ArticleCell reports methods2023
Characterization of cell-fate decision landscapes by estimating transcription factor dynamics.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Rapid Transcription Dynamics Confers Cytarabine Resistance in Acute Myeloid Leukemia.Blood cancer discovery · 2026Article
- Unraveling enteroendocrine cell lineage dynamics and associated gene regulatory networks during intestinal development.Biology open · 2025Article
- Synergistic DNA and RNA binding of the Hox transcription factor Ultrabithorax coordinates splicing and shapes in vivo homeotic functions.Nucleic acids research · 2025Article
- Advancing cell therapies with artificial intelligence and synthetic biology.Current opinion in biomedical engineering · 2025Article
- FrozONE: quick cell nucleus enrichment for comprehensive proteomics analysis of frozen tissues.Life science alliance · 2025Article
- Linking signaling dynamics and cell fate decisions through single-cell imaging: evidence and challenges.Frontiers in cell and developmental biology · 2025Review
- SinCMat: A single-cell-based method for predicting functional maturation transcription factors.Stem cell reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Time-specific modulation of gene expression during differentiation by transcription factors promotes cell diversity. However, estimating their dynamic regulatory activity at the single-cell level and in a high-throughput manner remains challenging. We present FateCompass, an integrative approach that utilizes single-cell transcriptomics data to identify lineage-specific transcription factors throughout differentiation. By combining a probabilistic framework with RNA velocities or differentiation potential, we estimate transition probabilities, while a linear model of gene regulation is employed to compute transcription factor activities. Considering dynamic changes and correlations of expression and activities, FateCompass identifies lineage-specific regulators. Our validation using
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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 Socratic graph.