Enhanced CD95 and interleukin 18 signalling accompany T cell receptor Vβ21.3+ activation in multi-inflammatory syndrome in children

Multisystem inflammatory syndrome in children is a post-infectious presentation SARS-CoV-2 associated with expansion of the T cell receptor Vβ21.3+ T-cell subgroup. Here we apply muti-single cell omics to compare the inflammatory process in children with acute respiratory COVID-19 and those presenting with non SARS-CoV-2 infections in children. Here we show that in Multi-Inflammatory Syndrome in Children (MIS-C), the natural killer cell and monocyte population demonstrate heightened CD95 (Fas) and Interleuking 18 receptor expression. Additionally, TCR Vβ21.3+ CD4+ T-cells exhibit skewed differentiation towards T helper 1, 17 and regulatory T cells, with increased expression of the co-stimulation receptors ICOS, CD28 and interleukin 18 receptor. We observe no functional evidence for NLRP3 inflammasome pathway overactivation, though MIS-C monocytes show elevated active caspase 8. This, coupled with raised IL18 mRNA expression in CD16- NK cells on single cell RNA sequencing analysis, suggests interleukin 18 and CD95 signalling may trigger activation of TCR Vβ21.3+ T-cells in MIS-C, driven by increased IL-18 production from activated monocytes and CD16- Natural Killer cells.

Human skeletal muscle aging atlas

Skeletal muscle aging is a key contributor to age-related frailty and sarcopenia with substantial implications for global health. Here we profiled 90,902 single cells and 92,259 single nuclei from 17 donors to map the aging process in the adult human intercostal muscle, identifying cellular changes in each muscle compartment. We found that distinct subsets of muscle stem cells exhibit decreased ribosome biogenesis genes and increased CCL2 expression, causing different aging phenotypes. Our atlas also highlights an expansion of nuclei associated with the neuromuscular junction, which may reflect re-innervation, and outlines how the loss of fast-twitch myofibers is mitigated through regeneration and upregulation of fast-type markers in slow-twitch myofibers with age. Furthermore, we document the function of aging muscle microenvironment in immune cell attraction. Overall, we present a comprehensive human skeletal muscle aging resource ( https://www.muscleageingcellatlas.org/ ) together with an in-house mouse muscle atlas to study common features of muscle aging across species.

Age-specific nasal epithelial responses to SARS-CoV-2 infection

Children infected with SARS-CoV-2 rarely progress to respiratory failure. However, the risk of mortality in infected people over 85 years of age remains high. Here we investigate differences in the cellular landscape and function of paediatric (<12 years), adult (30-50 years) and older adult (>70 years) ex vivo cultured nasal epithelial cells in response to infection with SARS-CoV-2. We show that cell tropism of SARS-CoV-2, and expression of ACE2 and TMPRSS2 in nasal epithelial cell subtypes, differ between age groups. While ciliated cells are viral replication centres across all age groups, a distinct goblet inflammatory subtype emerges in infected paediatric cultures and shows high expression of interferon-stimulated genes and incomplete viral replication. In contrast, older adult cultures infected with SARS-CoV-2 show a proportional increase in basaloid-like cells, which facilitate viral spread and are associated with altered epithelial repair pathways. We confirm age-specific induction of these cell types by integrating data from in vivo COVID-19 studies and validate that our in vitro model recapitulates early epithelial responses to SARS-CoV-2 infection.

Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk

BACKGROUND

Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers.

METHODS

We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer.

RESULTS

We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier.

CONCLUSIONS

Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins

Assessment of single-cell gene expression (single-cell RNA sequencing) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of nonproductive and partially spliced contigs. We devised a strategy to create an AIR feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double-positive T cells to mature single-positive CD4/CD8 T cells, generating predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is available at https://www.github.com/zktuong/dandelion .

Automatic cell-type harmonization and integration across Human Cell Atlas datasets

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.

Early human lung immune cell development and its role in epithelial cell fate

Studies of human lung development have focused on epithelial and mesenchymal cell types and function, but much less is known about the developing lung immune cells, even though the airways are a major site of mucosal immunity after birth. An unanswered question is whether tissue-resident immune cells play a role in shaping the tissue as it develops in utero. Here, we profiled human embryonic and fetal lung immune cells using scRNA-seq, smFISH, and immunohistochemistry. At the embryonic stage, we observed an early wave of innate immune cells, including innate lymphoid cells, natural killer cells, myeloid cells, and lineage progenitors. By the canalicular stage, we detected naive T lymphocytes expressing high levels of cytotoxicity genes and the presence of mature B lymphocytes, including B-1 cells. Our analysis suggests that fetal lungs provide a niche for full B cell maturation. Given the presence and diversity of immune cells during development, we also investigated their possible effect on epithelial maturation. We found that IL-1β drives epithelial progenitor exit from self-renewal and differentiation to basal cells in vitro. In vivo, IL-1β-producing myeloid cells were found throughout the lung and adjacent to epithelial tips, suggesting that immune cells may direct human lung epithelial development.

FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets

Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions.

Preliminary validity testing of the eHealth Literacy Questionnaire (eHLQ): a Confirmatory Factor Analysis (CFA) in Norwegian hospitalized patients

AIMS

To perform the first psychometric analysis of the Norwegian version of the eHLQ using confirmative factor analysis (CFA) procedures in a population of patients admitted to hospital using a cross-sectional design. The eHLQ consists of 35 items capturing the 7-dimensional eHealth Literacy Framework (eHLF) which describes users' attributes, user's interaction with technologies and user's experience with digital health systems.

