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Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.
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Linhagem da Célula/genética , Biologia Computacional/métodos , Sequenciamento de Nucleotídeos em Larga Escala/estatística & dados numéricos , RNA Mensageiro/genética , Análise de Célula Única/estatística & dados numéricos , Transcriptoma , Análise por Conglomerados , Epigênese Genética , Células Eucarióticas/classificação , Células Eucarióticas/citologia , Células Eucarióticas/metabolismo , Perfilação da Expressão Gênica , Humanos , RNA Mensageiro/química , RNA Mensageiro/metabolismo , Análise de Célula Única/métodos , Aprendizado de Máquina não SupervisionadoRESUMO
During typesetting of this article, errors were inadvertently introduced to the hyperlinked URLs of some of the clustering tools in table 1 (Seurat, CIDR, pcaReduce and mpath), as well as to the numbering of the bold-text annotations in the reference list. The article has now been corrected online. The editors apologize for this error.
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BACKGROUND & AIMS: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation. METHODS: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function. RESULTS: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation. CONCLUSIONS: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments. IMPACT AND IMPLICATIONS: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.
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Colangite Esclerosante , Humanos , Cicatriz/metabolismo , Cicatriz/patologia , Ecossistema , Fígado/patologia , Cirrose Hepática/patologia , Citocinas/metabolismo , Inflamação/metabolismo , Perfilação da Expressão GênicaRESUMO
Eukaryotic elongation factor 1A1 (EEF1A1), originally identified for its role in protein synthesis, has additional functions in diverse cellular processes. Of note, we previously discovered a role for EEF1A1 in hepatocyte lipotoxicity. We also demonstrated that a two-week intervention with the EEF1A1 inhibitor didemnin B (DB) (50 µg/kg) decreased liver steatosis in a mouse model of obesity and metabolic dysfunction-associated steatotic liver disease (MASLD) (129S6/SvEvTac mice fed western diet (42% fat) for 26 weeks). Here, we further characterized hepatic changes occurring in these mice by assessing lipid droplet (LD) size, bulk differential expression, and cell type-associated alterations in gene expression. Consistent with the previously demonstrated decrease in hepatic steatosis, we observed decreased median LD size in response to DB. Bulk RNA-seq followed by gene set enrichment analysis revealed alterations in pathways related to energy metabolism and proteostasis in DB-treated mouse livers. Deconvolution of bulk data identified decreased cell-type association scores for cholangiocytes, mononuclear phagocytes, and mesenchymal cells in response to DB. Overrepresentation analyses of bulk data using cell type marker gene sets further identified hepatocytes and cholangiocytes as the primary contributors to bulk differential expression in response to DB. Thus, we show that chemical inhibition of EEF1A1 decreases hepatic LD size and decreases gene expression signatures associated with several liver cell types implicated in MASLD progression. Furthermore, changes in hepatic gene expression were primarily attributable to hepatocytes and cholangiocytes. This work demonstrates that EEF1A1 inhibition may be a viable strategy to target aspects of liver biology implicated in MASLD progression.
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Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.
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Perfilação da Expressão Gênica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Análise por Conglomerados , Conjuntos de Dados como Assunto , Células-Tronco Hematopoéticas/citologia , Humanos , Máquina de Vetores de SuporteRESUMO
MOTIVATION: Most genomes contain thousands of genes, but for most functional responses, only a subset of those genes are relevant. To facilitate many single-cell RNASeq (scRNASeq) analyses the set of genes is often reduced through feature selection, i.e. by removing genes only subject to technical noise. RESULTS: We present M3Drop, an R package that implements popular existing feature selection methods and two novel methods which take advantage of the prevalence of zeros (dropouts) in scRNASeq data to identify features. We show these new methods outperform existing methods on simulated and real datasets. AVAILABILITY AND IMPLEMENTATION: M3Drop is freely available on github as an R package and is compatible with other popular scRNASeq tools: https://github.com/tallulandrews/M3Drop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Software , Genoma , Análise de Sequência de RNA , Análise de Célula ÚnicaRESUMO
Akt is a pro-survival kinase frequently activated in human cancers and is associated with more aggressive tumors that resist therapy. Here, we connect Akt pathway activation to reduced sensitivity to chemotherapy via Akt phosphorylation of Bax at residue S184, one of the pro-apoptotic Bcl-2 family proteins required for cells to undergo apoptosis. We show that phosphorylation by Akt converts the pro-apoptotic protein Bax into an anti-apoptotic protein. Mechanistically, we show that phosphorylation (i) enables Bax binding to pro-apoptotic BH3 proteins in solution, and (ii) prevents Bax inserting into mitochondria. Together, these alterations promote resistance to apoptotic stimuli by sequestering pro-apoptotic activator BH3 proteins. Bax phosphorylation correlates with cellular resistance to BH3 mimetics in primary ovarian cancer cells. Further, analysis of the TCGA database reveals that 98% of cancer patients with increased BAX levels also have an upregulated Akt pathway, compared to 47% of patients with unchanged or decreased BAX levels. These results suggest that in patients, increased phosphorylated anti-apoptotic Bax promotes resistance of cancer cells to inherent and drug-induced apoptosis.
