Nanog-dependent feedback loops regulate murine embryonic stem cell heterogeneity

Nanog-independent reprogramming to iPSCs with canonical factors

It has been suggested that the transcription factor Nanog is essential for the establishment of pluripotency during the derivation of embryonic stem cells and induced pluripotent stem cells (iPSCs). However, successful reprogramming to pluripotency with a growing list of divergent transcription factors, at ever-increasing efficiencies, suggests that there may be many distinct routes to a pluripotent state. Here, we have investigated whether Nanog is necessary for reprogramming murine fibroblasts under highly efficient conditions using the canonical-reprogramming factors Oct4, Sox2, Klf4, and cMyc. In agreement with prior results, the efficiency of reprogramming Nanog (-/-) fibroblasts was significantly lower than that of control fibroblasts. However, in contrast to previous findings, we were able to reproducibly generate iPSCs from Nanog (-/-) fibroblasts that effectively contributed to the germline of chimeric mice. Thus, whereas Nanog may be an important mediator of reprogramming, it is not required for establishing pluripotency in the mouse, even under standard conditions.

Transcriptional mechanisms of cell fate decisions revealed by single cell expression profiling

Transcriptional networks regulate cell fate decisions, which occur at the level of individual cells. However, much of what we know about their structure and function comes from studies averaging measurements over large populations of cells, many of which are functionally heterogeneous. Such studies conceal the variability between cells and so prevent us from determining the nature of heterogeneity at the molecular level. In recent years, many protocols and platforms have been developed that allow the high throughput analysis of gene expression in single cells, opening the door to a new era of biology. Here, we discuss the need for single cell gene expression analysis to gain deeper insights into the transcriptional control of cell fate decisions, and consider the insights it has provided so far into transcriptional regulatory networks in development.

Heterogeneity of the level of activity of lgr5+ intestinal stem cells

Intestinal stem cells (ISCs) are a group of rare cells located in the intestinal crypts which are responsible for the maintenance of the intestinal epithelial homeostasis and regeneration following injury or inflammation. Lineage tracing experiments in mice have proven that ISCs can repopulate the entire intestinal crypt. It is noteworthy that in such experiments, only a subset of intestinal crypts is marked by the specific marker. This is suggestive of different levels of activity of stem cells in different crypts i.e. intracryptal variation. Niche succession i.e. dominating the entire crypt by the progenies of one stem cell is also suggestive of the intercryptal stem cell heterogeneity. Regional differences in crypt size, proliferative index, and distribution of proliferative cells along the crypt axis have been reported. It is conceivable that ISCs are heterogeneous in terms of their levels of activity. Appreciation of such heterogeneity will significantly challenge the way in which ISCs are investigated. A better understanding of ISC biology will in turn improve our mechanistic understanding of major intestinal disease including inflammatory bowel disease and colorectal cancer.

PhysioSpace: relating gene expression experiments from heterogeneous sources using shared physiological processes

Relating expression signatures from different sources such as cell lines, in vitro cultures from primary cells and biopsy material is an important task in drug development and translational medicine as well as for tracking of cell fate and disease progression. Especially the comparison of large scale gene expression changes to tissue or cell type specific signatures is of high interest for the tracking of cell fate in (trans-) differentiation experiments and for cancer research, which increasingly focuses on shared processes and the involvement of the microenvironment. These signature relation approaches require robust statistical methods to account for the high biological heterogeneity in clinical data and must cope with small sample sizes in lab experiments and common patterns of co-expression in ubiquitous cellular processes. We describe a novel method, called PhysioSpace, to position dynamics of time series data derived from cellular differentiation and disease progression in a genome-wide expression space. The PhysioSpace is defined by a compendium of publicly available gene expression signatures representing a large set of biological phenotypes. The mapping of gene expression changes onto the PhysioSpace leads to a robust ranking of physiologically relevant signatures, as rigorously evaluated via sample-label permutations. A spherical transformation of the data improves the performance, leading to stable results even in case of small sample sizes. Using PhysioSpace with clinical cancer datasets reveals that such data exhibits large heterogeneity in the number of significant signature associations. This behavior was closely associated with the classification endpoint and cancer type under consideration, indicating shared biological functionalities in disease associated processes. Even though the time series data of cell line differentiation exhibited responses in larger clusters covering several biologically related patterns, top scoring patterns were highly consistent with a priory known biological information and separated from the rest of response patterns.