METHODS

The 7 independent scales of the eHLQ was translated from Danish and culturally adapted into the Norwegian language following a standardised protocol. Assessment of construct validity of the eHLQ was undertaken using data from a cross-sectional survey of 260 patients hospitalized at a Norwegian University Hospital in the Oslo area during a two-week period in June 2021. The analysis included using correlation analysis (Pearsons R), internal consistency (Cronbach's alpha) and confirmatory factor analysis (CFA).

RESULTS

All factor loadings were high to acceptable (i.e. > 0.6), except for five items which had somewhat lower loadings. Regarding internal consistency, alpha ranged from 0.73 to 0.90. For optimal CFA fit for the different scale models, correlated residuals were required for five of the seven scales. Overall our analysis shows an intermediate fit of the orginal construct. Scale intercorrelations were all below 0.8, indicating an overall acceptable discriminant validity between the 7 dimensions.

CONCLUSIONS

The results from the CFA analysis indicate that for almost all 7 eHLQ scales, an acceptable model fit was achieved. The 260 hospitalized patients included in this study represented a variety of diagnoses, recruited from a geographically limited area. Further studies on psychometric properties of the Norwegian version of eHLQ in larger samples, diverse settings and by using more comprehensive approaches are warranted.

Precise identification of cell states altered in disease using healthy single-cell references

Joint analysis of single-cell genomics data from diseased tissues and a healthy reference can reveal altered cell states. We investigate whether integrated collections of data from healthy individuals (cell atlases) are suitable references for disease-state identification and whether matched control samples are needed to minimize false discoveries. We demonstrate that using a reference atlas for latent space learning followed by differential analysis against matched controls leads to improved identification of disease-associated cells, especially with multiple perturbed cell types. Additionally, when an atlas is available, reducing control sample numbers does not increase false discovery rates. Jointly analyzing data from a COVID-19 cohort and a blood cell atlas, we improve detection of infection-related cell states linked to distinct clinical severities. Similarly, we studied disease states in pulmonary fibrosis using a healthy lung atlas, characterizing two distinct aberrant basal states. Our analysis provides guidelines for designing disease cohort studies and optimizing cell atlas use.

Yolk sac cell atlas reveals multiorgan functions during human early development

The extraembryonic yolk sac (YS) ensures delivery of nutritional support and oxygen to the developing embryo but remains ill-defined in humans. We therefore assembled a comprehensive multiomic reference of the human YS from 3 to 8 postconception weeks by integrating single-cell protein and gene expression data. Beyond its recognized role as a site of hematopoiesis, we highlight roles in metabolism, coagulation, vascular development, and hematopoietic regulation. We reconstructed the emergence and decline of YS hematopoietic stem and progenitor cells from hemogenic endothelium and revealed a YS-specific accelerated route to macrophage production that seeds developing organs. The multiorgan functions of the YS are superseded as intraembryonic organs develop, effecting a multifaceted relay of vital functions as pregnancy proceeds.

Systematic benchmarking of single-cell ATAC-sequencing protocols

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.

Mapping interindividual dynamics of innate immune response at single-cell resolution

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution.

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Th2 high and mast cell gene signatures are associated with corticosteroid sensitivity in COPD

RATIONALE

Severe asthma and chronic obstructive pulmonary disease (COPD) share common pathophysiological traits such as relative corticosteroid insensitivity. We recently published three transcriptome-associated clusters (TACs) using hierarchical analysis of the sputum transcriptome in asthmatics from the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) cohort comprising one Th2-high inflammatory signature (TAC1) and two Th2-low signatures (TAC2 and TAC3).

OBJECTIVE

We examined whether gene expression signatures obtained in asthma can be used to identify the subgroup of patients with COPD with steroid sensitivity.

METHODS

Using gene set variation analysis, we examined the distribution and enrichment scores (ES) of the 3 TACs in the transcriptome of bronchial biopsies from 46 patients who participated in the Groningen Leiden Universities Corticosteroids in Obstructive Lung Disease COPD study that received 30 months of treatment with inhaled corticosteroids (ICS) with and without an added long-acting β-agonist (LABA). The identified signatures were then associated with longitudinal clinical variables after treatment. Differential gene expression and cellular convolution were used to define key regulated genes and cell types.

MEASUREMENTS AND MAIN RESULTS

Bronchial biopsies in patients with COPD at baseline showed a wide range of expression of the 3 TAC signatures. After ICS±LABA treatment, the ES of TAC1 was significantly reduced at 30 months, but those of TAC2 and TAC3 were unaffected. A corticosteroid-sensitive TAC1 signature was developed from the TAC1 ICS-responsive genes. This signature consisted of mast cell-specific genes identified by single-cell RNA-sequencing and positively correlated with bronchial biopsy mast cell numbers following ICS±LABA. Baseline levels of gene transcription correlated with the change in RV/TLC %predicted following 30-month ICS±LABA.

CONCLUSION

Sputum-derived transcriptomic signatures from an asthma cohort can be recapitulated in bronchial biopsies of patients with COPD and identified a signature of airway mast cells as a predictor of corticosteroid responsiveness.

