Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis.

Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations.

Molecular characterization of human cytomegalovirus infection with single-cell transcriptomics.

Human cytomegalovirus (HCMV) can result in either productive or non-productive infection, with the latter potentially leading to viral latency. The molecular factors dictating these outcomes are poorly understood. Here we used single-cell transcriptomics to analyse HCMV infection progression in monocytes, which are latently infected, and macrophages, considered to be permissive for productive infection.

Temporal dynamics of HCMV gene expression in lytic and latent infections.

During productive human cytomegalovirus (HCMV) infection, viral genes are expressed in a coordinated cascade that conventionally relies on the dependencies of viral genes on protein synthesis and viral DNA replication. By contrast, the transcriptional landscape of HCMV latency is poorly understood. Here, we examine viral gene expression dynamics during the establishment of both productive and latent HCMV infections.

Single cell analysis reveals human cytomegalovirus drives latently infected cells towards an anergic-like monocyte state.

Human cytomegalovirus (HCMV) causes a lifelong infection through establishment of latency. Although reactivation from latency can cause life-threatening disease, our molecular understanding of HCMV latency is incomplete. Here we use single cell RNA-seq analysis to characterize latency in monocytes and hematopoietic stem and progenitor cells (HSPCs).