Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

scDiffCom: a tool for differential analysis of cell-cell interactions provides a mouse atlas of aging changes in intercellular communication.

Dysregulation of intercellular communication is a hallmark of aging. To better quantify and explore changes in intercellular communication, we present scDiffCom and scAgeCom. scDiffCom is an R package, relying on approximately 5,000 curated ligand-receptor interactions, that performs differential intercellular communication analysis between two conditions from single-cell transcriptomics data. Built upon scDiffCom, scAgeCom is an atlas of age-related cell-cell communication changes covering 23 mouse tissues from 58 single-cell RNA sequencing datasets from Tabula Muris Senis and the Calico murine aging cell atlas. It offers a comprehensive resource of tissue-specific and sex-specific aging dysregulations and highlights age-related intercellular communication changes widespread across the whole body, such as the upregulation of immune system processes and inflammation, the downregulation of developmental processes, angiogenesis and extracellular matrix organization and the deregulation of lipid metabolism. Our analysis emphasizes the relevance of the specific ligands, receptors and cell types regulating these processes. The atlas is available online ( https://scagecom.org ).

Single-nuclei characterization of pervasive transcriptional signatures across organs in response to COVID-19.

Background: Infection by coronavirus SARS-CoV2 is a severe and often deadly disease that has implications for the respiratory system and multiple organs across the human body. While the effects in the lung have been extensively studied, less is known about the impact COVID-19 has across other organs. Methods: Here, we contribute a single-nuclei RNA-sequencing atlas comprising six human organs across 20 autopsies where we analyzed the transcriptional changes due to COVID-19 in multiple cell types. The integration of data from multiple organs enabled the identification of systemic transcriptional changes. Results: Computational cross-organ analysis for endothelial cells and macrophages identified systemic transcriptional changes in these cell types in COVID-19 samples. In addition, analysis of gene modules showed enrichment of specific signaling pathways across multiple organs in COVID-19 autopsies. Conclusions: Altogether, the COVID Tissue Atlas enables the investigation of both cell type-specific and cross-organ transcriptional responses to COVID-19, providing insights into the molecular networks affected by the disease and highlighting novel potential targets for therapies and drug development. Funding: The Chan-Zuckerberg Initiative, The Chan-Zuckerberg Biohub.

Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution.

Aging is characterized by a decline in tissue function, but the underlying changes at cellular resolution across the organism remain unclear. Here, we present the Aging Fly Cell Atlas, a single-nucleus transcriptomic map of the whole aging Drosophila. We characterized 163 distinct cell types and performed an in-depth analysis of changes in tissue cell composition, gene expression, and cell identities. We further developed aging clock models to predict fly age and show that ribosomal gene expression is a conserved predictive factor for age. Combining all aging features, we find distinctive cell type-specific aging patterns. This atlas provides a valuable resource for studying fundamental principles of aging in complex organisms.

A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants.

The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false-negative viral PCR test results. Such tests are also susceptible to false-positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses, and nonviral conditions (n = 318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with nonviral conditions, such as IFI6, and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5. The IFI6+GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n = 553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improved accuracy of COVID-19 diagnostics. IMPORTANCE In this work, we study upper respiratory tract gene expression to develop and validate a 2-gene host-based COVID-19 diagnostic classifier and then demonstrate its implementation in a clinically practical qPCR assay. We find that the host classifier has utility for mitigating false-negative results, for example due to SARS-CoV-2 variants harboring mutations at primer target sites, and for mitigating false-positive viral PCR results due to laboratory cross-contamination. Both types of error carry serious consequences of either unrecognized viral transmission or unnecessary isolation and contact tracing. This work is directly relevant to the ongoing COVID-19 pandemic given the continued emergence of viral variants and the continued challenges of false-positive PCR assays. It also suggests the feasibility of pan-respiratory virus host-based diagnostics that would have value in congregate settings, such as hospitals and nursing homes, where unrecognized respiratory viral transmission is of particular concern.

The hepatic integrated stress response suppresses the somatotroph axis to control liver damage in nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) can be ameliorated by calorie restriction, which leads to the suppressed somatotroph axis. Paradoxically, the suppressed somatotroph axis is associated with patients with NAFLD and is correlated with the severity of fibrosis. How the somatotroph axis becomes dysregulated and whether the repressed somatotroph axis impacts liver damage during the progression of NAFLD are unclear. Here, we identify a regulatory branch of the hepatic integrated stress response (ISR), which represses the somatotroph axis in hepatocytes through ATF3, resulting in enhanced cell survival and reduced cell proliferation. In mouse models of NAFLD, the ISR represses the somatotroph axis, leading to reduced apoptosis and inflammation but decreased hepatocyte proliferation and exacerbated fibrosis in the liver. NAD+ repletion reduces the ISR, rescues the dysregulated somatotroph axis, and alleviates NAFLD. These results establish that the hepatic ISR suppresses the somatotroph axis to control cell fate decisions and liver damage in NAFLD.

