ReviewCells2020
Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing.
Review in Cells, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- Bibliometric and visual analysis of single-cell multiomics in neurodegenerative disease arrest studies.Frontiers in neurology · 2024Pooled it
- Decoding Cellular Heterogeneity with Microfluidic Single-Cell Secretion Analysis Tools.ACS omega · 2026Review
- Decoding dry eye disease based on bioinformatics and in vitro experimental: the role of immune responses and natural product intervention.Human genomics · 2026Article
- Single-cell transcriptome reveals keratinocyte subclusters contributing to altered differentiation and inflammatory responses in atopic dermatitis.Nature communications · 2026Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
- Therapeutic synergies that overcome carboplatin resistance in triple-negative breast cancer.Journal of experimental & clinical cancer research : CR · 2026Article
- Opportunities for RNA sequencing in physiology: from big data to understanding homeostasis and heterogeneity.Function (Oxford, England) · 2026Review
- ALPCAHUS: Subspace Clustering for Heteroscedastic Data.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2026Article
- PSMA4 as a Druggable Target in Hidradenitis Suppurativa: Evidence From Mendelian Randomization and Single-Cell Transcriptomics.Mediators of inflammation · 2026Article
- Unveiling the therapeutic potential of senescence-related IQGAP2 in pancreatic Cancer through post-GWAS genomic and scRNA-seq analyses.Discover oncology · 2025Article
- DiSC: a statistical tool for fast differential expression analysis of individual-level single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2025Article
- SCPEP1Frontiers in immunology · 2025Article
- Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma.Frontiers in oncology · 2025Article
- MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.Analytical chemistry · 2024Article
- A composite scaling network of EfficientNet for improving spatial domain identification performance.Communications biology · 2024Article
- Multi-scalar data integration decoding risk genes for chronic kidney disease.BMC nephrology · 2024Article
- Review
- Single-cell and genome-wide Mendelian randomization identifies causative genes for gout.Arthritis research & therapy · 2024Article
- Advances in single-cell long-read sequencing technologies.NAR genomics and bioinformatics · 2024Article
- Single-cell combined with transcriptome sequencing to explore the molecular mechanism of cell communication in idiopathic pulmonary fibrosis.Journal of cellular and molecular medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer's disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.
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.