Hierarchical clustering of ECM and growth factor genes.

<p>Gene expression fold changes compared to undifferentiated ESCs were log2-transformed and analyzed using hierarchical clustering on Genesis software. Of the 84 extracellular matrix genes analyzed (A), approximately 34% of the genes demonstrated either an overall decrease (green) or increase (red) in gene expression relative to undifferentiated ESCs, whereas approximately 60% of the 84 growth factor genes (B) resulted in overall expression changes relative to ESCs. Black bars highlight noticeable gene clusters in each array set.</p

Gene Expression Signatures of Extracellular Matrix and Growth Factors during Embryonic Stem Cell Differentiation

<div><p>Pluripotent stem cells are uniquely capable of differentiating into somatic cell derivatives of all three germ lineages, therefore holding tremendous promise for developmental biology studies and regenerative medicine therapies. Although temporal patterns of phenotypic gene expression have been relatively well characterized during the course of differentiation, coincident patterns of endogenous extracellular matrix (ECM) and growth factor expression that accompany pluripotent stem cell differentiation remain much less well-defined. Thus, the objective of this study was to examine the global dynamic profiles of ECM and growth factor genes associated with early stages of pluripotent mouse embryonic stem cell (ESC) differentiation. Gene expression analysis of ECM and growth factors by ESCs differentiating as embryoid bodies for up to 14 days was assessed using PCR arrays (172 unique genes total), and the results were examined using a variety of data mining methods. As expected, decreases in the expression of genes regulating pluripotent stem cell fate preceded subsequent increases in morphogen expression associated with differentiation. Pathway analysis generated solely from ECM and growth factor gene expression highlighted morphogenic cell processes within the embryoid bodies, such as cell growth, migration, and intercellular signaling, that are required for primitive tissue and organ developmental events. In addition, systems analysis of ECM and growth factor gene expression alone identified intracellular molecules and signaling pathways involved in the progression of pluripotent stem cell differentiation that were not contained within the array data set. Overall, these studies represent a novel framework to dissect the complex, dynamic nature of the extracellular biochemical milieu of stem cell microenvironments that regulate pluripotent cell fate decisions and morphogenesis.</p> </div

Embryoid body morphology and differentiation.

<p>(A–D) EBs cultured using rotary orbital culture maintain morphological homogeneity and increase in size over the course of differentiation. (E) Gene expression of the pluripotent marker <i>Nanog</i>, relative to ESC levels, decreases as EB differentiation proceeds. (F) Conversely, gene expression of α<i>-fetoprotein</i> (<i>Afp</i>), a marker of endoderm differentiation, increases significantly by day 14. * ANOVA: p<0.05 compared to all other time points.</p

K-means clustering of combined array sets.

<p>(A) The hierarchical cluster produced by merging data from both the ECM and growth factor arrays exhibits several identifiable clusters, indicated by groups I–V. (B–M) K-means clustering of the combined data set highlights the subtle temporal changes in gene expression while clustering genes that follow similar patterns of expression over time. In the k-means graphs, the x-axis at zero represents the undifferentiated ESC baseline. The centroid of each cluster is indicated by a black line (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042580#pone-0042580-g003" target="_blank">Figure 3B–M</a>), while colored lines in each panel correspond to genes in the color-coded groups (I–V) established by hierarchical clustering (A).</p

Aggregate modeling methodology.

<p>Dissociated mouse embryonic stem cells (mESCs) were analyzed via Coulter counter for surface area (A) and radius (B). <i>In silico</i> aggregates were generated in a physical modeling process in which cells were generated and then forcibly aggregated (C). Embryoid bodies (EBs) were formed via ultra-high throughput methods for two initial cell numbers: 250 and 1000 - a representative image for 1000 cell EB is shown (D). The black box in first column is digitally enlarged in the second column. Circularity was calculated by fitting the EB to an ellipse and taking the ratio of the two radii labeled R1 and R2 respectively. EBs were analyzed for two macro scale aggregate properties: radius (E) and circularity (F). Confocal images were used to analyze local aggregate properties –a representative 1000 cell EB image is shown (G). Aggregate local properties were assessed by two metrics: connection length (H) and number of connections (I).</p

