ReviewGenome research2015
Defining cell types and states with single-cell genomics.
Review in Genome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 447 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
447 citing papers in PubMed, 1 synthesis or guideline pooled it, 859 citations in OpenAlex.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Donor-aware integrative single-cell analysis distinguishes consistent memory B-cell programmes from donor-sensitive state boundaries in untreated rheumatoid arthritis.Functional & integrative genomics · 2026Article
- Standardizing Cell Ontology terms for cross-study integration in the female reproductive tract.Science advances · 2026Review
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- A statistical framework for disease classification with scRNA-Seq Data.bioRxiv : the preprint server for biology · 2026Article
- Unicellular and Multicellular Modes of Selection Impose Distinct Constraints on Cellular Phenotype Evolution.bioRxiv : the preprint server for biology · 2026Article
- Condensates and cell states: A new paradigm for understanding tumor biology.Biophysical journal · 2026Review
- Multiscale modeling primer: Focus on chromatin and epigenetics.The Journal of biological chemistry · 2026Review
- Single-cell transcriptomics of human brain disorders.Molecular psychiatry · 2026Review
- SemanticST: A Scalable Multi-Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi-Sample Integration in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multi-Technique Integration Identifies O-GlcNAc Transferase in Fibroblast-Like Synoviocytes as a Therapeutic Target for Rheumatoid Arthritis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single-cell profiling decodes patagium development in gliding mammal.Nature communications · 2026Article
- ScanNet: Single-cell annotation informed by transcriptional regulation Network via iterative heterogeneous graph learning.PLoS computational biology · 2026Article
- CroCoNet: a framework for the quantitative comparison of gene regulatory networks across species.Genome biology · 2026Article
- The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors.Cell genomics · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Sensing and perturbing mammalian cell states with reprogrammable ADAR sensors (RADARS).Nature protocols · 2026Review
- Spatial Atlas Creation at the Single Cell Level: Unveiling the Human Kidney Metaverse.Clinical journal of the American Society of Nephrology : CJASN · 2026Article
- Amaranth: enhanced single-cell transcript assembly via discriminative modelling of UMI reads and internal reads.Bioinformatics (Oxford, England) · 2026Article
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
387 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
A revolution in cellular measurement technology is under way: For the first time, we have the ability to monitor global gene regulation in thousands of individual cells in a single experiment. Such experiments will allow us to discover new cell types and states and trace their developmental origins. They overcome fundamental limitations inherent in measurements of bulk cell population that have frustrated efforts to resolve cellular states. Single-cell genomics and proteomics enable not only precise characterization of cell state, but also provide a stunningly high-resolution view of transitions between states. These measurements may finally make explicit the metaphor that C.H. Waddington posed nearly 60 years ago to explain cellular plasticity: Cells are residents of a vast "landscape" of possible states, over which they travel during development and in disease. Single-cell technology helps not only locate cells on this landscape, but illuminates the molecular mechanisms that shape the landscape itself. However, single-cell genomics is a field in its infancy, with many experimental and computational advances needed to fully realize its full potential.
Indexed as
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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.