Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders.

Positional information encoded in the dynamic differences between neighboring oscillators during vertebrate segmentation

A central problem in developmental biology is to understand how cells interpret their positional information to give rise to spatial patterns, such as the process of periodic segmentation of the vertebrate embryo into somites. For decades, somite formation has been interpreted according to the clock-and-wavefront model. In this conceptual framework, molecular oscillators set the frequency of somite formation while the positional information is encoded in signaling gradients. Recent experiments using ex vivo explants have challenged this interpretation, suggesting that positional information is encoded in the properties of the oscillators, independent of long-range modulations such as signaling gradients. Here, we propose that positional information is encoded in the difference in the levels of neighboring oscillators. The differences gradually increase because both the amplitude and the period of the oscillators increase with time. When this difference exceeds a certain threshold, the segmentation program starts. Using this framework, we quantitatively fit experimental data from in vivo and ex vivo mouse segmentation, and propose mechanisms of somite scaling. Our results suggest a novel mechanism of spatial pattern formation based on the local interactions between dynamic molecular oscillators.

Id4 Downstream of Notch2 Maintains Neural Stem Cell Quiescence in the Adult Hippocampus

Neural stem cells (NSCs) in the adult mouse hippocampal dentate gyrus (DG) are mostly quiescent, and only a few are in cell cycle at any point in time. DG NSCs become increasingly dormant with age and enter mitosis less frequently, which impinges on neurogenesis. How NSC inactivity is maintained is largely unknown. Here, we found that Id4 is a downstream target of Notch2 signaling and maintains DG NSC quiescence by blocking cell-cycle entry. Id4 expression is sufficient to promote DG NSC quiescence and Id4 knockdown rescues Notch2-induced inhibition of NSC proliferation. Id4 deletion activates NSC proliferation in the DG without evoking neuron generation, and overexpression increases NSC maintenance while promoting astrogliogenesis at the expense of neurogenesis. Together, our findings indicate that Id4 is a major effector of Notch2 signaling in NSCs and a Notch2-Id4 axis promotes NSC quiescence in the adult DG, uncoupling NSC activation from neuronal differentiation.

A Shh/Gli-driven three-node timer motif controls temporal identity and fate of neural stem cells

How time is measured by neural stem cells during temporal neurogenesis has remained unresolved. By combining experiments and computational modeling, we define a Shh/Gli-driven three-node timer underlying the sequential generation of motor neurons (MNs) and serotonergic neurons in the brainstem. The timer is founded on temporal decline of Gli-activator and Gli-repressor activities established through down-regulation of Gli transcription. The circuitry conforms an incoherent feed-forward loop, whereby Gli proteins not only promote expression of Phox2b and thereby MN-fate but also account for a delayed activation of a self-promoting transforming growth factor-β (Tgfβ) node triggering a fate switch by repressing Phox2b. Hysteresis and spatial averaging by diffusion of Tgfβ counteract noise and increase temporal accuracy at the population level, providing a functional rationale for the intrinsically programmed activation of extrinsic switch signals in temporal patterning. Our study defines how time is reliably encoded during the sequential specification of neurons.

Patterning via local cell-cell interactions in developing systems

Spatial patterning during embryonic development emerges from the differentiation of progenitor cells that share the same genetic program. One of the main challenges in systems biology is to understand the relationship between gene network and patterning, especially how the cells communicate to coordinate their differentiation. This review aims to describe the principles of pattern formation from local cell-cell interactions mediated by the Notch signalling pathway. Notch mediates signalling via direct cell-cell contact and regulates cell fate decisions in many tissues during embryonic development. Here, I will describe the patterning mechanisms via different Notch ligands and the critical role of Notch oscillations during the segmentation of the vertebrate body, brain development, and blood vessel formation.

Differential Expression Analysis in RNA-seq Data Using a Geometric Approach

Although differential gene expression (DGE) profiling in RNA-seq is used by many researchers, new packages and pipelines are continuously being presented as a result of an ongoing investigation. In this work, a geometric approach based on Supervised Variational Relevance Learning (Suvrel) was compared with DEpackages (edgeR, DESEq, baySeq, PoissonSeq, and limma) in the DGE profiling. The Suvrel method seeks to determine the relevance of characteristics (e.g., gene or transcript) based on intraclass and interclass distances. The comparison was performed using technical and biological replicates. For technical replicates, we used receiver operating characteristic (ROC) analysis, while for the other ones, we used robustness analysis. From ROC analysis, we found that geometric approach had a better performance than the DEpackages. Particularly, for a reduced list of differentially expressed genes (DEG), we noticed that this method had a remarkable advantage in ranking of most DEG (with a specificity ranging from 1 to 0.8). From robustness analysis associated to biological replicates, we found that geometric approach has comparable performance to the DEpackages. We conclude that the geometric approach had a slight overall better performance than the other methods. Moreover, it is a simple method that does not make any assumption about the distribution associated with RNA-seq data set. From this perspective, the relevance of this study was to show that a simple method can provide as good performance as more complex methods.