Spatial Pattern Dynamics of 3D Stem Cell Loss of Pluripotency via Rules-Based Computational Modeling

<div><p>Pluripotent embryonic stem cells (ESCs) have the unique ability to differentiate into cells from all germ lineages, making them a potentially robust cell source for regenerative medicine therapies, but difficulties in predicting and controlling ESC differentiation currently limit the development of therapies and applications from such cells. A common approach to induce the differentiation of ESCs <i>in vitro</i> is via the formation of multicellular aggregates known as embryoid bodies (EBs), yet cell fate specification within EBs is generally considered an ill-defined and poorly controlled process. Thus, the objective of this study was to use rules-based cellular modeling to provide insight into which processes influence initial cell fate transitions in 3-dimensional microenvironments. Mouse embryonic stem cells (D3 cell line) were differentiated to examine the temporal and spatial patterns associated with loss of pluripotency as measured through Oct4 expression. Global properties of the multicellular aggregates were accurately recapitulated by a physics-based aggregation simulation when compared to experimentally measured physical parameters of EBs. Oct4 expression patterns were analyzed by confocal microscopy over time and compared to simulated trajectories of EB patterns. The simulations demonstrated that loss of Oct4 can be modeled as a binary process, and that associated patterns can be explained by a set of simple rules that combine baseline stochasticity with intercellular communication. Competing influences between Oct4+ and Oct4− neighbors result in the observed patterns of pluripotency loss within EBs, establishing the utility of rules-based modeling for hypothesis generation of underlying ESC differentiation processes. Importantly, the results indicate that the rules dominate the emergence of patterns independent of EB structure, size, or cell division. In combination with strategies to engineer cellular microenvironments, this type of modeling approach is a powerful tool to predict stem cell behavior under a number of culture conditions that emulate characteristics of 3D stem cell niches.</p> </div

Parallel ANOVA analysis.

<p>(A) ANOVAs were performed across each time point for individual genes from both ECM and growth factor arrays. Results are depicted using a tree schematic, where the slope of the line between each time point indicates a significant increase (positive slope), significant decrease (negative slope), or no significance (zero slope). The number of genes in individual categories is shown on each line. A majority of the genes analyzed do not change significantly by 14 days of EB culture; however, a large number of genes significantly increase in expression between days 10 and 14. (B–F) Each graph highlights individual profiles of statistical changes in gene expression, including the number of genes encompassed in the respective profile and a sample of specific genes.</p

Biological functions related to global gene expression changes.

<p>Graphs of biological functions were generated using “focus” genes and the database of gene interactions present within Ingenuity Pathway Analysis software presented according to day 4 EB ranking results. (A) The ten highest-ranked biological functions that were significant at different differentiation time points consisted of processes typically related to development, including morphogenesis and proliferation. (B) In contrast, the ten lowest-ranked biological processes included disease states not typically associated with development.</p

Transition times for differentiation patterns within EBs vary as a function of size.

<p>Confocal images of EBs stained with DAPI (blue), phalloidin (red), and Oct4 (green) shown at a depth of 25 µm for EBs of (A) 250 and (B) 1000 cells. (C, E) Temporal dynamics of observed patterns for 250 cell EBs (C) and 1000 cell EBs (E). (D, F) the overall distribution of observed patterns for 250 cell EBs over 6 days in culture (D) and 1000 cell EBs over 7 days in culture (F). Scale bars on all images are 25 µm.</p

Classification of differentiation spatial patterns within EBs.

<p>Six classifications are used to described Oct4 expression patterns: undifferentiated, inside-out, outside-in, connected, random, and differentiated. Confocal images shown on top are stained with DAPI (blue), phalloidin (red) and Oct4 (green) (with a scale bar of 25 µm). The model generates patterns similar to confocal images with Oct4− cells (dark blue) and Oct4+ cells (cyan). Scale bar represents 25 µm.</p