ArticleScience (New York, N.Y.)2014
Systems biology. Conditional density-based analysis of T cell signaling in single-cell data.
Article in Science (New York, N.Y.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 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
124 citing papers in PubMed, 242 citations in OpenAlex.
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- Characterizing gene perturbations in single cells via network divergence analysis.Nature communications · 2026Article
- Cell-State-Specific Drug Responses are Associated With Differences in Signaling Network Wiring.Molecular & cellular proteomics : MCP · 2026Article
- Existence of Causation without Correlation in Transcriptional Networks.bioRxiv : the preprint server for biology · 2026Article
- Reconstructing and comparing signal transduction networks from single-cell protein quantification data.Bioinformatics (Oxford, England) · 2026Article
- Article
- Phenoscaping Reveals Multimodal γδ T-cell Cytotoxicity as a Strategy to Overcome Cancer Cell-Mediated Immunomodulation.Cancer research · 2025Article
- The kinase ERK plays a conserved dominant role in the heterogeneity of epithelial-mesenchymal transition in pancreatic cancer cells.Science signaling · 2025Article
- CyTOF as a suitable tool for stratification and monitoring of cancer patients.Journal of translational medicine · 2025Review
- Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry.Nature biotechnology · 2025Article
- Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age.Genome biology · 2025Article
- STAT1 regulates immune-mediated intestinal stem cell proliferation and epithelial regeneration.Nature communications · 2025Article
- Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data.NPJ systems biology and applications · 2024Article
- VI-VS: calibrated identification of feature dependencies in single-cell multiomics.Genome biology · 2024Article
- Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data.bioRxiv : the preprint server for biology · 2024Article
- Non-Equilibrium Enhancement of Classical Information Transmission.Entropy (Basel, Switzerland) · 2024Article
- SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.Bioinformatics (Oxford, England) · 2024Article
64 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4(+) T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells as compared with antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals.
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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.