ReviewProteomics2018
Defining Cell Identity with Single-Cell Omics.
Review in Proteomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed.
- Single-cell marker gene clustering: A unified deep learning framework for marker gene-based clustering of single-cell RNA-sequencing data.Quantitative biology (Beijing, China) · 2026Article
- Decoding Microbiota-Immune Interplay in Viral Pathogenesis: Toward Next-Generation Antiviral Therapies.Probiotics and antimicrobial proteins · 2026Review
- Cell-type-specific alkaloid and terpenoid biosynthesis in glandular trichomes: single-cell and spatial transcriptomic perspectives.Frontiers in plant science · 2026Review
- Branch-specific gene discovery in cell differentiation using multi-omics graph attention.PLoS computational biology · 2025Article
- Gene spatial integration: enhancing spatial transcriptomics analysis via deep learning and batch effect mitigation.Bioinformatics (Oxford, England) · 2025Article
- Single-cell lipidomics: protocol development for reliable cellular profiling using capillary sampling.The Analyst · 2025Article
- Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.Nucleic acids research · 2025Article
- Cell manufacturing for cell-based tissue engineering: a focus on vascularized, skeletal muscle regeneration.Frontiers in chemical engineering · 2025Article
- Single-cell transcriptome profiling of mMolecular biomedicine · 2024Article
- FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics.Nature protocols · 2024Review
- Understanding Cell Differentiation Through Single-Cell Approaches: Conceptual Challenges of the Systemic Approach.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Mass Spectrometry-based Single-Cell Lipidomics: Advancements, Challenges, and the Path Forward.Trends in analytical chemistry : TRAC · 2023Article
- Optofluidic Flow Cytometer with In-Plane Spherical Mirror for Signal Enhancement.Sensors (Basel, Switzerland) · 2023Article
- Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease.Biomolecules · 2023Review
- Article
- Deciphering endothelial heterogeneity in health and disease at single-cell resolution: progress and perspectives.Cardiovascular research · 2023Article
- Learning cell identity in immunology, neuroscience, and cancer.Seminars in immunopathology · 2023Review
- Single-cell genomics reveals population structures fromMicrobial genomics · 2022Article
- Developments of Conventional and Microfluidic Flow Cytometry Enabling High-Throughput Characterization of Single Cells.Biosensors · 2022Review
- Review
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
Cells are a fundamental unit of life, and the ability to study the phenotypes and behaviors of individual cells is crucial to understanding the workings of complex biological systems. Cell phenotypes (epigenomic, transcriptomic, proteomic, and metabolomic) exhibit dramatic heterogeneity between and within the different cell types and states underlying cellular functional diversity. Cell genotypes can also display heterogeneity throughout an organism, in the form of somatic genetic variation-most notably in the emergence and evolution of tumors. Recent technical advances in single-cell isolation and the development of omics approaches sensitive enough to reveal these aspects of cell identity have enabled a revolution in the study of multicellular systems. In this review, we discuss the technologies available to resolve the genomes, epigenomes, transcriptomes, proteomes, and metabolomes of single cells from a wide variety of living systems.
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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.