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.

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.

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.

Proteome and Secretome Characterization of Glioblastoma-Derived Neural Stem Cells.

Glioblastoma multiforme (GBM) (grade IV astrocytoma) is the most common and aggressive primary brain tumor. GBM consists of heterogeneous cell types including a subset of stem cell-like cells thought to sustain tumor growth. These tumor-initiating glioblastoma multiforme-derived neural stem (GNS) cells as well as their genetically normal neural stem (NS) counterparts can be propagated in culture as relatively pure populations. Here, we perform quantitative proteomics to globally characterize and compare total proteome plus the secreted proteome (secretome) between GNS cells and NS cells. Proteins and pathways that distinguish malignant cancer (GNS) stem cells from their genetically normal counterparts (NS cells) might have value as new biomarkers or therapeutic targets. Our analysis identified and quantified ∼7,500 proteins in the proteome and ∼2,000 in the secretome, 447 and 138 of which were differentially expressed, respectively. Notable tumor-associated processes identified using gene set enrichment analysis included: extracellular matrix interactions, focal adhesion, cell motility, and cell signaling. We focused on differentially expressed surface proteins, and identified 26 that participate in ligand-receptor pairs that play a prominent role in tumorigenesis. Immunocytochemistry and immunoblotting confirmed that CD9, a recently identified marker of adult subventricular zone NS cells, was consistently enriched across a larger set of primary GNS cell lines. CD9 may, therefore, have value as a GNS-specific surface marker and a candidate therapeutic target. Altogether, these findings support the notion that increased cell-matrix and cell-cell adhesion molecules play a crucial role in promoting the tumor initiating and infiltrative properties of GNS cells. Stem Cells 2017;35:967-980.

The RNA helicase DDX6 regulates cell-fate specification in neural stem cells via miRNAs.

In neural stem cells (NSCs), the balance between stem cell maintenance and neuronal differentiation depends on cell-fate determinants such as TRIM32. Previously, we have shown that TRIM32 associates with the RNA-induced silencing complex and increases the activity of microRNAs such as Let-7a. However, the exact mechanism of microRNA regulation by TRIM32 during neuronal differentiation has yet to be elucidated. Here, we used a mass spectrometry approach to identify novel protein-protein interaction partners of TRIM32 during neuronal differentiation. We found that TRIM32 associates with proteins involved in neurogenesis and RNA-related processes, such as the RNA helicase DDX6, which has been implicated in microRNA regulation. We demonstrate, that DDX6 colocalizes with TRIM32 in NSCs and neurons and that it increases the activity of Let-7a. Furthermore, we provide evidence that DDX6 is necessary and sufficient for neuronal differentiation and that it functions in cooperation with TRIM32.

SinCMat: A single-cell-based method for predicting functional maturation transcription factors.

A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.

A molecular index for biological age identified from the metabolome and senescence-associated secretome in humans.

Unlike chronological age, biological age is a strong indicator of health of an individual. However, the molecular fingerprint associated with biological age is ill-defined. To define a high-resolution signature of biological age, we analyzed metabolome, circulating senescence-associated secretome (SASP)/inflammation markers and the interaction between them, from a cohort of healthy and rapid agers. The balance between two fatty acid oxidation mechanisms, ß-oxidation and ω-oxidation, associated with the extent of functional aging. Furthermore, a panel of 25 metabolites, Healthy Aging Metabolic (HAM) index, predicted healthy agers regardless of gender and race. HAM index was also validated in an independent cohort. Causal inference with machine learning implied three metabolites, ß-cryptoxanthin, prolylhydroxyproline, and eicosenoylcarnitine as putative drivers of biological aging. Multiple SASP markers were also elevated in rapid agers. Together, our findings reveal that a network of metabolic pathways underlie biological aging, and the HAM index could serve as a predictor of phenotypic aging in humans.