Obesity Is Associated with Attenuated Tissue Immunity in COVID-19

Rationale: Obesity affects 40% of U.S. adults, is associated with a proinflammatory state, and presents a significant risk factor for the development of severe coronavirus disease (COVID-19). To date, there is limited information on how obesity might affect immune cell responses in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objectives: To determine the impact of obesity on respiratory tract immunity in COVID-19 across the human lifespan. Methods: We analyzed single-cell transcriptomes from BAL in three ventilated adult cohorts with (n = 24) or without (n = 9) COVID-19 from nasal immune cells in children with (n = 14) or without (n = 19) COVID-19, and from peripheral blood mononuclear cells in an independent adult COVID-19 cohort (n = 42), comparing obese and nonobese subjects. Measurements and Main Results: Surprisingly, we found that obese adult subjects had attenuated lung immune or inflammatory responses in SARS-CoV-2 infection, with decreased expression of IFN-α, IFN-γ, and TNF-α (tumor necrosis factor α) response gene signatures in almost all lung epithelial and immune cell subsets, and lower expression of IFNG and TNF in specific lung immune cells. Peripheral blood immune cells in an independent adult cohort showed a similar but less marked reduction in type-I IFN and IFNγ response genes, as well as decreased serum IFNα, in obese patients with SARS-CoV-2. Nasal immune cells from obese children with COVID-19 also showed reduced enrichment of IFN-α and IFN-γ response genes. Conclusions: These findings show blunted tissue immune responses in obese patients with COVID-19, with implications for treatment stratification, supporting the specific application of inhaled recombinant type-I IFNs in this vulnerable subset.

Organoid modeling of human fetal lung alveolar development reveals mechanisms of cell fate patterning and neonatal respiratory disease

Variation in lung alveolar development is strongly linked to disease susceptibility. However, underlying cellular and molecular mechanisms are difficult to study in humans. We have identified an alveolar-fated epithelial progenitor in human fetal lungs, which we grow as self-organizing organoids that model key aspects of cell lineage commitment. Using this system, we have functionally validated cell-cell interactions in the developing human alveolar niche, showing that Wnt signaling from differentiating fibroblasts promotes alveolar-type-2 cell identity, whereas myofibroblasts secrete the Wnt inhibitor, NOTUM, providing spatial patterning. We identify a Wnt-NKX2.1 axis controlling alveolar differentiation. Moreover, we show that differential binding of NKX2.1 coordinates alveolar maturation, allowing us to model the effects of human genetic variation in NKX2.1 on alveolar differentiation. Our organoid system recapitulates key aspects of human fetal lung stem cell biology allowing mechanistic experiments to determine the cellular and molecular regulation of human development and disease.

A spatially resolved atlas of the human lung characterizes a gland-associated immune niche

Single-cell transcriptomics has allowed unprecedented resolution of cell types/states in the human lung, but their spatial context is less well defined. To (re)define tissue architecture of lung and airways, we profiled five proximal-to-distal locations of healthy human lungs in depth using multi-omic single cell/nuclei and spatial transcriptomics (queryable at lungcellatlas.org ). Using computational data integration and analysis, we extend beyond the suspension cell paradigm and discover macro and micro-anatomical tissue compartments including previously unannotated cell types in the epithelial, vascular, stromal and nerve bundle micro-environments. We identify and implicate peribronchial fibroblasts in lung disease. Importantly, we discover and validate a survival niche for IgA plasma cells in the airway submucosal glands (SMG). We show that gland epithelial cells recruit B cells and IgA plasma cells, and promote longevity and antibody secretion locally through expression of CCL28, APRIL and IL-6. This new 'gland-associated immune niche' has implications for respiratory health.

Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer

Tumor behavior is intricately dependent on the oncogenic properties of cancer cells and their multi-cellular interactions. To understand these dependencies within the wider microenvironment, we studied over 270,000 single-cell transcriptomes and 100 microdissected whole exomes from 12 patients with kidney tumors, prior to validation using spatial transcriptomics. Tissues were sampled from multiple regions of the tumor core, the tumor-normal interface, normal surrounding tissues, and peripheral blood. We find that the tissue-type location of CD8+ T cell clonotypes largely defines their exhaustion state with intra-tumoral spatial heterogeneity that is not well explained by somatic heterogeneity. De novo mutation calling from single-cell RNA-sequencing data allows us to broadly infer the clonality of stromal cells and lineage-trace myeloid cell development. We report six conserved meta-programs that distinguish tumor cell function, and find an epithelial-mesenchymal transition meta-program highly enriched at the tumor-normal interface that co-localizes with IL1B-expressing macrophages, offering a potential therapeutic target.

A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates

We present a multiomic cell atlas of human lung development that combines single-cell RNA and ATAC sequencing, high-throughput spatial transcriptomics, and single-cell imaging. Coupling single-cell methods with spatial analysis has allowed a comprehensive cellular survey of the epithelial, mesenchymal, endothelial, and erythrocyte/leukocyte compartments from 5-22 post-conception weeks. We identify previously uncharacterized cell states in all compartments. These include developmental-specific secretory progenitors and a subtype of neuroendocrine cell related to human small cell lung cancer. Our datasets are available through our web interface (https://lungcellatlas.org). To illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signaling and transcription factor hierarchies which we rigorously test using organoid models.

SOX9 maintains human foetal lung tip progenitor state by enhancing WNT and RTK signalling

The balance between self-renewal and differentiation in human foetal lung epithelial progenitors controls the size and function of the adult organ. Moreover, progenitor cell gene regulation networks are employed by both regenerating and malignant lung cells, where modulators of their effects could potentially be of therapeutic value. Details of the molecular networks controlling human lung progenitor self-renewal remain unknown. We performed the first CRISPRi screen in primary human lung organoids to identify transcription factors controlling progenitor self-renewal. We show that SOX9 promotes proliferation of lung progenitors and inhibits precocious airway differentiation. Moreover, by identifying direct transcriptional targets using Targeted DamID, we place SOX9 at the centre of a transcriptional network, which amplifies WNT and RTK signalling to stabilise the progenitor cell state. In addition, the proof-of-principle CRISPRi screen and Targeted DamID tools establish a new workflow for using primary human organoids to elucidate detailed functional mechanisms underlying normal development and disease.

