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As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.
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Algoritmos , Análisis de la Célula Individual , Análisis de la Célula Individual/métodos , Humanos , Biología Computacional/métodos , Análisis de Datos , Animales , Análisis por ConglomeradosRESUMEN
Deciphering the cell-state transitions underlying immune adaptation across time is fundamental for advancing biology. Empirical in vivo genomic technologies that capture cellular dynamics are currently lacking. We present Zman-seq, a single-cell technology recording transcriptomic dynamics across time by introducing time stamps into circulating immune cells, tracking them in tissues for days. Applying Zman-seq resolved cell-state and molecular trajectories of the dysfunctional immune microenvironment in glioblastoma. Within 24 hours of tumor infiltration, cytotoxic natural killer cells transitioned to a dysfunctional program regulated by TGFB1 signaling. Infiltrating monocytes differentiated into immunosuppressive macrophages, characterized by the upregulation of suppressive myeloid checkpoints Trem2, Il18bp, and Arg1, over 36 to 48 hours. Treatment with an antagonistic anti-TREM2 antibody reshaped the tumor microenvironment by redirecting the monocyte trajectory toward pro-inflammatory macrophages. Zman-seq is a broadly applicable technology, enabling empirical measurements of differentiation trajectories, which can enhance the development of more efficacious immunotherapies.
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Glioblastoma , Humanos , Perfilación de la Expresión Génica , Glioblastoma/patología , Inmunoterapia , Células Asesinas Naturales , Macrófagos , Microambiente Tumoral , Análisis de la Célula IndividualRESUMEN
Technological advances in a variety of scientific disciplines are being applied in the life sciences leading to an increase in the number scientists who see themselves or are classed as being multidisciplinary. Although their diverse skills are celebrated and needed to understand the immense complexity of life, being a multidisciplinary researcher can pose unique challenges. We asked multidisciplinary researchers and the director of an institute that fosters multidisciplinary research for their thoughts on what they see as the challenges or obstacles that multidisciplinary scientists can often face.
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Investigación Interdisciplinaria , Investigadores , HumanosRESUMEN
There is pressing urgency to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2), which causes the disease COVID-19. SARS-CoV-2 spike (S) protein binds angiotensin-converting enzyme 2 (ACE2), and in concert with host proteases, principally transmembrane serine protease 2 (TMPRSS2), promotes cellular entry. The cell subsets targeted by SARS-CoV-2 in host tissues and the factors that regulate ACE2 expression remain unknown. Here, we leverage human, non-human primate, and mouse single-cell RNA-sequencing (scRNA-seq) datasets across health and disease to uncover putative targets of SARS-CoV-2 among tissue-resident cell subsets. We identify ACE2 and TMPRSS2 co-expressing cells within lung type II pneumocytes, ileal absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discovered that ACE2 is a human interferon-stimulated gene (ISG) in vitro using airway epithelial cells and extend our findings to in vivo viral infections. Our data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection.
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Células Epiteliales Alveolares/metabolismo , Enterocitos/metabolismo , Células Caliciformes/metabolismo , Interferón Tipo I/metabolismo , Mucosa Nasal/citología , Peptidil-Dipeptidasa A/genética , Adolescente , Células Epiteliales Alveolares/inmunología , Enzima Convertidora de Angiotensina 2 , Animales , Betacoronavirus/fisiología , COVID-19 , Línea Celular , Células Cultivadas , Niño , Infecciones por Coronavirus/virología , Enterocitos/inmunología , Células Caliciformes/inmunología , Infecciones por VIH/inmunología , Humanos , Gripe Humana/inmunología , Interferón Tipo I/inmunología , Pulmón/citología , Pulmón/patología , Macaca mulatta , Ratones , Mycobacterium tuberculosis , Mucosa Nasal/inmunología , Pandemias , Peptidil-Dipeptidasa A/metabolismo , Neumonía Viral/virología , Receptores Virales/genética , SARS-CoV-2 , Serina Endopeptidasas/metabolismo , Análisis de la Célula Individual , Tuberculosis/inmunología , Regulación hacia ArribaRESUMEN
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Perfilación de la Expresión Génica , Proteómica , Perfilación de la Expresión Génica/métodos , Análisis de la Célula Individual/métodosRESUMEN
Single-cell ATAC sequencing coverage in regulatory regions is typically binarized as an indicator of open chromatin. Here we show that binarization is an unnecessary step that neither improves goodness of fit, clustering, cell type identification nor batch integration. Fragment counts, but not read counts, should instead be modeled, which preserves quantitative regulatory information. These results have immediate implications for single-cell ATAC sequencing analysis.
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Secuenciación de Inmunoprecipitación de Cromatina , Secuenciación de Nucleótidos de Alto Rendimiento , Análisis de Secuencia de ADN/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Cromatina/genética , Análisis de la Célula IndividualRESUMEN
Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.
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RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show that veloVI compares favorably to previous approaches with respect to goodness of fit, consistency across transcriptionally similar cells and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that veloVI's posterior velocity uncertainty can be used to assess whether velocity analysis is appropriate for a given dataset. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.
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ARN , Transcriptoma , ARN/genética , AprendizajeRESUMEN
Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.
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Análisis de la Célula Individual , Análisis de la Célula Individual/métodos , Humanos , Aprendizaje Automático , Animales , Biología Computacional/métodos , Genómica/métodosRESUMEN
Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.
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A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.
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Algoritmos , Análisis de la Célula Individual , Animales , Humanos , Ratones , Análisis de Secuencia de ARN/métodos , Análisis por Conglomerados , Análisis de la Célula Individual/métodos , Perfilación de la Expresión Génica/métodosRESUMEN
Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease.
