Single-Cell RNA Sequencing Supports Preferential Bioactivation of Remdesivir in the Liver.
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ABSTRACT:
SUBMITTER: Yan VC
PROVIDER: S-EPMC8448088 | biostudies-literature | 2021 Sep
REPOSITORIES: biostudies-literature
Publications
Single-Cell RNA Sequencing Supports Preferential Bioactivation of Remdesivir in the Liver.
Yan Victoria C VC Muller Florian L FL
Antimicrobial agents and chemotherapy 20210917 10
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Project description:Background & aimsThe multiple vital functions of the human liver are performed by highly specialised parenchymal and non-parenchymal cells organised in complex collaborative sinusoidal units. Although crucial for homeostasis, the cellular make-up of the human liver remains to be fully elucidated. Here, single-cell RNA-sequencing was used to unravel the heterogeneity of human liver cells, in particular of hepatocytes (HEPs) and hepatic stellate cells (HSCs).MethodThe transcriptome of ~25,000 freshly isolated human liver cells was profiled using droplet-based RNA-sequencing. Recently published data sets and RNA in situ hybridisation were integrated to validate and locate newly identified cell populations.ResultsIn total, 22 cell populations were annotated that reflected the heterogeneity of human parenchymal and non-parenchymal liver cells. More than 20,000 HEPs were ordered along the portocentral axis to confirm known, and reveal previously undescribed, zonated liver functions. The existence of 2 subpopulations of human HSCs with unique gene expression signatures and distinct intralobular localisation was revealed (i.e. portal and central vein-concentrated GPC3 + HSCs and perisinusoidally located DBH + HSCs). In particular, these data suggest that, although both subpopulations collaborate in the production and organisation of extracellular matrix, GPC3 + HSCs specifically express genes involved in the metabolism of glycosaminoglycans, whereas DBH + HSCs display a gene signature that is reminiscent of antigen-presenting cells.ConclusionsThis study highlights metabolic zonation as a key determinant of HEP transcriptomic heterogeneity and, for the first time, outlines the existence of heterogeneous HSC subpopulations in the human liver. These findings call for further research on the functional implications of liver cell heterogeneity in health and disease.Lay summaryThis study resolves the cellular landscape of the human liver in an unbiased manner and at high resolution to provide new insights into human liver cell biology. The results highlight the physiological heterogeneity of human hepatic stellate cells.
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Project description:The liver is the largest solid organ in the body and is critical for metabolic and immune functions. However, little is known about the cells that make up the human liver and its immune microenvironment. Here we report a map of the cellular landscape of the human liver using single-cell RNA sequencing. We provide the transcriptional profiles of 8444 parenchymal and non-parenchymal cells obtained from the fractionation of fresh hepatic tissue from five human livers. Using gene expression patterns, flow cytometry, and immunohistochemical examinations, we identify 20 discrete cell populations of hepatocytes, endothelial cells, cholangiocytes, hepatic stellate cells, B cells, conventional and non-conventional T cells, NK-like cells, and distinct intrahepatic monocyte/macrophage populations. Together, our study presents a comprehensive view of the human liver at single-cell resolution that outlines the characteristics of resident cells in the liver, and in particular provides a map of the human hepatic immune microenvironment.
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Project description:ObjectiveThis review aimed to summarize the application of single-cell transcriptome sequencing technology in liver diseases.BackgroundThe increasing application of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) in life science and biomedical research has greatly improved our understanding of cellular heterogeneity in immunology, oncology, and developmental biology. scRNA-seq has proven to be a powerful tool for identifying and classifying cell subsets, characterizing rare or small cell subsets and tracking cell differentiation along the dynamic cell stages. Globally, liver disease has high rates of morbidity and mortality, and its exact pathological mechanism remains unclear, current treatment options are limited to clearance of the underlying cause or liver transplantation, which cannot overwhelm and cure liver diseases. scRNA-seq provides many novel insights for healthy and diseased livers.MethodsIn this review, we searched for related articles in the PubMed database and summarized the advances of scRNA-seq in revealing the molecular mechanisms of liver development, regeneration, and disease. We also discussed the challenges and future application potential of scRNA-seq, which is expected to enhance the ability to explore the field of liver research and accelerate the clinical application of liver precision medicine.ConclusionsWith the continuous improvement of scRNA-seq technology, scRNA-seq is expected to unlock new avenues for liver biology exploration, liver disease diagnosis, and personalized treatment, which will pave the way for breakthrough innovation in personalized medicine.