Cross-tissue immune cell analysis reveals tissue-specific features in humans

Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing.

Altered TMPRSS2 usage by SARS-CoV-2 Omicron impacts infectivity and fusogenicity

The SARS-CoV-2 Omicron BA.1 variant emerged in 20211 and has multiple mutations in its spike protein2. Here we show that the spike protein of Omicron has a higher affinity for ACE2 compared with Delta, and a marked change in its antigenicity increases Omicron's evasion of therapeutic monoclonal and vaccine-elicited polyclonal neutralizing antibodies after two doses. mRNA vaccination as a third vaccine dose rescues and broadens neutralization. Importantly, the antiviral drugs remdesivir and molnupiravir retain efficacy against Omicron BA.1. Replication was similar for Omicron and Delta virus isolates in human nasal epithelial cultures. However, in lung cells and gut cells, Omicron demonstrated lower replication. Omicron spike protein was less efficiently cleaved compared with Delta. The differences in replication were mapped to the entry efficiency of the virus on the basis of spike-pseudotyped virus assays. The defect in entry of Omicron pseudotyped virus to specific cell types effectively correlated with higher cellular RNA expression of TMPRSS2, and deletion of TMPRSS2 affected Delta entry to a greater extent than Omicron. Furthermore, drug inhibitors targeting specific entry pathways3 demonstrated that the Omicron spike inefficiently uses the cellular protease TMPRSS2, which promotes cell entry through plasma membrane fusion, with greater dependency on cell entry through the endocytic pathway. Consistent with suboptimal S1/S2 cleavage and inability to use TMPRSS2, syncytium formation by the Omicron spike was substantially impaired compared with the Delta spike. The less efficient spike cleavage of Omicron at S1/S2 is associated with a shift in cellular tropism away from TMPRSS2-expressing cells, with implications for altered pathogenesis.

TreeAndLeaf: an R/Bioconductor package for graphs and trees with focus on the leaves

MOTIVATION

Dendrogram is a classical diagram for visualizing binary trees. Although efficient to represent hierarchical relations, it provides limited space for displaying information on the leaf elements, especially for large trees.

RESULTS

Here, we present TreeAndLeaf, an R/Bioconductor package that implements a hybrid layout strategy to represent tree diagrams with focus on the leaves. The TreeAndLeaf package combines force-directed graph and tree layout algorithms using a single visualization system, allowing projection of multiple layers of information onto a graph-tree diagram. The Supplementary Information provides two case studies that use breast cancer data from epidemiological and experimental studies.

AVAILABILITY AND IMPLEMENTATION

TreeAndLeaf is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/TreeAndLeaf/ (version≥1.4.2).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Local and systemic responses to SARS-CoV-2 infection in children and adults

It is not fully understood why COVID-19 is typically milder in children1-3. Here, to examine the differences between children and adults in their response to SARS-CoV-2 infection, we analysed paediatric and adult patients with COVID-19 as well as healthy control individuals (total n = 93) using single-cell multi-omic profiling of matched nasal, tracheal, bronchial and blood samples. In the airways of healthy paediatric individuals, we observed cells that were already in an interferon-activated state, which after SARS-CoV-2 infection was further induced especially in airway immune cells. We postulate that higher paediatric innate interferon responses restrict viral replication and disease progression. The systemic response in children was characterized by increases in naive lymphocytes and a depletion of natural killer cells, whereas, in adults, cytotoxic T cells and interferon-stimulated subpopulations were significantly increased. We provide evidence that dendritic cells initiate interferon signalling in early infection, and identify epithelial cell states associated with COVID-19 and age. Our matching nasal and blood data show a strong interferon response in the airways with the induction of systemic interferon-stimulated populations, which were substantially reduced in paediatric patients. Together, we provide several mechanisms that explain the milder clinical syndrome observed in children.

The discovAIR project: a roadmap towards the Human Lung Cell Atlas

The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The Lung Biological Network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and cell–cell interactions that characterise normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease. The discovAIR project is one of six pilot actions for the HCA funded by the European Commission in the context of the H2020 framework programme. discovAIR aims to establish the first draft of an integrated Human Lung Cell Atlas, combining single-cell transcriptional and epigenetic profiling with spatially resolving techniques on matched tissue samples, as well as including a number of chronic and infectious diseases of the lung. The integrated Human Lung Cell Atlas will be available as a resource for the wider respiratory community, including basic and translational scientists, clinical medicine, and the private sector, as well as for patients with lung disease and the interested lay public. We anticipate that the Human Lung Cell Atlas will be the founding stone for a more detailed understanding of the pathogenesis of lung diseases, guiding the design of novel diagnostics and preventive or curative interventions.

