Transcriptional and Chromatin Accessibility Profiling of Neural Stem Cells Differentiating into Astrocytes Reveal Dynamic Signatures Affected under Inflammatory Conditions.

Astrocytes arise from multipotent neural stem cells (NSCs) and represent the most abundant cell type of the central nervous system (CNS), playing key roles in the developing and adult brain. Since the differentiation of NSCs towards a gliogenic fate is a precisely timed and regulated process, its perturbation gives rise to dysfunctional astrocytic phenotypes. Inflammation, which often underlies neurological disorders, including neurodevelopmental disorders and brain tumors, disrupts the accurate developmental process of NSCs. However, the specific consequences of an inflammatory environment on the epigenetic and transcriptional programs underlying NSCs' differentiation into astrocytes is unexplored. Here, we address this gap by profiling in mice glial precursors from neural tissue derived from early embryonic stages along their astrocytic differentiation trajectory in the presence or absence of tumor necrosis factor (TNF), a master pro-inflammatory cytokine. By using a combination of RNA- and ATAC-sequencing approaches, together with footprint and integrated gene regulatory network analyses, we here identify key differences during the differentiation of NSCs into astrocytes under physiological and inflammatory settings. In agreement with its role to turn cells resistant to inflammatory challenges, we detect Nrf2 as a master transcription factor supporting the astrocytic differentiation under TNF exposure. Further, under these conditions, we unravel additional transcriptional regulatory hubs, including Stat3, Smad3, Cebpb, and Nfkb2, highlighting the interplay among pathways underlying physiological astrocytic developmental processes and those involved in inflammatory responses, resulting in discrete astrocytic phenotypes. Overall, our study reports key transcriptional and epigenetic changes leading to the identification of molecular regulators of astrocytic differentiation. Furthermore, our analyses provide a valuable resource for understanding inflammation-induced astrocytic phenotypes that might contribute to the development and progression of CNS disorders with an inflammatory component.

Computational modelling of stem cell-niche interactions facilitates discovery of strategies to enhance tissue regeneration and counteract ageing.

The stem cell niche is a specialized microenvironment for stem cells in an adult tissue. The niche provides cues for the maintenance and regulation of stem cell activities and thus presents a target for potential rejuvenating strategies. García-Prat et al. found that in the heterogeneous population of quiescent stem cells of skeletal muscles, a fraction of cells responsible for regeneration and having genuine 'stemness' properties deteriorates only in extremely old age. An essential tool used in this analysis of stem cell-niche interactions is the computational tool, NicheHotSpotter, which proved to be instrumental for identifying niche and cell signalling factors that contribute to the maintenance of the pool of genuine quiescent stem cells. NicheHotSpotter predicts candidate factors by analysing signalling interactome and gene regulatory network data in combination with expression profiles. The effect of the niche environment on stem cells is modelled as a mean field of niche cues that induce sustained activation or inhibition of signalling pathways. In this way, NicheHotSpotter has been successful in delineating novel strategies to enhance stemness, which may rejuvenate skeletal muscle cells at the extreme old age.

A Catalogus Immune Muris of the mouse immune responses to diverse pathogens.

Immunomodulation strategies are crucial for several biomedical applications. However, the immune system is highly heterogeneous and its functional responses to infections remains elusive. Indeed, the characterization of immune response particularities to different pathogens is needed to identify immunomodulatory candidates. To address this issue, we compiled a comprehensive map of functional immune cell states of mouse in response to 12 pathogens. To create this atlas, we developed a single-cell-based computational method that partitions heterogeneous cell types into functionally distinct states and simultaneously identifies modules of functionally relevant genes characterizing them. We identified 295 functional states using 114 datasets of six immune cell types, creating a Catalogus Immune Muris. As a result, we found common as well as pathogen-specific functional states and experimentally characterized the function of an unknown macrophage cell state that modulates the response to Salmonella Typhimurium infection. Thus, we expect our Catalogus Immune Muris to be an important resource for studies aiming at discovering new immunomodulatory candidates.

FoxO maintains a genuine muscle stem-cell quiescent state until geriatric age.