Post-transcriptional regulation of IFI16 promotes inflammatory endothelial pathophenotypes observed in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a progressive disease driven by endothelial cell inflammation and dysfunction, resulting in the pathological remodeling of the pulmonary vasculature. Innate immune activation has been linked to PAH development; however, the regulation, propagation, and reversibility of the induction of inflammation in PAH is poorly understood. Here, we demonstrate a role for interferon inducible protein 16 (IFI16), an innate immune sensor, as a modulator of endothelial inflammation in pulmonary hypertension, utilizing human pulmonary artery endothelial cells (PAECs). Inflammatory stimulus of PAECs with IL-1ß up-regulates IFI16 expression, inducing proinflammatory cytokine up-regulation and cellular apoptosis. IFI16 mRNA stability is regulated by post-transcriptional m6A modification, mediated by Wilms' tumor 1-associated protein (WTAP), a structural stabilizer of the methyltransferase complex, via regulation of m6A methylation of IFI16. Additionally, m6A levels are increased in the peripheral blood mononuclear cells of PAH patients compared to control, indicating that quantifying this epigenetic change in patients may hold potential as a biomarker for disease identification. In summary, our study demonstrates IFI16 mediates inflammatory endothelial pathophenotypes seen in pulmonary arterial hypertension.

Ice recrystallization inhibitors enable efficient cryopreservation of induced pluripotent stem cells: A functional and transcriptomic analysis.

The successful use of human induced pluripotent stem cells (iPSCs) for research or clinical applications requires the development of robust, efficient, and reproducible cryopreservation protocols. After cryopreservation, the survival rate of iPSCs is suboptimal and cell line-dependent. We assessed the use of ice recrystallization inhibitors (IRIs) for cryopreservation of human iPSCs. A toxicity screening study was performed to assess specific small-molecule carbohydrate-based IRIs and concentrations for further evaluation. Then, a cryopreservation study compared the cryoprotective efficiency of 15 mM IRIs in 5 % or 10 % DMSO-containing solutions and with CryoStor® CS10. Three iPSC lines were cryopreserved as single-cell suspensions in the cryopreservation solutions and post-thaw characteristics, including pluripotency and differential gene expression were assessed. We demonstrate the fitness-for-purpose of 15 mM IRI in 5 % DMSO as an efficient cryoprotective solution for iPSCs in terms of post-thaw recovery, viability, pluripotency, and transcriptomic changes. This mRNA sequencing dataset has the potential to be used for molecular mechanism analysis relating to cryopreservation. Use of IRIs can reduce DMSO concentrations and its associated toxicities, thereby improving the utility, effectiveness, and efficiency of cryopreservation.

Pten regulates endocytic trafficking of cell adhesion and Wnt signaling molecules to pattern the retina.

The retina is exquisitely patterned, with neuronal somata positioned at regular intervals to completely sample the visual field. Here, we show that phosphatase and tensin homolog (Pten) controls starburst amacrine cell spacing by modulating vesicular trafficking of cell adhesion molecules and Wnt proteins. Single-cell transcriptomics and double-mutant analyses revealed that Pten and Down syndrome cell adhesion molecule Dscam) are co-expressed and function additively to pattern starburst amacrine cell mosaics. Mechanistically, Pten loss accelerates the endocytic trafficking of DSCAM, FAT3, and MEGF10 off the cell membrane and into endocytic vesicles in amacrine cells. Accordingly, the vesicular proteome, a molecular signature of the cell of origin, is enriched in exocytosis, vesicle-mediated transport, and receptor internalization proteins in Pten conditional knockout (PtencKO) retinas. Wnt signaling molecules are also enriched in PtencKO retinal vesicles, and the genetic or pharmacological disruption of Wnt signaling phenocopies amacrine cell patterning defects. Pten thus controls vesicular trafficking of cell adhesion and signaling molecules to establish retinal amacrine cell mosaics.

Allele-specific control of rodent and human lncRNA KMT2E-AS1 promotes hypoxic endothelial pathology in pulmonary hypertension.