The discovAIR project contributes to the Human Cell Atlas Lung Biological Network by establishing a first draft of the Human Lung Cell Atlas, advancing our insight into the cellular complexity and spatial organisation of the lung in health and diseasehttps://bit.ly/3zX4cad

Cells of the human intestinal tract mapped across space and time

The cellular landscape of the human intestinal tract is dynamic throughout life, developing in utero and changing in response to functional requirements and environmental exposures. Here, to comprehensively map cell lineages, we use single-cell RNA sequencing and antigen receptor analysis of almost half a million cells from up to 5 anatomical regions in the developing and up to 11 distinct anatomical regions in the healthy paediatric and adult human gut. This reveals the existence of transcriptionally distinct BEST4 epithelial cells throughout the human intestinal tract. Furthermore, we implicate IgG sensing as a function of intestinal tuft cells. We describe neural cell populations in the developing enteric nervous system, and predict cell-type-specific expression of genes associated with Hirschsprung's disease. Finally, using a systems approach, we identify key cell players that drive the formation of secondary lymphoid tissue in early human development. We show that these programs are adopted in inflammatory bowel disease to recruit and retain immune cells at the site of inflammation. This catalogue of intestinal cells will provide new insights into cellular programs in development, homeostasis and disease.

Molecular phenotyping reveals the identity of Barrett's esophagus and its malignant transition

The origin of human metaplastic states and their propensity for cancer is poorly understood. Barrett's esophagus is a common metaplastic condition that increases the risk for esophageal adenocarcinoma, and its cellular origin is enigmatic. To address this, we harvested tissues spanning the gastroesophageal junction from healthy and diseased donors, including isolation of esophageal submucosal glands. A combination of single-cell transcriptomic profiling, in silico lineage tracing from methylation, open chromatin and somatic mutation analyses, and functional studies in organoid models showed that Barrett's esophagus originates from gastric cardia through c-MYC and HNF4A-driven transcriptional programs. Furthermore, our data indicate that esophageal adenocarcinoma likely arises from undifferentiated Barrett's esophagus cell types even in the absence of a pathologically identifiable metaplastic precursor, illuminating early detection strategies.

Integrated Single-Cell Atlas of Endothelial Cells of the Human Lung

BACKGROUND

Cellular diversity of the lung endothelium has not been systematically characterized in humans. We provide a reference atlas of human lung endothelial cells (ECs) to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium.

METHODS

We reprocessed human control single-cell RNA sequencing (scRNAseq) data from 6 datasets. EC populations were characterized through iterative clustering with subsequent differential expression analysis. Marker genes were validated by fluorescent microscopy and in situ hybridization. scRNAseq of primary lung ECs cultured in vitro was performed. The signaling network between different lung cell types was studied. For cross-species analysis or disease relevance, we applied the same methods to scRNAseq data obtained from mouse lungs or from human lungs with pulmonary hypertension.

RESULTS

Six lung scRNAseq datasets were reanalyzed and annotated to identify >15 000 vascular EC cells from 73 individuals. Differential expression analysis of EC revealed signatures corresponding to endothelial lineage, including panendothelial, panvascular, and subpopulation-specific marker gene sets. Beyond the broad cellular categories of lymphatic, capillary, arterial, and venous ECs, we found previously indistinguishable subpopulations; among venous EC, we identified 2 previously indistinguishable populations: pulmonary-venous ECs (COL15A1neg) localized to the lung parenchyma and systemic-venous ECs (COL15A1pos) localized to the airways and the visceral pleura; among capillary ECs, we confirmed their subclassification into recently discovered aerocytes characterized by EDNRB, SOSTDC1, and TBX2 and general capillary EC. We confirmed that all 6 endothelial cell types, including the systemic-venous ECs and aerocytes, are present in mice and identified endothelial marker genes conserved in humans and mice. Ligand-receptor connectome analysis revealed important homeostatic crosstalk of EC with other lung resident cell types. scRNAseq of commercially available primary lung ECs demonstrated a loss of their native lung phenotype in culture. scRNAseq revealed that endothelial diversity is maintained in pulmonary hypertension. Our article is accompanied by an online data mining tool (www.LungEndothelialCellAtlas.com).

CONCLUSIONS

Our integrated analysis provides a comprehensive and well-crafted reference atlas of ECs in the normal lung and confirms and describes in detail previously unrecognized endothelial populations across a large number of humans and mice.

Single cell derived mRNA signals across human kidney tumors

Tumor cells may share some patterns of gene expression with their cell of origin, providing clues into the differentiation state and origin of cancer. Here, we study the differentiation state and cellular origin of 1300 childhood and adult kidney tumors. Using single cell mRNA reference maps of normal tissues, we quantify reference "cellular signals" in each tumor. Quantifying global differentiation, we find that childhood tumors exhibit fetal cellular signals, replacing the presumption of "fetalness" with a quantitative measure of immaturity. By contrast, in adult cancers our assessment refutes the suggestion of dedifferentiation towards a fetal state in most cases. We find an intimate connection between developmental mesenchymal populations and childhood renal tumors. We demonstrate the diagnostic potential of our approach with a case study of a cryptic renal tumor. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer.

Single-cell multi-omics analysis of the immune response in COVID-19

Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

Periostin: contributor to abnormal airway epithelial function in asthma?

