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Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.
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Metadatos , Análisis de la Célula Individual , Análisis de la Célula Individual/métodos , Análisis de la Célula Individual/normas , Reproducibilidad de los Resultados , Humanos , Programas InformáticosRESUMEN
Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.
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Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.
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Perfilación de la Expresión Génica , Difusión de la Información , Animales , Ratones , Humanos , Análisis de la Célula Individual , TranscriptomaRESUMEN
Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.
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Secuenciación de Nucleótidos de Alto Rendimiento , Neoplasias , Humanos , Neoplasias/genética , Análisis de Secuencia de ARN , Transcriptoma/genéticaRESUMEN
Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.
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Encéfalo , Neurociencias , Animales , Humanos , Ratones , Ecosistema , NeuronasRESUMEN
A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of feature-observation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple list-like schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.
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We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.
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Biología Computacional , Células Vegetales , Animales , Humanos , Ratones , Plantas/genéticaRESUMEN
Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org).
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Encéfalo , Bases de Datos Genéticas , Epigenómica , Multiómica , Transcriptoma , Animales , Ratones , Genómica , Mamíferos , Primates , Encéfalo/citología , Encéfalo/metabolismoRESUMEN
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.
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Biología Computacional , Microbioma Gastrointestinal , Análisis Multivariante , Simulación por Computador , Humanos , Enfermedades Inflamatorias del Intestino/genética , Enfermedades Inflamatorias del Intestino/metabolismo , Enfermedades Inflamatorias del Intestino/patologíaRESUMEN
Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA's model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA's performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA's utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.
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Microbiota , Modelos Estadísticos , Algoritmos , Benchmarking , Biología Computacional/métodos , Simulación por ComputadorRESUMEN
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.
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Nube Computacional/economía , Biología Computacional/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Análisis de Secuencia de ARN/métodos , Análisis de la Célula Individual/métodos , Biología Computacional/economía , Secuenciación de Nucleótidos de Alto Rendimiento/economía , Análisis de Secuencia de ARN/economíaRESUMEN
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Single-cell genomics is essential to chart tumor ecosystems. Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each requires customization to different tissue and tumor types, posing a barrier to adoption. Here, we have developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 cells and nuclei from 40 samples across 23 specimens spanning eight tumor types of varying tissue and sample characteristics. We evaluated protocols by cell and nucleus quality, recovery rate and cellular composition. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, but at different proportions. Our work provides guidance for studies in a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.
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Algoritmos , Núcleo Celular/genética , Genómica/métodos , Neoplasias/genética , RNA-Seq/métodos , Análisis de la Célula Individual/métodos , Adulto , Animales , Núcleo Celular/química , Núcleo Celular/metabolismo , Niño , Biología Computacional/métodos , Femenino , Congelación , Perfilación de la Expresión Génica/métodos , Regulación Neoplásica de la Expresión Génica , Humanos , Ratones , Ratones Noqueados , Ratones Desnudos , Neoplasias/metabolismo , Neoplasias/patología , Análisis de Secuencia de ARN/métodos , Células Tumorales Cultivadas , Secuenciación del Exoma/métodosRESUMEN
During postnatal life, thymopoiesis depends on the continuous colonization of the thymus by bone-marrow-derived hematopoietic progenitors that migrate through the bloodstream. The current understanding of the nature of thymic immigrants is largely based on data from pre-clinical models. Here, we employed single-cell RNA sequencing (scRNA-seq) to examine the immature postnatal thymocyte population in humans. Integration of bone marrow and peripheral blood precursor datasets identified two putative thymus seeding progenitors that varied in expression of CD7; CD10; and the homing receptors CCR7, CCR9, and ITGB7. Whereas both precursors supported T cell development, only one contributed to intrathymic dendritic cell (DC) differentiation, predominantly of plasmacytoid dendritic cells. Trajectory inference delineated the transcriptional dynamics underlying early human T lineage development, enabling prediction of transcription factor (TF) modules that drive stage-specific steps of human T cell development. This comprehensive dataset defines the expression signature of immature human thymocytes and provides a resource for the further study of human thymopoiesis.
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Diferenciación Celular , Regulación del Desarrollo de la Expresión Génica , Células Progenitoras Linfoides/citología , Células Progenitoras Linfoides/metabolismo , ARN Citoplasmático Pequeño/genética , Timocitos/citología , Timocitos/metabolismo , Biomarcadores , Diferenciación Celular/genética , Diferenciación Celular/inmunología , Linaje de la Célula/genética , Perfilación de la Expresión Génica , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Inmunofenotipificación , Análisis de la Célula Individual , Timocitos/inmunología , TranscriptomaRESUMEN
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.
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Feto/citología , Hematopoyesis , Hígado/citología , Hígado/embriología , Células Sanguíneas/citología , Microambiente Celular , Femenino , Feto/metabolismo , Citometría de Flujo , Perfilación de la Expresión Génica , Humanos , Hígado/metabolismo , Tejido Linfoide/citología , Análisis de la Célula Individual , Células Madre/metabolismoRESUMEN
Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan.
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Desarrollo Embrionario/genética , Redes Reguladoras de Genes/genética , Pediatría/tendencias , Análisis de la Célula Individual/métodos , Perfilación de la Expresión Génica , Regulación del Desarrollo de la Expresión Génica/genética , Humanos , Distribución Tisular/genéticaRESUMEN
Oral squamous cell carcinomas are a major cause of morbidity and mortality, and tobacco usage, alcohol consumption, and poor oral hygiene are established risk factors. To date, no large-scale case-control studies have considered the effects of these risk factors on the composition of the oral microbiome, nor microbial community associations with oral cancer. We compared the composition, diversity, and function of the oral microbiomes of 121 oral cancer patients to 242 age- and gender-matched controls using a metagenomic multivariate analysis pipeline. Significant shifts in composition and function of the oral microbiome were observed with poor oral hygiene, tobacco smoking, and oral cancer. Specifically, we observed dramatically altered community composition and function after tooth loss, with smaller alterations in current tobacco smokers, increased production of antioxidants in individuals with periodontitis, and significantly decreased glutamate metabolism metal transport in oral cancer patients. Although the alterations in the oral microbiome of oral cancer patients were significant, they were of substantially lower effect size relative to microbiome shifts after tooth loss. Alterations following tooth loss, itself a major risk factor for oral cancer, are likely a result of severe ecological disruption due to habitat loss but may also contribute to the development of the disease.
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Bacterias/patogenicidad , Microbiota/fisiología , Neoplasias de la Boca/etiología , Neoplasias de la Boca/microbiología , Neoplasias Orofaríngeas/etiología , Neoplasias Orofaríngeas/microbiología , Anciano , Consumo de Bebidas Alcohólicas/efectos adversos , Carcinoma de Células Escamosas/etiología , Carcinoma de Células Escamosas/microbiología , Estudios de Casos y Controles , Femenino , Humanos , Masculino , Persona de Mediana Edad , Higiene Bucal/efectos adversos , Factores de Riesgo , Fumar/efectos adversos , Uso de Tabaco/efectos adversos , Pérdida de Diente/etiología , Pérdida de Diente/microbiologíaRESUMEN
This corrects the article DOI: 10.1038/nature24029.
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Type 2 innate lymphoid cells (ILC2s) both contribute to mucosal homeostasis and initiate pathologic inflammation in allergic asthma. However, the signals that direct ILC2s to promote homeostasis versus inflammation are unclear. To identify such molecular cues, we profiled mouse lung-resident ILCs using single-cell RNA sequencing at steady state and after in vivo stimulation with the alarmin cytokines IL-25 and IL-33. ILC2s were transcriptionally heterogeneous after activation, with subpopulations distinguished by expression of proliferative, homeostatic and effector genes. The neuropeptide receptor Nmur1 was preferentially expressed by ILC2s at steady state and after IL-25 stimulation. Neuromedin U (NMU), the ligand of NMUR1, activated ILC2s in vitro, and in vivo co-administration of NMU with IL-25 strongly amplified allergic inflammation. Loss of NMU-NMUR1 signalling reduced ILC2 frequency and effector function, and altered transcriptional programs following allergen challenge in vivo. Thus, NMUR1 signalling promotes inflammatory ILC2 responses, highlighting the importance of neuro-immune crosstalk in allergic inflammation at mucosal surfaces.
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Hipersensibilidad/inmunología , Hipersensibilidad/patología , Inflamación/inmunología , Inflamación/patología , Pulmón/patología , Linfocitos/inmunología , Neuropéptidos/metabolismo , Animales , Femenino , Regulación de la Expresión Génica , Inmunidad Innata/inmunología , Interleucina-17/inmunología , Interleucina-33/inmunología , Ligandos , Pulmón/inmunología , Masculino , Ratones , Ratones Endogámicos C57BL , Receptores de Neurotransmisores/biosíntesis , Receptores de Neurotransmisores/genética , Receptores de Neurotransmisores/metabolismo , Mucosa Respiratoria/inmunología , Mucosa Respiratoria/patología , Transducción de Señal , Transcripción GenéticaRESUMEN
Mammals have extremely limited regenerative capabilities; however, axolotls are profoundly regenerative and can replace entire limbs. The mechanisms underlying limb regeneration remain poorly understood, partly because the enormous and incompletely sequenced genomes of axolotls have hindered the study of genes facilitating regeneration. We assembled and annotated a de novo transcriptome using RNA-sequencing profiles for a broad spectrum of tissues that is estimated to have near-complete sequence information for 88% of axolotl genes. We devised expression analyses that identified the axolotl orthologs of cirbp and kazald1 as highly expressed and enriched in blastemas. Using morpholino anti-sense oligonucleotides, we find evidence that cirbp plays a cytoprotective role during limb regeneration whereas manipulation of kazald1 expression disrupts regeneration. Our transcriptome and annotation resources greatly complement previous transcriptomic studies and will be a valuable resource for future research in regenerative biology.