ReviewClinical and translational medicine2025
Clinical application of single-cell RNA sequencing in disease and therapy.
Review in Clinical and translational medicine, 2025. 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.
- Hepatic metabolic adaptation to endurance exercise: temporal and sex differences by multiomics integration and validation.Cell communication and signaling : CCS · 2026Article
- Comprehensive Analysis of Starvation Response-Related Genes in the Diagnosis of Ischemic Stroke: Based on Machine Learning and Single-Cell RNA Sequencing Data.Journal of molecular neuroscience : MN · 2026Article
- RNA Sequencing Technologies in Acute Lymphoblastic Leukemia: A Comparative Technical Review.Current issues in molecular biology · 2026Review
- Advancing clinical precision medicine via peripheral blood immune single-cell omics.Clinical and translational medicine · 2026Article
- Single-cell RNA sequencing of intestinal Behçet's disease identifies putative pathogenic programs and potential therapeutic targets.Biomolecules & biomedicine · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Integrative multi-omics and radiomics reveal a TMSB10-driven cell state for non-invasive assessment and precision stratification in breast cancer.Frontiers in immunology · 2026Article
- Regulatory T cells in pregnancy disorders: a multi-dimensional framework for biomarkers and therapeutic strategies.Frontiers in immunology · 2026Review
- Single-cell Transcriptomics Uncovers the Tumor Microenvironment and Collagen-CD44 Axis in HIV Positive Cervical Squamous Cell Carcinoma.Journal of Cancer · 2026Article
- Clinical application of single-cell RNA sequencing in disease and therapy.Clinical and translational medicine · 2025Review
- Advances in the identification of novel cell signatures in benign prostatic hyperplasia and prostate cancer using single-cell RNA sequencing.Frontiers in immunology · 2025Review
- Emerging technologies and current challenges in intratumoral microbiota research.Frontiers in cellular and infection microbiology · 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
4 authors.
Funding
Abstract
backgroundThe emergence of single-cell RNA sequencing (scRNA-seq) technology has revolutionized our capacity to study cell functions in complex tissue microenvironments. Traditional transcriptomic approaches, such as microarrays and bulk RNA sequencing, lacked the resolution to distinguish signals from heterogeneous cell populations or rare cell types, limiting their clinical utility. Since 2009, scRNA-seq has evolved as a new and powerful tool for revisiting somatic evolution and functions under physiological or pathological conditions. MAIN TOPICS COVERED: This review focus on elaborating on the clinical applications of scRNA-seq technology, with a particular emphasis on the application of scRNA-seq methods in revisiting the somatic cell evolution in human diseases. We further provide a snapshot of the scRNA-seq applications in biomarker discovery and drug development, current challenges associated with the technology, and future directions.
conclusionsWith the recent progresses in single cell and spatial transcriptome technologies, scRNA-seq enables a deeper understanding of the complexity of human diseases. The integration of AI and machine learning algorithms into big data analysis offers hope for overcoming these hurdles, potentially allowing scRNA-seq and multi-omics approaches to bridge the gap in our understanding of complex biological systems and advances the development of precision medicine. HIGHLIGHTS: This review provides a systematic overview of the application of scRNA-seq technology in understanding of disease mechanisms. We cover applications in respiratory diseases, metabolic disorders, cardiovascular diseases, cancers, autoimmune and auto-inflammatory diseases, neurodegenerative diseases, and infectious diseases. This review also explores promises and challenges for the emerging application of scRNA-seq in drug discovery.
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What Socratic 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 Socratic graph.