Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Single-cell RNA sequencing of human liver cells

ABSTRACT: To examine the heterogeneity and dynamic crosstalk of human liver cells, ~18,000 freshly isolated human liver cells from 6 donors were profiled using the 10x Genomics Chromium platform.

INSTRUMENT(S): Illumina HiSeq 2500

ORGANISM(S): Homo sapiens

SUBMITTER: Zhongyi Wang

PROVIDER: E-MTAB-10553 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis.

Wang Zhong-Yi ZY Keogh Adrian A Waldt Annick A Cuttat Rachel R Neri Marilisa M Zhu Shanshan S Schuierer Sven S Ruchti Alexandra A Crochemore Christophe C Knehr Judith J Bastien Julie J Ksiazek Iwona I Sánchez-Taltavull Daniel D Ge Hui H Wu Jing J Roma Guglielmo G Helliwell Stephen B SB Stroka Deborah D Nigsch Florian F

Scientific reports 20210929 1

Fibrosis is characterized by the excessive production of collagen and other extracellular matrix (ECM) components and represents a leading cause of morbidity and mortality worldwide. Previous studies of nonalcoholic steatohepatitis (NASH) with fibrosis were largely restricted to bulk transcriptome profiles. Thus, our understanding of this disease is limited by an incomplete characterization of liver cell types in general and hepatic stellate cells (HSCs) in particular, given that activated HSCs ...[more]

PMID: 34588551

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Project description:Genetic and non-genetic heterogeneity within cancer cell populations represents a major challenge to anti-cancer therapies. We currently lack robust methods to determine how pre-existing and adaptive features affect cellular responses to therapies. Here, by conducting clonal fitness mapping and transcriptional characterization using expressed barcodes and single-cell RNA-sequencing, we have developed TraCe-seq, a method that captures at clonal resolution the origin, fate, and differential early adaptive transcriptional programs of cells in a complex population in response to distinct treatments. We used TraCe-seq to benchmark how next-generation dual EGFR inhibitors-degraders compare to standard EGFR kinase inhibitors in EGFR-mutant lung cancer cells. To QC the TraCe-seq strategy, single-cell RNA-seq libraries were generated from a variety of human cancer cell lines transduced with the TraCe-seq library to validate the TraCe-seq strategy. Specifically, 5 different cell lines (PC9, MCF-10A, MDA-MB-231, NCI-H358, and NCI-H1373) were each transduced with a unique TraCe-seq barcode. The transduced cells were selected with puromycin only, dissociated to single cell suspensions, and then mixed together. The complex mixture of the 5 cell lines was profiled by 10X scRNA-seq. Furthermore, transduced NCI-H1373 cells were sorted by FACS to enrich for the top 50% of eGFP positive cells, and sorted cells were cultured briefly and used to construct scRNA-seq libraries and profiled by 10x scRNA-seq. To carry out the full TraCe-seq experiment, ~600 PC9 cells carrying unique TraCe-seq barcodes were expanded over 12 doublings to establish the barcoded population. A subset of the barcoded PC9 population was used to generate scRNA-seq libraries and profiled by 10x scRNA-seq prior to treatment to establish a baseline transcription profile for each barcoded clone. The rest of the cells were then treated for four days with 1 µM erlotinib, 1 µM GNE-069, or 1 µM GNE-104 respectively. scRNA-seq libraries were then generated form the treated cells and profiled by 10x scRNA-seq.

Dataset Information

Single-cell RNA sequencing of human liver cells

Publications

Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis.

Similar Datasets

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