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Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.

ABSTRACT: 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.

SUBMITTER: Hou Y

PROVIDER: S-EPMC4783472 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.

Hou Yu Y Guo Huahu H Cao Chen C Li Xianlong X Hu Boqiang B Zhu Ping P Wu Xinglong X Wen Lu L Tang Fuchou F Huang Yanyi Y Peng Jirun J

Cell research 20160223 3

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 ...[more]

PMID: 26902283

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Project description:BackgroundHepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile of the whole genome of thousands of single cells. Thus, the purpose of this study was to identify novel prognostic markers for HCC based on single-cell sequencing data.MethodsSingle-cell sequencing of 21 HCC samples and 256 normal liver tissue samples in the GSE124395 dataset was collected from the Gene Expression Omnibus (GEO) database. The quality-controlled cells were grouped by unsupervised cluster analysis and identified the marker genes of each cell cluster. Hereafter, these cell clusters were annotated by singleR and CellMarker according to the expression patterns of the marker genes. Pseudotime analysis was performed to construct the trajectory of cell evolution and to define hub genes in the evolution process. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the potential regulatory mechanism of hub genes in HCC. Next, the differential expression of hub genes and the correlation of the expression of these genes with patients' survival and diagnosis were investigated in The Cancer Genome Atlas (TCGA) database.ResultsA total of 9 clusters corresponding to 9 cell types, including NKT cells, hepatocytes, endothelial cells, Kupffer cells, EPCAM+ cells, cancer cells, plasma cells (B cells), immature B cells, and myofibroblasts were identified. We screened 63 key genes related to cell differentiation through trajectory analysis, which were enriched in the process of coagulation. Ultimately, we identified 10 survival-related hub genes in the TCGA database, namely ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN.ConclusionIn conclusion, ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN may serve as hub genes in the diagnosis and prognosis for HCC.

Project description:Metaplastic breast carcinoma is a rare and aggressive histologic type of breast cancer, preferentially displaying a triple-negative phenotype. We sought to define the transcriptomic heterogeneity of metaplastic breast cancers on the basis of current gene expression microarray-based classifiers, and to determine whether these tumors display gene copy number profiles consistent with those of BRCA1-associated breast cancers. Twenty-eight consecutive triple-negative metaplastic breast carcinomas were reviewed, and the metaplastic component present in each frozen specimen was defined (ie, spindle cell, squamous, chondroid metaplasia). RNA and DNA extracted from frozen sections with tumor cell content >60% were subjected to gene expression (Illumina HumanHT-12 v4) and copy number profiling (Affymetrix SNP 6.0), respectively. Using the best practice PAM50/claudin-low microarray-based classifier, all metaplastic breast carcinomas with spindle cell metaplasia were of claudin-low subtype, whereas those with squamous or chondroid metaplasia were preferentially of basal-like subtype. Triple-negative breast cancer subtyping using a dedicated website (http://cbc.mc.vanderbilt.edu/tnbc/) revealed that all metaplastic breast carcinomas with chondroid metaplasia were of mesenchymal-like subtype, spindle cell carcinomas preferentially of unstable or mesenchymal stem-like subtype, and those with squamous metaplasia were of multiple subtypes. None of the cases was classified as immunomodulatory or luminal androgen receptor subtype. Integrative clustering, combining gene expression and gene copy number data, revealed that metaplastic breast carcinomas with spindle cell and chondroid metaplasia were preferentially classified as of integrative clusters 4 and 9, respectively, whereas those with squamous metaplasia were classified into six different clusters. Eight of the 26 metaplastic breast cancers subjected to SNP6 analysis were classified as BRCA1-like. The diversity of histologic features of metaplastic breast carcinomas is reflected at the transcriptomic level, and an association between molecular subtypes and histology was observed. BRCA1-like genomic profiles were found only in a subset (31%) of metaplastic breast cancers, and were not associated with a specific molecular or histologic subtype.

Dataset Information

Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.

Publications

Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.

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