Deconstructing stem cell population heterogeneity: single-cell analysis and modeling approaches

Isogenic stem cell populations display cell-to-cell variations in a multitude of attributes including gene or protein expression, epigenetic state, morphology, proliferation and proclivity for differentiation. The origins of the observed heterogeneity and its roles in the maintenance of pluripotency and the lineage specification of stem cells remain unclear. Addressing pertinent questions will require the employment of single-cell analysis methods as traditional cell biochemical and biomolecular assays yield mostly population-average data. In addition to time-lapse microscopy and flow cytometry, recent advances in single-cell genomic, transcriptomic and proteomic profiling are reviewed. The application of multiple displacement amplification, next generation sequencing, mass cytometry and spectrometry to stem cell systems is expected to provide a wealth of information affording unprecedented levels of multiparametric characterization of cell ensembles under defined conditions promoting pluripotency or commitment. Establishing connections between single-cell analysis information and the observed phenotypes will also require suitable mathematical models. Stem cell self-renewal and differentiation are orchestrated by the coordinated regulation of subcellular, intercellular and niche-wide processes spanning multiple time scales. Here, we discuss different modeling approaches and challenges arising from their application to stem cell populations. Integrating single-cell analysis with computational methods will fill gaps in our knowledge about the functions of heterogeneity in stem cell physiology. This combination will also aid the rational design of efficient differentiation and reprogramming strategies as well as bioprocesses for the production of clinically valuable stem cell derivatives.

From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of “supercell statistics”, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet's disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet's disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8⁺ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8⁺ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.

The behavior of organisms is based on the concerted action occurring on an astonishing range of scales from the molecular to the organismal level. Molecular properties control the function of a cell, while cell ensembles form tissues and organs, which work together as an organism. In order to understand and characterize the molecular nature of the emergent properties of a cell, it is essential that multiple components of the cell are measured simultaneously in the same cell. Similarly, multiple cells must be measured in order to understand health and disease in the organism. In this work, we develop an approach that is able to determine how many cells, how many measurements per cell, and which measurements are needed to reliably diagnose disease. We apply this method to two different problems: the diagnosis of a premature aging disorder using images of cell nuclei, and the distinction between two similar autoimmune eye diseases using stained cells from patients' blood samples. Our findings shed new light on the role of specific kinds of immune system cells in systemic inflammatory diseases and may lead to improved diagnosis and treatment.

Targeting cancer stem cells: emerging role of Nanog transcription factor

The involvement of stemness factors in cancer initiation and progression has drawn much attention recently, especially after the finding that introducing four stemness factors in somatic cells is able to reprogram the cells back to an embryonic stem cell-like state. Following accumulating data revealing abnormal elevated expression levels of key stemness factors, like Nanog, Oct4, and Sox2, in several types of cancer stem cells; the importance and therapeutic potential of targeting these stemness regulators in cancers has turned to research focus. Nanog determines cell fate in both embryonic and cancer stem cells; activating Nanog at an inappropriate time would result in cancer stem cells rather than normal pluripotent stem cells or differentiated somatic cells. Upregulated Nanog is correlated with poor survival outcome of patients with various types of cancer. The discoveries of downstream regulatory pathways directly or indirectly mediated by Nanog indicate that Nanog regulates several aspects of cancer development such as tumor cell proliferation, self-renewal, motility, epithelial-mesenchymal transition, immune evasion, and drug-resistance, which are all defined features for cancer stem cells. The current review paper illustrates the central role of Nanog in the regulatory networks of cancer malignant development and stemness acquirement, as well as in the communication between cancer cells and the surrounding stroma. Though a more defined model is needed to test the therapeutic efficacy of targeting Nanog as a cancer treatment method, current animal experiments using siNanog or shNanog have shown the promising therapeutic potential of Nanog targeting in several types of cancer.

A molecular basis for developmental plasticity in early mammalian embryos

Early mammalian embryos exhibit remarkable plasticity, as highlighted by the ability of separated early blastomeres to produce a whole organism. Recent work in the mouse implicates a network of transcription factors in governing the establishment of the primary embryonic lineages. A combination of genetics and embryology has uncovered the organisation and function of the components of this network, revealing a gradual resolution from ubiquitous to lineage-specific expression through a combination of defined regulatory relationships, spatially organised signalling, and biases from mechanical inputs. Here, we summarise this information, link it to classical embryology and propose a molecular framework for the establishment and regulation of developmental plasticity.

Transcriptional regulation of lineage commitment--a stochastic model of cell fate decisions

Molecular mechanisms employed by individual multipotent cells at the point of lineage commitment remain largely uncharacterized. Current paradigms span from instructive to noise-driven mechanisms. Of considerable interest is also whether commitment involves a limited set of genes or the entire transcriptional program, and to what extent gene expression configures multiple trajectories into commitment. Importantly, the transient nature of the commitment transition confounds the experimental capture of committing cells. We develop a computational framework that simulates stochastic commitment events, and affords mechanistic exploration of the fate transition. We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. We define putative regulators of commitment and probabilistic rules of transition through machine learning methods, and employ clustering and correlation analyses to interrogate gene regulatory interactions in multipotent cells. Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. Monte Carlo time is converted to physical time using cell culture kinetic data. Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. Our approach should be applicable to similar differentiating systems where single cell data is available. Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to molecular scenarios of entry into commitment. The model suggests distinct dependencies of different commitment-associated genes on mRNA dynamics and promoter activity, which globally influence the probability of lineage commitment.