Periostin (POSTN) may serve as a biomarker for Type-2 mediated eosinophilic airway inflammation in asthma. We hypothesised that a Type-2 cytokine, interleukin (IL)-13, induces airway epithelial expression of POSTN, which in turn contributes to epithelial changes observed in asthma.We studied the effect of IL-13 on POSTN expression in BEAS-2B and air-liquid interface differentiated primary bronchial epithelial cells (PBECs). Additionally, the effects of recombinant human POSTN on epithelial-to-mesenchymal transition (EMT) markers and mucin genes were assessed. POSTN single cell gene expression and protein levels were analysed in bronchial biopsies and induced sputum from asthma patients and healthy controls.IL-13 increased POSTN expression in both cell types and this was accompanied by EMT-related features in BEAS-2B. In air-liquid interface differentiated PBECs, IL-13 increased POSTN basolateral and apical release. Apical administration of POSTN increased the expression of MMP-9, MUC5B and MUC5AC In bronchial biopsies, POSTN expression was mainly confined to basal epithelial cells, ionocytes, endothelial cells and fibroblasts, showing higher expression in basal epithelial cells from asthma patients versus those from controls. A higher level of POSTN protein expression in epithelial and subepithelial layers was confirmed in bronchial biopsies from asthma patients when compared to healthy controls. Although sputum POSTN levels were not higher in asthma, levels correlated with eosinophil numbers and with the coughing-up of mucus.POSTN expression is increased by IL-13 in bronchial epithelial cells and is higher in bronchial biopsies from asthma patients. This may have important consequences, as administration of POSTN increases epithelial expression of mucin genes, supporting the relationship of POSTN with Type-2 mediated asthma and mucus secretion.

Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell

Neuroblastoma is a childhood cancer that resembles developmental stages of the neural crest. It is not established what developmental processes neuroblastoma cancer cells represent. Here, we sought to reveal the phenotype of neuroblastoma cancer cells by comparing cancer (n = 19,723) with normal fetal adrenal single-cell transcriptomes (n = 57,972). Our principal finding was that the neuroblastoma cancer cell resembled fetal sympathoblasts, but no other fetal adrenal cell type. The sympathoblastic state was a universal feature of neuroblastoma cells, transcending cell cluster diversity, individual patients, and clinical phenotypes. We substantiated our findings in 650 neuroblastoma bulk transcriptomes and by integrating canonical features of the neuroblastoma genome with transcriptional signals. Overall, our observations indicate that a pan-neuroblastoma cancer cell state exists, which may be attractive for novel immunotherapeutic and targeted avenues.

Developmental cell programs are co-opted in inflammatory skin disease

The skin confers biophysical and immunological protection through a complex cellular network established early in embryonic development. We profiled the transcriptomes of more than 500,000 single cells from developing human fetal skin, healthy adult skin, and adult skin with atopic dermatitis and psoriasis. We leveraged these datasets to compare cell states across development, homeostasis, and disease. Our analysis revealed an enrichment of innate immune cells in skin during the first trimester and clonal expansion of disease-associated lymphocytes in atopic dermatitis and psoriasis. We uncovered and validated in situ a reemergence of prenatal vascular endothelial cell and macrophage cellular programs in atopic dermatitis and psoriasis lesional skin. These data illustrate the dynamism of cutaneous immunity and provide opportunities for targeting pathological developmental programs in inflammatory skin diseases.

A cell atlas of human thymic development defines T cell repertoire formation

The thymus provides a nurturing environment for the differentiation and selection of T cells, a process orchestrated by their interaction with multiple thymic cell types. We used single-cell RNA sequencing to create a cell census of the human thymus across the life span and to reconstruct T cell differentiation trajectories and T cell receptor (TCR) recombination kinetics. Using this approach, we identified and located in situ CD8αα⁺ T cell populations, thymic fibroblast subtypes, and activated dendritic cell states. In addition, we reveal a bias in TCR recombination and selection, which is attributed to genomic position and the kinetics of lineage commitment. Taken together, our data provide a comprehensive atlas of the human thymus across the life span with new insights into human T cell development.

Distinct microbial and immune niches of the human colon

Gastrointestinal microbiota and immune cells interact closely and display regional specificity; however, little is known about how these communities differ with location. Here, we simultaneously assess microbiota and single immune cells across the healthy, adult human colon, with paired characterization of immune cells in the mesenteric lymph nodes, to delineate colonic immune niches at steady state. We describe distinct helper T cell activation and migration profiles along the colon and characterize the transcriptional adaptation trajectory of regulatory T cells between lymphoid tissue and colon. Finally, we show increasing B cell accumulation, clonal expansion and mutational frequency from the cecum to the sigmoid colon and link this to the increasing number of reactive bacterial species.

DNA Repair Biomarker for Lung Cancer Risk and its Correlation With Airway Cells Gene Expression

BACKGROUND

Improving lung cancer risk assessment is required because current early-detection screening criteria miss most cases. We therefore examined the utility for lung cancer risk assessment of a DNA Repair score obtained from OGG1, MPG, and APE1 blood tests. In addition, we examined the relationship between the level of DNA repair and global gene expression.

METHODS

We conducted a blinded case-control study with 150 non-small cell lung cancer case patients and 143 control individuals. DNA Repair activity was measured in peripheral blood mononuclear cells, and the transcriptome of nasal and bronchial cells was determined by RNA sequencing. A combined DNA Repair score was formed using logistic regression, and its correlation with disease was assessed using cross-validation; correlation of expression to DNA Repair was analyzed using Gene Ontology enrichment.

RESULTS

DNA Repair score was lower in case patients than in control individuals, regardless of the case's disease stage. Individuals at the lowest tertile of DNA Repair score had an increased risk of lung cancer compared to individuals at the highest tertile, with an odds ratio (OR) of 7.2 (95% confidence interval [CI] = 3.0 to 17.5; P < .001), and independent of smoking. Receiver operating characteristic analysis yielded an area under the curve  of 0.89 (95% CI = 0.82 to 0.93). Remarkably, low DNA Repair score correlated with a broad upregulation of gene expression of immune pathways in patients but not in control individuals.

CONCLUSIONS

The DNA Repair score, previously shown to be a lung cancer risk factor in the Israeli population, was validated in this independent study as a mechanism-based cancer risk biomarker and can substantially improve current lung cancer risk prediction, assisting prevention and early detection by computed tomography scanning.

RTNduals: an R/Bioconductor package for analysis of co-regulation and inference of dual regulons

MOTIVATION

Transcription factors (TFs) are key regulators of gene expression, and can activate or repress multiple target genes, forming regulatory units, or regulons. Understanding downstream effects of these regulators includes evaluating how TFs cooperate or compete within regulatory networks. Here we present RTNduals, an R/Bioconductor package that implements a general method for analyzing pairs of regulons.

RESULTS

RTNduals identifies a dual regulon when the number of targets shared between a pair of regulators is statistically significant. The package extends the RTN (Reconstruction of Transcriptional Networks) package, and uses RTN transcriptional networks to identify significant co-regulatory associations between regulons. The Supplementary Information reports two case studies for TFs using the METABRIC and TCGA breast cancer cohorts.

AVAILABILITY AND IMPLEMENTATION

RTNduals is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNduals/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RTNsurvival: an R/Bioconductor package for regulatory network survival analysis

MOTIVATION

Transcriptional networks are models that allow the biological state of cells or tumours to be described. Such networks consist of connected regulatory units known as regulons, each comprised of a regulator and its targets. Inferring a transcriptional network can be a helpful initial step in characterizing the different phenotypes within a cohort. While the network itself provides no information on molecular differences between samples, the per-sample state of each regulon, i.e. the regulon activity, can be used for describing subtypes in a cohort. Integrating regulon activities with clinical data and outcomes would extend this characterization of differences between subtypes.

RESULTS

We describe RTNsurvival, an R/Bioconductor package that calculates regulon activity profiles using transcriptional networks reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichment Analysis. Given regulon activity profiles across a cohort, RTNsurvival can perform Kaplan-Meier analyses and Cox Proportional Hazards regressions, while also considering confounding variables. The Supplementary Information provides two case studies that use data from breast and liver cancer cohorts and features uni- and multivariate regulon survival analysis.

AVAILABILITY AND IMPLEMENTATION

RTNsurvival is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNsurvival/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decoding human fetal liver haematopoiesis

Definitive haematopoiesis in the fetal liver supports self-renewal and differentiation of haematopoietic stem cells and multipotent progenitors (HSC/MPPs) but remains poorly defined in humans. Here, using single-cell transcriptome profiling of approximately 140,000 liver and 74,000 skin, kidney and yolk sac cells, we identify the repertoire of human blood and immune cells during development. We infer differentiation trajectories from HSC/MPPs and evaluate the influence of the tissue microenvironment on blood and immune cell development. We reveal physiological erythropoiesis in fetal skin and the presence of mast cells, natural killer and innate lymphoid cell precursors in the yolk sac. We demonstrate a shift in the haemopoietic composition of fetal liver during gestation away from being predominantly erythroid, accompanied by a parallel change in differentiation potential of HSC/MPPs, which we functionally validate. Our integrated map of fetal liver haematopoiesis provides a blueprint for the study of paediatric blood and immune disorders, and a reference for harnessing the therapeutic potential of HSC/MPPs.

BBKNN: fast batch alignment of single cell transcriptomes

MOTIVATION

Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods.

AVAILABILITY AND IMPLEMENTATION

BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

BBKNN: fast batch alignment of single cell transcriptomes

Motivation

Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods.

Availability and implementation

BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip.

Supplementary information

Supplementary data are available at Bioinformatics online.

Single-cell landscape in mammary epithelium reveals bipotent-like cells associated with breast cancer risk and outcome

Adult stem-cells may serve as the cell-of-origin for cancer, yet their unbiased identification in single cell RNA sequencing data is challenging due to the high dropout rate. In the case of breast, the existence of a bipotent stem-like state is also controversial. Here we apply a marker-free algorithm to scRNA-Seq data from the human mammary epithelium, revealing a high-potency cell-state enriched for an independent mammary stem-cell expression module. We validate this stem-like state in independent scRNA-Seq data. Our algorithm further predicts that the stem-like state is bipotent, a prediction we are able to validate using FACS sorted bulk expression data. The bipotent stem-like state correlates with clinical outcome in basal breast cancer and is characterized by overexpression of YBX1 and ENO1, two modulators of basal breast cancer risk. This study illustrates the power of a marker-free computational framework to identify a novel bipotent stem-like state in the mammary epithelium.

The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease

Lung disease accounts for every sixth death globally. Profiling the molecular state of all lung cell types in health and disease is currently revolutionizing the identification of disease mechanisms and will aid the design of novel diagnostic and personalized therapeutic regimens. Recent progress in high-throughput techniques for single-cell genomic and transcriptomic analyses has opened up new possibilities to study individual cells within a tissue, classify these into cell types, and characterize variations in their molecular profiles as a function of genetics, environment, cell-cell interactions, developmental processes, aging, or disease. Integration of these cell state definitions with spatial information allows the in-depth molecular description of cellular neighborhoods and tissue microenvironments, including the tissue resident structural and immune cells, the tissue matrix, and the microbiome. The Human Cell Atlas consortium aims to characterize all cells in the healthy human body and has prioritized lung tissue as one of the flagship projects. Here, we present the rationale, the approach, and the expected impact of a Human Lung Cell Atlas.