Travelling waves in somitogenesis: Collective cellular properties emerge from time-delayed juxtacrine oscillation coupling

The sculpturing of the vertebrate body plan into segments begins with the sequential formation of somites in the presomitic mesoderm (PSM). The rhythmicity of this process is controlled by travelling waves of gene expression. These kinetic waves emerge from coupled cellular oscillators and sweep across the PSM. In zebrafish, the oscillations are driven by autorepression of her genes and are synchronized via Notch signalling. Mathematical modelling has played an important role in explaining how collective properties emerge from the molecular interactions. Increasingly more quantitative experimental data permits the validation of those mathematical models, yet leads to increasingly more complex model formulations that hamper an intuitive understanding of the underlying mechanisms. Here, we review previous efforts, and design a mechanistic model of the her1 oscillator, which represents the experimentally viable her7;hes6 double mutant. This genetically simplified system is ideally suited to conceptually recapitulate oscillatory entrainment and travelling wave formation, and to highlight open questions. It shows that three key parameters, the autorepression delay, the juxtacrine coupling delay, and the coupling strength, are sufficient to understand the emergence of the collective period, the collective amplitude, and the synchronization of neighbouring Her1 oscillators. Moreover, two spatiotemporal time delay gradients, in the autorepression and in the juxtacrine signalling, are required to explain the collective oscillatory dynamics and synchrony of PSM cells. The highlighted developmental principles likely apply more generally to other developmental processes, including neurogenesis and angiogenesis.

Differential interactions between Notch and ID factors control neurogenesis by modulating Hes factor autoregulation

During embryonic and adult neurogenesis, neural stem cells (NSCs) generate the correct number and types of neurons in a temporospatial fashion. Control of NSC activity and fate is crucial for brain formation and homeostasis. Neurogenesis in the embryonic and adult brain differ considerably, but Notch signaling and inhibitor of DNA-binding (ID) factors are pivotal in both. Notch and ID factors regulate NSC maintenance; however, it has been difficult to evaluate how these pathways potentially interact. Here, we combined mathematical modeling with analysis of single-cell transcriptomic data to elucidate unforeseen interactions between the Notch and ID factor pathways. During brain development, Notch signaling dominates and directly regulates Id4 expression, preventing other ID factors from inducing NSC quiescence. Conversely, during adult neurogenesis, Notch signaling and Id2/3 regulate neurogenesis in a complementary manner and ID factors can induce NSC maintenance and quiescence in the absence of Notch. Our analyses unveil key molecular interactions underlying NSC maintenance and mechanistic differences between embryonic and adult neurogenesis. Similar Notch and ID factor interactions may be crucial in other stem cell systems.

Summary: Computational analysis of transcriptome data from neural stem cells reveals key differences in the synergistic interactions between Notch and inhibitor of DNA-binding factors during embryonic and adult neurogenesis.

Notch-Jagged signalling can give rise to clusters of cells exhibiting a hybrid epithelial/mesenchymal phenotype

Metastasis can involve repeated cycles of epithelial-to-mesenchymal transition (EMT) and its reverse mesenchymal-to-epithelial transition. Cells can also undergo partial transitions to attain a hybrid epithelial/mesenchymal (E/M) phenotype that allows the migration of adhering cells to form a cluster of circulating tumour cells. These clusters can be apoptosis-resistant and possess an increased metastatic propensity as compared to the cells that undergo a complete EMT (mesenchymal cells). Hence, identifying the key players that can regulate the formation and maintenance of such clusters may inform anti-metastasis strategies. Here, we devise a mechanism-based theoretical model that links cell–cell communication via Notch-Delta-Jagged signalling with the regulation of EMT. We demonstrate that while both Notch-Delta and Notch-Jagged signalling can induce EMT in a population of cells, only Jagged-dominated Notch signalling, but not Delta-dominated signalling, can lead to the formation of clusters containing hybrid E/M cells. Our results offer possible mechanistic insights into the role of Jagged in tumour progression, and offer a framework to investigate the effects of other microenvironmental signals during metastasis.

