ReviewJournal of clinical and translational hepatology2024
Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology.
Review in Journal of clinical and translational hepatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 4 citations in OpenAlex.
- Single-cell atlas of hepatic cellular plasticity and immune niche reprogramming in liver cirrhosis.Journal of translational medicine · 2026Article
- Editorial: Advancing immune research in chronic liver diseases through new approach methodologies.Frontiers in immunology · 2026Article
- Cell-type-specific alkaloid and terpenoid biosynthesis in glandular trichomes: single-cell and spatial transcriptomic perspectives.Frontiers in plant science · 2026Review
- Systematic Comparison of Droplet-Based and Microwell-Based Platforms for Comprehensive Single-Cell Transcriptomic Analysis in Clinical Samples.IET nanobiotechnology · 2026Article
- Integrated multi-omics, spatial profiling and organoid modeling drive transformative advances in chronic liver disease and hepatocellular carcinoma immunomicroenvironment research.Frontiers in immunology · 2026Review
- FGF7-FGFR2 Signaling Enhances Contractile Activation of CAFs in Ovarian Cancer: Integrative Single-Cell and Spatial Transcriptomics.Journal of inflammation research · 2026Article
- Decoding Liver Fibrosis: How Omics Technologies and Innovative Modeling Can Guide Precision Medicine.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.