a series of experiments comprising 14 datasets from the Gene Expression Omnibus, we demonstrate that the approach is robust and has excellent prediction ability both within a specific cell line and across different cell lines.

AVAILABILITY

Model inference and computational evaluation procedures in the form of Python scripts and accompanying datasets are available at http://www.biolab.si/supp/stagerank.

CONTACT

blaz.zupan@fri.uni-lj.si

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovering biological progression underlying microarray samples

In biological systems that undergo processes such as differentiation, a clear concept of progression exists. We present a novel computational approach, called Sample Progression Discovery (SPD), to discover patterns of biological progression underlying microarray gene expression data. SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression), and that each sample represents one unknown point along the progression of that process. SPD aims to organize the samples in a manner that reveals the underlying progression and to simultaneously identify subsets of genes that are responsible for that progression. We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process. When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time order and identified many genes that have been associated with the cell cycle. When applied to B-cell differentiation data, SPD recovered the correct order of stages of normal B-cell differentiation and the linkage between preB-ALL tumor cells with their cell origin preB. When applied to mouse embryonic stem cell differentiation data, SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes. When applied to a prostate cancer microarray dataset, SPD identified gene modules that reflect a progression consistent with disease stages. SPD may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the candidate genes that regulate that progression.

We present a novel computational approach, Sample Progression Discovery (SPD), to discover biological progression underlying a microarray dataset. In contrast to the majority of microarray data analysis methods which identify differences between sample groups (normal vs. cancer, treated vs. control), SPD aims to identify an underlying progression among individual samples, both within and across sample groups. We validated SPD's ability to discover biological progression using datasets of cell cycle, B-cell differentiation, and mouse embryonic stem cell differentiation. We view SPD as a hypothesis generation tool when applied to datasets where the progression is unclear. For example, when applied to a microarray dataset of cancer samples, SPD assumes that the cancer samples collected from individual patients represent different stages during an intrinsic progression underlying cancer development. The inferred relationship among the samples may therefore indicate a trajectory or hierarchy of cancer progression, which serves as a hypothesis to be tested. SPD is not limited to microarray data analysis, and can be applied to a variety of high-dimensional datasets. We implemented SPD using MATLAB graphical user interface, which is available at http://icbp.stanford.edu/software/SPD/.

Gene set assembly for quantitative prediction of developmental toxicity in the embryonic stem cell test

The embryonic stem cell test (EST) is an in vitro method for predicting developmental toxicity based on compound-induced inhibition of embryonic stem cell (ESC) differentiation. We previously described how gene expression analysis in the EST can be used to describe normal ESC differentiation as well as identify compound developmental toxicity, by means of our differentiation track algorithm. In this study, we combined raw data from our three previous studies in a new integrated analysis, to identify a gene set that allows for improved prediction. By evaluating predictions of 100,000 randomly selected gene sets, we identified which genes contribute significantly to the prediction reliability. By additional cross-validation, we identified a set of 52 genes that allows for improved prediction of toxicity. The correlation between the predictions using this gene set and the magnitude of the EST endpoint was 0.85, and the gene set predicted developmental toxicity with 83% accuracy (area under the curve 89%). If compounds with ineffective data because of a too low tested concentration or too much variation between samples were excluded, even 100% accuracy could be reached based on 15 compounds. This novel gene set consists mainly of genes involved in the stem cell differentiation or other developmental processes. We expect that this set can be of use in future studies aimed at improving the EST for risk assessment, thus making a next step towards regulatory implementation of this method.

Responsiveness of genes to manipulation of transcription factors in ES cells is associated with histone modifications and tissue specificity

BACKGROUND

In addition to determining static states of gene expression (high vs. low), it is important to characterize their dynamic status. For example, genes with H3K27me3 chromatin marks are not only suppressed but also poised for activation. However, the responsiveness of genes to perturbations has never been studied systematically. To distinguish gene responses to specific factors from responsiveness in general, it is necessary to analyze gene expression profiles of cells responding to a large variety of disturbances, and such databases did not exist before.

RESULTS

We estimated the responsiveness of all genes in mouse ES cells using our recently published database on expression change after controlled induction of 53 transcription factors (TFs) and other genes. Responsive genes (N=4746), which were readily upregulated or downregulated depending on the kind of perturbation, mostly have regulatory functions and a propensity to become tissue-specific upon differentiation. Tissue-specific expression was evaluated on the basis of published (GNF) and our new data for 15 organs and tissues. Non-responsive genes (N=9562), which did not change their expression much following any perturbation, were enriched in housekeeping functions. We found that TF-responsiveness in ES cells is the best predictor known for tissue-specificity in gene expression. Among genes with CpG islands, high responsiveness is associated with H3K27me3 chromatin marks, and low responsiveness is associated with H3K36me3 chromatin, stronger tri-methylation of H3K4, binding of E2F1, and GABP binding motifs in promoters.

