Full-length single-cell RNA-seq applied to a viral human cancer: applications to HPV expression and splicing analysis in HeLa S3 cells

Abstract

Background: Viral infection causes multiple forms of human cancer, and HPV infection is the primary factor in cervical carcinomas. Recent single-cell RNA-seq studies highlight the tumor heterogeneity present in most cancers, but virally induced tumors have not been studied. HeLa is a well characterized HPV+ cervical cancer cell line.

Result: We developed a new high throughput platform to prepare single-cell RNA on a nanoliter scale based on a customized microwell chip. Using this method, we successfully amplified full-length transcripts of 669 single HeLa S3 cells and 40 of them were randomly selected to perform single-cell RNA sequencing. Based on these data, we obtained a comprehensive understanding of the heterogeneity of HeLa S3 cells in gene expression, alternative splicing and fusions. Furthermore, we identified a high diversity of HPV-18 expression and splicing at the single-cell level. By co-expression analysis we identified 283 E6, E7 co-regulated genes, including CDC25, PCNA, PLK4, BUB1B and IRF1 known to interact with HPV viral proteins.

Conclusion: Our results reveal the heterogeneity of a virus-infected cell line. It not only provides a transcriptome characterization of HeLa S3 cells at the single cell level, but is a demonstration of the power of single cell RNA-seq analysis of virally infected cells and cancers.

Keywords: Cancer; HPV; HeLa; RNA splicing; Single-cell transcriptome; Tumor heterogeneity; Virus.

Fig. 1

The schematic diagram of MIRALCS. a The flowchart of the MIRALCS. b The box plot of Ct (left) and Tm (right) value of the 20 % Percoll solution (negative control) and 10 pg total RNA (positive control), respectively. c The Ct value and Tm value distribution of 20 % Percoll solution, 10 pg total RNA, non-target well and target well during cDNA amplification process in microwells. The target wells (well with cell) and the non-target wells (without cells) were validated by Agilent 2100 Bioanlyzer. The line denotes Ct median. Horizontal bars denote ± 0.5

Fig. 2

A high sensitivity, accuracy and reproducibility of MIRALCS. a Comparison of gene number between single cell (the smaller circle) and the 5 ng bulk sample (the larger circle). The left, a typical cell; the right, 5 randomly selected cells (randomly sampling 0.4 million reads per cell) vs. the 5 ng bulk sample (2 million reads). b Gene detection in MIRALCS single-cells, regular tube-based single cells and 5 ng bulk RNA sample. c The distribution of gene number on gene expression along sequencing depths. d The correlation of the mean expression (FPKM) and the number of input molecules of spike-ins of all MIRALCS single-cell libraries. e The reads coverage along the transcript position from 5′ to 3′end. Error bar stands for the standard deviation. f The correlation of spike-ins expression (FPKM) between two randomly selected MIRALCS single cells. g Heat map of correlation coefficients of spike-ins expression levels with input molecules >1 for each library (n = 19). h The correlation of gene expression (FPKM) between technical replicates. Left: two randomly selected MIRALCS 10 pg replicates. Right: two randomly selected tube-based 10 pg replicates. i The pair-wise correlation in MIRALCS 10 pg RNA replicates and tube-based 10 pg RNA replicates

Fig. 3

Heterogeneity of gene expression in HeLa S3 single cells. a The mRNA molecular number in single cells and 10 pg RNA replicates. b The heat map of the FPKM values of extremely highly expressed genes (FPKM > 500 in bulk RNA) in single cells and 10 pg replicates. c Single-cell subpopulations identification based on cell cycle relative genes. The cells with underline are in G2/M phase. d Gene co-expression modules derived from 19 single cells based on RNA molecular number (modules are distinguished by colors). The detailed of each module stands for were shown on Additional file 4: Table S7. The weighted gene correlation network was constructed using the WCGNA R package [38]

Fig. 4

Heterogeneity of alternative splicing and distributions of splices in in single cells. a The sequencing depth for genes of NPM1, YWHAB, YWHAQ and GAPDH in single cells. b The frequency distribution of detected annotated and novel spliced junctions. c The distributions of the ψ scores of annotated and novel spliced junctions in the bulk RNA (upper) and single cells (lower)

Fig. 5

The landscape of the HPV-18/cellular fusion and diversity of HPV-host splicing and expression in HeLa S3 cells. a The overview of the HPV-18 cellular fusion based on HeLa cell transcriptome. Blue lines denote fusion events. b The read coverage of HPV-18 genome in single cells and the bulk RNA. Colored vertical lines denote nucleotides of SNPs detected in the transcriptome. Light green, A; red, T; orange, G; blue, C. c The read coverage of the host region on chromosome 8 in single cells and the bulk RNA. d The schematic diagram of the inferred HPV integration structure (upper) and splicing forms (lower). RPM stands for reads per million