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Apoptose , Resistencia a Medicamentos Antineoplásicos , Proteínas Proto-Oncogênicas c-akt/metabolismo , Proteína X Associada a bcl-2/metabolismo , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/metabolismo , Permeabilidade da Membrana Celular , Células Cultivadas , Feminino , Humanos , Células MCF-7 , Mitocôndrias/metabolismo , Mutação , Neoplasias Ovarianas/tratamento farmacológico , Neoplasias Ovarianas/metabolismo , Fragmentos de Peptídeos/metabolismo , Fosforilação , Ligação Proteica , Proteínas Proto-Oncogênicas/metabolismo , Proteína X Associada a bcl-2/genéticaRESUMO
Clusters of functionally related genes can be disrupted by a single copy number variant (CNV). We demonstrate that the simultaneous disruption of multiple functionally related genes is a frequent and significant characteristic of de novo CNVs in patients with developmental disorders (P = 1 × 10(-3)). Using three different functional networks, we identified unexpectedly large numbers of functionally related genes within de novo CNVs from two large independent cohorts of individuals with developmental disorders. The presence of multiple functionally related genes was a significant predictor of a CNV's pathogenicity when compared to CNVs from apparently healthy individuals and a better predictor than the presence of known disease or haploinsufficient genes for larger CNVs. The functionally related genes found in the de novo CNVs belonged to 70% of all clusters of functionally related genes found across the genome. De novo CNVs were more likely to affect functional clusters and affect them to a greater extent than benign CNVs (P = 6 × 10(-4)). Furthermore, such clusters of functionally related genes are phenotypically informative: Different patients possessing CNVs that affect the same cluster of functionally related genes exhibit more similar phenotypes than expected (P < 0.05). The spanning of multiple functionally similar genes by single CNVs contributes substantially to how these variants exert their pathogenic effects.
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Variações do Número de Cópias de DNA , Deficiências do Desenvolvimento/genética , Família Multigênica , Cromossomos Humanos/genética , Análise por Conglomerados , Bases de Dados Genéticas , Deficiências do Desenvolvimento/diagnóstico , Redes Reguladoras de Genes , Genoma Humano , Voluntários Saudáveis , Humanos , Modelos Logísticos , FenótipoRESUMO
Readily-accessible and standardised capture of genotypic variation has revolutionised our understanding of the genetic contribution to disease. Unfortunately, the corresponding systematic capture of patient phenotypic variation needed to fully interpret the impact of genetic variation has lagged far behind. Exploiting deep and systematic phenotyping of a cohort of 197 patients presenting with heterogeneous developmental disorders and whose genomes harbour de novo CNVs, we systematically applied a range of commonly-used functional genomics approaches to identify the underlying molecular perturbations and their phenotypic impact. Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups. We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype. Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.
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Variações do Número de Cópias de DNA/genética , Deficiências do Desenvolvimento/genética , Redes e Vias Metabólicas/genética , Mapas de Interação de Proteínas/genética , Animais , Deficiências do Desenvolvimento/metabolismo , Deficiências do Desenvolvimento/patologia , Expressão Gênica , Estudos de Associação Genética , Genoma Humano , Genótipo , Humanos , Camundongos , Fenótipo , Mapeamento de Interação de ProteínasRESUMO
SUMMARY: We present GeneNet Toolbox for MATLAB (also available as a set of standalone applications for Linux). The toolbox, available as command-line or with a graphical user interface, enables biologists to assess connectivity among a set of genes of interest ('seed-genes') within a biological network of their choosing. Two methods are implemented for calculating the significance of connectivity among seed-genes: 'seed randomization' and 'network permutation'. Options include restricting analyses to a specified subnetwork of the primary biological network, and calculating connectivity from the seed-genes to a second set of interesting genes. Pre-analysis tools help the user choose the best connectivity-analysis algorithm for their network. The toolbox also enables visualization of the connections among seed-genes. GeneNet Toolbox functions execute in reasonable time for very large networks (â¼10 million edges) on a desktop computer. AVAILABILITY AND IMPLEMENTATION: GeneNet Toolbox is open source and freely available from http://avigailtaylor.github.io/gntat14. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: avigail.taylor@dpag.ox.ac.uk.
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Algoritmos , Biologia Computacional/métodos , Bases de Dados Genéticas , Redes Reguladoras de Genes , Redes e Vias Metabólicas/genética , Análise de Sequência de DNA/métodos , Software , Gráficos por Computador , Regulação da Expressão Gênica , Humanos , Armazenamento e Recuperação da Informação , Interface Usuário-ComputadorRESUMO
Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.
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Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.
