bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2025–05–18
eleven papers selected by
Xiao Qin, University of Oxford
  1. An integrated transcriptomic cell atlas of human endoderm-derived organoids.
  2. Three-dimensional genome landscape of primary human cancers.
  3. Fitness and transcriptional plasticity of human breast cancer single-cell-derived clones.
  4. Predicting gene expression from DNA sequence using deep learning models.
  5. Single-cell MultiOmics and spatial transcriptomics demonstrate neuroblastoma developmental plasticity.
  6. Simultaneous single-cell sequencing of RNA and DNA at scale with DEFND-seq.
  7. A systematic assessment of transcription factor function.
  8. 'AI models are capable of novel research': OpenAI's chief scientist on what to expect.
  9. Temporally discordant chromatin accessibility and DNA demethylation define short- and long-term enhancer regulation during cell fate specification.
  10. Cell-type deconvolution methods for spatial transcriptomics.
  11. NOTUM-mediated stem cell competition in CRC.
  1. Nat Genet. 2025 May;57(5): 1201-1212
    An integrated transcriptomic cell atlas of human endoderm-derived organoids.
    Quan Xu, Lennard Halle, Soroor Hediyeh-Zadeh, Merel Kuijs, Rya Riedweg, Umut Kilik, Timothy Recaldin, Qianhui Yu, Isabell Rall, Tristan Frum, Lukas Adam, Shrey Parikh, Raphael Kfuri-Rubens, Manuel Gander, Dominik Klein, Fabiola Curion, Zhisong He, Jonas Simon Fleck, Koen Oost, Maurice Kahnwald, Silvia Barbiero, Olga Mitrofanova, Grzegorz Jerzy Maciag, Kim B Jensen, Matthias Lutolf, Prisca Liberali, Jason R Spence, Nikolche Gjorevski, Joep Beumer, Barbara Treutlein, Fabian J Theis, J Gray Camp.
      Human pluripotent stem cells and tissue-resident fetal and adult stem cells can generate epithelial tissues of endodermal origin in vitro that recapitulate aspects of developing and adult human physiology. Here, we integrate single-cell transcriptomes from 218 samples covering organoids and other models of diverse endoderm-derived tissues to establish an initial version of a human endoderm-derived organoid cell atlas. The integration includes nearly one million cells across diverse conditions, data sources and protocols. We compare cell types and states between organoid models and harmonize cell annotations through mapping to primary tissue counterparts. Focusing on the intestine and lung, we provide examples of mapping data from new protocols and show how the atlas can be used as a diverse cohort to assess perturbations and disease models. The human endoderm-derived organoid cell atlas makes diverse datasets centrally available and will be valuable to assess fidelity, characterize perturbed and diseased states, and streamline protocol development.
    DOI:  https://doi.org/10.1038/s41588-025-02182-6
  2. Nat Genet. 2025 May;57(5): 1189-1200
    Three-dimensional genome landscape of primary human cancers.
    Cancer Genome Atlas Analysis Network
      Genome conformation underlies transcriptional regulation by distal enhancers, and genomic rearrangements in cancer can alter critical regulatory interactions. Here we profiled the three-dimensional genome architecture and enhancer connectome of 69 tumor samples spanning 15 primary human cancer types from The Cancer Genome Atlas. We discovered the following three archetypes of enhancer usage for over 100 oncogenes across human cancers: static, selective gain or dynamic rewiring. Integrative analyses revealed the enhancer landscape of noncancer cells in the tumor microenvironment for genes related to immune escape. Deep whole-genome sequencing and enhancer connectome mapping provided accurate detection and validation of diverse structural variants across cancer genomes and revealed distinct enhancer rewiring consequences from noncoding point mutations, genomic inversions, translocations and focal amplifications. Extrachromosomal DNA promoted more extensive enhancer rewiring among several types of focal amplification mechanisms. These results suggest a systematic approach to understanding genome topology in cancer etiology and therapy.
