Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states.

Single-cell genomics technology has transformed our understanding of complex cellular systems. However, excessive cost and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high-content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all main hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables the functional capacities of precisely mapped cell states to be measured at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry.

SpaceM reveals metabolic states of single cells.

A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

Single-cell transcriptomics reveals immune response of intestinal cell types to viral infection.

Human intestinal epithelial cells form a primary barrier protecting us from pathogens, yet only limited knowledge is available about individual contribution of each cell type to mounting an immune response against infection. Here, we developed a framework combining single-cell RNA-Seq and highly multiplex RNA FISH and applied it to human intestinal organoids infected with human astrovirus, a model human enteric virus. We found that interferon controls the infection and that astrovirus infects all major cell types and lineages and induces expression of the cell proliferation marker MKI67. Intriguingly, each intestinal epithelial cell lineage exhibits a unique basal expression of interferon-stimulated genes and, upon astrovirus infection, undergoes an antiviral transcriptional reprogramming by upregulating distinct sets of interferon-stimulated genes. These findings suggest that in the human intestinal epithelium, each cell lineage plays a unique role in resolving virus infection. Our framework is applicable to other organoids and viruses, opening new avenues to unravel roles of individual cell types in viral pathogenesis.

Single-cell analyses reveal SARS-CoV-2 interference with intrinsic immune response in the human gut.

Exacerbated pro-inflammatory immune response contributes to COVID-19 pathology. However, despite the mounting evidence about SARS-CoV-2 infecting the human gut, little is known about the antiviral programs triggered in this organ. To address this gap, we performed single-cell transcriptomics of SARS-CoV-2-infected intestinal organoids. We identified a subpopulation of enterocytes as the prime target of SARS-CoV-2 and, interestingly, found the lack of positive correlation between susceptibility to infection and the expression of ACE2. Infected cells activated strong pro-inflammatory programs and produced interferon, while expression of interferon-stimulated genes was limited to bystander cells due to SARS-CoV-2 suppressing the autocrine action of interferon. These findings reveal that SARS-CoV-2 curtails the immune response and highlights the gut as a pro-inflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.

PySpacell: A Python Package for Spatial Analysis of Cell Images.

Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single-cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single-cell level is one of the grand challenges and a key to understanding the effects of cell-cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single-cell read-out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. The proposed Python module can be used for any light microscopy images of cells as well as other types of single-cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. © 2019 International Society for Advancement of Cytometry.

Single-cell division tracing and transcriptomics reveal cell types and differentiation paths in the regenerating lung.

Understanding the molecular and cellular processes involved in lung epithelial regeneration may fuel the development of therapeutic approaches for lung diseases. We combine mouse models allowing diphtheria toxin-mediated damage of specific epithelial cell types and parallel GFP-labeling of functionally dividing cells with single-cell transcriptomics to characterize the regeneration of the distal lung. We uncover cell types, including Krt13+ basal and Krt15+ club cells, detect an intermediate cell state between basal and goblet cells, reveal goblet cells as actively dividing progenitor cells, and provide evidence that adventitial fibroblasts act as supporting cells in epithelial regeneration. We also show that diphtheria toxin-expressing cells can persist in the lung, express specific inflammatory factors, and transcriptionally resemble a previously undescribed population in the lungs of COVID-19 patients. Our study provides a comprehensive single-cell atlas of the distal lung that characterizes early transcriptional and cellular responses to concise epithelial injury, encompassing proliferation, differentiation, and cell-to-cell interactions.

Scalable, compressed phenotypic screening using pooled perturbations.

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

Natural history of Ebola virus disease in rhesus monkeys shows viral variant emergence dynamics and tissue-specific host responses.

Ebola virus (EBOV) causes Ebola virus disease (EVD), marked by severe hemorrhagic fever; however, the mechanisms underlying the disease remain unclear. To assess the molecular basis of EVD across time, we performed RNA sequencing on 17 tissues from a natural history study of 21 rhesus monkeys, developing new methods to characterize host-pathogen dynamics. We identified alterations in host gene expression with previously unknown tissue-specific changes, including downregulation of genes related to tissue connectivity. EBOV was widely disseminated throughout the body; using a new, broadly applicable deconvolution method, we found that viral load correlated with increased monocyte presence. Patterns of viral variation between tissues differentiated primary infections from compartmentalized infections, and several variants impacted viral fitness in a EBOV/Kikwit minigenome system, suggesting that functionally significant variants can emerge during early infection. This comprehensive portrait of host-pathogen dynamics in EVD illuminates new features of pathogenesis and establishes resources to study other emerging pathogens.

Mapping the epithelial-immune cell interactome upon infection in the gut and the upper airways.

Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial-immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach. In this work, we propose an integrated computational biology framework that models how infection alters intracellular signalling of epithelial cells and how this change impacts the systemic immune response through modified interactions between epithelial cells and local immune cell populations. As a proof-of-concept, we focused on the role of intestinal and upper-airway epithelial infection. To characterise the modified epithelial-immune interactome, we integrated intra- and intercellular networks with single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids as well as from infected airway ciliated epithelial cells. This integrated methodology has proven useful to point out specific epithelial-immune interactions driving inflammation during disease response, and propose relevant molecular targets to guide focused experimental analysis.