Hypoxic reprogramming of vasculature relies on genetic, epigenetic, and metabolic circuitry, but the control points are unknown. In pulmonary arterial hypertension (PAH), a disease driven by hypoxia inducible factor (HIF)-dependent vascular dysfunction, HIF-2α promoted expression of neighboring genes, long noncoding RNA (lncRNA) histone lysine N-methyltransferase 2E-antisense 1 (KMT2E-AS1) and histone lysine N-methyltransferase 2E (KMT2E). KMT2E-AS1 stabilized KMT2E protein to increase epigenetic histone 3 lysine 4 trimethylation (H3K4me3), driving HIF-2α-dependent metabolic and pathogenic endothelial activity. This lncRNA axis also increased HIF-2α expression across epigenetic, transcriptional, and posttranscriptional contexts, thus promoting a positive feedback loop to further augment HIF-2α activity. We identified a genetic association between rs73184087, a single-nucleotide variant (SNV) within a KMT2E intron, and disease risk in PAH discovery and replication patient cohorts and in a global meta-analysis. This SNV displayed allele (G)-specific association with HIF-2α, engaged in long-range chromatin interactions, and induced the lncRNA-KMT2E tandem in hypoxic (G/G) cells. In vivo, KMT2E-AS1 deficiency protected against PAH in mice, as did pharmacologic inhibition of histone methylation in rats. Conversely, forced lncRNA expression promoted more severe PH. Thus, the KMT2E-AS1/KMT2E pair orchestrates across convergent multi-ome landscapes to mediate HIF-2α pathobiology and represents a key clinical target in pulmonary hypertension.

Properties of isotope patterns and their utility for peptide identification in large-scale proteomic experiments.

RATIONALE: In proteomic experiments, isotope patterns are routinely generated for all detected peptides. While this pattern is determined by peptide composition, it has not been evaluated as a parameter that can help in the process of peptide identification. METHODS: First, we investigated how the relative isotope abundance (RIA) accuracy in proteomic data sets depends on the spectral intensity, resolution, and the number of mass spectrometry (MS) 1 scans, using an Orbitrap Velos mass spectrometer. Next, we explored the discriminatory power of isotope patterns in the context of proteome analyses of various complexities, either alone or in combination with a Mascot database search. Finally, we provide a theoretical framework for the required accuracies of both peptide mass and RIA for peptide identification. RESULTS: We demonstrate that the RIA error obtained in routine proteome analyses is 4-5%, and that this is only modestly influenced by spectral intensity, resolution, and the number of MS1 scans. While RIA alone has no discriminatory power, in a Mascot search isotope patterns can distinguish top scoring hits from runner-up hits in 70-95% of cases. Our theoretical approach shows that RIA accuracy needs to be ~0.2% in order to uniquely identify peptides in full proteomes. CONCLUSIONS: Our results demonstrate that isotope patterns can have discriminatory power when used in combination with a classical database search. Inclusion of this parameter in proteomic workflows may help to increase confidence in peptide identification, but in practical terms this will be limited to small proteomes.

Aphasia with No Apparent Paralysis in Progressive Stroke of the Anterior Choroidal Artery.

Some anterior choroidal artery (AChA) infarctions in the posterior limbs of the internal capsule (plIC) have been reported to cause aphasia, typically with apparent paralysis. We herein report an 84-year-old woman with AChA infarction. Although her dysarthria remained mild with no apparent paralysis, we overlooked progression to branch atheromatous disease-related infarct with exacerbation of her anomia, which delayed the initiation of more intense therapy. Even in AChA infarction, especially when the lesion is located mainly in the anterior part of the plIC, as in our case, it is possible to encounter progressive stroke predominantly with aphasia.

A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion.