Tissue regeneration declines with ageing but little is known about whether this arises from changes in stem-cell heterogeneity. Here, in homeostatic skeletal muscle, we identify two quiescent stem-cell states distinguished by relative CD34 expression: CD34High, with stemness properties (genuine state), and CD34Low, committed to myogenic differentiation (primed state). The genuine-quiescent state is unexpectedly preserved into later life, succumbing only in extreme old age due to the acquisition of primed-state traits. Niche-derived IGF1-dependent Akt activation debilitates the genuine stem-cell state by imposing primed-state features via FoxO inhibition. Interventions to neutralize Akt and promote FoxO activity drive a primed-to-genuine state conversion, whereas FoxO inactivation deteriorates the genuine state at a young age, causing regenerative failure of muscle, as occurs in geriatric mice. These findings reveal transcriptional determinants of stem-cell heterogeneity that resist ageing more than previously anticipated and are only lost in extreme old age, with implications for the repair of geriatric muscle.

SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.

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.

Single-cell analysis of cardiogenesis reveals basis for organ-level developmental defects.

Organogenesis involves integration of diverse cell types; dysregulation of cell-type-specific gene networks results in birth defects, which affect 5% of live births. Congenital heart defects are the most common malformations, and result from disruption of discrete subsets of cardiac progenitor cells1, but the transcriptional changes in individual progenitors that lead to organ-level defects remain unknown. Here we used single-cell RNA sequencing to interrogate early cardiac progenitor cells as they become specified during normal and abnormal cardiogenesis, revealing how dysregulation of specific cellular subpopulations has catastrophic consequences. A network-based computational method for single-cell RNA-sequencing analysis that predicts lineage-specifying transcription factors2,3 identified Hand2 as a specifier of outflow tract cells but not right ventricular cells, despite the failure of right ventricular formation in Hand2-null mice4. Temporal single-cell-transcriptome analysis of Hand2-null embryos revealed failure of outflow tract myocardium specification, whereas right ventricular myocardium was specified but failed to properly differentiate and migrate. Loss of Hand2 also led to dysregulation of retinoic acid signalling and disruption of anterior-posterior patterning of cardiac progenitors. This work reveals transcriptional determinants that specify fate and differentiation in individual cardiac progenitor cells, and exposes mechanisms of disrupted cardiac development at single-cell resolution, providing a framework for investigating congenital heart defects.

Modeling Cellular Differentiation and Reprogramming with Gene Regulatory Networks.

Gene expression regulation is a fundamental cellular process that enables robust functioning of cells. How different genes interact among themselves to coordinate and maintain the overall gene expression profile observed in a cell is a key question in cellular biology. However, the immense complexity arising due to the scale and the nature of gene-gene interactions often hinders obtaining a global understanding of gene regulation. In this regard, network models of gene regulation based on gene-gene interactions, commonly referred to as gene regulatory networks (GRNs), serve important purpose of describing the overall interactions within a cell and provide a systematic approach to study their global behavior. In particular, in the context of cellular differentiation and reprogramming, where regulated changes in gene expression play a crucial role, precise knowledge of a cell type-specific GRN can enable control of the eventual cell fates with potential clinical applications. In this chapter, we describe our computational methodologies that we have tailor-made with purpose of applications to cell fate control. Briefly, we introduce the process of cellular differentiation and reprogramming, describe GRNs and common strategies to model them, and, finally, introduce the concept of determinants of cellular reprogramming and differentiation. In the Methods section, we elaborate on the different steps involved in the computational pipeline, including initial gene expression data processing, characterization of prior knowledge network, algorithm to remove non-cell type-specific edges, topological characterization of the inferred network, and Boolean network simulations to mimic cellular transitions. Finally, we provide a strategy to identify determinants of cellular reprogramming and differentiation based on the proposed computational methods.

Quiescence Modulates Stem Cell Maintenance and Regenerative Capacity in the Aging Brain.

The function of somatic stem cells declines with age. Understanding the molecular underpinnings of this decline is key to counteract age-related disease. Here, we report a dramatic drop in the neural stem cells (NSCs) number in the aging murine brain. We find that this smaller stem cell reservoir is protected from full depletion by an increase in quiescence that makes old NSCs more resistant to regenerate the injured brain. Once activated, however, young and old NSCs show similar proliferation and differentiation capacity. Single-cell transcriptomics of NSCs indicate that aging changes NSCs minimally. In the aging brain, niche-derived inflammatory signals and the Wnt antagonist sFRP5 induce quiescence. Indeed, intervention to neutralize them increases activation of old NSCs during homeostasis and following injury. Our study identifies quiescence as a key feature of old NSCs imposed by the niche and uncovers ways to activate NSCs to repair the aging brain.

