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.

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.

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.

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.

Measuring biological age using a functionally interpretable multi-tissue RNA clock.

The quantification of the biological age of cells yields great promises for accelerating the discovery of novel rejuvenation strategies. Here, we present MultiTIMER, the first multi-tissue aging clock that measures the biological, rather than chronological, age of cells from their transcriptional profiles by evaluating key cellular processes. We applied MultiTIMER to more than 70,000 transcriptional profiles and demonstrate that it accurately responds to cellular stressors and known interventions while informing about dysregulated cellular functions.

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.

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.

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.

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.

Combinatorial analysis reveals highly coordinated early-stage immune reactions that predict later antiviral immunity in mild COVID-19 patients.

While immunopathology has been widely studied in patients with severe COVID-19, immune responses in non-hospitalized patients have remained largely elusive. We systematically analyze 484 peripheral cellular or soluble immune features in a longitudinal cohort of 63 mild and 15 hospitalized patients versus 14 asymptomatic and 26 household controls. We observe a transient increase of IP10/CXCL10 and interferon-ß levels, coordinated responses of dominant SARS-CoV-2-specific CD4 and fewer CD8 T cells, and various antigen-presenting and antibody-secreting cells in mild patients within 3 days of PCR diagnosis. The frequency of key innate immune cells and their functional marker expression are impaired in hospitalized patients at day 1 of inclusion. T cell and dendritic cell responses at day 1 are highly predictive for SARS-CoV-2-specific antibody responses after 3 weeks in mild but not hospitalized patients. Our systematic analysis reveals a combinatorial picture and trajectory of various arms of the highly coordinated early-stage immune responses in mild COVID-19 patients.

Computational Methods to Identify Cell-Fate Determinants, Identity Transcription Factors, and Niche-Induced Signaling Pathways for Stem Cell Research.

The large-scale development of high-throughput sequencing technologies has not only allowed the generation of reliable omics data related to various regulatory layers but also the development of novel computational models in the field of stem cell research. These computational approaches have enabled the disentangling of a complex interplay between these interrelated layers of regulation by interpreting large quantities of biomedical data in a systematic way. In the context of stem cell research, network modeling of complex gene-gene interactions has been successfully used for understanding the mechanisms underlying stem cell differentiation and cellular conversion. Notably, it has proven helpful for predicting cell-fate determinants and signaling molecules controlling such processes. This chapter will provide an overview of various computational approaches that rely on single-cell and/or bulk RNA sequencing data for elucidating the molecular underpinnings of cell subpopulation identities, lineage specification, and the process of cell-fate decisions. Furthermore, we discuss how these computational methods provide the right framework for computational modeling of biological systems in order to address long-standing challenges in the stem cell field by guiding experimental efforts in stem cell research and regenerative medicine.

Home Treatment Compared to Initial Hospitalization in Normotensive Patients with Acute Pulmonary Embolism in the Netherlands: A Cost Analysis.

BACKGROUND: Venous thromboembolism constitutes substantial health care costs amounting to approximately 60 million euros per year in the Netherlands. Compared with initial hospitalization, home treatment of pulmonary embolism (PE) is associated with a cost reduction. An accurate estimation of cost savings per patient treated at home is currently lacking. AIM: The aim of this study was to compare health care utilization and costs during the first 3 months after a PE diagnosis in patients who are treated at home versus those who are initially hospitalized. METHODS: Patient-level data of the YEARS cohort study, including 383 normotensive patients diagnosed with PE, were used to estimate the proportion of patients treated at home, mean hospitalization duration in those who were hospitalized, and rates of PE-related readmissions and complications. To correct for baseline differences within the two groups, regression analyses was performed. The primary outcome was the average total health care costs during a 3-month follow-up period for patients initially treated at home or in hospital. RESULTS: Mean hospitalization duration for the initial treatment was 0.69 days for those treated initially at home (n = 181) and 4.3 days for those initially treated in hospital (n = 202). Total average costs per hospitalized patient were €3,209 and €1,512 per patient treated at home. The adjusted mean difference was €1,483 (95% confidence interval: €1,181-1,784). CONCLUSION: Home treatment of hemodynamically stable patients with acute PE was associated with an estimated net cost reduction of €1,483 per patient. This difference underlines the advantage of triage-based home treatment of these patients.

