ArticleNature communications2024
Reassessment of marker genes in human induced pluripotent stem cells for enhanced quality control.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Micropatterned neural induction with heat-inactivated extracellular matrix protein by on-demand high-speed laser.Regenerative therapy · 2026Article
- Ploidy-Dependent Response to Anticancer Drugs of Human Embryonic Stem Cells.Cell proliferation · 2026Article
- Application of human induced pluripotent stem cells for tissue modeling and therapy: are we on track?Stem cells (Dayton, Ohio) · 2026Review
- KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease.iScience · 2026Article
- Allogeneic islet products for type 1 diabetes: Navigating nonclinical and manufacturing regulatory expectations.Stem cell reports · 2026Review
- Bridging population and cell: modelling complex diseases with human induced pluripotent stem cells.European journal of human genetics : EJHG · 2026Review
- Deep Learning-Guided Holotomography Reveals Early Structural Remodelling During Pluripotency Exit.bioRxiv : the preprint server for biology · 2026Article
- Charcot-Marie-Tooth disease and related neuropathies.Nature reviews. Disease primers · 2026Review
- Transcriptomic Profile of Directed Differentiation of iPSCs into Hepatocyte-like Cells.International journal of molecular sciences · 2026Article
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Proteomic Signatures in Extracellular Vesicles as Predictive Markers of Neural and Cardiac Differentiation from Human iPSCs.Mass spectrometry (Tokyo, Japan) · 2026Article
- Modeling myeloid cell development in health and disease using induced pluripotent stem cells.Frontiers in immunology · 2026Review
- Overcoming the eIF2α Brake in Human Cell-Derived Translation Systems.bioRxiv : the preprint server for biology · 2025Article
- Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- Transposable element expression and sub-cellular dynamics during hPSC differentiation to endoderm, mesoderm, and ectoderm lineages.Nature communications · 2025Article
- SPRINGing forward: Advancing RNA editing efficiency and precision with engineered ADAR2.Molecular therapy. Nucleic acids · 2025Article
- Single-cell ultra-high-throughput multiplexed chromatin and RNA profiling reveals gene regulatory dynamics.Nature methods · 2025Article
- Review
- CRISPR/Cas-edited iPSCs and mesenchymal stem cells: a concise review of their potential in thalassemia therapy.Frontiers in cell and developmental biology · 2025Review
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
Human induced pluripotent stem cells (iPSCs) have great potential in research, but pluripotency testing faces challenges due to non-standardized methods and ambiguous markers. Here, we use long-read nanopore transcriptome sequencing to discover 172 genes linked to cell states not covered by current guidelines. We validate 12 genes by qPCR as unique markers for specific cell fates: pluripotency (CNMD, NANOG, SPP1), endoderm (CER1, EOMES, GATA6), mesoderm (APLNR, HAND1, HOXB7), and ectoderm (HES5, PAMR1, PAX6). Using these genes, we develop a machine learning-based scoring system, "hiPSCore", trained on 15 iPSC lines and validated on 10 more. hiPSCore accurately classifies pluripotent and differentiated cells and predicts their potential to become specialized 2D cells and 3D organoids. Our re-evaluation of cell fate marker genes identifies key targets for future studies on cell fate assessment. hiPSCore improves iPSC testing by reducing time, subjectivity, and resource use, thus enhancing iPSC quality for scientific and medical applications.
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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.