Ben D. MacArthur

Mesenchymal and haematopoietic stem cells form a unique bone marrow niche

The cellular constituents forming the haematopoietic stem cell (HSC) niche in the bone marrow are unclear, with studies implicating osteoblasts, endothelial and perivascular cells. Here we demonstrate that mesenchymal stem cells (MSCs), identified using nestin expression, constitute an essential HSC niche component. Nestin+ MSCs contain all the bone-marrow colony-forming-unit fibroblastic activity and can be propagated as non-adherent ‘mesenspheres’ that can self-renew and expand in serial transplantations. Nestin+ MSCs are spatially associated with HSCs and adrenergic nerve fibres, and highly express HSC maintenance genes. These genes, and others triggering osteoblastic differentiation, are selectively downregulated during enforced HSC mobilization or β3 adrenoreceptor activation. Whereas parathormone administration doubles the number of bone marrow nestin+ cells and favours their osteoblastic differentiation, in vivo nestin+ cell depletion rapidly reduces HSC content in the bone marrow. Purified HSCs home near nestin+ MSCs in the bone marrow of lethally irradiated mice, whereas in vivo nestin+ cell depletion significantly reduces bone marrow homing of haematopoietic progenitors. These results uncover an unprecedented partnership between two distinct somatic stem-cell types and are indicative of a unique niche in the bone marrow made of heterotypic stem-cell pairs.

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 a more 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.

Statistical Mechanics of Pluripotency

Recent reports using single-cell profiling have indicated a remarkably dynamic view of pluripotent stem cell identity. Here, we argue that the pluripotent state is not well defined at the single-cell level but rather is a statistical property of stem cell populations, amenable to analysis using the tools of statistical mechanics and information theory.

Single-Cell Analyses of ESCs Reveal Alternative Pluripotent Cell States and Molecular Mechanisms that Control Self-Renewal

Summary Analyses of gene expression in single mouse embryonic stem cells (mESCs) cultured in serum and LIF revealed the presence of two distinct cell subpopulations with individual gene expression signatures. Comparisons with published data revealed that cells in the first subpopulation are phenotypically similar to cells isolated from the inner cell mass (ICM). In contrast, cells in the second subpopulation appear to be more mature. Pluripotency Gene Regulatory Network (PGRN) reconstruction based on single-cell data and published data suggested antagonistic roles for Oct4 and Nanog in the maintenance of pluripotency states. Integrated analyses of published genomic binding (ChIP) data strongly supported this observation. Certain target genes alternatively regulated by OCT4 and NANOG, such as Sall4 and Zscan10, feed back into the top hierarchical regulator Oct4. Analyses of such incoherent feedforward loops with feedback (iFFL-FB) suggest a dynamic model for the maintenance of mESC pluripotency and self-renewal.

GATE: Software for the Analysis and Visualization of High- Dimensional Time-series Expression Data

SUMMARY We present Grid Analysis of Time series Expression (GATE), an integrated computational software platform for the analysis and visualization of high-dimensional biomolecular time series. GATE uses a correlation-based clustering algorithm to arrange molecular time series on a two-dimensional hexagonal array and dynamically colors individual hexagons according to the expression level of the molecular component to which they are assigned, to create animated movies of systems-level molecular regulatory dynamics. In order to infer potential regulatory control mechanisms from patterns of correlation, GATE also allows interactive interroga-tion of movies against a wide variety of prior knowledge datasets. GATE movies can be paused and are interactive, allowing users to reconstruct networks and perform functional enrichment analyses. Movies created with GATE can be saved in Flash format and can be inserted directly into PDF manuscript files as interactive figures. AVAILABILITY GATE is available for download and is free for academic use from http://amp.pharm.mssm.edu/maayan-lab/gate.htm

Toward stem cell systems biology: from molecules to networks and landscapes.

The last few years have seen significant advances in our understanding of the molecular mechanisms of stem-cell-fate specification. New and emerging high-throughput techniques, as well as increasingly accurate loss-of-function perturbation techniques, are allowing us to dissect the interplay among genetic, epigenetic, proteomic, and signaling mechanisms in stem-cell-fate determination with ever-increasing fidelity (Boyer et al. 2005, 2006; Ivanova et al. 2006; Loh et al. 2006; Cole et al. 2008; Jiang et al. 2008; Johnson et al. 2008; Kim et al. 2008; Liu et al. 2008; Marson et al. 2008; Mathur et al. 2008). Taken together, recent reports using these new techniques demonstrate that stem-cell-fate specification is an extremely complex process, regulated by multiple mutually interacting molecular mechanisms involving multiple regulatory feedback loops. Given this complexity and the sensitive dependence of stem cell differentiation on signaling cues from the extracellular environment, how are we best to develop a coherent quantitative understanding of stem cell fate at the systems level? One approach that we and other researchers have begun to investigate is the application of techniques derived in the computational disciplines (mathematics, physics, computer science, etc.) to problems in stem cell biology. Here, we briefly sketch a few pertinent results from the literature in this area and discuss future potential applications of computational techniques to stem cell systems biology.

Spectral characteristics of network redundancy

Many real-world complex networks contain a significant amount of structural redundancy, in which multiple vertices play identical topological roles. Such redundancy arises naturally from the simple growth processes which form and shape many real-world systems. Since structurally redundant elements may be permuted without altering network structure, redundancy may be formally investigated by examining network automorphism (symmetry) groups. Here, we use a group-theoretic approach to give a complete description of spectral signatures of redundancy in undirected networks. In particular, we describe how a networks automorphism group may be used to directly associate specific eigenvalues and eigenvectors with specific network motifs.

Microdynamics and Criticality of Adaptive Regulatory Networks

We present a model of adaptive regulatory networks consisting of a simple biologically motivated rewiring procedure coupled to an elementary stability criterion. The resulting networks exhibit a characteristic stationary heavy-tailed degree distribution, show complex structural microdynamics, and self-organize to a dynamically critical state. We show analytically that the observed criticality results from the formation and breaking of transient feedback loops during the adaptive process.

Entropy, Ergodicity, and Stem Cell Multipotency.

Populations of mammalian stem cells commonly exhibit considerable cell-cell variability. However, the functional role of this diversity is unclear. Here, we analyze expression fluctuations of the stem cell surface marker Sca1 in mouse hematopoietic progenitor cells using a simple stochastic model and find that the observed dynamics naturally lie close to a critical state, thereby producing a diverse population that is able to respond rapidly to environmental changes. We propose an information-theoretic interpretation of these results that views cellular multipotency as an instance of maximum entropy statistical inference.

Identification of candidate regulators of multipotency in human skeletal progenitor cells

Stem cell differentiation is controlled intrinsically by dynamic networks of interacting lineage-specifying and multipotency genes. However, the relationship between internal genetic dynamics and extrinsic regulation of internal dynamics is complex and, in the case of skeletal progenitor cell differentiation, incompletely understood. In this study we elucidate a set of candidate markers of multipotency in human skeletal progenitor cells by systematic study of the relationships between gene expression and environmental stimulus. We used full genome cDNA microarrays to explore gene expression profiles in skeletal progenitor enriched populations derived from adult human bone marrow, minimally cultured in basal, osteogenic, chondrogenic, and adipogenic lineage-specifying culture conditions. We then used a variety of statistical clustering procedures to identify a small subset of genes which are related to these stromal lineages but are specific to none. For a selection of 11 key genes, conclusions of the microarray study were confirmed using quantitative real-time PCR.