Single-cell genome, DNA methylome, and transcriptome sequencing methods have been separately developed. However, to accurately analyze the mechanism by which transcriptome, genome and DNA methylome regulate each other, these omic methods need to be performed in the same single cell. Here we demonstrate a single-cell triple omics sequencing technique, scTrio-seq, that can be used to simultaneously analyze the genomic copy-number variations (CNVs), DNA methylome, and transcriptome of an individual mammalian cell. We show that large-scale CNVs cause proportional changes in RNA expression of genes within the gained or lost genomic regions, whereas these CNVs generally do not affect DNA methylation in these regions. Furthermore, we applied scTrio-seq to 25 single cancer cells derived from a human hepatocellular carcinoma tissue sample. We identified two subpopulations within these cells based on CNVs, DNA methylome, or transcriptome of individual cells. Our work offers a new avenue of dissecting the complex contribution of genomic and epigenomic heterogeneities to the transcriptomic heterogeneity within a population of cells.

Background: Tumor-reactive CD8+ tumor-infiltrating lymphocytes (TILs) represent a subtype of T cells that can recognize and destroy tumor specifically. Understanding the regulatory mechanism of tumor-reactive CD8+ T cells has important therapeutic implications. Yet the DNA methylation status of this T cell subtype has not been elucidated. Results: In this study, we segregate tumor-reactive and bystander CD8+ TILs, as well as naïve and effector memory CD8+ T cell subtypes as controls from colorectal cancer patients, to compare their transcriptome and methylome characteristics. Transcriptome profiling confirms previous conclusions that tumor-reactive TILs have an exhausted tissue-resident memory signature. Whole-genome methylation profiling identifies a distinct methylome pattern of tumor-reactive CD8+ T cells, with tumor-reactive markers CD39 and CD103 being specifically demethylated. In addition, dynamic changes are observed during the transition of naïve T cells into tumor-reactive CD8+ T cells. Transcription factor binding motif enrichment analysis identifies several immune-related transcription factors, including three exhaustion-related genes (NR4A1, BATF, and EGR2) and VDR, which potentially play an important regulatory role in tumor-reactive CD8+ T cells. Conclusion: Our study supports the involvement of DNA methylation in shaping tumor-reactive and bystander CD8+ TILs, and provides a valuable resource for the development of novel DNA methylation markers and future therapeutics.

Despite advances in DNA methylome analyses of cells and tissues, current techniques for genome-scale profiling of DNA methylation in circulating cell-free DNA (ccfDNA) remain limited. Here we describe a methylated CpG tandems amplification and sequencing (MCTA-Seq) method that can detect thousands of hypermethylated CpG islands simultaneously in ccfDNA. This highly sensitive technique can work with genomic DNA as little as 7.5 pg, which is equivalent to 2.5 copies of the haploid genome. We have analyzed a cohort of tissue and plasma samples (n = 151) of hepatocellular carcinoma (HCC) patients and control subjects, identifying dozens of high-performance markers in blood for detecting small HCC (≤ 3 cm). Among these markers, 4 (RGS10, ST8SIA6, RUNX2 and VIM) are mostly specific for cancer detection, while the other 15, classified as a novel set, are already hypermethylated in the normal liver tissues. Two corresponding classifiers have been established, combination of which achieves a sensitivity of 94% with a specificity of 89% for the plasma samples from HCC patients (n = 36) and control subjects including cirrhosis patients (n = 17) and normal individuals (n = 38). Notably, all 15 alpha-fetoprotein-negative HCC patients were successfully identified. Comparison between matched plasma and tissue samples indicates that both the cancer and noncancerous tissues contribute to elevation of the methylation markers in plasma. MCTA-Seq will facilitate the development of ccfDNA methylation biomarkers and contribute to the improvement of cancer detection in a clinical setting.

Comprehensive analysis of the tissue of origin of plasma cell-free DNA (cfDNA) remains insufficient. A genome-scale DNA methylation method for this analysis is of both biological and clinical interest. We used the methylated CpG tandem amplification and sequencing (MCTA-Seq), which is a genome-scale DNA methylation method, for analyzing cfDNA. We performed MCTA-Seq to pair plasma cfDNA and white blood cell genomic DNA from 14 healthy individuals for comparative analysis, with eight tissues being analyzed for identifying tissue-specific markers. The relative contributions of multiple tissues to cfDNA were calculated for plasma cfDNA obtained from healthy adults (n = 25), cholelithiasis patients (n = 13), liver cirrhosis patients (n = 17), hepatocellular carcinoma patients (n = 30), and acute pancreatitis patients (n = 8). We identified a total of 146 tissue-specific hypermethylation markers. Simulation analysis showed that MCTA-Seq can accurately measure DNA fractions contributed by multiple tissues to cfDNA. We demonstrated that the liver is the major non-hematopoietic tissue contributing to plasma cfDNA in healthy adults. The method also detected increases in the liver-derived DNA in the blood from patients with liver diseases, which correlate with an increase in the liver enzyme level. Furthermore, the results indicated that blood cells make a major contribution to the elevation of cfDNA levels in acute pancreatitis, liver cirrhosis, and hepatocellular carcinoma patients. Finally, we characterized a novel set of tissue-specific hypermethylation markers for cfDNA detection, which are located within the intragenic regions of tissue-specific highly expressed genes. We have used MCTA-Seq for simultaneously measuring cfDNA fractions contributed by multiple tissues. Applying this approach to healthy adults and liver and pancreas disease patients revealed the tissue of origin of cfDNA. The approach and the identified markers should facilitate assessing the cfDNA dynamics in a variety of human diseases.