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.

Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration.

Tissue regeneration requires coordination between resident stem cells and local niche cells1,2. Here we identify that senescent cells are integral components of the skeletal muscle regenerative niche that repress regeneration at all stages of life. The technical limitation of senescent-cell scarcity3 was overcome by combining single-cell transcriptomics and a senescent-cell enrichment sorting protocol. We identified and isolated different senescent cell types from damaged muscles of young and old mice. Deeper transcriptome, chromatin and pathway analyses revealed conservation of cell identity traits as well as two universal senescence hallmarks (inflammation and fibrosis) across cell type, regeneration time and ageing. Senescent cells create an aged-like inflamed niche that mirrors inflammation associated with ageing (inflammageing4) and arrests stem cell proliferation and regeneration. Reducing the burden of senescent cells, or reducing their inflammatory secretome through CD36 neutralization, accelerates regeneration in young and old mice. By contrast, transplantation of senescent cells delays regeneration. Our results provide a technique for isolating in vivo senescent cells, define a senescence blueprint for muscle, and uncover unproductive functional interactions between senescent cells and stem cells in regenerative niches that can be overcome. As senescent cells also accumulate in human muscles, our findings open potential paths for improving muscle repair throughout life.

Detecting expressed genes in cell populations at the single-cell level with scGeneXpress.

Determining whether genes are expressed or not remains a challenge in single-cell RNAseq experiments due to their different expression spectra, which are influenced by genetics, the microenvironment and gene length. Current approaches for addressing this issue fail to provide a comprehensive landscape of expressed genes, since they neglect the inherent differences in the expression ranges and distributions of genes. Here, we present scGeneXpress, a method for detecting expressed genes in cell populations of single-cell RNAseq samples based on gene-specific reference distributions. We demonstrate that scGeneXpress accurately detects expressed cell markers and identity genes in 34 human and mouse tissues and can be employed to improve differential expression analysis of single-cell RNAseq data.

Fostering experimental and computational synergy to modulate hyperinflammation.

The molecular underpinnings of the uncontrolled release of proinflammatory cytokines and chemokines ('cytokine storm'), which can cause organ damage and even mortality, are not completely understood. Furthermore, targeted therapeutic options to dampen such hyperinflammation are scarce. Here, we highlight the ways in which technological advances have set the stage for a new age of synergy between experimental and computational researchers to guide the discovery of novel therapeutic targets for modulating hyperinflammation.

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.

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells.

Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbation responses and drugs in non-cancer cells compared to those based onto cancer databases. In particular, ChemPert correctly predicted drug effects for treating hepatitis and novel drugs for osteoarthritis. The ChemPert web interface is user-friendly and allows easy access of the entire datasets and the computational tool, providing valuable resources for both experimental researchers who wish to find datasets relevant to their research and computational researchers who need comprehensive non-cancer perturbation transcriptomics datasets for developing novel algorithms. Overall, ChemPert will facilitate future in silico compound screening for non-cancer cells.

FunRes: resolving tissue-specific functional cell states based on a cell-cell communication network model.

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.

Epi-Impute: Single-Cell RNA-seq Imputation via Integration with Single-Cell ATAC-seq.

Single-cell RNA-seq data contains a lot of dropouts hampering downstream analyses due to the low number and inefficient capture of mRNAs in individual cells. Here, we present Epi-Impute, a computational method for dropout imputation by reconciling expression and epigenomic data. Epi-Impute leverages single-cell ATAC-seq data as an additional source of information about gene activity to reduce the number of dropouts. We demonstrate that Epi-Impute outperforms existing methods, especially for very sparse single-cell RNA-seq data sets, significantly reducing imputation error. At the same time, Epi-Impute accurately captures the primary distribution of gene expression across cells while preserving the gene-gene and cell-cell relationship in the data. Moreover, Epi-Impute allows for the discovery of functionally relevant cell clusters as a result of the increased resolution of scRNA-seq data due to imputation.

Integrative and perturbation-based analysis of the transcriptional dynamics of TGFß/BMP system components in transition from embryonic stem cells to neural progenitors.

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 ß (TGFß) 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 TGFß 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.

A general computational approach to predicting synergistic transcriptional cores that determine cell subpopulation identities.

Advances in single-cell RNA-sequencing techniques reveal the existence of distinct cell subpopulations. Identification of transcription factors (TFs) that define the identity of these subpopulations poses a challenge. Here, we postulate that identity depends on background subpopulations, and is determined by a synergistic core combination of TFs mainly uniquely expressed in each subpopulation, but also TFs more broadly expressed across background subpopulations. Building on this view, we develop a new computational method for determining such synergistic identity cores of subpopulations within a given cell population. Our method utilizes an information-theoretic measure for quantifying transcriptional synergy, and implements a novel algorithm for searching for optimal synergistic cores. It requires only single-cell RNA-seq data as input, and does not rely on any prior knowledge of candidate genes or gene regulatory networks. Hence, it can be directly applied to any cellular systems, including those containing novel subpopulations. The method is capable of recapitulating known experimentally validated identity TFs in eight published single-cell RNA-seq datasets. Furthermore, some of these identity TFs are known to trigger cell conversions between subpopulations. Thus, this methodology can help design strategies for cell conversion within a cell population, guiding experimentalists in the field of stem cell research and regenerative medicine.

An integrative method to predict signalling perturbations for cellular transitions.

Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material. Here we present the first general computational method that systematically predicts signalling molecules, whose perturbations induce desired cellular transitions. This probabilistic method integrates gene regulatory networks (GRNs) with manually-curated signalling pathways obtained from MetaCore from Clarivate Analytics, to model how signalling cues are received and processed in the GRN. The method was applied to 219 cellular transition examples, including cell type transitions, and overall correctly predicted experimentally validated signalling molecules, consistently outperforming other well-established approaches, such as differential gene expression and pathway enrichment analyses. Further, we validated our method predictions in the case of rat cirrhotic liver, and identified the activation of angiopoietins receptor Tie2 as a potential target for reverting the disease phenotype. Experimental results indicated that this perturbation induced desired changes in the gene expression of key TFs involved in fibrosis and angiogenesis. Importantly, this method only requires gene expression data of the initial and desired cell states, and therefore is suited for the discovery of signalling interventions for disease treatments and cellular therapies.

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons.

The stochastic dynamics and regulatory mechanisms that govern differentiation of individual human neural precursor cells (NPC) into mature neurons are currently not fully understood. Here, we used single-cell RNA-sequencing (scRNA-seq) of developing neurons to dissect/identify NPC subtypes and critical developmental stages of alternative lineage specifications. This study comprises an unsupervised, high-resolution strategy for identifying cell developmental bifurcations, tracking the stochastic transcript kinetics of the subpopulations, elucidating regulatory networks, and finding key regulators. Our data revealed the bifurcation and developmental tracks of the two NPC subpopulations, and we captured an early (24 h) transition phase that leads to alternative neuronal specifications. The consequent up-regulation and down-regulation of stage- and subpopulation-specific gene groups during the course of maturation revealed biological insights with regard to key regulatory transcription factors and lincRNAs that control cellular programs in the identified neuronal subpopulations.

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.

Wnt/Tcf1 pathway restricts embryonic stem cell cycle through activation of the Ink4/Arf locus.

Understanding the mechanisms regulating cell cycle, proliferation and potency of pluripotent stem cells guarantees their safe use in the clinic. Embryonic stem cells (ESCs) present a fast cell cycle with a short G1 phase. This is due to the lack of expression of cell cycle inhibitors, which ultimately determines naïve pluripotency by holding back differentiation. The canonical Wnt/ß-catenin pathway controls mESC pluripotency via the Wnt-effector Tcf3. However, if the activity of the Wnt/ß-catenin controls the cell cycle of mESCs remains unknown. Here we show that the Wnt-effector Tcf1 is recruited to and triggers transcription of the Ink4/Arf tumor suppressor locus. Thereby, the activation of the Wnt pathway, a known mitogenic pathway in somatic tissues, restores G1 phase and drastically reduces proliferation of mESCs without perturbing pluripotency. Tcf1, but not Tcf3, is recruited to a palindromic motif enriched in the promoter of cell cycle repressor genes, such as p15Ink4b, p16Ink4a and p19Arf, which mediate the Wnt-dependent anti-proliferative effect in mESCs. Consistently, ablation of ß-catenin or Tcf1 expression impairs Wnt-dependent cell cycle regulation. All together, here we showed that Wnt signaling controls mESC pluripotency and proliferation through non-overlapping functions of distinct Tcf factors.

Transcriptome of human foetal heart compared with cardiomyocytes from pluripotent stem cells.

Differentiated derivatives of human pluripotent stem cells (hPSCs) are often considered immature because they resemble foetal cells more than adult, with hPSC-derived cardiomyocytes (hPSC-CMs) being no exception. Many functional features of these cardiomyocytes, such as their cell morphology, electrophysiological characteristics, sarcomere organization and contraction force, are underdeveloped compared with adult cardiomyocytes. However, relatively little is known about how their gene expression profiles compare with the human foetal heart, in part because of the paucity of data on the human foetal heart at different stages of development. Here, we collected samples of matched ventricles and atria from human foetuses during the first and second trimester of development. This presented a rare opportunity to perform gene expression analysis on the individual chambers of the heart at various stages of development, allowing us to identify not only genes involved in the formation of the heart, but also specific genes upregulated in each of the four chambers and at different stages of development. The data showed that hPSC-CMs had a gene expression profile similar to first trimester foetal heart, but after culture in conditions shown previously to induce maturation, they cluster closer to the second trimester foetal heart samples. In summary, we demonstrate how the gene expression profiles of human foetal heart samples can be used for benchmarking hPSC-CMs and also contribute to determining their equivalent stage of development.

RefBool: a reference-based algorithm for discretizing gene expression data.

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. AVAILABILITY AND IMPLEMENTATION: The software is available as MATLAB files in the Supplementary Information and as an online repository ( https://github.com/saschajung/RefBool ). CONTACT: antonio.delsol@uni.lu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation.

Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self-renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation-specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

Identification of large-scale genomic variation in cancer genomes using in silico reference models.

Identifying large-scale structural variation in cancer genomes continues to be a challenge to researchers. Current methods rely on genome alignments based on a reference that can be a poor fit to highly variant and complex tumor genomes. To address this challenge we developed a method that uses available breakpoint information to generate models of structural variations. We use these models as references to align previously unmapped and discordant reads from a genome. By using these models to align unmapped reads, we show that our method can help to identify large-scale variations that have been previously missed.