Using human iPSC-derived kidney organoids to decipher SARS-CoV-2 pathology on single cell level.

We describe a protocol for single-cell RNA sequencing of SARS-CoV-2-infected human induced pluripotent stem cell (iPSC)-derived kidney organoids. After inoculation of kidney organoids with virus, we use mechanical and enzymatic disruption to obtain single cell suspensions. Next, we process the organoid-derived cells into sequencing-ready SARS-CoV-2-targeted libraries. Subsequent sequencing analysis reveals changes in kidney cells after virus infection. The protocol was designed for kidney organoids cultured in a 6-well transwell format but can be adapted to organoids with different organ backgrounds. For complete details on the use and execution of this protocol, please refer to Jansen et al. (2022).

A Model for Network-Based Identification and Pharmacological Targeting of Aberrant, Replication-Permissive Transcriptional Programs Induced by Viral Infection.

Precise characterization and targeting of host cell transcriptional machinery hijacked by viral infection remains challenging. Here, we show that SARS-CoV-2 hijacks the host cell transcriptional machinery to induce a phenotypic state amenable to its replication. Specifically, analysis of Master Regulator (MR) proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2 in infected cells revealed coordinated inactivation of MRs enriched in physical interactions with SARS-CoV-2 proteins, suggesting their mechanistic role in maintaining a host cell state refractory to virus replication. To test their functional relevance, we measured SARS-CoV-2 replication in epithelial cells treated with drugs predicted to activate the entire repertoire of repressed MRs, based on their experimentally elucidated, context-specific mechanism of action. Overall, >80% of drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 replication, without affecting cell viability. This model for host-directed pharmacological therapy is fully generalizable and can be deployed to identify drugs targeting host cell-based MR signatures induced by virtually any pathogen.

A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection.

SARS-CoV-2 hijacks the host cell transcriptional machinery to induce a phenotypic state amenable to its replication. Here we show that analysis of Master Regulator proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2 in infected cells revealed coordinated inactivation of Master Regulators enriched in physical interactions with SARS-CoV-2 proteins, suggesting their mechanistic role in maintaining a host cell state refractory to virus replication. To test their functional relevance, we measured SARS-CoV-2 replication in epithelial cells treated with drugs predicted to activate the entire repertoire of repressed Master Regulators, based on their experimentally elucidated, context-specific mechanism of action. Overall, 15 of the 18 drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 replication, without affecting cell viability. This model for host-directed pharmacological therapy is fully generalizable and can be deployed to identify drugs targeting host cell-based Master Regulator signatures induced by virtually any pathogen.

SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids.

Kidney failure is frequently observed during and after COVID-19, but it remains elusive whether this is a direct effect of the virus. Here, we report that SARS-CoV-2 directly infects kidney cells and is associated with increased tubule-interstitial kidney fibrosis in patient autopsy samples. To study direct effects of the virus on the kidney independent of systemic effects of COVID-19, we infected human-induced pluripotent stem-cell-derived kidney organoids with SARS-CoV-2. Single-cell RNA sequencing indicated injury and dedifferentiation of infected cells with activation of profibrotic signaling pathways. Importantly, SARS-CoV-2 infection also led to increased collagen 1 protein expression in organoids. A SARS-CoV-2 protease inhibitor was able to ameliorate the infection of kidney cells by SARS-CoV-2. Our results suggest that SARS-CoV-2 can directly infect kidney cells and induce cell injury with subsequent fibrosis. These data could explain both acute kidney injury in COVID-19 patients and the development of chronic kidney disease in long COVID.

Critical Role of Type III Interferon in Controlling SARS-CoV-2 Infection in Human Intestinal Epithelial Cells.

Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is an unprecedented worldwide health problem that requires concerted and global approaches to stop the coronavirus 2019 (COVID-19) pandemic. Although SARS-CoV-2 primarily targets lung epithelium cells, there is growing evidence that the intestinal epithelium is also infected. Here, using both colon-derived cell lines and primary non-transformed colon organoids, we engage in the first comprehensive analysis of the SARS-CoV-2 life cycle in human intestinal epithelial cells (hIECs). Our results demonstrate that hIECs fully support SARS-CoV-2 infection, replication, and production of infectious de novo virus particles. We found that viral infection elicits an extremely robust intrinsic immune response where interferon-mediated responses are efficient at controlling SARS-CoV-2 replication and de novo virus production. Taken together, our data demonstrate that hIECs are a productive site of SARS-CoV-2 replication and suggest that the enteric phase of SARS-CoV-2 may participate in the pathologies observed in COVID-19 patients by contributing to increasing patient viremia and fueling an exacerbated cytokine response.

Analysis of Malassezia Lipidome Disclosed Differences Among the Species and Reveals Presence of Unusual Yeast Lipids.