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In mice, the first liver-resident macrophages, known as Kupffer cells (KCs), are thought to derive from yolk sac (YS) hematopoietic progenitors that are specified prior to the emergence of the hematopoietic stem cell (HSC). To investigate human KC development, we recapitulated YS-like hematopoiesis from human pluripotent stem cells (hPSCs) and transplanted derivative macrophage progenitors into NSG mice previously humanized with hPSC-liver sinusoidal endothelial cells (LSECs). We demonstrate that hPSC-LSECs facilitate stable hPSC-YS-macrophage engraftment for at least 7 weeks. Single-cell RNA sequencing (scRNA-seq) of engrafted YS-macrophages revealed a homogeneous MARCO-expressing KC gene signature and low expression of monocyte-like macrophage genes. In contrast, human cord blood (CB)-derived macrophage progenitors generated grafts that contain multiple hematopoietic lineages in addition to KCs. Functional analyses showed that the engrafted KCs actively perform phagocytosis and erythrophagocytosis in vivo. Taken together, these findings demonstrate that it is possible to generate human KCs from hPSC-derived, YS-like progenitors.
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Diferenciação Celular , Células Endoteliais , Células de Kupffer , Fígado , Células-Tronco Pluripotentes , Humanos , Células de Kupffer/metabolismo , Células de Kupffer/citologia , Células-Tronco Pluripotentes/metabolismo , Células-Tronco Pluripotentes/citologia , Células Endoteliais/metabolismo , Células Endoteliais/citologia , Animais , Fígado/citologia , Fígado/metabolismo , Camundongos , Fagocitose , HematopoeseRESUMO
Progressive Supranuclear palsy (PSP) is a 4-repeat (4-R) tauopathy. We hypothesized that the molecular diversity of tau could explain the heterogeneity seen in PSP disease progression. To test this hypothesis, we performed an extensive biochemical characterisation of the high molecular weight tau species (HMW-Tau) in 20 different brain regions of 25 PSP patients. We found a correlation between the HMW-Tau species and tau seeding capacity in the primary motor cortex, where we confirmed that an elevated 4R-Tau seeding activity correlates with a shorter disease duration. To identify factors that contribute to these differences, we performed proteomic and spatial transcriptomic analysis that revealed key mechanistic pathways, in particular those involving the immune system, that defined patients demonstrating high and low tau seeding capacity. These observations suggest that differences in the tau seeding activity may contribute to the considerable heterogeneity seen in disease progression of patients suffering from PSP.
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BACKGROUND: Lung transplant recipients experience episodes of immune-mediated acute lung allograft dysfunction (ALAD). ALAD episodes are a risk factor for chronic lung allograft dysfunction (CLAD), the major cause of death after lung transplantation. Our objective was to determine key cellular elements in dysfunctional lung allografts, with a focus on macrophages. METHODS: We have applied single-cell RNA sequencing (scRNAseq) to bronchoalveolar lavage cells from stable and ALAD patients and to cells from explanted CLAD lung tissue. RESULTS: We identified 2 alveolar macrophage (AM) subsets uniquely represented in ALAD. Using pathway analysis and differentially expressed genes, we annotated these as pro-inflammatory interferon-stimulated gene (ISG) and metallothionein-mediated inflammatory (MT) AMs. Functional analysis of an independent set of AMs in vitro revealed that ALAD AMs exhibited a higher expression of CXCL10, a marker of ISG AMs, and increased secretion of pro-inflammatory cytokines compared to AMs from stable patients. Using publicly available bronchoalveolar lavage scRNAseq datasets, we found that ISG and MT AMs are associated with more severe inflammation in COVID-19 patients. Analysis of cells from 4 explanted CLAD lungs revealed similar macrophage populations. Donor and recipient cells were identified using expressed single nucleotide variations. We demonstrated contributions of donor and recipient cells to all AM subsets early post-transplant, with loss of donor-derived cells over time. CONCLUSIONS: Our data reveal extensive heterogeneity among lung macrophages after lung transplantation and indicates that specific sub-populations may be associated with allograft dysfunction, raising the possibility that these cells may represent important therapeutic targets.
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COVID-19 , Transplante de Pulmão , Humanos , Interferons , Metalotioneína/genética , Rejeição de Enxerto , Líquido da Lavagem Broncoalveolar , Transplante de Pulmão/efeitos adversos , Pulmão , Macrófagos Alveolares , AloenxertosRESUMO
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.
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Fígado , Análise de Célula Única , Núcleo Celular/genética , Humanos , Análise de Sequência de RNA , Transcriptoma/genéticaRESUMO
The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.
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Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Here we present an overview of the computational workflow involved in processing scRNA-seq data. We discuss some of the most common tasks and the tools available for addressing central biological questions. In this article and our companion website ( https://scrnaseq-course.cog.sanger.ac.uk/website/index.html ), we provide guidelines regarding best practices for performing computational analyses. This tutorial provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods.