Estimation of immune cell content in tumor using single-cell RNA-seq reference data

Xiaoqing Yu,Y Ann Chen,Christine H Chung,Jose R Conejo-Garcia,Xuefeng Wang

doi:10.1186/s12885-019-5927-3

Abstract

BackgroundThe rapid development of single-cell RNA sequencing (scRNA-seq) provides unprecedented opportunities to study the tumor ecosystem that involves a heterogeneous mixture of cell types. However, the majority of previous and current studies related to translational and molecular oncology have only focused on the bulk tumor and there is a wealth of gene expression data accumulated with matched clinical outcomes.ResultsIn this paper, we introduce a scheme for characterizing cell compositions from bulk tumor gene expression by integrating signatures learned from scRNA-seq data. We derived the reference expression matrix to each cell type based on cell subpopulations identified in head and neck cancer dataset. Our results suggest that scRNA-Seq-derived reference matrix outperforms the existing gene panel and reference matrix with respect to distinguishing immune cell subtypes.ConclusionsFindings and resources created from this study enable future and secondary analysis of tumor RNA mixtures in head and neck cancer for a more accurate cellular deconvolution, and can facilitate the profiling of the immune infiltration in other solid tumors due to the expression homogeneity observed in immune cells.

Highlights

The rapid development of single-cell RNA sequencing provides unprecedented opportunities to study the tumor ecosystem that involves a heterogeneous mixture of cell types
Identifiable cell types using head and neck squamous cell carcinoma (HNSCC) single cell data Overall, the adaptive clustering analysis on single-cell transcriptome data pooled from all HNSCC tumor samples identified distinct 11 cell clusters to be used in generating reference gene expression profile (GEP)
At this stage, we had no information about cell types underlying these cell groups and the number of clusters might differ subject to the perplexity parameter choice in t-Distributed Stochastic Neighbor Embedding (t-SNE)

Summary

Introduction

The rapid development of single-cell RNA sequencing (scRNA-seq) provides unprecedented opportunities to study the tumor ecosystem that involves a heterogeneous mixture of cell types. The presence and higher content of tumor-infiltrating lymphocytes (TILs) is believed to be associated with response to the immunotherapy. It was found that the composition of immune cells such as CD8+ cytotoxic lymphocytes and dendritic cells are strong prognostic predictors themselves and are associated with overall clinical outcomes. There are still considerable technological and analytical barriers to assess cancer and immune cell compositions in the tumor quantitatively. The pathological approaches such as immunohistochemical (IHC) staining and flow cytometry analysis are labor intensive and often

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Conclusion