bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2026–05–03
fourteen papers selected by
Xiao Qin, University of Oxford
  1. Decoding tumor heterogeneity and plasticity.
  2. Protocol for identifying cellular reprogramming minimal networks using combinatorial transcription factor screening.
  3. Multimodal foundation models in colorectal cancer: from prediction to trustworthy clinical insight.
  4. Aged differentiated cells reverse into native stemness-like state by niche cytokines to sustain lifelong homeostasis and tissue repair.
  5. NOD2 drives regenerative fetal-like reprogramming in the intestinal epithelium.
  6. Fibroblast dynamics in colorectal cancer: stability, plasticity, and novel markers.
  7. Tracing the rise of biomedical foundation models.
  8. When tissue tension creates a mutagenic niche.
  9. This organoid can menstruate - and shows how tissue can repair itself.
  10. Unravelling bacterial complexity at high resolution with single-cell transcriptomics.
  11. Decoding Human T Cell Immunity with Artificial Intelligence and Single-Cell Genomics.
  12. Call your AI agent.
  13. SOX9 as a key regulator of tissue remodelling and epithelial cell fate transitions.
  14. From bench to byte: A UK perspective on data-driven cancer research.
  1. Nat Cancer. 2026 Apr;7(4): 553
    Decoding tumor heterogeneity and plasticity.
      
    DOI:  https://doi.org/10.1038/s43018-026-01166-7
  2. STAR Protoc. 2026 Apr 28. pii: S2666-1667(26)00180-2. [Epub ahead of print]7(2): 104527
    Protocol for identifying cellular reprogramming minimal networks using combinatorial transcription factor screening.
    Abigail R Altman, Diogo Pértiga-Cabral, Carlos-Filipe Pereira, Ilia Kurochkin.
      Direct reprogramming offers a powerful approach to generate therapeutic cell types, but progress is limited by an incomplete understanding of transcription factor (TF) cooperativity. Here, we present a protocol for performing combinatorial TF screening to resolve reprogramming factor networks that drive cell identity. We describe steps for arrayed lentiviral production, transduction, and reprogramming of human fibroblasts into distinct immune cells. We detail procedures for cell purification, library preparation, sequencing, and analysis to resolve TF combinations and dynamics. For complete details on the use and execution of this protocol, please refer to Kurochkin et al.1.
    Keywords:  Bioinformatics; Flow Cytometry; Immunology; RNAseq; Sequencing; Single Cell; Stem Cells
    DOI:  https://doi.org/10.1016/j.xpro.2026.104527
  3. Brief Bioinform. 2026 Mar 01. pii: bbag179. [Epub ahead of print]27(2):
    Multimodal foundation models in colorectal cancer: from prediction to trustworthy clinical insight.
    Danial Gharaie Amirabadi, Adib Miraki Feriz, Hossein Safarpour.
      Colorectal cancer (CRC) is characterized by profound, multi-layered heterogeneity that limits the precision of conventional single-modality clinical tools. The emergence of multimodal foundation models (MFMs) represents a conceptual paradigm shift, moving beyond static biomarkers to capture the dynamic and evolving nature of CRC. MFMs integrate histopathology, radiology, multi-omics data (including the critical regulatory layer of epigenomics), and clinical variables into shared high-dimensional representational spaces. This integration enables improved prognostication, refined molecular subtyping, and in silico simulation of therapeutic perturbations within the tumor's functional landscape, thereby supporting rational and model-driven drug development. In this review, we synthesize the rapidly expanding body of CRC-specific MFM research and critically examine the unresolved challenges that currently limit clinical translation. We place particular emphasis on the transition from correlation to causal inference, the establishment of cross-population generalizability, and the resolution of key issues related to trustworthiness and clinical interpretability. Finally, we propose an actionable roadmap outlining regulatory, data governance, and translational requirements, including the lab-in-the-loop paradigm, necessary to position MFMs as a robust and equitable framework in clinical oncology.
    Keywords:  colorectal cancer; functional space navigation; lab-in-the-loop; multimodal foundation models; precision oncology; trustworthy AI
    DOI:  https://doi.org/10.1093/bib/bbag179
  4. Nat Commun. 2026 Apr 25.
    Aged differentiated cells reverse into native stemness-like state by niche cytokines to sustain lifelong homeostasis and tissue repair.
    Shalini Dimri-Wagh, Swarnabh Bhattacharya, Gharam Yassen, Ofir Shorer, Aya Amitai-Lange, Anna Altshuler, Shira Hadad-Porat, Keren Yizhak, Yuval Shaked, Michael Mimouni, Beatrice Tiosano, Ramez Barbara, Ruby Shalom-Feuerstein.