    DOI:  https://doi.org/10.1038/s41588-025-02188-0
  3. Cell Rep. 2025 May 12. pii: S2211-1247(25)00470-X. [Epub ahead of print]44(5): 115699
    Fitness and transcriptional plasticity of human breast cancer single-cell-derived clones.
    Long V Nguyen, Yaniv Eyal-Lubling, Daniel Guerrero-Romero, Sarah Kronheim, Suet-Feung Chin, Raquel Manzano Garcia, Stephen-John Sammut, Giulia Lerda, Allan J W Lui, Helen A Bardwell, Wendy Greenwood, Hee Jin Shin, Riccardo Masina, Katarzyna Kania, Alejandra Bruna, Elham Esmaeilishirazifard, Emily A Kolyvas, Samuel Aparicio, Oscar M Rueda, Carlos Caldas.
      Clonal fitness and plasticity drive cancer heterogeneity. We used expressed lentiviral-based cellular barcodes combined with single-cell RNA sequencing to associate single-cell profiles with in vivo clonal growth. This generated a significant resource of growth measurements from over 20,000 single-cell-derived clones in 110 xenografts from 26 patient-derived breast cancer xenograft models. 167,375 single-cell RNA profiles were obtained from 5 models and revealed that rare propagating clones display a highly conserved model-specific differentiation program with reproducible regeneration of the entire transcriptomic landscape of the original xenograft. In 2 models of basal breast cancer, propagating clones demonstrated remarkable transcriptional plasticity at single-cell resolution. Dichotomous cell populations with different clonal growth properties, signaling pathways, and metabolic programs were characterized. By directly linking clonal growth with single-cell transcriptomes, these findings provide a profound understanding of clonal fitness and plasticity with implications for cancer biology and therapy.
    Keywords:  CP: Cancer; breast cancer; cancer stem cells; cellular barcoding; clonal heterogeneity; clonal tracking; patient-derived tumor xenografts; plasticity; single-cell sequencing
    DOI:  https://doi.org/10.1016/j.celrep.2025.115699
  4. Nat Rev Genet. 2025 May 13.
    Predicting gene expression from DNA sequence using deep learning models.
    Lucía Barbadilla-Martínez, Noud Klaassen, Bas van Steensel, Jeroen de Ridder.
      Transcription of genes is regulated by DNA elements such as promoters and enhancers, the activity of which are in turn controlled by many transcription factors. Owing to the highly complex combinatorial logic involved, it has been difficult to construct computational models that predict gene activity from DNA sequence. Recent advances in deep learning techniques applied to data from epigenome mapping and high-throughput reporter assays have made substantial progress towards addressing this complexity. Such models can capture the regulatory grammar with remarkable accuracy and show great promise in predicting the effects of non-coding variants, uncovering detailed molecular mechanisms of gene regulation and designing synthetic regulatory elements for biotechnology. Here, we discuss the principles of these approaches, the types of training data sets that are available and the strengths and limitations of different approaches.
    DOI:  https://doi.org/10.1038/s41576-025-00841-2
  5. Dev Cell. 2025 May 06. pii: S1534-5807(25)00251-5. [Epub ahead of print]
    Single-cell MultiOmics and spatial transcriptomics demonstrate neuroblastoma developmental plasticity.
    Yunyun Xu, Daohua Lou, Ping Chen, Gang Li, Dimtry Usoskin, Jian Pan, Fang Li, Shungen Huang, Caroline Hess, Ruze Tang, Xiaohan Hu, Juanjuan Yu, Maria Arceo, Ronald R de Krijger, Arthur S Tischler, Susanne Schlisio, Patrik Ernfors, Yizhou Hu, Jian Wang.
