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Cell Rep ; 37(1): 109793, 2021 10 05.
Artigo em Inglês | MEDLINE | ID: mdl-34587478


The mortality risk of coronavirus disease 2019 (COVID-19) patients has been linked to the cytokine storm caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the inflammatory responses shared between COVID-19 and other infectious diseases that feature cytokine storms may therefore help in developing improved therapeutic strategies. Here, we use integrative analysis of single-cell transcriptomes to characterize the inflammatory signatures of peripheral blood mononuclear cells from patients with COVID-19, sepsis, and HIV infection. We identify ten hyperinflammatory cell subtypes in which monocytes are the main contributors to the transcriptional differences in these infections. Monocytes from COVID-19 patients share hyperinflammatory signatures with HIV infection and immunosuppressive signatures with sepsis. Finally, we construct a "three-stage" model of heterogeneity among COVID-19 patients, related to the hyperinflammatory and immunosuppressive signatures in monocytes. Our study thus reveals cellular and molecular insights about inflammatory responses to SARS-CoV-2 infection and provides therapeutic guidance to improve treatments for subsets of COVID-19 patients.

COVID-19/sangue , COVID-19/imunologia , Infecções por HIV/sangue , Leucócitos Mononucleares/metabolismo , SARS-CoV-2/imunologia , Sepse/sangue , Transcriptoma , COVID-19/virologia , Síndrome da Liberação de Citocina/sangue , Síndrome da Liberação de Citocina/imunologia , Citocinas/sangue , Análise de Dados , Conjuntos de Dados como Assunto , Infecções por HIV/imunologia , HIV-1/imunologia , Humanos , Inflamação/sangue , Leucócitos Mononucleares/imunologia , Sepse/imunologia , Análise de Célula Única
Brief Bioinform ; 22(5)2021 09 02.
Artigo em Inglês | MEDLINE | ID: mdl-33834202


The low capture rate of expressed RNAs from single-cell sequencing technology is one of the major obstacles to downstream functional genomics analyses. Recently, a number of imputation methods have emerged for single-cell transcriptome data, however, recovering missing values in very sparse expression matrices remains a substantial challenge. Here, we propose a new algorithm, WEDGE (WEighted Decomposition of Gene Expression), to impute gene expression matrices by using a biased low-rank matrix decomposition method. WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations and improved the clustering of cells, performing impressively for applications with sparse datasets. Overall, this study shows a potent approach for imputing sparse expression matrix data, and our WEDGE algorithm should help many researchers to more profitably explore the biological meanings embedded in their single-cell RNA sequencing datasets. The source code of WEDGE has been released at

Algoritmos , Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , RNA-Seq/métodos , Análise de Célula Única/métodos , COVID-19/sangue , COVID-19/genética , COVID-19/virologia , Análise por Conglomerados , Simulação por Computador , Genômica/métodos , Humanos , Leucócitos Mononucleares/classificação , Leucócitos Mononucleares/metabolismo , Reprodutibilidade dos Testes , SARS-CoV-2/fisiologia , Índice de Gravidade de Doença
Cell ; 184(7): 1895-1913.e19, 2021 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-33657410


A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.

COVID-19/imunologia , Megacariócitos/imunologia , Monócitos/imunologia , RNA Viral , SARS-CoV-2/genética , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , China , Estudos de Coortes , Citocinas/metabolismo , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , RNA Viral/sangue , RNA Viral/isolamento & purificação , Análise de Célula Única , Transcriptoma/imunologia , Adulto Jovem