Genome-wide CRISPR Screens in T Helper Cells Reveal Pervasive Crosstalk between Activation and Differentiation

T helper type 2 (Th2) cells are important regulators of mammalian adaptive immunity and have relevance for infection, autoimmunity, and tumor immunology. Using a newly developed, genome-wide retroviral CRISPR knockout (KO) library, combined with RNA-seq, ATAC-seq, and ChIP-seq, we have dissected the regulatory circuitry governing activation and differentiation of these cells. Our experiments distinguish cell activation versus differentiation in a quantitative framework. We demonstrate that these two processes are tightly coupled and are jointly controlled by many transcription factors, metabolic genes, and cytokine/receptor pairs. There are only a small number of genes regulating differentiation without any role in activation. By combining biochemical and genetic data, we provide an atlas for Th2 differentiation, validating known regulators and identifying factors, such as Pparg and Bhlhe40, as part of the core regulatory network governing Th2 helper cell fates.

Single-cell reconstruction of the early maternal-fetal interface in humans

During early human pregnancy the uterine mucosa transforms into the decidua, into which the fetal placenta implants and where placental trophoblast cells intermingle and communicate with maternal cells. Trophoblast-decidual interactions underlie common diseases of pregnancy, including pre-eclampsia and stillbirth. Here we profile the transcriptomes of about 70,000 single cells from first-trimester placentas with matched maternal blood and decidual cells. The cellular composition of human decidua reveals subsets of perivascular and stromal cells that are located in distinct decidual layers. There are three major subsets of decidual natural killer cells that have distinctive immunomodulatory and chemokine profiles. We develop a repository of ligand-receptor complexes and a statistical tool to predict the cell-type specificity of cell-cell communication via these molecular interactions. Our data identify many regulatory interactions that prevent harmful innate or adaptive immune responses in this environment. Our single-cell atlas of the maternal-fetal interface reveals the cellular organization of the decidua and placenta, and the interactions that are critical for placentation and reproductive success.

Genome-wide analyses reveal the IRE1a-XBP1 pathway promotes T helper cell differentiation by resolving secretory stress and accelerating proliferation

Background

The IRE1a-XBP1 pathway is a conserved adaptive mediator of the unfolded protein response. The pathway is indispensable for the development of secretory cells by facilitating protein folding and enhancing secretory capacity. In the immune system, it is known to function in dendritic cells, plasma cells, and eosinophil development and differentiation, while its role in T helper cell is unexplored. Here, we investigated the role of the IRE1a-XBP1 pathway in regulating activation and differentiation of type-2 T helper cell (Th2), a major T helper cell type involved in allergy, asthma, helminth infection, pregnancy, and tumor immunosuppression.

Methods

We perturbed the IRE1a-XBP1 pathway and interrogated its role in Th2 cell differentiation. We performed genome-wide transcriptomic analysis of differential gene expression to reveal IRE1a-XBP1 pathway-regulated genes and predict their biological role. To identify direct target genes of XBP1 and define XBP1’s regulatory network, we performed XBP1 ChIPmentation (ChIP-seq). We validated our predictions by flow cytometry, ELISA, and qPCR. We also used a fluorescent ubiquitin cell cycle indicator mouse to demonstrate the role of XBP1 in the cell cycle.

Results

We show that Th2 lymphocytes induce the IRE1a-XBP1 pathway during in vitro and in vivo activation. Genome-wide transcriptomic analysis of differential gene expression by perturbing the IRE1a-XBP1 pathway reveals XBP1-controlled genes and biological pathways. Performing XBP1 ChIPmentation (ChIP-seq) and integrating with transcriptomic data, we identify XBP1-controlled direct target genes and its transcriptional regulatory network. We observed that the IRE1a-XBP1 pathway controls cytokine secretion and the expression of two Th2 signature cytokines, IL13 and IL5. We also discovered that the IRE1a-XBP1 pathway facilitates activation-dependent Th2 cell proliferation by facilitating cell cycle progression through S and G2/M phase.

Conclusions

We confirm and detail the critical role of the IRE1a-XBP1 pathway during Th2 lymphocyte activation in regulating cytokine expression, secretion, and cell proliferation. Our high-quality genome-wide XBP1 ChIP and gene expression data provide a rich resource for investigating XBP1-regulated genes. We provide a browsable online database available at http://data.teichlab.org.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0589-3) contains supplementary material, which is available to authorized users.

From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.

ERα Binding by Transcription Factors NFIB and YBX1 Enables FGFR2 Signaling to Modulate Estrogen Responsiveness in Breast Cancer

Two opposing clusters of transcription factors (TF) have been associated with the differential risks of estrogen receptor positive or negative breast cancers, but the mechanisms underlying the opposing functions of the two clusters are undefined. In this study, we identified NFIB and YBX1 as novel interactors of the estrogen receptor (ESR1). NFIB and YBX1 are both risk TF associated with progression of ESR1-negative disease. Notably, they both interacted with the ESR1-FOXA1 complex and inhibited the transactivational potential of ESR1. Moreover, signaling through FGFR2, a known risk factor in breast cancer development, augmented these interactions and further repressed ESR1 target gene expression. We therefore show that members of two opposing clusters of risk TFs associated with ESR1-positive and -negative breast cancer can physically interact. We postulate that this interaction forms a toggle between two developmental pathways affected by FGFR2 signaling, possibly offering a junction to exploit therapeutically.Significance: Binding of the transcription factors NFIB and YBX1 to the estrogen receptor can promote an estrogen-independent phenotype that can be reverted by inhibiting FGFR2 signaling. Cancer Res; 78(2); 410-21. ©2017 AACR.