ArticleCell2015
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.
Article in Cell, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,772 papers, 3 of them syntheses 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
1,772 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- A systematic review of single-cell RNA sequencing applications and insights in Oryza sativa response to environmental stresses.Molecular biology reports · 2025Pooled it
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Thermally Triggered Double Emulsion-Integrated Hydrogel Microparticles for Multiplexed Molecular Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Trial
- Integrative single-cell and spatial transcriptomic approaches to decipher the tumor microenvironment and therapeutic resistance in pancreatic cancer.Cancer biology & therapy · 2026Review
- Single-cell RNA sequencing unravels mosquito biology and host-pathogen interactions.Parasites & vectors · 2026Review
- A Method for Imputing scRNA-seq Data Based on Mobile Graph Convolutional Generative Adversarial Networks.Biochemical genetics · 2026Article
- Applications of Acoustic Waves in Micro-Droplet Technologies.Biosensors · 2026Review
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq nanopore long reads data.NAR genomics and bioinformatics · 2026Article
- Crosstalk Between CTSBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A single-cell transcriptome atlas reveals paradoxical monoallelic expression of RNA editing and heterozygous SNPs.Nucleic acids research · 2026Article
- Genomics in containment: BSL-4-compatible workflows enable high-resolution genomic and transcriptomic analyses of Risk Group 4 viruses.iScience · 2026Article
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Advances in sex-specific single-cell transcriptomic profiling in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Single-cell insights into plant growth, adaptation, and evolution.Journal of integrative plant biology · 2026Review
- Decoding Cardiac Development and Maturation at Single-Cell and Spatial Transcriptomic Resolution.Circulation research · 2026Review
- Bacterial surface display enables lysis-independent joint host-pathogen single-cell profiling.bioRxiv : the preprint server for biology · 2026Article
- scFANCL: Dual contrastive learning with false-negative correction at cell level for single-cell RNA-seq clustering.BMC genomics · 2026Article
- scHashFormer: a hash-driven graph transformer for scalable scRNA-seq clustering.Briefings in bioinformatics · 2026Article
1,712 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
Abstract
It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT.
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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.