Single cell lineage analysis of mouse embryonic stem cells at the exit from pluripotency

Understanding how interactions between extracellular signalling pathways and transcription factor networks influence cellular decision making will be crucial for understanding mammalian embryogenesis and for generating specialised cell types in vitro. To this end, pluripotent mouse Embryonic Stem (mES) cells have proven to be a useful model system. However, understanding how transcription factors and signalling pathways affect decisions made by individual cells is confounded by the fact that measurements are generally made on groups of cells, whilst individual mES cells differentiate at different rates and towards different lineages, even in conditions that favour a particular lineage. Here we have used single-cell measurements of transcription factor expression and Wnt/β-catenin signalling activity to investigate their effects on lineage commitment decisions made by individual cells. We find that pluripotent mES cells exhibit differing degrees of heterogeneity in their expression of important regulators from pluripotency, depending on the signalling environment to which they are exposed. As mES cells differentiate, downregulation of Nanog and Oct4 primes cells for neural commitment, whilst loss of Sox2 expression primes cells for primitive streak commitment. Furthermore, we find that Wnt signalling acts through Nanog to direct cells towards a primitive streak fate, but that transcriptionally active β-catenin is associated with both neural and primitive streak commitment. These observations confirm and extend previous suggestions that pluripotency genes influence lineage commitment and demonstrate how their dynamic expression affects the direction of lineage commitment, whilst illustrating two ways in which the Wnt signalling pathway acts on this network during cell fate assignment.

Distinct allelic patterns of nanog expression impart embryonic stem cell population heterogeneity

Nanog is a principal pluripotency regulator exhibiting a disperse distribution within stem cell populations in vivo and in vitro. Increasing evidence points to a functional role of Nanog heterogeneity on stem cell fate decisions. Allelic control of Nanog gene expression was reported recently in mouse embryonic stem cells. To better understand how this mode of regulation influences the observed heterogeneity of NANOG in stem cell populations, we assembled a multiscale stochastic population balance equation framework. In addition to allelic control, gene expression noise and random partitioning at cell division were considered. As a result of allelic Nanog expression, the distribution of Nanog exhibited three distinct states but when combined with transcriptional noise the profile became bimodal. Regardless of their allelic expression pattern, initially uniform populations of stem cells gave rise to the same Nanog heterogeneity within ten cell cycles. Depletion of NANOG content in cells switching off both gene alleles was slower than the accumulation of intracellular NANOG after cells turned on at least one of their Nanog gene copies pointing to Nanog state-dependent dynamics. Allelic transcription of Nanog also raises issues regarding the use of stem cell lines with reporter genes knocked in a single allelic locus. Indeed, significant divergence was observed in the reporter and native protein profiles depending on the difference in their half-lives and insertion of the reporter gene in one or both alleles. In stem cell populations with restricted Nanog expression, allelic regulation facilitates the maintenance of fractions of self-renewing cells with sufficient Nanog content to prevent aberrant loss of pluripotency. Our findings underline the role of allelic control of Nanog expression as a prime determinant of stem cell population heterogeneity and warrant further investigation in the contexts of stem cell specification and cell reprogramming.

Nanog is a key factor influencing the decision of a stem cell to remain pluripotent or differentiate. Each embryonic stem cell (ESC) in a population exhibits fluctuating Nanog levels resulting in heterogeneity which affects cell fate specification. The allelic regulation of Nanog was demonstrated recently but its implications on population heterogeneity are unclear. We developed a multiscale population balance equation (PBE) model and compared our results with pertinent experimental studies. Under allelic control the profile of Nanog features three peaks or distinct states. Transcriptional noise causes the distribution to become bimodal as suggested previously. When stem cells carrying a reporter transgene in an allelically regulated locus were examined, we observed non-matching distributions of the endogenous and reporter proteins. This led us to investigate the performance of reporter systems depending on insertion of the transgene in one or both alleles and the protein degradation dynamics. Lastly, our model was employed to address how allelic regulation affects the maintenance of pluripotency in stem cells with a single Nanog allele deletion. A fraction of these cells remains pluripotent while deletion of a single allele does not simply reduce NANOG uniformly for all ESCs but modulates NANOG heterogeneity directly.