CONCLUSIONS

We thus propose the responsiveness of expression to perturbations as a new way to define the dynamic status of genes, which brings new insights into mechanisms of regulation of gene expression and tissue specificity.

Epigenetic switch involved in activation of pioneer factor FOXA1-dependent enhancers

Transcription factors (TFs) bind specifically to discrete regions of mammalian genomes called cis-regulatory elements. Among those are enhancers, which play key roles in regulation of gene expression during development and differentiation. Despite the recognized central regulatory role exerted by chromatin in control of TF functions, much remains to be learned regarding the chromatin structure of enhancers and how it is established. Here, we have analyzed on a genomic-scale enhancers that recruit FOXA1, a pioneer transcription factor that triggers transcriptional competency of these cis-regulatory sites. Importantly, we found that FOXA1 binds to genomic regions showing local DNA hypomethylation and that its cell-type-specific recruitment to chromatin is linked to differential DNA methylation levels of its binding sites. Using neural differentiation as a model, we showed that induction of FOXA1 expression and its subsequent recruitment to enhancers is associated with DNA demethylation. Concomitantly, histone H3 lysine 4 methylation is induced at these enhancers. These epigenetic changes may both stabilize FOXA1 binding and allow for subsequent recruitment of transcriptional regulatory effectors. Interestingly, when cloned into reporter constructs, FOXA1-dependent enhancers were able to recapitulate their cell type specificity. However, their activities were inhibited by DNA methylation. Hence, these enhancers are intrinsic cell-type-specific regulatory regions of which activities have to be potentiated by FOXA1 through induction of an epigenetic switch that includes notably DNA demethylation.

Concentration-dependent gene expression responses to flusilazole in embryonic stem cell differentiation cultures

The murine embryonic stem cell test (EST) is designed to evaluate developmental toxicity based on compound-induced inhibition of embryonic stem cell (ESC) differentiation into cardiomyocytes. The addition of transcriptomic evaluation within the EST may result in enhanced predictability and improved characterization of the applicability domain, therefore improving usage of the EST for regulatory testing strategies. Transcriptomic analyses assessing factors critical for risk assessment (i.e. dose) are needed to determine the value of transcriptomic evaluation in the EST. Here, using the developmentally toxic compound, flusilazole, we investigated the effect of compound concentration on gene expression regulation and toxicity prediction in ESC differentiation cultures. Cultures were exposed for 24 h to multiple concentrations of flusilazole (0.54-54 μM) and RNA was isolated. In addition, we sampled control cultures 0, 24, and 48 h to evaluate the transcriptomic status of the cultures across differentiation. Transcriptomic profiling identified a higher sensitivity of development-related processes as compared to cell division-related processes in flusilazole-exposed differentiation cultures. Furthermore, the sterol synthesis-related mode of action of flusilazole toxicity was detected. Principal component analysis using gene sets related to normal ESC differentiation was used to describe the dynamics of ESC differentiation, defined as the 'differentiation track'. The concentration-dependent effects on development were reflected in the significance of deviation of flusilazole-exposed cultures from this transcriptomic-based differentiation track. Thus, the detection of developmental toxicity in EST using transcriptomics was shown to be compound concentration-dependent. This study provides further insight into the possible application of transcriptomics in the EST as an improved alternative model system for developmental toxicity testing.

Hematopoietic differentiation: a coordinated dynamical process towards attractor stable states

Background

The differentiation process, proceeding from stem cells towards the different committed cell types, can be considered as a trajectory towards an attractor of a dynamical process. This view, taking into consideration the transcriptome and miRNome dynamics considered as a whole, instead of looking at few 'master genes' driving the system, offers a novel perspective on this phenomenon. We investigated the 'differentiation trajectories' of the hematopoietic system considering a genome-wide scenario.

Results

We developed serum-free liquid suspension unilineage cultures of cord blood (CB) CD34⁺ hematopoietic progenitor cells through erythroid (E), megakaryocytic (MK), granulocytic (G) and monocytic (Mo) pathways. These cultures recapitulate physiological hematopoiesis, allowing the analysis of almost pure unilineage precursors starting from initial differentiation of HPCs until terminal maturation. By analyzing the expression profile of protein coding genes and microRNAs in unilineage CB E, MK, G and Mo cultures, at sequential stages of differentiation and maturation, we observed a coordinated, fully interconnected and scalable character of cell population behaviour in both transcriptome and miRNome spaces reminiscent of an attractor-like dynamics. MiRNome and transcriptome space differed for a still not terminally committed behaviour of microRNAs.

Conclusions

Consistent with their roles, the transcriptome system can be considered as the state space of a cell population, while the continuously evolving miRNA space corresponds to the tuning system necessary to reach the attractor. The behaviour of miRNA machinery could be of great relevance not only for the promise of reversing the differentiated state but even for tumor biology.