      Neuroblastoma, the most prevalent extracranial pediatric solid tumor, arises from neural crest progeny cells. It exhibits substantial developmental plasticity and intratumoral heterogeneity, leading to survival rates below 50% in high-risk cases. The regulatory mechanisms underlying this plasticity remain largely elusive. In this integrative study, we used single-cell MultiOmics from a mouse spontaneous tumor model and spatial transcriptomics from human patient samples to dissect the transcriptional and epigenetic landscapes that govern developmental states in neuroblastoma. We identified developmental intermediate states in high-risk neuroblastomas critical for malignant transitions and uncovered extensive epigenetic priming with latent capacity for diverse state transitions. Furthermore, we mapped enhancer gene regulatory networks (eGRNs) and tumor microenvironments sustaining these aggressive states. State transitions and malignancy could be interfered with by targeting transcription factors controlling the eGRNs.
    Keywords:  development; developmental plasticity; epigenetic priming; gene regulatory network; intermediate state; intratumoral heterogeneity; microenvironment; neuroblastoma; single-cell MultiOmics; spatial transcriptomics
    DOI:  https://doi.org/10.1016/j.devcel.2025.04.013
  6. Nat Rev Genet. 2025 May 12.
    Simultaneous single-cell sequencing of RNA and DNA at scale with DEFND-seq.
    Timothy R Olsen.
      
    DOI:  https://doi.org/10.1038/s41576-025-00853-y
  7. Nat Genet. 2025 May;57(5): 1061
    A systematic assessment of transcription factor function.
    Chiara Anania.
      
    DOI:  https://doi.org/10.1038/s41588-025-02216-z
  8. Nature. 2025 May 12.
    'AI models are capable of novel research': OpenAI's chief scientist on what to expect.
    Elizabeth Gibney.
      
    Keywords:  Machine learning
    DOI:  https://doi.org/10.1038/d41586-025-01485-2
  9. Cell Rep. 2025 May 09. pii: S2211-1247(25)00451-6. [Epub ahead of print]44(5): 115680
    Temporally discordant chromatin accessibility and DNA demethylation define short- and long-term enhancer regulation during cell fate specification.
    Lindsey N Guerin, Timothy J Scott, Jacqueline A Yap, Annelie Johansson, Fabio Puddu, Tom Charlesworth, Yilin Yang, Alan J Simmons, Ken S Lau, Rebecca A Ihrie, Emily Hodges.
      Chromatin and DNA modifications mediate the transcriptional activity of lineage-specifying enhancers, but recent work challenges the dogma that joint chromatin accessibility and DNA demethylation are prerequisites for transcription. To understand this paradox, we established a highly resolved timeline of their dynamics during neural progenitor cell differentiation. We discovered that, while complete demethylation appears delayed relative to shorter-lived chromatin changes for thousands of enhancers, DNA demethylation actually initiates with 5-hydroxymethylation before appreciable accessibility and transcription factor occupancy is observed. The extended timeline of DNA demethylation creates temporal discordance appearing as heterogeneity in enhancer regulatory states. Few regions ever gain methylation, and resulting enhancer hypomethylation persists long after chromatin activities have dissipated. We demonstrate that the temporal methylation status of CpGs (mC/hmC/C) predicts past, present, and future chromatin accessibility using machine learning models. Thus, chromatin and DNA methylation collaborate on different timescales to shape short- and long-term enhancer regulation during cell fate specification.
    Keywords:  5-hydroxymethylation; 6-base sequencing; ATAC-Me; CP: Developmental biology; CP: Molecular biology; DNA methylation; chromatin accessibility; differentiation; enhancers; epigenetics; machine learning; neural progenitor cells
    DOI:  https://doi.org/10.1016/j.celrep.2025.115680
  10. Nat Rev Genet. 2025 May 14.
    Cell-type deconvolution methods for spatial transcriptomics.
    Lucie C Gaspard-Boulinc, Luca Gortana, Thomas Walter, Emmanuel Barillot, Florence M G Cavalli.
      Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection.
    DOI:  https://doi.org/10.1038/s41576-025-00845-y
  11. Nat Rev Cancer. 2025 May 12.
    NOTUM-mediated stem cell competition in CRC.
    Praver Gupta, Tamal Das.
      
    DOI:  https://doi.org/10.1038/s41568-025-00827-6
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