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

Upper airway gene expression shows a more robust adaptive immune response to SARS-CoV-2 in children.

Unlike other respiratory viruses, SARS-CoV-2 disproportionately causes severe disease in older adults whereas disease burden in children is lower. To investigate whether differences in the upper airway immune response may contribute to this disparity, we compare nasopharyngeal gene expression in 83 children (<19-years-old; 38 with SARS-CoV-2, 11 with other respiratory viruses, 34 with no virus) and 154 older adults (>40-years-old; 45 with SARS-CoV-2, 28 with other respiratory viruses, 81 with no virus). Expression of interferon-stimulated genes is robustly activated in both children and adults with SARS-CoV-2 infection compared to the respective non-viral groups, with only subtle distinctions. Children, however, demonstrate markedly greater upregulation of pathways related to B cell and T cell activation and proinflammatory cytokine signaling, including response to TNF and production of IFNγ, IL-2 and IL-4. Cell type deconvolution confirms greater recruitment of B cells, and to a lesser degree macrophages, to the upper airway of children. Only children exhibit a decrease in proportions of ciliated cells, among the primary targets of SARS-CoV-2, upon infection. These findings demonstrate that children elicit a more robust innate and especially adaptive immune response to SARS-CoV-2 in the upper airway that likely contributes to their protection from severe disease in the lower airway.

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans.

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual.

Dynamical Systems Model of RNA Velocity Improves Inference of Single-cell Trajectory, Pseudo-time and Gene Regulation.

Recent development in inferring RNA velocity from single-cell RNA-seq opens up exciting new vista into developmental lineage and cellular dynamics. However, the estimated velocity only gives a snapshot of how the transcriptome instantaneously changes in individual cells, and it does not provide quantitative predictions and insights about the whole system. In this work, we develop RNA-ODE, a principled computational framework that extends RNA velocity to quantify systems level dynamics and improve single-cell data analysis. We model the gene expression dynamics by an ordinary differential equation (ODE) based formalism. Given a snapshot of gene expression at one time, RNA-ODE is able to predict and extrapolate the expression trajectory of each cell by solving the dynamic equations. Systematic experiments on simulations and on new data from developing brain demonstrate that RNA-ODE substantially improves many aspects of standard single-cell analysis. By leveraging temporal dynamics, RNA-ODE more accurately estimates cell state lineage and pseudo-time compared to previous state-of-the-art methods. It also infers gene regulatory networks and identifies influential genes whose expression changes can decide cell fate. We expect RNA-ODE to be a Swiss army knife that aids many facets of single-cell RNA-seq analysis.

Transcriptomic profiling of blood from autoimmune hepatitis patients reveals potential mechanisms with implications for management.

Autoimmune hepatitis (AIH) is a poorly understood, chronic disease, for which corticosteroids are still the mainstay of therapy and most patients undergo liver biopsy to obtain a diagnosis. We aimed to determine if there was a transcriptomic signature of AIH in the peripheral blood and investigate underlying biologic pathways revealed by gene expression analysis. Whole blood RNA from 75 AIH patients and 25 healthy volunteers was extracted and sequenced. Differential gene expression analysis revealed 249 genes that were significantly differentially expressed in AIH patients compared to controls. Using a random forest algorithm, we determined that less than 10 genes were sufficient to differentiate the two groups in our cohort. Interferon signaling was more active in AIH samples compared to controls, regardless of treatment status. Pegivirus sequences were detected in five AIH samples and 1 healthy sample. The gene expression data and clinical metadata were used to determine 12 genes that were significantly associated with advanced fibrosis in AIH. AIH patients with a partial response to therapy demonstrated decreased evidence of a CD8+ T cell gene expression signal. These findings represent progress in understanding a disease in need of better tests, therapies, and biomarkers.

Molecular hallmarks of heterochronic parabiosis at single-cell resolution.

