ReviewLife medicine2022
Decoding liver fibrogenesis with single-cell technologies.
Review in Life medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Single-cell and spatial transcriptomics reveal Thbs1-CD36 crosstalk between Kupffer and hepatic stellate cells following mesenchymal stem cell-derived small extracellular vesicles transplantation, alleviating liver fibrosis.Journal of nanobiotechnology · 2026Article
- Single-cell analysis reveals the spatiotemporal effects of long-term electromagnetic field exposure on the liver.Frontiers in cell and developmental biology · 2025Article
- Modeling metabolic-associated steatohepatitis with human pluripotent stem cell-derived liver organoids.Hepatology communications · 2024Article
- Article
- Aging hallmarks of the primate ovary revealed by spatiotemporal transcriptomics.Protein & cell · 2024Article
- Degeneration Directory: a multi-omics web resource for degenerative diseases.Protein & cell · 2024Article
- A single-nucleus transcriptomic atlas of primate liver aging uncovers the pro-senescence role of SREBP2 in hepatocytes.Protein & cell · 2024Article
- Single-cell profiling reveals a potent role of quercetin in promoting hair regeneration.Protein & cell · 2023Article
- Biomarkers of aging.Science China. Life sciences · 2023Review
- Single-cell transcriptomic atlas of mouse cochlear aging.Protein & cell · 2023Article
- A single-cell transcriptomic atlas of exercise-induced anti-inflammatory and geroprotective effects across the body.Innovation (Cambridge (Mass.)) · 2023Article
- Macrophages and platelets in liver fibrosis and hepatocellular carcinoma.Frontiers in immunology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver fibrogenesis is a highly dynamic and complex process that drives the progression of chronic liver disease toward liver failure and end-stage liver diseases. Despite decades of intense studies, the cellular and molecular mechanisms underlying liver fibrogenesis remain elusive, and no approved therapies to treat liver fibrosis are currently available. The rapid development of single-cell RNA sequencing (scRNA-seq) technologies allows the characterization of cellular alterations under healthy and diseased conditions at an unprecedented resolution. In this Review, we discuss how the scRNA-seq studies are transforming our understanding of the regulatory mechanisms of liver fibrosis. We specifically emphasize discoveries on disease-relevant cell subpopulations, molecular events, and cell interactions on cell types including hepatocytes, liver sinusoidal endothelial cells, myofibroblasts, and macrophages. These discoveries have uncovered critical pathophysiological changes during liver fibrogenesis. Further efforts are urged to fully understand the functional contributions of these changes to liver fibrogenesis, and to translate the new knowledge into effective therapeutic approaches.
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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.