ArticleiScience2020
Alignment of Cell Lineage Trees Elucidates Genetic Programs for the Development and Evolution of Cell Types.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Stereotyped Subclones Revealed by High-Density Single-Cell Lineage Tracing Support Robust Development.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A statistical method for quantifying progenitor cells reveals incipient cell fate commitments.Nature methods · 2024Article
- A topological look into the evolution of developmental programs.Biophysical journal · 2021Article
- CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology.iScience · 2021Article
- Cell Tracking for Organoids: Lessons From Developmental Biology.Frontiers in cell and developmental biology · 2021Review
- Expression Partitioning of Duplicate Genes at Single Cell Resolution inFrontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A full understanding of the developmental process requires fine-scale characterization of cell divisions and cell types, which are naturally organized as the developmental cell lineage tree (CLT). Technological breakthroughs facilitated determination of more CLTs, but complete comprehension of the data remains difficult without quantitative comparison among CLTs. We hereby quantified phenotypic similarity between CLTs using a novel computational method that exhaustively searches for optimal correspondence between individual cells meanwhile retaining their topological relationships. The revealed CLT similarities allowed us to infer functional similarity at the transcriptome level, identify cell fate transformations, predict functional relationships between mutants, and find evolutionary correspondence between cell types of different species. By allowing quantitative comparison between CLTs, our work is expected to greatly enhance the interpretability of relevant data and help answer the myriad of questions surrounding the developmental process.
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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.