The ability to slow or reverse biological ageing would have major implications for mitigating disease risk and maintaining vitality1. Although an increasing number of interventions show promise for rejuvenation2, their effectiveness on disparate cell types across the body and the molecular pathways susceptible to rejuvenation remain largely unexplored. Here we performed single-cell RNA sequencing on 20 organs to reveal cell-type-specific responses to young and aged blood in heterochronic parabiosis. Adipose mesenchymal stromal cells, haematopoietic stem cells and hepatocytes are among those cell types that are especially responsive. On the pathway level, young blood invokes new gene sets in addition to reversing established ageing patterns, with the global rescue of genes encoding electron transport chain subunits pinpointing a prominent role of mitochondrial function in parabiosis-mediated rejuvenation. We observed an almost universal loss of gene expression with age that is largely mimicked by parabiosis: aged blood reduces global gene expression, and young blood restores it in select cell types. Together, these data lay the groundwork for a systemic understanding of the interplay between blood-borne factors and cellular integrity.

Leveraging the Cell Ontology to classify unseen cell types.

Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

Single-Cell Analysis for Whole-Organism Datasets.

Cell atlases are essential companions to the genome as they elucidate how genes are used in a cell type-specific manner or how the usage of genes changes over the lifetime of an organism. This review explores recent advances in whole-organism single-cell atlases, which enable understanding of cell heterogeneity and tissue and cell fate, both in health and disease. Here we provide an overview of recent efforts to build cell atlases across species and discuss the challenges that the field is currently facing. Moreover, we propose the concept of having a knowledgebase that can scale with the number of experiments and computational approaches and a new feedback loop for development and benchmarking of computational methods that includes contributions from the users. These two aspects are key for community efforts in single-cell biology that will help produce a comprehensive annotated map of cell types and states with unparalleled resolution.

Tracheal aspirate RNA sequencing identifies distinct immunological features of COVID-19 ARDS.

The immunological features that distinguish COVID-19-associated acute respiratory distress syndrome (ARDS) from other causes of ARDS are incompletely understood. Here, we report the results of comparative lower respiratory tract transcriptional profiling of tracheal aspirate from 52 critically ill patients with ARDS from COVID-19 or from other etiologies, as well as controls without ARDS. In contrast to a "cytokine storm," we observe reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes. COVID-19 ARDS is characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity. In silico analysis of gene expression identifies several candidate drugs that may modulate gene expression in COVID-19 ARDS, including dexamethasone and granulocyte colony stimulating factor. Compared to ARDS due to other types of viral pneumonia, COVID-19 is characterized by impaired interferon-stimulated gene (ISG) expression. The relationship between SARS-CoV-2 viral load and expression of ISGs is decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19. In summary, assessment of host gene expression in the lower airways of patients reveals distinct immunological features of COVID-19 ARDS.

Upper airway gene expression reveals a more robust innate and adaptive immune response to SARS-CoV-2 in children compared with older adults.

Unlike other respiratory viruses, SARS-CoV-2 disproportionately causes severe disease in older adults and only rarely in children. To investigate whether differences in the upper airway immune response could contribute to this disparity, we compared nasopharyngeal gene expression in 83 children (<19-years-old; 38 with SARS-CoV-2, 11 with other respiratory viruses, 34 with no virus) and 154 adults (>40-years-old; 45 with SARS-CoV-2, 28 with other respiratory viruses, 81 with no virus). Expression of interferon-stimulated genes (ISGs) was robustly activated in both children and adults with SARS-CoV-2 compared to the respective non-viral groups, with only relatively subtle distinctions. Children, however, demonstrated markedly greater upregulation of pathways related to B cell and T cell activation and proinflammatory cytokine signaling, including TNF, IFNγ, IL-2 and IL-4 production. Cell type deconvolution confirmed greater recruitment of B cells, and to a lesser degree macrophages, to the upper airway of children. Only children exhibited a decrease in proportions of ciliated cells, the primary target of SARS-CoV-2, upon infection with the virus. These findings demonstrate that children elicit a more robust innate and adaptive immune response to SARS-CoV-2 infection in the upper airway that likely contributes to their protection from severe disease in the lower airway.

Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures.

Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq data set to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. Altogether, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

COVID-19 ARDS is characterized by a dysregulated host response that differs from cytokine storm and is modified by dexamethasone.

We performed comparative lower respiratory tract transcriptional profiling of 52 critically ill patients with the acute respiratory distress syndrome (ARDS) from COVID-19 or from other etiologies, as well as controls without ARDS. In contrast to a cytokine storm, we observed reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes. COVID-19 ARDS was characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity that were predicted to be modulated by dexamethasone and granulocyte colony stimulating factor. Compared to ARDS due to other types of viral pneumonia, COVID-19 was characterized by impaired interferon-stimulated gene expression (ISG). We found that the relationship between SARS-CoV-2 viral load and expression of ISGs was decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19. In summary, assessment of host gene expression in the lower airways of patients with COVID-19 ARDS did not demonstrate cytokine storm but instead revealed a unique and dysregulated host response predicted to be modified by dexamethasone.