ArticleNature biotechnology2012
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells.
Article in Nature biotechnology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 929 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
929 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- Review
- scASprofiler: profiling single-cell RNA splicing with a deep convolutional generative network.Briefings in bioinformatics · 2026Article
- A single-cell transcriptome atlas reveals paradoxical monoallelic expression of RNA editing and heterozygous SNPs.Nucleic acids research · 2026Article
- Hematopoietic stem cell aging: a review of transcriptional and multi-omics insights and potential paths for AI integration.Experimental & molecular medicine · 2026Review
- Advances in sex-specific single-cell transcriptomic profiling in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Organism-scale annotation with Pan-human Azimuth.bioRxiv : the preprint server for biology · 2026Article
- Single-nucleus chromatin accessibility and gene expression co-profiling by ISSAAC-seq.Nature protocols · 2026Review
- Integrating Single-Cell Sequencing and Proteomics to Unravel Stem Cell Heterogeneity: Implications for Precision Regenerative Medicine.Stem cell reviews and reports · 2026Review
- Review
- Single-Cell Omics Advances in Understanding Tissue Development and Complex Trait Formation in Sheep and Goats.Animals : an open access journal from MDPI · 2026Review
- Comprehensive assessment of alternative splicing analysis methods for single-cell RNA-seq.iScience · 2026Article
- scTACL: a multitask topology-aware contrastive learning approach for single-cell transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Immune profiling of T cells: A comparative analysis of single-cell TCR sequencing using 10X Genomics and Parse Biosciences platforms.Biochemistry and biophysics reports · 2026Article
- Annotating genomes at increased scale and resolution.Nature reviews. Genetics · 2026Review
- Acoustic separation and isolation of viruses, small extracellular vesicles and other nanoscale bioparticles.Nature protocols · 2026Review
- Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 2026Article
- Current trends and challenges in deciphering single molecule resolution maps of single cell transcriptomes.Briefings in bioinformatics · 2026Review
- The impact of transcriptome assembly algorithms on downstream quantification in RNA-seq data analysis.Briefings in bioinformatics · 2026Article
- A CD57+CD8+ T cell subset links T cell cytotoxicity to fibrotic lung disease in systemic sclerosis.The Journal of clinical investigation · 2026Article
869 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease- and cell type–specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.
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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.