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Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis 1 . To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA sequencing to profile ~11,000 cells from 22 ascites specimens from 11 patients with HGSOC. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We found that the previously described immunoreactive and mesenchymal subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells 2 . Malignant cell variability was partly explained by heterogeneous copy number alteration patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in single-cell RNA sequencing of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient ascites-derived xenograft models. Inhibition of the JAK/STAT pathway, which was expressed in both malignant cells and cancer-associated fibroblasts, had potent anti-tumor activity in primary short-term cultures and patient-derived xenograft models. Our work contributes to resolving the HSGOC landscape 3 – 5 and provides a resource for the development of novel therapeutic approaches. Single-cell transcriptomics analysis of malignant ascites samples from patients with high-grade serous ovarian cancer reveals inter- and intra-patient heterogeneity in malignant cells, cancer-associated fibroblasts and macrophages.

Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus—a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies. Cumulus is a cloud-based framework enabling large-scale single-cell and single-nucleus RNA sequencing data analysis.

The airways of the lung are the primary sites of disease in asthma and cystic fibrosis. Here we study the cellular composition and hierarchy of the mouse tracheal epithelium by single-cell RNA-sequencing (scRNA-seq) and in vivo lineage tracing. We identify a rare cell type, the Foxi1 + pulmonary ionocyte; functional variations in club cells based on their location; a distinct cell type in high turnover squamous epithelial structures that we term ‘hillocks’; and disease-relevant subsets of tuft and goblet cells. We developed ‘pulse-seq’, combining scRNA-seq and lineage tracing, to show that tuft, neuroendocrine and ionocyte cells are continually and directly replenished by basal progenitor cells. Ionocytes are the major source of transcripts of the cystic fibrosis transmembrane conductance regulator in both mouse ( Cftr ) and human ( CFTR ). Knockout of Foxi1 in mouse ionocytes causes loss of Cftr expression and disrupts airway fluid and mucus physiology, phenotypes that are characteristic of cystic fibrosis. By associating cell-type-specific expression programs with key disease genes, we establish a new cellular narrative for airways disease. Single-cell RNA sequencing analysis identifies cell types and lineages in airway epithelium, including the pulmonary ionocyte, a new cell type predominantly expressing the cystic fibrosis gene CFTR .

Human iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we profile four human iPSC lines for a total of 450,118 single cells to show how organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes are largely reproducible across time points, protocols, and replicates, we detect variability in cell proportions between different iPSC lines, largely due to off-target cells. To address this, we analyze organoids transplanted under the mouse kidney capsule and find diminished off-target cells. Our work shows how single cell RNA-seq (scRNA-seq) can score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells. How reproducible human kidney organoids derived from different iPSC lines are, and how faithful they are to human kidney tissue remain unclear. Here, the authors use four human iPSC lines to derive kidney organoids and show how organoid composition is reproducible, comparable to human tissue and of improved quality after transplantation.

Systemic immunity supports lifelong brain function. Obesity posits a chronic burden on systemic immunity. Independently, obesity was shown as a risk factor for Alzheimer’s disease (AD). Here we show that high-fat obesogenic diet accelerated recognition-memory impairment in an AD mouse model (5xFAD). In obese 5xFAD mice, hippocampal cells displayed only minor diet-related transcriptional changes, whereas the splenic immune landscape exhibited aging-like CD4 + T-cell deregulation. Following plasma metabolite profiling, we identified free N -acetylneuraminic acid (NANA), the predominant sialic acid, as the metabolite linking recognition-memory impairment to increased splenic immune-suppressive cells in mice. Single-nucleus RNA-sequencing revealed mouse visceral adipose macrophages as a potential source of NANA. In vitro, NANA reduced CD4 + T-cell proliferation, tested in both mouse and human. In vivo, NANA administration to standard diet-fed mice recapitulated high-fat diet effects on CD4 + T cells and accelerated recognition-memory impairment in 5xFAD mice. We suggest that obesity accelerates disease manifestation in a mouse model of AD via systemic immune exhaustion. Obesity and aging increase Alzheimer’s disease (AD) risk. Here, using an AD mouse model and high-fat diet, we suggest that immune exhaustion links the two risk factors, and identify a metabolite that can hasten immune dysfunction and memory deficit.