Single-cell analysis reveals that expression of nanog is biallelic and equally variable as that of other pluripotency factors in mouse ESCs

The homeodomain transcription factor Nanog is a central part of the core pluripotency transcriptional network and plays a critical role in embryonic stem cell (ESC) self-renewal. Several reports have suggested that Nanog expression is allelically regulated and that transient downregulation of Nanog in a subset of pluripotent cells predisposes them toward differentiation. Using single-cell gene expression analyses combined with different reporters for the two alleles of Nanog, we show that Nanog is biallelically expressed in ESCs independently of culture condition. We also show that the overall variation in endogenous Nanog expression in ESCs is very similar to that of several other pluripotency markers. Our analysis suggests that reporter-based studies of gene expression in pluripotent cells can be significantly influenced by the gene-targeting strategy and genetic background employed.

Next-generation qPCR for the high-throughput measurement of gene expression in multiple leukocyte subsets

Clinical studies of gene expression are increasingly using the whole blood, peripheral blood mononuclear cells, and leukocyte subsets involved in the innate and adaptive immune responses. However, the small amount of RNA available in the clinical setting is a limitation for commonly used methods such as quantitative polymerase chain reactions (qPCR) and microarrays. Our aim was to design 96 gene assays to simultaneously measure gene expression in the whole blood and seven leukocyte subsets using a new-generation qPCR method--high-throughput nanofluidic reverse transcription qPCR (HT RT-qPCR). The leukocyte subset purity was 94% to 98% for seven subsets and was less for the γδ T-cell receptor subset (80%). The HT RT-qPCR replicate sample measurements were highly reproducible (r = 0.997, p < 2.2 × 10(-16)), and the ΔΔCt values from HT RT-qPCR correlated significantly with those from qPCR. The control genes were differentially expressed across the eight leukocyte subsets in the control subjects (p = 1.3 × 10(-5), analysis of variance). Two analytical methods, absolute and relative, gave concordant results and were significantly correlated (p = 1.9 × 10(-9)). HT RT-qPCR permits the rapid, reproducible, and quantitative measurement of multiple transcripts using minimal sample amounts. The protocol described yielded leukocyte subsets of high purity and identified two analytic methods for use.

Origins and implications of pluripotent stem cell variability and heterogeneity

Pluripotent stem cells constitute a platform to model disease and developmental processes and can potentially be used in regenerative medicine. However, not all pluripotent cell lines are equal in their capacity to differentiate into desired cell types in vitro. Genetic and epigenetic variations contribute to functional variability between cell lines and heterogeneity within clones. These genetic and epigenetic variations could 'lock' the pluripotency network resulting in residual pluripotent cells or alter the signalling response of developmental pathways leading to lineage bias. The molecular contributors to functional variability and heterogeneity in both embryonic stem (ES) cells and induced pluripotent stem (iPS) cells are only beginning to emerge, yet they are crucial to the future of the stem cell field.

Standardization of human stem cell pluripotency using bioinformatics

The study of cell differentiation, embryonic development, and personalized regenerative medicine are all possible through the use of human stem cells. The propensity for these cells to differentiate into all three germ layers of the body with the potential to generate any cell type opens a number of promising avenues for studying human development and disease. One major hurdle to the development of high-throughput production of human stem cells for use in regenerative medicine has been standardization of pluripotency assays. In this review we discuss technologies currently being deployed to produce standardized, high-quality stem cells that can be scaled for high-throughput derivation and screening in regenerative medicine applications. We focus on assays for pluripotency using bioinformatics and gene expression profiling. We review a number of approaches that promise to improve unbiased prediction of utility of both human induced pluripotent stem cells and embryonic stem cells.

OCT4/SOX2-independent Nanog autorepression modulates heterogeneous Nanog gene expression in mouse ES cells

NANOG, OCT4 and SOX2 form the core network of transcription factors supporting embryonic stem (ES) cell self-renewal. While OCT4 and SOX2 expression is relatively uniform, ES cells fluctuate between states of high NANOG expression possessing high self-renewal efficiency, and low NANOG expression exhibiting increased differentiation propensity. NANOG, OCT4 and SOX2 are currently considered to activate transcription of each of the three genes, an architecture that cannot readily account for NANOG heterogeneity. Here, we examine the architecture of the Nanog-centred network using inducible NANOG gain- and loss-of-function approaches. Rather than activating itself, Nanog activity is autorepressive and OCT4/SOX2-independent. Moreover, the influence of Nanog on Oct4 and Sox2 expression is minimal. Using Nanog:GFP reporters, we show that Nanog autorepression is a major regulator of Nanog transcription switching. We conclude that the architecture of the pluripotency gene regulatory network encodes the capacity to generate reversible states of Nanog transcription via a Nanog-centred autorepressive loop. Therefore, cellular variability in self-renewal efficiency is an emergent property of the pluripotency gene regulatory network.