CHARTS: a web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets

Matthew N Bernstein,Zijian Ni,Mark E Burkard,Ron Stewart,Michael Collins,Christina Kendziorski

doi:10.1186/s12859-021-04021-x

Abstract

BackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. This is especially important in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer data sets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a web application for exploring publicly available scRNA-seq cancer data sets in the NCBI’s Gene Expression Omnibus. More specifically, CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across tumors and data sets. Along with the web application, we also make available the backend computational pipeline that was used to produce the analyses that are available for exploration in the web application.ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer data sets. CHARTS is freely available at charts.morgridge.org.

Highlights

Single-cell RNA-seq enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue
Available scRNAseq cancer data sets offer unprecedented opportunity to better understand the mechanisms of tumor progression, metastasis, drug resistance, and immune evasion
We present CHARacterizing Tumor Subpopulations (CHARTS), a web application and associated computational pipeline for analyzing and characterizing publicly available cancer scRNA-seq data sets

Summary

Results

Two case studies demonstrate how CHARTS can be used, both to examine and generate new hypotheses. Using the gene set enrichment feature of CHARTS, we further found that dysfunctional cells are enriched for cell cycle genes, which may indicate that these dysfunctional CD8 + T cells are highly proliferative in lung adenocarcinoma, as has been recently observed in melanoma. Using the gene set enrichment feature of CHARTS, we observed that cells expressing MCT1 tended to express genes enriched for hypoxia, whereas cells expressing MCT4 tended to express genes that were less enriched for hypoxia (Fig. 3a, b) This observation indicates a possible metabolic symbiosis between malignant cells and myeloid cells in the tumor microenvironment of glioblastoma, which to the best of our knowledge, has not been well characterized

Background

Conclusion