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Isolation of nuclei from flash-frozen liver tissue for single-cell multiomics.

J. Vis. Exp., DOI: 10.3791/64792 (2022)
Open Access Green: Postprint online verfügbar 01/2025
The liver is a complex and heterogenous tissue responsible for carrying out many critical physiological functions, such as the maintenance of energy homeostasis and the metabolism of xenobiotics, among others. These tasks are performed through tight coordination between hepatic parenchymal and non-parenchymal cells. Additionally, various metabolic activities are confined to specific areas of the hepatic lobule-a phenomenon called liver zonation. Recent advances in single-cell sequencing technologies have empowered researchers to investigate tissue heterogeneity at a single-cell resolution. In many complex tissues, including the liver, harsh enzymatic and/or mechanical dissociation protocols can negatively affect the viability or the quality of the single-cell suspensions needed to comprehensively characterize this organ in health and disease. This paper describes a robust and reproducible protocol for isolating nuclei from frozen, archived liver tissues. This method yields high-quality nuclei that are compatible with downstream, single-cell omics approaches, including single-nucleus RNA-seq, assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq), as well as multimodal omics (joint RNA-seq and ATAC-seq). This method has been successfully used for the isolation of nuclei from healthy and diseased human, mouse, and non-human primate frozen liver samples. This approach allows the unbiased isolation of all the major cell types in the liver and, therefore, offers a robust methodology for studying the liver at the single-cell resolution.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 1940-087X
e-ISSN 1940-087X
Quellenangaben Band: , Heft: 190 Seiten: , Artikelnummer: , Supplement: ,
Verlag JoVE
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Pioneer Campus (HPC)
Institute of Computational Biology (ICB)