Computational Strategies for Niche-Dependent Cell Conversion to Assist Stem Cell Therapy.

The field of regenerative medicine has blossomed in recent decades. However, the ultimate goal of tissue regeneration - replacing damaged or aged cells with healthy functioning cells - still faces a number of challenges. In particular, better understanding of the role of the cellular niche in shaping stem cell phenotype and conversion would aid in improving current protocols for stem cell therapies. In this regard, the implementation of novel computational approaches that consider the niche effect on stem cells would be valuable. Here we discuss current problems in stem cell transplantation and rejuvenation, and we propose computational strategies to control niche-dependent cell conversion to overcome them.

Transcriptional synergy as an emergent property defining cell subpopulation identity enables population shift.

Single-cell RNA sequencing allows defining molecularly distinct cell subpopulations. However, the identification of specific sets of transcription factors (TFs) that define the identity of these subpopulations remains a challenge. Here we propose that subpopulation identity emerges from the synergistic activity of multiple TFs. Based on this concept, we develop a computational platform (TransSyn) for identifying synergistic transcriptional cores that determine cell subpopulation identities. TransSyn leverages single-cell RNA-seq data, and performs a dynamic search for an optimal synergistic transcriptional core using an information theoretic measure of synergy. A large-scale TransSyn analysis identifies transcriptional cores for 186 subpopulations, and predicts identity conversion TFs between 3786 pairs of cell subpopulations. Finally, TransSyn predictions enable experimental conversion of human hindbrain neuroepithelial cells into medial floor plate midbrain progenitors, capable of rapidly differentiating into dopaminergic neurons. Thus, TransSyn can facilitate designing strategies for conversion of cell subpopulation identities with potential applications in regenerative medicine.

Integrative Computational Network Analysis Reveals Site-Specific Mediators of Inflammation in Alzheimer's Disease.

Alzheimer's disease (AD) is a major neurodegenerative disease and is one of the most common cause of dementia in older adults. Among several factors, neuroinflammation is known to play a critical role in the pathogenesis of chronic neurodegenerative diseases. In particular, studies of brains affected by AD show a clear involvement of several inflammatory pathways. Furthermore, depending on the brain regions affected by the disease, the nature and the effect of inflammation can vary. Here, in order to shed more light on distinct and common features of inflammation in different brain regions affected by AD, we employed a computational approach to analyze gene expression data of six site-specific neuronal populations from AD patients. Our network based computational approach is driven by the concept that a sustained inflammatory environment could result in neurotoxicity leading to the disease. Thus, our method aims to infer intracellular signaling pathways/networks that are likely to be constantly activated or inhibited due to persistent inflammatory conditions. The computational analysis identified several inflammatory mediators, such as tumor necrosis factor alpha (TNF-a)-associated pathway, as key upstream receptors/ligands that are likely to transmit sustained inflammatory signals. Further, the analysis revealed that several inflammatory mediators were mainly region specific with few commonalities across different brain regions. Taken together, our results show that our integrative approach aids identification of inflammation-related signaling pathways that could be responsible for the onset or the progression of AD and can be applied to study other neurodegenerative diseases. Furthermore, such computational approaches can enable the translation of clinical omics data toward the development of novel therapeutic strategies for neurodegenerative diseases.

Identifying niche-mediated regulatory factors of stem cell phenotypic state: a systems biology approach.

Understanding how the cellular niche controls the stem cell phenotype is often hampered due to the complexity of variegated niche composition, its dynamics, and nonlinear stem cell-niche interactions. Here, we propose a systems biology view that considers stem cell-niche interactions as a many-body problem amenable to simplification by the concept of mean field approximation. This enables approximation of the niche effect on stem cells as a constant field that induces sustained activation/inhibition of specific stem cell signaling pathways in all stem cells within heterogeneous populations exhibiting the same phenotype (niche determinants). This view offers a new basis for the development of single cell-based computational approaches for identifying niche determinants, which has potential applications in regenerative medicine and tissue engineering.

A new method of finding groups of coexpressed genes and conditions of coexpression.