      Recent studies report that epithelial differentiated cells can undergo a reverse process called dedifferentiation in response to stem cell loss. However, the extent of this reversion and the plasticity of young versus aged-differentiated cells remain unclear. Here we show that dedifferentiated corneal epithelial cells acquire a transcriptomic state closely resembling native stem cells, sustain tissue homeostasis across lifespan and efficiently repair repeated tissue injury. Transplantation of stage-specific genetically traceable aged differentiated epithelial cells onto a denuded niche reveals reversion into a stemness-like state, restoring both quiescent and active stem cell compartments. This plasticity operates within the epithelial lineage, allowing transitions along the differentiation axis, but remains restricted across lineages, as transplanted conjunctival cells fail to regenerate the corneal stem cell pool. Mechanistically, we identify niche-derived cytokines that trigger reprogramming in vivo and enhance stemness in primary human corneal epithelial cells, revealing a conserved and therapeutically exploitable pathway for epithelial regeneration.
    DOI:  https://doi.org/10.1038/s41467-026-72331-w
  5. Proc Natl Acad Sci U S A. 2026 May 05. 123(18): e2519981123
    NOD2 drives regenerative fetal-like reprogramming in the intestinal epithelium.
    Derek K L Tsang, Charles Maisonneuve, Arshad Ayyaz, Elisabeth G Foerster, Marry Nissan, Lauren Baerg, Daniel Trcka, Bibaswan Ghoshal, Boyan K Tsankov, Giuliano Bayer, Madison Denney, Catherine J Streutker, Jeffrey L Wrana, Stephen E Girardin, Dana J Philpott.
      Inflammatory injury to the intestine triggers a reprogramming of the intestinal epithelium to a fetal-like state that drives rapid restoration of the epithelial barrier. Although the intestinal microbiota is a key modulator of inflammation, its role in influencing epithelial fetal-like stem cell reprogramming and consequent restitution remains unclear. Using irradiation (IR) injury as a model for small intestinal epithelium injury and repair, we found that the intestinal microbiota accelerated epithelial restitution by amplifying a repair-associated inflammatory response that promoted the emergence of fetal-like intestinal epithelial cells (IECs), marked by Ly6a and Clu. NOD2, the strongest genetic link to the development of Crohn's disease, was found to be expressed in fetal-like IECs following injury. Employing an ileal organoid model, we demonstrated that NOD2 activation by its peptidoglycan ligand potentiated an inflammatory gene signature characterized by interferon signaling, concurrent with enterocyte recovery. NOD2 deficiency exacerbated epithelial apoptosis following IR injury, whereas epithelial-specific NOD2 signaling promoted fetal-like IEC emergence and increased epithelial proliferation. Collectively, these findings reveal a pivotal role for the microbiota and NOD2-mediated microbial sensing in regulating fetal-like IEC fate after injury, thus contributing to the protective function of this microbial sensor during intestinal inflammation.
    Keywords:  NOD2; fetal-like reversion; inflammation; intestinal regeneration; microbiota
    DOI:  https://doi.org/10.1073/pnas.2519981123
  6. Oncogene. 2026 Apr 28.
    Fibroblast dynamics in colorectal cancer: stability, plasticity, and novel markers.
    Richard Demmler, Charles G Anchang, Yongsong Yong, Andreas Ramming, Simon Rauber, Vera S Schellerer, Benjamin Schmid, Arndt Hartmann, Susanne Merkel, Katharina Imkeller, Elisabeth Naschberger, Michael Stürzl.
      Colorectal cancer (CRC) is one of the most commonly diagnosed and globally spread malignant diseases. Cancer-associated fibroblasts (CAFs) are key architects of the tumor microenvironment, yet their origin, stability, and interconvertibility remain poorly understood. Using transcriptomic profiling of fibroblasts from colorectal cancer (CRC) patients, we identify highly expressed (HEX) markers that define fibroblast subpopulations and uncover mechanisms governing their plasticity. We find that ADH1B marks normal colon-associated fibroblasts (NAFs), which consist of PI16-NAFs and ADAMDEC1-NAFs. ITGA3 delineates the total CAF population, which comprises myofibroblastic CAFs (myCAFs), whose characterizing markers were associated with poor prognosis and proteolytic inflammatory CAFs (piCAFs), characterized by markers not associated with prognosis. An AGT/TGM2-expressing fibroblast subset is present in both healthy and tumor tissues, suggesting alternative trajectories to the classical NAF-to-CAF transition model. While PI16-NAFs, AGT/TGM2-fibroblasts, and myCAFs maintain stable identities in long-term culture, the ADAMDEC1-NAF and piCAF phenotypes are lost in vitro. ITGA3-CAFs demonstrate dynamic plasticity, with TGF-β stably inducing myCAF formation and TNF-α or inhibition of DNA methylation promoting transient piCAF emergence. These findings redefine fibroblast heterogeneity in CRC and reveal a coexisting stable and plastic fibroblast network that may be amenable to modulation and provides a framework for future functional and translational studies. We identified highly expressed markers (HEX markers) to distinguish CAFs, NAFs and corresponding subpopulations in CRC. ADH1B characterized NAFs, which consisted of stable (solid outline) PI16-NAFs and unstable (dashed outline) ADAMDEC1-NAFs. ITGA3 identified CAFs consisting of stable myCAFs associated with poor prognosis and unstable piCAFs not associated with prognosis. AGT/TGM2 fibroblasts did not express ADH1B or ITGA3, were stable in culture and could be detected in both healthy colon and CRC. Treatment of PI16-NAFs with LPS or IFN-γ induced ADAMDEC1-NAFs, TGF-β the formation of myCAFs, while treatment with TNF-α led to the formation of piCAFs. Reduced DNA methylation converted myCAFs and PI16-NAFs into piCAFs.
