ArticleNature biotechnology2014
Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.
Article in Nature biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 485 papers.
What it found
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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.
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Who cites it
485 citing papers in PubMed, 989 citations in OpenAlex.
- A graph-attentive GAN for rare-cell-aware single-cell RNA-seq data generation.PLoS computational biology · 2026Article
- 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
- RareCapsNet: An explainable capsule network enables robust discovery of rare cell populations from large-scale single-cell transcriptomics.PLoS computational biology · 2026Article
- ScEnsemble: weighted hypergraph ensemble clustering for single-cell RNA sequencing.BMC bioinformatics · 2026Article
- Single-cell RNA sequencing in stroke and traumatic brain injury: Current achievements, challenges and future perspectives on transcriptomic profiling.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising.Interdisciplinary sciences, computational life sciences · 2026Article
- scTACL: a multitask topology-aware contrastive learning approach for single-cell transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- SurvMarker: an R package for identifying survival-associated molecular features using PCA-based weighted scores.BMC bioinformatics · 2026Article
- Modern Mining: The Role of Single-cell RNA Sequencing in Advancing Neuroscience Research.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
- Unravelling bacterial complexity at high resolution with single-cell transcriptomics.Nature microbiology · 2026Review
- Hyperdynamic microtubule-based structural changes in Monocyte-Derived-Neuronal-like cells from patients with schizophrenia.Schizophrenia (Heidelberg, Germany) · 2026Article
- Mosaic: Single-Cell Atlas of Stress.Cells · 2026Review
- Dissecting molecular heterogeneity in primary gastric cancer by IGFBP7-related analysis.Journal of translational medicine · 2026Article
- Compact and informative representation learning for scRNA-seq data clustering with masked information bottleneck.BMC biology · 2026Article
- Cell-Based Immuno-Biosensors Using Microfluidics.Sensors (Basel, Switzerland) · 2026Review
- Single-molecule peptide sequencing through reverse translation of peptides into DNA.Nature biotechnology · 2026Article
- scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- VillageNet: Graph-based, Easily-interpretable, Unsupervised Clustering for Broad Biomedical Applications.ArXiv · 2026Article
- Srnc: semi-supervised learning for robust novel cell-type identification in single cell RNA sequencing data.BMC bioinformatics · 2026Article
- scAURA: Alignment- and Uniformity-based Graph Debiased Contrastive Representation Architecture for Self-Supervised Clustering of Single-Cell Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
425 more citing papers are in PubMed but not listed here.
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Authors and funding
30 authors at 3 institutions in 1 country.
Funding
Abstract
Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.
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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.