Malassezia yeasts are lipid dependent and part of the human and animal skin microbiome. However, they are also associated with a variety of dermatological conditions and even cause systemic infections. How these yeasts can live as commensals on the skin and switch to a pathogenic stage has long been a matter of debate. Lipids are important cellular molecules, and understanding the lipid metabolism and composition of Malassezia species is crucial to comprehending their biology and host-microbe interaction. Here, we investigated the lipid composition of Malassezia strains grown to the stationary phase in a complex Dixon medium broth. In this study, we perform a lipidomic analysis of a subset of species; in addition, we conducted a gene prediction analysis for the detection of lipid metabolic proteins. We identified 18 lipid classes and 428 lipidic compounds. The most commonly found lipids were triglycerides (TAG), sterol (CH), diglycerides (DG), fatty acids (FAs), phosphatidylcholine (PC), phosphatidylethanolamine (PE), ceramides, cholesteryl ester (CE), sphingomyelin (SM), acylcarnitine, and lysophospholipids. Particularly, we found a low content of CEs in Malassezia furfur, atypical M. furfur, and Malassezia pachydermatis and undetectable traces of these components in Malassezia globosa, Malassezia restricta, and Malassezia sympodialis. Remarkably, uncommon lipids in yeast, like diacylglyceryltrimethylhomoserine and FA esters of hydroxyl FAs, were found in a variable concentration in these Malassezia species. The latter are bioactive lipids recently reported to have antidiabetic and anti-inflammatory properties. The results obtained can be used to discriminate different Malassezia species and offer a new overview of the lipid composition of these yeasts. We could confirm the presence and the absence of certain lipid-biosynthesis genes in specific species. Further analyses are necessary to continue disclosing the complex lipidome of Malassezia species and the impact of the lipid metabolism in connection with the host interaction.

New Therapeutic Candidates for the Treatment of Malassezia pachydermatis -Associated Infections.

The opportunistic pathogen Malassezia pachydermatis causes bloodstream infections in preterm infants or individuals with immunodeficiency disorders and has been associated with a broad spectrum of diseases in animals such as seborrheic dermatitis, external otitis and fungemia. The current approaches to treat these infections are failing as a consequence of their adverse effects, changes in susceptibility and antifungal resistance. Thus, the identification of novel therapeutic targets against M. pachydermatis infections are highly relevant. Here, Gene Essentiality Analysis and Flux Variability Analysis was applied to a previously reported M. pachydermatis metabolic network to identify enzymes that, when absent, negatively affect biomass production. Three novel therapeutic targets (i.e., homoserine dehydrogenase (MpHSD), homocitrate synthase (MpHCS) and saccharopine dehydrogenase (MpSDH)) were identified that are absent in humans. Notably, L-lysine was shown to be an inhibitor of the enzymatic activity of MpHCS and MpSDH at concentrations of 1 mM and 75 mM, respectively, while L-threonine (1 mM) inhibited MpHSD. Interestingly, L- lysine was also shown to inhibit M. pachydermatis growth during in vitro assays with reference strains and canine isolates, while it had a negligible cytotoxic activity on HEKa cells. Together, our findings form the bases for the development of novel treatments against M. pachydermatis infections.

Lipid Metabolic Versatility in Malassezia spp. Yeasts Studied through Metabolic Modeling.

Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur, and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis. The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa, M. sympodialis, M. pachydermatis, and the atypical variant of M. furfur, but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia.

Manifestaciones radiológicas del síndrome del nevus azul. Presentación de caso / Manifestaciones radiológicas del síndrome del nevus azul. Presentación de caso

El síndrome del nevus azul es una entidad infrecuente que consiste en múltiples malformaciones venosas que comprometen la piel y el resto de los órganos. Los pacientes pueden ser asintomáticos o pueden cursar con diversas manifestaciones dependiendo del lugar donde se encuentren estas alteraciones vasculares. Las distintas modalidades diagnósticas permiten identificar claramente las malformaciones vasculares; sin embargo, usualmente son consideradas un hallazgo incidental y rara vez son el motivo de la realización del estudio. A continuación, se describe el caso de un paciente de 65 años de edad con diagnóstico de síndrome del nevus azul, con malformaciones venosas evidentes, en modalidades como tomografía computarizada, ultrasonido y resonancia magnética.

The blue rubber bleb nevus syndrome is a rare vascular disorder entity that consists of multiple venous malformations that involve the skin, gastrointestinal tract and viscera. Patients may be asymptomatic or may present with various manifestations depending on the site of these vascular alterations and organs involved. The different diagnostic modalities allow for clear identification of vascular malformations; however, they are usually considered an incidental finding and are rarely the reason for the study to be performed. We describe the case of a 65-year-old patient with a diagnosis of blue nevus syndrome with evident venous malformations in computed tomography, ultrasound and magnetic resonance imaging.

Draft Genome Sequence of the Animal and Human Pathogen Malassezia pachydermatis Strain CBS 1879.

Malassezia pachydermatis is a basidiomycetous yeast that causes infections in humans and animals. Here, we report the genome sequence of Malassezia pachydermatis strain CBS 1879, which will facilitate the study of mechanisms underlying pathogenicity of the only non-lipid-dependent Malasezzia species.