The critical functions of the human liver are coordinated through the interactions of hepatic parenchymal and non‐parenchymal cells. Recent advances in single‐cell transcriptional approaches have enabled an examination of the human liver with unprecedented resolution. However, dissociation‐related cell perturbation can limit the ability to fully capture the human liver’s parenchymal cell fraction, which limits the ability to comprehensively profile this organ. Here, we report the transcriptional landscape of 73,295 cells from the human liver using matched single‐cell RNA sequencing (scRNA‐seq) and single‐nucleus RNA sequencing (snRNA‐seq). The addition of snRNA‐seq enabled the characterization of interzonal hepatocytes at a single‐cell resolution, revealed the presence of rare subtypes of liver mesenchymal cells, and facilitated the detection of cholangiocyte progenitors that had only been observed during in vitro differentiation experiments. However, T and B lymphocytes and natural killer cells were only distinguishable using scRNA‐seq, highlighting the importance of applying both technologies to obtain a complete map of tissue‐resident cell types. We validated the distinct spatial distribution of the hepatocyte, cholangiocyte, and mesenchymal cell populations by an independent spatial transcriptomics data set and immunohistochemistry. Conclusion: Our study provides a systematic comparison of the transcriptomes captured by scRNA‐seq and snRNA‐seq and delivers a high‐resolution map of the parenchymal cell populations in the healthy human liver.

Human airways contain specialized rare epithelial cells including CFTR-rich ionocytes that regulate airway surface physiology and chemosensory tuft cells that produce asthma-associated inflammatory mediators. Here, using a lung cell atlas of 311,748 single cell RNA-Seq profiles, we identify 687 ionocytes (0.45%). In contrast to prior reports claiming a lack of ionocytes in the small airways, we demonstrate that ionocytes are present in small and large airways in similar proportions. Surprisingly, we find only 3 mature tuft cells (0.002%), and demonstrate that previously annotated tuft-like cells are instead highly replicative progenitor cells. These tuft-ionocyte progenitor (TIP) cells produce ionocytes as a default lineage. However, Type 2 and Type 17 cytokines divert TIP cell lineage in vitro, resulting in the production of mature tuft cells at the expense of ionocyte differentiation. Our dataset thus provides an updated understanding of airway rare cell composition, and further suggests that clinically relevant cytokines may skew the composition of disease-relevant rare cells. Human airway contains physiologically relevant yet rare cells, but their scarcity prevents thorough profiling and differentiation studies. Here the authors use single cell RNA sequencing to identify rare ionocytes and tuft cells, as well as a potential progenitor population with cytokine-guided differentiation into either the ionocytes or tuft cell lineage.

Solid tumors are spatially heterogeneous in their genetic, molecular, and cellular composition, but recent spatial profiling studies have mostly charted genetic and RNA variation in tumors separately. To leverage the potential of RNA to identify copy number alterations (CNAs), we develop SlideCNA, a computational tool to extract CNA signals from sparse spatial transcriptomics data with near single cellular resolution. SlideCNA uses expression-aware spatial binning to overcome sparsity limitations while maintaining spatial signal to recover CNA patterns. We test SlideCNA on simulated and real Slide-seq data of (metastatic) breast cancer and demonstrate its potential for spatial subclone detection.

In atopic dermatitis (AD), skin barrier and immune dysfunction result in chronic tissue inflammation, yet our understanding of the tissue ecosystem remains incomplete. Here, we generate a multi-modal census of 280,518 cells from whole skin tissue samples from 17 adults, including 11 AD patients, integrating it with 430,186 cell profiles from four previous studies into a comprehensive human skin cell atlas. Reconstruction of keratinocyte differentiation revealed disrupted cornification in AD associated with signals from an immune and stromal multicellular community – comprising MMP12 + and migratory dendritic cells (DCs), cycling innate lymphoid cells (ILC), natural killer cells, inflammatory CCL19 + IL4I1 + fibroblasts, and clonally expanded IL13 + IL22 + IL26 + T cells connected by intercellular feedback loops predicted to impact community assembly. Subsets from this community, along with disrupted cornified keratinocytes, were enriched in GWAS, suggesting that dysfunction in this communication network may initiate AD. Our work highlights disease-associated cell subsets and interactions in chronic skin inflammation. In atopic dermatitis (AD), skin barrier disruption leads to chronic inflammation. Here, the authors use single-cell sequencing to map human skin, uncovering AD-specific cell states and populations involved in immune responses and cell differentiation.

Adipose tissue is usually classified on the basis of its function as white, brown or beige (brite) 1 . It is an important regulator of systemic metabolism, as shown by the fact that dysfunctional adipose tissue in obesity leads to a variety of secondary metabolic complications 2 , 3 . In addition, adipose tissue functions as a signalling hub that regulates systemic metabolism through paracrine and endocrine signals 4 . Here we use single-nucleus RNA-sequencing (snRNA-seq) analysis in mice and humans to characterize adipocyte heterogeneity. We identify a rare subpopulation of adipocytes in mice that increases in abundance at higher temperatures, and we show that this subpopulation regulates the activity of neighbouring adipocytes through acetate-mediated modulation of their thermogenic capacity. Human adipose tissue contains higher numbers of cells of this subpopulation, which could explain the lower thermogenic activity of human compared to mouse adipose tissue and suggests that targeting this pathway could be used to restore thermogenic activity. Single-nucleus RNA sequencing in mouse and human adipose tissue identifies a subpopulation of adipocytes that regulates thermogenesis in neighbouring adipocytes in a paracrine manner by modulating acetate signalling.