ArticlePloS one2018
Learning time-varying information flow from single-cell epithelial to mesenchymal transition data.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry.Nature biotechnology · 2025Article
- Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer.Cancer research · 2025Article
- Unravelling the Mysteries of the Sonic Hedgehog Pathway in Cancer Stem Cells: Activity, Crosstalk and Regulation.Current issues in molecular biology · 2024Review
- The actin cytoskeleton: Morphological changes in pre- and fully developed lung cancer.Biophysics reviews · 2022Review
- A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition.Biophysical journal · 2022Article
- Cancer progression as a learning process.iScience · 2022Review
- Cancer Stem Cells and Epithelial-to-Mesenchymal Transition in Cancer Metastasis.Cold Spring Harbor perspectives in medicine · 2020Review
- Profiling Cell Signaling Networks at Single-cell Resolution.Molecular & cellular proteomics : MCP · 2020Review
- Fabrication of Hybrid Silver Microstructures from Vermiculite Templates as SERS Substrates.Nanomaterials (Basel, Switzerland) · 2020Article
- Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition.Frontiers in physiology · 2020Article
- Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.Nature communications · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in protein abundance and confirmation. However, typical computational approaches treat them as static interaction networks derived from a single time point. Here, we provide methods for learning the dynamic modulation of relationships between proteins from static single-cell data. We demonstrate our approach using TGFß induced epithelial-to-mesenchymal transition (EMT) in murine breast cancer cell line, profiled with mass cytometry. We take advantage of the asynchronous rate of transition to EMT in the data and derive a pseudotime EMT trajectory. We propose methods for visualizing and quantifying time-varying edge behavior over the trajectory, and a metric of edge dynamism to predict the effect of drug perturbations on EMT.
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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.