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Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity.

ABSTRACT: To better understand cellular communication driving diverse behaviors, we need to uncover the molecular mechanisms of within-cell-type functional heterogeneity. While single-cell RNA sequencing (scRNAseq) has advanced our understanding of cell heterogeneity, linking individual cell phenotypes to transcriptomic data remains challenging. Here, we used a phenotypic cell sorting technique to ask whether phenotypically supervised scRNAseq analysis (pheno-scRNAseq) can provide more insight into heterogeneous cell behaviors than unsupervised scRNAseq. Using a simple 3D in vitro breast cancer (BRCA) model, we conducted pheno-scRNAseq on invasive and non-invasive cells and compared the results to phenotype-agnostic scRNAseq analysis. Pheno-scRNAseq identified unique and more selective differentially expressed genes than unsupervised scRNAseq analysis. Functional studies validated the utility of pheno-scRNAseq in understanding within-cell-type functional heterogeneity and revealed that migration phenotypes were coordinated with specific metabolic, proliferation, stress, and immune phenotypes. This approach lends new insight into the molecular systems underlying BRCA cell phenotypic heterogeneity.

SUBMITTER: Chen K

PROVIDER: S-EPMC7808958 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity.

Chen Kevin K Ozturk Kivilcim K Contreras Ryne L RL Simon Jessica J McCann Sean S Chen Wei Ji WJ Carter Hannah H Fraley Stephanie I SI

iScience 20201226 1

To better understand cellular communication driving diverse behaviors, we need to uncover the molecular mechanisms of within-cell-type functional heterogeneity. While single-cell RNA sequencing (scRNAseq) has advanced our understanding of cell heterogeneity, linking individual cell phenotypes to transcriptomic data remains challenging. Here, we used a phenotypic cell sorting technique to ask whether phenotypically supervised scRNAseq analysis (pheno-scRNAseq) can provide more insight into hetero ...[more]

PMID: 33490901

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Project description:West Nile virus (WNV) is a neurotropic mosquito-borne flavivirus of global importance. Neuroinvasive WNV infection results in encephalitis and can lead to prolonged neurological impairment or death. Type I interferon (IFN-I) is crucial for promoting antiviral defenses through the induction of antiviral effectors, which function to restrict viral replication and spread. However, our understanding of the antiviral response to WNV infection is mostly derived from analysis of bulk cell populations. It is becoming increasingly apparent that substantial heterogeneity in cellular processes exists among individual cells, even within a seemingly homogenous cell population. Here, we present WNV-inclusive single-cell RNA sequencing (scRNA-seq), an approach to examine the transcriptional variation and viral RNA burden across single cells. We observed that only a few cells within the bulk population displayed robust transcription of IFN-β mRNA, and this did not appear to depend on viral RNA abundance within the same cell. Furthermore, we observed considerable transcriptional heterogeneity in the IFN-I response, with genes displaying high unimodal and bimodal expression patterns. Broadly, IFN-stimulated genes negatively correlated with viral RNA abundance, corresponding with a precipitous decline in expression in cells with high viral RNA levels. Altogether, we demonstrated the feasibility and utility of WNV-inclusive scRNA-seq as a high-throughput technique for single-cell transcriptomics and WNV RNA detection. This approach can be implemented in other models to provide insights into the cellular features of protective immunity and identify novel therapeutic targets.IMPORTANCE West Nile virus (WNV) is a clinically relevant pathogen responsible for recurrent epidemics of neuroinvasive disease. Type I interferon is essential for promoting an antiviral response against WNV infection; however, it is unclear how heterogeneity in the antiviral response at the single-cell level impacts viral control. Specifically, conventional approaches lack the ability to distinguish differences across cells with varying viral abundance. The significance of our research is to demonstrate a new technique for studying WNV infection at the single-cell level. We discovered extensive variation in antiviral gene expression and viral abundance across cells. This protocol can be applied to primary cells or in vivo models to better understand the underlying cellular heterogeneity following WNV infection for the development of targeted therapeutic strategies.

Dataset Information

Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity.

Publications

Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity.

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