Operating principles of tristable circuits regulating cellular differentiation

Many cell-fate decisions during embryonic development are governed by a motif comprised of two transcription factors (TFs) A and B that mutually inhibit each other and may self-activate. This motif, called as a self-activating toggle switch (SATS), can typically have three stable states (phenotypes)-two corresponding to differentiated cell fates, each of which has a much higher level of one TF than the other-[Formula: see text] or [Formula: see text]-and the third state corresponding to an 'undecided' stem-like state with similar levels of both A and B-[Formula: see text]. Furthermore, two or more SATSes can be coupled together in various topologies in different contexts, thereby affecting the coordination between multiple cellular decisions. However, two questions remain largely unanswered: (a) what governs the co-existence and relative stability of these three stable states? (b) What orchestrates the decision-making of coupled SATSes? Here, we first demonstrate that the co-existence and relative stability of the three stable states in an individual SATS can be governed by the relative strength of self-activation, external signals activating and/or inhibiting A and B, and mutual degradation between A and B. Simultaneously, we investigate the effects of these factors on the decision-making of two coupled SATSes. Our results offer novel understanding into the operating principles of individual and coupled tristable self-activating toggle switches (SATSes) regulating cellular differentiation and can yield insights into synthesizing three-way genetic circuits and understanding of cellular reprogramming.

Coupling the modules of EMT and stemness: A tunable 'stemness window' model

Metastasis of carcinoma involves migration of tumor cells to distant organs and initiate secondary tumors. Migration requires a complete or partial Epithelial-to-Mesenchymal Transition (EMT), and tumor-initiation requires cells possessing stemness. Epithelial cells (E) undergoing a complete EMT to become mesenchymal (M) have been suggested to be more likely to possess stemness. However, recent studies suggest that stemness can also be associated with cells undergoing a partial EMT (hybrid E/M phenotype). Therefore, the correlation between EMT and stemness remains elusive. Here, using a theoretical framework that couples the core EMT and stemness modules (miR-200/ZEB and LIN28/let-7), we demonstrate that the positioning of ‘stemness window’ on the ‘EMT axis’ need not be universal; rather it can be fine-tuned. Particularly, we present OVOL as an example of a modulating factor that, due to its coupling with miR-200/ZEB/LIN28/let-7 circuit, fine-tunes the EMT-stemness interplay. Coupling OVOL can inhibit the stemness likelihood of M and elevate that of the hybrid E/M (partial EMT) phenotype, thereby pulling the ‘stemness window’ away from the M end of ‘EMT axis’. Our results unify various apparently contradictory experimental findings regarding the interconnection between EMT and stemness, corroborate the emerging notion that partial EMT associates with stemness, and offer new testable predictions.

OVOL guides the epithelial-hybrid-mesenchymal transition

Metastasis involves multiple cycles of Epithelial-to-Mesenchymal Transition (EMT) and its reverse-MET. Cells can also undergo partial transitions to attain a hybrid epithelial/mesenchymal (E/M) phenotype that has maximum cellular plasticity and allows migration of Circulating Tumor Cells (CTCs) as a cluster. Hence, deciphering the molecular players helping to maintain the hybrid E/M phenotype may inform anti-metastasis strategies. Here, we devised a mechanism-based mathematical model to couple the transcription factor OVOL with the core EMT regulatory network miR-200/ZEB that acts as a three-way switch between the E, E/M and M phenotypes. We show that OVOL can modulate cellular plasticity in multiple ways - restricting EMT, driving MET, expanding the existence of the hybrid E/M phenotype and turning both EMT and MET into two-step processes. Our theoretical framework explains the differences between the observed effects of OVOL in breast and prostate cancer, and provides a platform for investigating additional signals during metastasis.