Zscan4 regulates telomere elongation and genomic stability in ES cells

Exceptional genomic stability is one of the hallmarks of mouse embryonic stem (ES) cells. However, the genes contributing to this stability remain obscure. We previously identified Zscan4 as a specific marker for 2-cell embryo and ES cells. Here we show that Zscan4 is involved in telomere maintenance and long-term-genomic stability in ES cells. Only 5% of ES cells express Zscan4 at a given time, but nearly all ES cells activate Zscan4 at least once within nine passages. The transient Zscan4-positive state is associated with rapid telomere extension by telomere recombination and upregulation of meiosis-specific homologous recombination genes, which encode proteins that are colocalized with ZSCAN4 on telomeres. Furthermore, Zscan4 knockdown shortens telomeres, increases karyotype abnormalities and spontaneous sister chromatid exchange, and slows down cell proliferation until reaching crisis by eight passages. Together, our data reveal a unique mode of genome maintenance in ES cells.

Gene expression profiling of mouse embryos with microarrays

Global expression profiling by DNA microarrays provides a snapshot of cell and tissue status and becomes an essential tool in biological and medical sciences. Typical questions that can be addressed by microarray analysis in developmental biology include: (1) to find a set of genes expressed in a specific cell type; (2) to identify genes expressed commonly in multiple cell types; (3) to follow the time-course changes of gene expression patterns; (4) to demonstrate cell's identity by showing similarities or differences among two or multiple cell types; (5) to find regulatory pathways and/or networks affected by gene manipulations, such as overexpression or repression of gene expression; (6) to find downstream target genes of transcription factors; (7) to find downstream target genes of cell signaling; (8) to examine the effects of environmental manipulation of cells on gene expression patterns; and (9) to find the effects of genetic manipulation in embryos and adults. Here, we describe strategies for executing these experiments and monitoring changes of cell state with gene expression microarrays in application to mouse embryology. Both statistical assessment and interpretation of data are discussed. We also present a protocol for performing microarray analysis on a small amount of embryonic materials.

Uncovering early response of gene regulatory networks in ESCs by systematic induction of transcription factors

To examine transcription factor (TF) network(s), we created mouse ESC lines, in each of which 1 of 50 TFs tagged with a FLAG moiety is inserted into a ubiquitously controllable tetracycline-repressible locus. Of the 50 TFs, Cdx2 provoked the most extensive transcriptome perturbation in ESCs, followed by Esx1, Sox9, Tcf3, Klf4, and Gata3. ChIP-Seq revealed that CDX2 binds to promoters of upregulated target genes. By contrast, genes downregulated by CDX2 did not show CDX2 binding but were enriched with binding sites for POU5F1, SOX2, and NANOG. Genes with binding sites for these core TFs were also downregulated by the induction of at least 15 other TFs, suggesting a common initial step for ESC differentiation mediated by interference with the binding of core TFs to their target genes. These ESC lines provide a fundamental resource to study biological networks in ESCs and mice.

Neural differentiation of embryonic stem cells in vitro: a road map to neurogenesis in the embryo

Background

The in vitro generation of neurons from embryonic stem (ES) cells is a promising approach to produce cells suitable for neural tissue repair and cell-based replacement therapies of the nervous system. Available methods to promote ES cell differentiation towards neural lineages attempt to replicate, in different ways, the multistep process of embryonic neural development. However, to achieve this aim in an efficient and reproducible way, a better knowledge of the cellular and molecular events that are involved in the process, from the initial specification of neuroepithelial progenitors to their terminal differentiation into neurons and glial cells, is required.

Methodology/Principal Findings

In this work, we characterize the main stages and transitions that occur when ES cells are driven into a neural fate, using an adherent monolayer culture system. We established improved conditions to routinely produce highly homogeneous cultures of neuroepithelial progenitors, which organize into neural tube-like rosettes when they acquire competence for neuronal production. Within rosettes, neuroepithelial progenitors display morphological and functional characteristics of their embryonic counterparts, namely, apico-basal polarity, active Notch signalling, and proper timing of production of neurons and glia. In order to characterize the global gene activity correlated with each particular stage of neural development, the full transcriptome of different cell populations that arise during the in vitro differentiation protocol was determined by microarray analysis. By using embryo-oriented criteria to cluster the differentially expressed genes, we define five gene expression signatures that correlate with successive stages in the path from ES cells to neurons. These include a gene signature for a primitive ectoderm-like stage that appears after ES cells enter differentiation, and three gene signatures for subsequent stages of neural progenitor development, from an early stage that follows neural induction to a final stage preceding terminal differentiation.

Conclusions/Significance

Overall, our work confirms and extends the cellular and molecular parallels between monolayer ES cell neural differentiation and embryonic neural development, revealing in addition novel aspects of the genetic network underlying the multistep process that leads from uncommitted cells to differentiated neurons.