ArticleNature biotechnology2015
Decoding the regulatory network of early blood development from single-cell gene expression measurements.
Article in Nature biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 208 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
208 citing papers in PubMed, 1 synthesis or guideline pooled it, 418 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
- A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.Nature communications · 2026Article
- Unveiling gene perturbation effects through gene regulatory networks inference from single-cell transcriptomic data.PLoS computational biology · 2026Article
- Molecular insights into heart field-specific cardiomyocyte differentiation - A computational study.PloS one · 2026Article
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- scTFBridge: a disentangled deep generative model informed by TF-motif binding for gene regulation inference in single-cell multi-omics.Nature communications · 2025Article
- Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.NPJ systems biology and applications · 2025Article
- SmartImpute is a targeted imputation framework for single-cell transcriptome data.Cell reports methods · 2025Article
- Facilitate integrated analysis of single cell multiomic data by binarizing gene expression values.Nature communications · 2025Article
- Deep exploration of logical models of cell differentiation in human preimplantation embryos.NPJ systems biology and applications · 2025Article
- Computing hematopoiesis plasticity in response to genetic mutations and environmental stimulations.Life science alliance · 2025Article
- Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Control of Cellular Differentiation Trajectories for Cancer Reversion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Unlocking hematopoietic stem cell potential: integrative computational approaches for genomic and transcriptomic analysis.Frontiers in cell and developmental biology · 2025Review
- HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling.Genome biology · 2024Article
- Immunological face of megakaryocytes.Frontiers of medicine · 2024Review
- OneSC: a computational platform for recapitulating cell state transitions.Bioinformatics (Oxford, England) · 2024Article
- Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation.Briefings in bioinformatics · 2024Article
- OneSC: A computational platform for recapitulating cell state transitions.bioRxiv : the preprint server for biology · 2024Article
- A logic-incorporated gene regulatory network deciphers principles in cell fate decisions.eLife · 2024Article
148 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
Abstract
Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.
Indexed as
Identifiers
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.