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Project description:BackgroundHepatocellular carcinoma (HCC) is a common primary liver cancer with poor overall survival. We hypothesized that there are HCC-associated cell-types that impact patient survival.MethodsWe combined liver single nucleus (snRNA-seq), single cell (scRNA-seq), and bulk RNA-sequencing (RNA-seq) data to search for cell-type differences in HCC. To first identify cell-types in HCC, adjacent non-tumor tissue, and normal liver, we integrated single-cell level data from a healthy liver cohort (n = 9 non-HCC samples) collected in the Strasbourg University Hospital; an HCC cohort (n = 1 non-HCC, n = 14 HCC-tumor, and n = 14 adjacent non-tumor samples) collected in the Singapore General Hospital and National University; and another HCC cohort (n = 3 HCC-tumor and n = 3 adjacent non-tumor samples) collected in the Dumont-UCLA Liver Cancer Center. We then leveraged these single cell level data to decompose the cell-types in liver bulk RNA-seq data from HCC patients' tumor (n = 361) and adjacent non-tumor tissue (n = 49) from the Cancer Genome Atlas (TCGA) multi-center cohort. For replication, we decomposed 221 HCC and 209 adjacent non-tumor liver microarray samples from the Liver Cancer Institute (LCI) cohort collected by the Liver Cancer Institute and Zhongshan Hospital of Fudan University.ResultsWe discovered a tumor-associated proliferative cell-type, Prol (80.4% tumor cells), enriched for cell cycle and mitosis genes. In the liver bulk tissue from the TCGA cohort, the proportion of the Prol cell-type is significantly increased in HCC and associates with a worse overall survival. Independently from our decomposition analysis, we reciprocally show that Prol nuclei/cells significantly over-express both tumor-elevated and survival-decreasing genes obtained from the bulk tissue. Our replication analysis in the LCI cohort confirmed that an increased estimated proportion of the Prol cell-type in HCC is a significant marker for a shorter overall survival. Finally, we show that somatic mutations in the tumor suppressor genes TP53 and RB1 are linked to an increase of the Prol cell-type in HCC.ConclusionsBy integrating liver single cell, single nucleus, and bulk expression data from multiple cohorts we identified a proliferating cell-type (Prol) enriched in HCC tumors, associated with a decreased overall survival, and linked to TP53 and RB1 somatic mutations.
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Project description:Healthy human skin tissue is often used as a control for comparison to diseased skin in patients with skin pathologies, including skin cancers or other inflammatory conditions such as atopic dermatitis or psoriasis. Although non-affected skin from these patients is a more appropriate choice for comparison, there is a paucity of studies examining such tissue. This lack is exacerbated by the difficulty of processing skin tissue for experimental analysis. In addition, choosing a processing protocol for skin tissue which preserves cell viability and identity while sufficiently dissociating cells for single-cell analysis is not a trivial task. Here, we compare three digestion methods for human skin tissue, evaluating the cell yield and viability for each protocol. We find that the use of a sequential dissociation method with multiple enzymatic digestion steps produces the highest cell viability. Using single-cell sequencing, we show this method results in a relative increase in the proportion of non-antigen-presenting mast cells and CD8 T cells as well as a relative decrease in the proportion of antigen-presenting mast cells and KYNU+ CD4 T cells. Overall, our findings support the use of this sequential digestion method on freshly processed human skin samples for optimal cell yield and viability.
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