BACKGROUND: To study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many algorithms are available which finds groups of co-expressed genes and associated conditions of co-expression that can help finding processes associated with particular condition. However, these algorithms depend on different input parameters for generating groups. For real datasets, it is difficult to use these algorithms due to unknown values of these parameters. RESULTS: We present here an algorithm, clustered groups, which finds groups of co-expressed genes and conditions of co-expression with minimal input from user. We used random datasets to derive a cutoff on the basis of which we filtered the resultant groups and showed that this can improve the relevance of obtained groups. We showed that the proposed algorithm performs better than other known algorithms on both real and synthetic datasets. We have also shown its application on a temporal microarray dataset by extracting biclusters and biological information hidden in those biclusters. CONCLUSIONS: Clustered groups is an algorithm which finds groups of co-expressed genes and conditions of co-expression using only a single parameter. We have shown that it works better than other existing algorithms. It can be used to find these groups in different data types such as microarray, proteomics, metabolomics etc.

A systems biology approach to identify niche determinants of cellular phenotypes.

Recent reports indicate a dominant role for cellular microenvironment or niche for stably maintaining cellular phenotypic states. Identification of key niche mediated signaling that maintains stem cells in specific phenotypic states remains a challenge, mainly due to the complex and dynamic nature of stem cell-niche interactions. In order to overcome this, we consider that stem cells maintain their phenotypic state by experiencing a constant effect created by the niche by integrating its signals via signaling pathways. Such a constant niche effect should induce sustained activation/inhibition of specific stem cell signaling pathways that controls the gene regulatory program defining the cellular phenotypic state. Based on this view, we propose a computational approach to identify the most likely receptor mediated signaling responsible for transmitting niche signals to the transcriptional regulatory network that maintain cell-specific gene expression patterns, termed as niche determinants. We demonstrate the utility of our method in different stem cell systems by identifying several known and novel niche determinants. Given the key role of niche in several degenerative diseases, identification of niche determinants can aid in developing strategies for potential applications in regenerative medicine.

Selective enrichment of mycobacterial proteins from infected host macrophages.

Upon infection, Mycobacterium tuberculosis (Mtb) deploys specialized secretion machinery to deliver virulent proteins with the capacity to modulate a variety of host-cellular pathways. Studies on the identification of intra-macrophage Mtb proteins, however, are constricted by an inability to selectively enrich these virulent effectors against overwhelming protein content of the host. Here, we introduce an Mtb-selective protein labeling method based on genetic incorporation of azidonorleucine (Anl) through the expression of a mutant methionyl-tRNA synthetase. Exclusive incorporation of Anl, into native Mtb proteins, provided a click handle to pull out low abundant secretory proteins from the lysates of infected cells. Further, temporal secretome profiling, upon infection with strains of varying degree of virulence, revealed the proficiency of virulent Mtb to secrete chaperones. This ability contributed at least partially to the mycobacterial virulence-specific suppression of ER stress in the host macrophage, representing an important facet of mycobacterial virulence. The Anl labeling approach should facilitate new exciting opportunities for imaging and proteomic investigations of differently virulent Mtb isolates to understand determinants of pathogenicity.

Bistability in a model of early B cell receptor activation and its role in tonic signaling and system tunability.

Activation of the antigen receptors on the surface of B cells in response to their cognate ligands is tightly controlled by feedback mechanisms. Apart from ligand induced signaling, B cell receptors (BCRs) emanate ligand independent tonic signaling crucial for B cell survival and development. In the absence of a ligand, BCR tonic signaling is controlled by the basal activity of the Src family protein tyrosine kinase Lyn and the protein tyrosine phosphatase SHP. The binding of an antigen to the BCR causes receptor clustering or aggregation which is one of the earliest events in B cell activation. Lyn binds to aggregated receptors and phosphorylates them. In turn phosphorylation enhances the stability of receptor clusters against dissociation into monomers as well as the binding of Lyn to the receptor clusters, thereby producing positive feedback loops that enhance receptor clustering and activation. Apart from Lyn mediated positive feedback loops, SHP and BCR aggregates mutually inhibit each other to form a double negative feedback loop. Here, we present a simple computational model of BCR proximal signaling that incorporates these multiple feedback loops between the three molecules BCR, Lyn and SHP and their complexes. The model predicts bistable behaviour in the system that explains both the tonic signaling and ligand mediated receptor activation and a range of other biological phenomena in a unified manner. We find the bistability to be highly tunable by changes in the protein levels while remaining sufficiently robust to changes in the rate constants. The nested architecture of multiple feedback loops enhances the robustness of the bistability. Our model explains the recent experimental observation of the lack of response of germinal center B cells to ligand stimulation in terms of the tunability of the bistable switch by modification of SHP levels.