Abstract B2-24: OVOL: A brake on EMT, driver of MET and expander of the hybrid E/M phenotype

Aberrant activation of Epithelial to Mesenchymal Transition (EMT) and its reverse MET is a hallmark of cancer metastasis. Also, the carcinoma cells can undergo partial transitions to attain a hybrid epithelial/mesenchymal (E/M) phenotype, which can allow them to migrate collectively and form clusters of Circulating Tumor Cells (CTCs). These clusters can be up to 50 times more metastatic compared to cells that undergo complete EMT or individually migrating CTCs. Here, using experimental data and bioinformatics, we devised a dedicated model to study the role of transcription factor OVOL in regulating these bidirectional epithelial-hybrid-mesenchymal transitions. The new model incorporates the coupling between OVOL and miR-200/ZEB - the three-way EMT/MET decision-making switch that allows for three phenotypes (E, M and E/M). We found that OVOL operates as a brake holder of complete EMT and as an expander of the hybrid E/M phenotype (it extends the range of physiological parameters for which the cells can attain the hybrid E/M phenotype). Inhibition of OVOL, for example by Wg signaling, removes that brake and supports a complete EMT. Conversely, overexpression of OVOL, for example by BMP7/Smad4, leads to the reverse of EMT, i.e. MET. In addition, OVOL elevates the phenotypic plasticity of cells by turning both EMT and MET into a two-step process (E to E/M to M, and M to E/M to E). We show that depending on the context-specific coupling manner of OVOL with miR-200/ZEB, OVOL can shepherd cellular plasticity in multiple ways - by driving MET, by restricting EMT, and by expanding the existence of the hybrid E/M phenotype. In particular, we explain how the differences between the observed effects of OVOL in breast and prostate cancer are associated with the results of different miR-200/ZEB/OVOL couplings. Our computational framework can be tailored to include other signals such as p53, TGF-beta, inflammation and hypoxia that affect the cellular plasticity during metastasis, thus providing a platform to design novel anti-metastasis therapies.

Citation Format: Dongya Jia, Mohit Kumar Jolly, Marcelo Boareto, Princy Parsana, Steven Mooney, Kenneth J. Pienta, Herbert Levine, Eshel Ben-Jacob. OVOL: A brake on EMT, driver of MET and expander of the hybrid E/M phenotype. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-24.

Implications of the Hybrid Epithelial/Mesenchymal Phenotype in Metastasis

Transitions between epithelial and mesenchymal phenotypes – the epithelial to ­mesenchymal transition (EMT) and its reverse the mesenchymal to epithelial transition (MET) – are hallmarks of cancer metastasis. While transitioning between the epithelial and mesenchymal phenotypes, cells can also attain a hybrid epithelial/mesenchymal (E/M) (i.e., partial or intermediate EMT) phenotype. Cells in this phenotype have mixed epithelial (e.g., adhesion) and mesenchymal (e.g., migration) properties, thereby allowing them to move collectively as clusters. If these clusters reach the bloodstream intact, they can give rise to clusters of circulating tumor cells (CTCs), as have often been seen experimentally. Here, we review the operating principles of the core regulatory network for EMT/MET that acts as a “three-way” switch giving rise to three distinct phenotypes – E, M and hybrid E/M – and present a theoretical framework that can elucidate the role of many other players in regulating epithelial plasticity. Furthermore, we highlight recent studies on partial EMT and its association with drug resistance and tumor-initiating potential; and discuss how cell–cell communication between cells in a partial EMT phenotype can enable the formation of clusters of CTCs. These clusters can be more apoptosis-resistant and have more tumor-initiating potential than singly moving CTCs with a wholly mesenchymal (complete EMT) phenotype. Also, more such clusters can be formed under inflammatory conditions that are often generated by various therapies. Finally, we discuss the multiple advantages that the partial EMT or hybrid E/M phenotype have as compared to a complete EMT phenotype and argue that these collectively migrating cells are the primary “bad actors” of metastasis.

Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data

Microarray data analysis typically consists in identifying a list of differentially expressed genes (DEG), i.e., the genes that are differentially expressed between two experimental conditions. Variance shrinkage methods have been considered a better choice than the standard t-test for selecting the DEG because they correct the dependence of the error with the expression level. This dependence is mainly caused by errors in background correction, which more severely affects genes with low expression values. Here, we propose a new method for identifying the DEG that overcomes this issue and does not require background correction or variance shrinkage. Unlike current methods, our methodology is easy to understand and implement. It consists of applying the standard t-test directly on the normalized intensity data, which is possible because the probe intensity is proportional to the gene expression level and because the t-test is scale- and location-invariant. This methodology considerably improves the sensitivity and robustness of the list of DEG when compared with the t-test applied to preprocessed data and to the most widely used shrinkage methods, Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA). Our approach is useful especially when the genes of interest have small differences in expression and therefore get ignored by standard variance shrinkage methods.