ArticleScientific reports2024
Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- A Conserved Macrophage-to-Hepatic Stellate Cell PDGF Axis in Human MASH Identified by Multi-Cohort sc/snRNA-Seq Re-Analysis.International journal of molecular sciences · 2026Article
- Conserved Ductular Reaction Mechanisms in Biliary Atresia and Primary Sclerosing Cholangitis Derived From Single-Cell and Spatial Transcriptomics.Cellular and molecular gastroenterology and hepatology · 2026Review
- Epigenetic reprogramming of hepatic antigen presenting cells in chronic liver disease.Frontiers in immunology · 2026Review
- Advanced omics approaches in liver transplant settings: current applications and future prospectives.Frontiers in immunology · 2025Review
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
Abstract
Single cell and spatially resolved 'omic' techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome 'spot' on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNAseq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Single cell mapping of the spatial transcriptome using paired snRNAseq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell-cell interactions predicted using ligand-receptor analysis of snRNAseq data poorly correlated with cellular relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell-cell interactions in biobanked clinical samples with advanced liver disease.
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Registered trials
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