Delineation of key regulatory elements identifies points of vulnerability in the mitogen-activated signaling network.

Drug development efforts against cancer are often hampered by the complex properties of signaling networks. Here we combined the results of an RNAi screen targeting the cellular signaling machinery, with graph theoretical analysis to extract the core modules that process both mitogenic and oncogenic signals to drive cell cycle progression. These modules encapsulated mechanisms for coordinating seamless transition of cells through the individual cell cycle stages and, importantly, were functionally conserved across different cancer cell types. Further analysis also enabled extraction of the core signaling axes that progressively guide commitment of cells to the division cycle. Importantly, pharmacological targeting of the least redundant nodes in these axes yielded a synergistic disruption of the cell cycle in a tissue-type-independent manner. Thus, the core elements that regulate temporally distinct stages of the cell cycle provide attractive targets for the development of multi-module-based chemotherapeutic strategies.

Defining the antigen receptor-dependent regulatory network that induces arrest of cycling immature B-lymphocytes.

BACKGROUND: Engagement of the antigen receptor on immature B-lymphocytes leads to cell cycle arrest, and subsequent apoptosis. This is an essential process for eliminating self reactive B cells during its different stages of development. However, the mechanism by which it is achieved is not completely understood. RESULTS: Here we employed a systems biology approach that combined extensive experimentation with in silico methodologies to chart the network of receptor-activated pathways that mediated the arrest of immature B cells in the G1 phase of the cell cycle. Interestingly, we found that only a sparse network of signaling intermediates was recruited upon engagement of the antigen receptor. This then led to the activation of a restricted subset of transcription factors, with the consequent induction of genes primarily involved in the cell death pathway. Subsequent experiments revealed that the weak initiation of intracellular signaling pathways derived from desensitization of the receptor-proximal protein tyrosine kinase Lyn, to receptor-dependent activation. Intriguingly, the desensitization was a result of the constitutive activation of this kinase in unstimulated cells, which was likely maintained through a regulatory feedback loop involving the p38 MAP kinase. The high basal activity then attenuated the ability of the antigen receptor to recruit Lyn, and thereby also the downstream signaling intermediates. Finally, integration of these results into a mathematical model provided further substantiation to the novel finding that the ground state of the intracellular signaling machinery constitutes an important determinant of the outcome of receptor-induced cellular responses. CONCLUSIONS: Our results identify the global events leading to the G1 arrest and subsequent apoptosis in immature B cells upon receptor activation.

Integration of signals from the B-cell antigen receptor and the IL-4 receptor leads to a cooperative shift in the cellular response axis.

Although intracellular signaling events activated through individual cell surface receptors have been characterized in detail, cells are often exposed to multiple stimuli simultaneously in physiological situations. The response elicited then is defined through the cooperative interactions between signals activated by these multiple stimuli. Examples of such instances include cooperativity between individual isoforms of G-protein-coupled receptors, between different growth factor receptors, or between growth factor and integrin receptors. Mechanisms by which the integration of signals emanating from independent receptors influences cellular responses, however, are unknown. In this report, we studied interactions between the antigen and the IL-4 receptors in immature B cells. While stimulation through the B-cell antigen receptor alone causes cell cycle arrest and subsequent apoptosis, the inclusion of IL-4 during stimulation provides a protective effect. We therefore sought to obtain a systems view on how crosstalk between the two respective cell surface receptors modulates the cellular response. We found that, in comparison to the effects of B-cell receptor activation alone, combined stimulation through both receptors enforced a marked reorientation in the 'survival vs. apoptosis' axis of the signaling machinery. The consequent modulation of transcription factor activities yielded an integrated network, spanning the signaling and the transcriptional regulatory components, that was now biased towards the recruitment of molecules with a pro-survival function. This alteration in network properties influenced early-induced gene expression, in a manner that could rationalize the antagonistic effect of the IL-4 receptor on B-cell receptor signaling. Importantly, this antagonism was achieved through an expansion in the repertoire of the genes expressed, wherein the newly generated products counteracted the effects of the B-cell receptor-specific subset. Thus the plasticity of the regulatory networks is also experienced at the level of gene expression, and is the resultant pattern obtained that then modulates cell-fate decisions.