Single-cell analyses allow uncovering cellular heterogeneity, not only per se, but also in response to viral infection. Similarly, single cell transcriptome analyses (scRNA-Seq) can highlight specific signatures, identifying cell subsets with particular phenotypes, which are relevant in the understanding of virus-host interactions.
Identification of specific cell signatures using single-cell RNA-seq and phenotypic analyses
The whole analysis pipeline can be divided in five steps. (i) Cells infected at population level are separated and isolated as single cells. (ii) Each individual cell is processed for RNA-Seq, requiring RNA extraction, reverse transcription (RT) with optional unique molecular identifier (UMI) barcoding, library preparation and amplification, and finally sequencing. (iii) Sequencing reads are further processed and aligned to host and virus genome reference sequences to assess abundance and structure of transcripts, thereby defining the gene expression profile of each single cell. Multiple transcriptomic analyses can be performed to inform about data structure and cell heterogeneity using dimensionality reduction plots, such as t-Distributed Stochastic Neighbor Embedding (t-SNE) or Principal Component Analysis (PCA). Cell clustering and differential expression analysis can provide specific gene expression profiles. Finally, single cell transcriptomes can be compared to cell population transcriptome or to quantitative RT-qPCR analyses. These three first steps characterize the process for single-cell transcriptomic analysis, allowing mostly revealing the level of cellular heterogeneity in a cell population, that is, identifying one or multiple cell subpopulations. (iv) Phenotypic analyses at single cell level (with direct association with corresponding scRNA-Seq) or using two cell populations with extreme opposite phenotypes can complement transcriptome analyses. Phenotypic analyses include assessment of protein expression level and localization, using FACS staining, immunofluorescence or western blots, or assessment of protein activity studies, using assays quantifying enzymatic activity, transport activity (channels) or translocation assays (metabolites). The analysis of specific phenotypes should also reveal single cell heterogeneity (i.e. single cells displaying different levels of protein expression or activity) or differences between multiple cell populations. (v) Finally, correlation analysis between transcriptomic data and phenotypical analyses should help identifying specific cell subsets displaying a specific phenotype and characterized by a defined transcriptional and molecular signature.
Cristinelli S, Ciuffi A. (2018) The use of single-cell RNA-Seq to understand virus-host interactions. Curr Opin Virol 29:39-50. [abstract]