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ARN , Análisis por ConglomeradosRESUMEN
The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.
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Genómica , Leucocitos Mononucleares , Humanos , Cromatina/genética , Análisis de la Célula IndividualRESUMEN
Lymphotoxin ß-receptor (LTßR) signalling promotes lymphoid neogenesis and the development of tertiary lymphoid structures1,2, which are associated with severe chronic inflammatory diseases that span several organ systems3-6. How LTßR signalling drives chronic tissue damage particularly in the lung, the mechanism(s) that regulate this process, and whether LTßR blockade might be of therapeutic value have remained unclear. Here we demonstrate increased expression of LTßR ligands in adaptive and innate immune cells, enhanced non-canonical NF-κB signalling, and enriched LTßR target gene expression in lung epithelial cells from patients with smoking-associated chronic obstructive pulmonary disease (COPD) and from mice chronically exposed to cigarette smoke. Therapeutic inhibition of LTßR signalling in young and aged mice disrupted smoking-related inducible bronchus-associated lymphoid tissue, induced regeneration of lung tissue, and reverted airway fibrosis and systemic muscle wasting. Mechanistically, blockade of LTßR signalling dampened epithelial non-canonical activation of NF-κB, reduced TGFß signalling in airways, and induced regeneration by preventing epithelial cell death and activating WNT/ß-catenin signalling in alveolar epithelial progenitor cells. These findings suggest that inhibition of LTßR signalling represents a viable therapeutic option that combines prevention of tertiary lymphoid structures1 and inhibition of apoptosis with tissue-regenerative strategies.
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Pulmón/efectos de los fármacos , Pulmón/fisiología , Receptor beta de Linfotoxina/antagonistas & inhibidores , Regeneración/efectos de los fármacos , Transducción de Señal/efectos de los fármacos , Proteínas Wnt/agonistas , Inmunidad Adaptativa , Envejecimiento/metabolismo , Células Epiteliales Alveolares/citología , Células Epiteliales Alveolares/efectos de los fármacos , Células Epiteliales Alveolares/metabolismo , Animales , Apoptosis/efectos de los fármacos , Enfisema/metabolismo , Femenino , Humanos , Inmunidad Innata , Pulmón/metabolismo , Receptor beta de Linfotoxina/metabolismo , Ratones , Ratones Endogámicos C57BL , FN-kappa B/metabolismo , Enfermedad Pulmonar Obstructiva Crónica/metabolismo , Humo/efectos adversos , Células Madre/efectos de los fármacos , Células Madre/metabolismo , Proteínas Wnt/metabolismo , beta Catenina/metabolismoRESUMEN
Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.
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Tratamiento Basado en Trasplante de Células y Tejidos , Atención a la Salud/métodos , Atención a la Salud/tendencias , Medicina/métodos , Medicina/tendencias , Patología , Análisis de la Célula Individual , Inteligencia Artificial , Atención a la Salud/ética , Atención a la Salud/normas , Diagnóstico Precoz , Educación Médica , Europa (Continente) , Femenino , Salud , Humanos , Legislación Médica , Masculino , Medicina/normasRESUMEN
Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank ( https://cellrank.org ) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.
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Algoritmos , Biología Computacional/métodos , Páncreas Exocrino/citología , Análisis de la Célula Individual/métodos , Programas Informáticos , Animales , Diferenciación Celular/genética , Linaje de la Célula , Reprogramación Celular , Humanos , Pulmón/citología , ARN , RegeneraciónRESUMEN
Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space. Our freely available Python module and benchmarking pipeline can identify optimal data integration methods for new data, benchmark new methods and improve method development.
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Biología Computacional/métodos , Genómica/métodos , Análisis de la Célula Individual/métodos , Programas Informáticos , Animales , Benchmarking , Bases de Datos Genéticas , Humanos , Sistema Inmunológico/citología , Ratones , Análisis de Secuencia de ARN/métodosRESUMEN
Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.
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Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Proteómica/métodos , Programas Informáticos , Animales , Visualización de Datos , Bases de Datos Factuales , Humanos , Procesamiento de Imagen Asistido por Computador , Ratones , Lenguajes de Programación , Flujo de TrabajoRESUMEN
SUMMARY: Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions. GraphCompass builds upon the Squidpy spatial omics toolbox and encompasses various statistical approaches to perform cross-condition analyses at the level of individual cell types, niches, and samples. Additionally, GraphCompass provides custom visualization functions that enable effective communication of results. We demonstrate how GraphCompass can be used to address key biological questions, such as how cellular organization and tissue architecture differ across various disease states and which spatial patterns correlate with a given pathological condition. GraphCompass can be applied to various popular omics techniques, including, but not limited to, spatial proteomics (e.g. MIBI-TOF), spot-based transcriptomics (e.g. 10× Genomics Visium), and single-cell resolved transcriptomics (e.g. Stereo-seq). In this work, we showcase the capabilities of GraphCompass through its application to three different studies that may also serve as benchmark datasets for further method development. With its easy-to-use implementation, extensive documentation, and comprehensive tutorials, GraphCompass is accessible to biologists with varying levels of computational expertise. By facilitating comparative analyses of cell spatial organization, GraphCompass promises to be a valuable asset in advancing our understanding of tissue function in health and disease. .
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Programas Informáticos , Humanos , Proteómica/métodos , Biología Computacional/métodos , Genómica/métodos , Animales , Transcriptoma , Análisis de la Célula Individual/métodosRESUMEN
SUMMARY: Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. AVAILABILITY AND IMPLEMENTATION: The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.