Cellular conversion can be induced by perturbing a handful of key transcription factors (TFs). Replacement of direct manipulation of key TFs with chemical compounds offers a less laborious and safer strategy to drive cellular conversion for regenerative medicine. Nevertheless, identifying optimal chemical compounds currently requires large-scale screening of chemical libraries, which is resource intensive. Existing computational methods aim at predicting cell conversion TFs, but there are no methods for identifying chemical compounds targeting these TFs. Here, we develop a single cell-based platform (SiPer) to systematically prioritize chemical compounds targeting desired TFs to guide cellular conversions. SiPer integrates a large compendium of chemical perturbations on non-cancer cells with a network model and predicted known and novel chemical compounds in diverse cell conversion examples. Importantly, we applied SiPer to develop a highly efficient protocol for human hepatic maturation. Overall, SiPer provides a valuable resource to efficiently identify chemical compounds for cell conversion.

Microbleeds Due to Reperfusion Enhance Early Seizures after Carotid Ligation in a Rat Ischemic Model.

Impaired reperfusion in ischemic brain disease is a condition that we are increasingly confronted with owing to recent advances in reperfusion therapy. In the present study, rat models of reperfusion were investigated to determine the causes of acute seizures using magnetic resonance imaging (MRI) and histopathological specimens. Rat models of bilateral common carotid artery ligation followed by reperfusion and complete occlusion were created. We compared the incidence of seizures, mortality within 24 h, MRI, and magnetic resonance spectroscopy (MRS) to evaluate ischemic or hemorrhagic changes and metabolites in the brain parenchyma. In addition, the histopathological specimens were compared with those observed on MRI. In multivariate analysis, the predictive factors of mortality were seizure (odds ratios (OR), 106.572), reperfusion or occlusion (OR, 0.056), and the apparent diffusion coefficient value of the striatum (OR, 0.396). The predictive factors of a convulsive seizure were reperfusion or occlusion (OR, 0.007) and the number of round-shaped hyposignals (RHS) on susceptibility-weighted imaging (SWI) (OR, 2.072). The incidence of convulsive seizures was significantly correlated with the number of RHS in the reperfusion model. RHS on SWI was confirmed pathologically as microbleeds in the extravasation of the brain parenchyma and was distributed around the hippocampus and cingulum bundle. MRS analysis showed that the N-acetyl aspartate level was significantly lower in the reperfusion group than in the occlusion group. In the reperfusion model, RHS on SWI was a risk factor for convulsive seizures. The location of the RHS also influenced the incidence of convulsive seizures.

Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages.

Glioblastoma is believed to originate from nervous system cells; however, a putative origin from vessel-associated progenitor cells has not been considered. We deeply single-cell RNA-sequenced glioblastoma progenitor cells of 18 patients and integrated 710 bulk tumors and 73,495 glioma single cells of 100 patients to determine the relation of glioblastoma cells to normal brain cell types. A novel neural network-based projection of the developmental trajectory of normal brain cells uncovered two principal cell-lineage features of glioblastoma, neural crest perivascular and radial glia, carrying defining methylation patterns and survival differences. Consistently, introducing tumorigenic alterations in naïve human brain perivascular cells resulted in brain tumors. Thus, our results suggest that glioblastoma can arise from the brains' vasculature, and patients with such glioblastoma have a significantly poorer outcome.

TransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments.

Generation of desired cell types by cell conversion remains a challenge. In particular, derivation of novel cell subtypes identified by single-cell technologies will open up new strategies for cell therapies. The recent increase in the generation of single-cell RNA-sequencing (scRNA-seq) data and the concomitant increase in the interest expressed by researchers in generating a wide range of functional cells prompted us to develop a computational tool for tackling this challenge. Here we introduce a web application, TransSynW, which uses scRNA-seq data for predicting cell conversion transcription factors (TFs) for user-specified cell populations. TransSynW prioritizes pioneer factors among predicted conversion TFs to facilitate chromatin opening often required for cell conversion. In addition, it predicts marker genes for assessing the performance of cell conversion experiments. Furthermore, TransSynW does not require users' knowledge of computer programming and computational resources. We applied TransSynW to different levels of cell conversion specificity, which recapitulated known conversion TFs at each level. We foresee that TransSynW will be a valuable tool for guiding experimentalists to design novel protocols for cell conversion in stem cell research and regenerative medicine.