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The functional specialization of cell types arises during development and is shaped by cell–cell communication networks determining a distribution of functional cell states that are collectively important for tissue functioning. However, the identification of these tissue-specific functional cell states remains challenging. Although a plethora of computational approaches have been successful in detecting cell types and subtypes, they fail in resolving tissue-specific functional cell states. To address this issue, we present FunRes, a computational method designed for the identification of functional cell states. FunRes relies on scRNA-seq data of a tissue to initially reconstruct the functional cell–cell communication network, which is leveraged for partitioning each cell type into functional cell states. We applied FunRes to 177 cell types in 10 different tissues and demonstrated that the detected states correspond to known functional cell states of various cell types, which cannot be recapitulated by existing computational tools. Finally, we characterize emerging and vanishing functional cell states in aging and disease, and demonstrate their involvement in key tissue functions. Thus, we believe that FunRes will be of great utility in the characterization of the functional landscape of cell types and the identification of dysfunctional cell states in aging and disease. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary Single-cell RNA-sequencing is increasingly employed to characterize disease or ageing cell subpopulation phenotypes. Despite exponential increase in data generation, systematic identification of key regulatory factors for controlling cellular phenotype to enable cell rejuvenation in disease or ageing remains a challenge. Here, we present SigHotSpotter, a computational tool to predict hotspots of signaling pathways responsible for the stable maintenance of cell subpopulation phenotypes, by integrating signaling and transcriptional networks. Targeted perturbation of these signaling hotspots can enable precise control of cell subpopulation phenotypes. SigHotSpotter correctly predicts the signaling hotspots with known experimental validations in different cellular systems. The tool is simple, user-friendly and is available as web-server or as stand-alone software. We believe SigHotSpotter will serve as a general purpose tool for the systematic prediction of signaling hotspots based on single-cell RNA-seq data, and potentiate novel cell rejuvenation strategies in the context of disease and ageing. Availability and implementation SigHotSpotter is at https://SigHotSpotter.lcsb.uni.lu as a web tool. Source code, example datasets and other information are available at https://gitlab.com/srikanth.ravichandran/sighotspotter. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: The identification of genes or molecular regulatory mechanisms implicated in biological processes often requires the discretization, and in particular booleanization, of gene expression measurements. However, currently used methods mostly classify each measurement into an active or inactive state regardless of its statistical support possibly leading to downstream analysis conclusions based on spurious booleanization results. Results: In order to overcome the lack of certainty inherent in current methodologies and to improve the process of discretization, we introduce RefBool, a reference-based algorithm for discretizing gene expression data. Instead of requiring each measurement to be classified as active or inactive, RefBool allows for the classification of a third state that can be interpreted as an intermediate expression of genes. Furthermore, each measurement is associated to a p- and q-value indicating the significance of each classification. Validation of RefBool on a neuroepithelial differentiation study and subsequent qualitative and quantitative comparison against 10 currently used methods supports its advantages and shows clear improvements of resulting clusterings. [ABSTRACT FROM AUTHOR]

Adult neural stem cells with the ability to generate neurons and glia cells are active throughout life in both the dentate gyrus (DG) and the subventricular zone (SVZ). Differentiation of adult neural stem cells is induced by cell fate determinants like the transcription factor Prox1. Evidence has been provided for a function of Prox1 as an inducer of neuronal differentiation within the DG. We now show that within the SVZ Prox1 induces differentiation into oligodendrocytes. Moreover, we find that loss of Prox1 expression in vivo reduces cell migration into the corpus callosum, where the few Prox1 deficient SVZ-derived remaining cells fail to differentiate into oligodendrocytes. Thus, our work uncovers a novel function of Prox1 as a fate determinant for oligodendrocytes in the adult mammalian brain. These data indicate that the neurogenic and oligodendrogliogenic lineages in the two adult neurogenic niches exhibit a distinct requirement for Prox1, being important for neurogenesis in the DG but being indispensable for oligodendrogliogenesis in the SVZ. S tem C ells 2016;34:2115-2129 [ABSTRACT FROM AUTHOR]

Cellular reprogramming can offer valuable insight into disease mechanism and has the potential to provide novel tools for regenerative medicine. Yet it remains an inefficient and often incomplete process. However, experiments show that almost all somatic cells eventually give rise to the pluripotent state, albeit at different latencies, as long as expression of reprogramming transcription factors is maintained. Furthermore, it appears that specific subpopulations of cells can be identified that show enhanced propensities to be reprogrammed to the pluripotent state. It has been proposed that an initial stochastic process is responsible for this initial priming that is followed by a deterministic process that directs the primed cells into the pluripotent state. Here, we propose a population shift view of cellular reprogramming, which explains these observations and reconciles the stochastic and deterministic nature of this process. According to this view, a small population of cells, whose states are closer to the pluripotent state and reside in pre-existing energetically favorable trajectories, will be initially selected for reprogramming. Moreover, by maintaining ectopic expression of reprogramming factors, other cells enter these pathways as a result of transcriptional and epigenetic stochastic variations. Consequently, increasing numbers of cells reach the pluripotent state, and the cell population distribution shifts toward this state. Importantly, additional perturbations can change the epigenetic landscape, allowing cells more access to the reprogramming trajectories, thereby increasing reprogramming efficiency. Knowledge of the initial cellular subpopulations and pathways of states that lead to the final cellular state should allow us to design alternative perturbation strategies to improve reprogramming efficiency and fidelity. S tem C ells 2014;32:1367-1372 [ABSTRACT FROM AUTHOR]

With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types. [ABSTRACT FROM AUTHOR]

ABSTRACT Transcription factor cross-repression is an important concept in cellular differentiation. A bistable toggle switch constitutes a molecular mechanism that determines cellular commitment and provides stability to transcriptional programs of binary cell fate choices. Experiments support that perturbations of these toggle switches can interconvert these binary cell fate choices, suggesting potential reprogramming strategies. However, more complex types of cellular transitions could involve perturbations of combinations of different types of multistable motifs. Here, we introduce a method that generalizes the concept of transcription factor cross-repression to systematically predict sets of genes, whose perturbations induce cellular transitions between any given pair of cell types. Furthermore, to our knowledge, this is the first method that systematically makes these predictions without prior knowledge of potential candidate genes and pathways involved, providing guidance on systems where little is known. Given the increasing interest of cellular reprogramming in medicine and basic research, our method represents a useful computational methodology to assist researchers in the field in designing experimental strategies. S tem C ells 2013;31:2127-2135 [ABSTRACT FROM AUTHOR]

Motivation: Protein dynamics is believed to influence protein function through a variety of mechanisms, some of which are not fully understood. Thus, prediction of protein flexibility from sequence or structural characteristics would assist in comprehension of the ways dynamics is linked to function, and would be important in protein modeling and design. In particular, quantitative description of side-chain dynamics would allow us to understand the role of side-chain flexibility in different functional processes, such as protein–ligand and protein–protein interactions. [ABSTRACT FROM PUBLISHER]