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A multi-omics approach to liver diseases: integration of Single Nuclei Transcriptomics with Proteomics and Hi-Cap Bulk Data in human liver

ABSTRACT: Most of the metabolic functions in the human body happen in the liver, which is the largest solid organ and is composed of diverse cell types. Here we used single nuclei RNA-seq (snRNA-seq) to explore the gene expression profiles of these discrete liver cell populations. We identified seven distinct sub-populations including hepatocytes, sinusoidal endothelial cells, cholangiocytes, Kupffer cells, Nk/T/B cells, cells of nervous/arterial origin and both activated and inactive hepatic stellate cells. We then focused on medical relevant genes associated to metabolism of cancer drugs and survival of hepatocellular carcinoma (HCC). We integrated snRNA-seq with long-range genomic interactions to identify putative candidate regulatory elements for these genes and mass spectrometry proteomics to evaluate their protein abundance. We identified a new putative polymorphic regulatory element for the DPYD gene that is involved in cancer drug toxicity and a complex regulatory network for the SLC2A2 gene, which is overexpressed in HCC. Our results highlight the potential of a multi-omics approach in the study of human diseases.

PROVIDER: PRJEB36609 | ENA |

REPOSITORIES: ENA

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	ERR4354267.fastq.gz	Fastqsanger.gz

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Project description:Each cell type responds uniquely to stress and fractionally contributes to global and tissue-specific stress responses. Hepatocytes, liver macrophages (M?), and sinusoidal endothelial cells (SEC) play functionally important and interdependent roles in adaptive processes such as wound healing, obesity, and tumor growth. Although these cell types demonstrate significant phenotypic and functional heterogeneity, their distinctions enabling disease-specific responses remain understudied. To address this, we developed a strategy for simultaneous isolation and quantification of these liver cell types based on antigenic cell surface marker expression in response to DEN and found that while there was only a marginal increase in hepatocyte number, M? and SEC populations were quantitatively increased. Global gene expression profiling of hepatocytes, M? and SEC identified characteristic gene “fingerprints” that define each cell type and their distinct physiological or oncogenic stress signatures. Integration of these cell-specific gene fingerprints with available hepatocellular carcinoma (HCC) patient microarray data demonstrates that the hepatocyte-specific response strongly correlates with the human HCC gene expression profile. Liver-specific M? and SEC gene signatures demonstrate significant alterations in inflammatory and angiogenic gene regulatory pathways, which may impact the hepatocyte response to oncogenic stress. Further validation confirms alterations in components of two key pathways, AP-1 and p53, that have been previously associated with HCC onset and progression. Our data reveal unique gene expression patterns that serve as molecular “fingerprints” for the cell-centric responses to pathologic stimuli in the distinct microenvironment of the liver. The technical advance highlighted in this study provides an essential resource for assessing hepatic cell-specific contributions to oncogenic stress, information that could unveil previously unappreciated molecular mechanisms for the cellular crosstalk that underlies the development of hepatic cancer. Total RNA isolated from each specific cell type (Hepatocyte (PH), Sinusoidal Endothelial Cell (SEC), and Macrophage (MP)) pooled from 3-4 DEN-treated mice and compared to their untreated counterparts.

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A multi-omics approach to liver diseases: integration of Single Nuclei Transcriptomics with Proteomics and Hi-Cap Bulk Data in human liver

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