Single Cell triple omics sequencing reveals genetic and epigenetic heterogeneity in hepatocellular carcinoma
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ABSTRACT:
PROVIDER: PRJNA273802 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1777080_1.fastq.gz | Fastqsanger.gz | |||
| SRR1777080_2.fastq.gz | Fastqsanger.gz | |||
| SRR1777090_1.fastq.gz | Fastqsanger.gz | |||
| SRR1777090_2.fastq.gz | Fastqsanger.gz | |||
| SRR1777100_1.fastq.gz | Fastqsanger.gz |
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Project description:Single cell genome, DNA methylome, and transcriptome sequencing has been achieved separately. However, to analyze the regulation of RNA expression by genetic and epigenetic factors within an individual cell, it is necessary to analyze these omics simultaneously from the same single cell. Here we developed a single cell triple omics sequencing technique- scTrio-seq, to analyze the genome, DNA methylome, and transcriptome concurrently of a mammalian cell.
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Project description:Molecular heterogeneity of multinodular hepatocellular carcinoma
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Project description: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.
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Project description:The inter-patient variability of tumor proteomes has been investigated on a large scale but many tumors display also intra-tumoral heterogeneity (ITH) regarding morphological and genetic features. To what extent the local proteome of tumors intrinsically differs remains largely unknown. Here, we used hepatocellular carcinoma (HCC) as a model system, to quantify both inter- and intra-tumor heterogeneity across human patient specimens with spatial resolution. We first defined proteomic features that robustly distinguish neoplastic from the directly adjacent non-neoplastic tissue by integrating proteomic data from human patient samples and genetically defined mouse models with available gene expression data. We then demonstrated the existence of intra-tumoral variations in protein abundance that re-occur across different patient samples, and affect clinically relevant proteins, even in the absence of obvious morphological differences or genetic alterations. Our work demonstrates the suitability and the benefits of using mass spectrometry based proteomics to analyze diagnostic tumor specimens with spatial resolution
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