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.

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.

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.

Proneural genes define ground-state rules to regulate neurogenic patterning and cortical folding.

Asymmetric neuronal expansion is thought to drive evolutionary transitions between lissencephalic and gyrencephalic cerebral cortices. We report that Neurog2 and Ascl1 proneural genes together sustain neurogenic continuity and lissencephaly in rodent cortices. Using transgenic reporter mice and human cerebral organoids, we found that Neurog2 and Ascl1 expression defines a continuum of four lineage-biased neural progenitor cell (NPC) pools. Double+ NPCs, at the hierarchical apex, are least lineage restricted due to Neurog2-Ascl1 cross-repression and display unique features of multipotency (more open chromatin, complex gene regulatory network, G2 pausing). Strikingly, selectively eliminating double+ NPCs by crossing Neurog2-Ascl1 split-Cre mice with diphtheria toxin-dependent "deleter" strains locally disrupts Notch signaling, perturbs neurogenic symmetry, and triggers cortical folding. In support of our discovery that double+ NPCs are Notch-ligand-expressing "niche" cells that control neurogenic periodicity and cortical folding, NEUROG2, ASCL1, and HES1 transcript distribution is modular (adjacent high/low zones) in gyrencephalic macaque cortices, prefiguring future folds.

A 3D system to model human pancreas development and its reference single-cell transcriptome atlas identify signaling pathways required for progenitor expansion.

Human organogenesis remains relatively unexplored for ethical and practical reasons. Here, we report the establishment of a single-cell transcriptome atlas of the human fetal pancreas between 7 and 10 post-conceptional weeks of development. To interrogate cell-cell interactions, we describe InterCom, an R-Package we developed for identifying receptor-ligand pairs and their downstream effects. We further report the establishment of a human pancreas culture system starting from fetal tissue or human pluripotent stem cells, enabling the long-term maintenance of pancreas progenitors in a minimal, defined medium in three-dimensions. Benchmarking the cells produced in 2-dimensions and those expanded in 3-dimensions to fetal tissue identifies that progenitors expanded in 3-dimensions are transcriptionally closer to the fetal pancreas. We further demonstrate the potential of this system as a screening platform and identify the importance of the EGF and FGF pathways controlling human pancreas progenitor expansion.

Risk of thrombotic complications in influenza versus COVID-19 hospitalized patients.

Background: Whereas accumulating studies on patients with coronavirus disease 2019 (COVID-19) report high incidences of thrombotic complications, large studies on clinically relevant thrombosis in patients with other respiratory tract infections are lacking. How this high risk in COVID-19 patients compares to those observed in hospitalized patients with other viral pneumonias such as influenza is unknown. Objectives: To assess the incidence of venous and arterial thrombotic complications in hospitalized patients with influenza as opposed to that observed in hospitalized patients with COVID-19. Methods: This was a retrospective cohort study; we used data from Statistics Netherlands (study period: 2018) on thrombotic complications in hospitalized patients with influenza. In parallel, we assessed the cumulative incidence of thrombotic complications-adjusted for competing risk of death-in patients with COVID-19 in three Dutch hospitals (February 24 to April 26, 2020). Results: Of the 13 217 hospitalized patients with influenza, 437 (3.3%) were diagnosed with thrombotic complications, versus 66 (11%) of the 579 hospitalized patients with COVID-19. The 30-day cumulative incidence of any thrombotic complication in influenza was 11% (95% confidence interval [CI], 9.4-12) versus 25% (95% CI, 18-32) in COVID-19. For venous thrombotic (VTC) complications and arterial thrombotic complications alone, these numbers were, respectively, 3.6% (95% CI, 2.7-4.6) and 7.5% (95% CI, 6.3-8.8) in influenza versus 23% (95% CI, 16-29) and 4.4% (95% CI, 1.9-8.8) in COVID-19. Conclusions: The incidence of thrombotic complications in hospitalized patients with influenza was lower than in hospitalized patients with COVID-19. This difference was mainly driven by a high risk of VTC complications in the patients with COVID-19 admitted to the Intensive Care Unit. Remarkably, patients with influenza were more often diagnosed with arterial thrombotic complications.