    DOI:  https://doi.org/10.1038/s41388-026-03809-6
  7. Nat Biotechnol. 2026 Apr 30.
    Tracing the rise of biomedical foundation models.
    Yuzhou Chang, Hao Cheng, Mirage Modi, Guangyu Wang, Dong Xu, Qin Ma.
      
    DOI:  https://doi.org/10.1038/s41587-026-03135-y
  8. Cancer Cell. 2026 Apr 30. pii: S1535-6108(26)00178-9. [Epub ahead of print]
    When tissue tension creates a mutagenic niche.
    Nicole Cruz-Reyes, Derek C Radisky.
      In this issue of Cancer Cell, Hayward et al. show that fibrotic tissue tension creates a mechanically organized mutagenic niche. A stiff stroma activates epithelial STAT3, recruits macrophages, and drives NOX-dependent lipid peroxidation, generating diffusible aldehydes that damage epithelial DNA in fibrotic tumors and mammographically dense breast tissue.
    DOI:  https://doi.org/10.1016/j.ccell.2026.04.001
  9. Nature. 2026 May 01.
    This organoid can menstruate - and shows how tissue can repair itself.
    Claudia Steiner.
      
    Keywords:  Medical research; Regeneration; Stem cells
    DOI:  https://doi.org/10.1038/d41586-026-01428-5
  10. Nat Microbiol. 2026 May 01.
    Unravelling bacterial complexity at high resolution with single-cell transcriptomics.
    Anne E Clatworthy, Vincenzo P DiNatale, Emanuel Burgos-Robles, Fernando Lopes, Chris Smillie, Deborah T Hung.
      Phenotypic heterogeneity, a feature of both bacteria and eukaryotic cells, arises from inherent cell-to-cell variability. In eukaryotes, single-cell RNA sequencing has led to an explosion in understanding how heterogeneity impacts different cell types and states in organs and tissues. While single-cell RNA sequencing analyses in bacteria have lagged behind eukaryotic studies, recent technological advances now enable similar, high-resolution studies to be performed at scale in bacteria, yielding fundamental insights into how heterogeneity influences bacterial physiology, metabolism, antibiotic resistance, pathogenesis and interactions within complex microbial communities. Here we review recent advances in bacterial single-cell RNA sequencing, including the methods developed so far and what has been learned from their application. We also discuss technological and computational challenges going forwards, the need for standardization and how that could be achieved, and how this emerging field is now poised to revolutionize our understanding of bacterial physiology, infection biology and interactions within bacterial communities, such as the microbiota.
    DOI:  https://doi.org/10.1038/s41564-026-02333-3
  11. Annu Rev Immunol. 2026 Apr;44(1): 381-405
    Decoding Human T Cell Immunity with Artificial Intelligence and Single-Cell Genomics.
    Lisa M Dratva, Sarah A Teichmann, Lorenz Kretschmer.
      T cells are key mediators of adaptive immunity, yet their highly dynamic response states and immense T cell receptor (TCR) diversity pose challenges in deciphering their functional roles in human diseases. Recent advances in single-cell genomics and TCR sequencing provide unprecedented opportunities to resolve T cell heterogeneity, decode clonal response dynamics, and facilitate high-throughput antigen specificity mapping. In this review, we summarize major technological innovations that have transformed T cell research, from experimental tools for antigen-specific T cell profiling to machine learning frameworks for predicting interactions between the TCR and peptide-MHC (pMHC) and structural modeling powered by deep learning. We discuss current bottlenecks, including data limitations and model generalizability, and explore emerging strategies to guide the next generation of T cell discoveries. We argue that artificial intelligence and single-cell genomics will collectively pave the way for dissecting T cell heterogeneity, mapping TCR sequence to pMHC specificity, and interpreting these features in the context of clinical outcomes.
    Keywords:  T cell receptor; TCR repertoire analysis; adaptive immunity; antigen specificity prediction; artificial intelligence; human T cell responses; single-cell genomics
    DOI:  https://doi.org/10.1146/annurev-immunol-082724-011631
  12. Nat Methods. 2026 May 01.
    Call your AI agent.
    Vivien Marx.
      
    DOI:  https://doi.org/10.1038/s41592-026-03088-9
  13. Curr Opin Genet Dev. 2026 Apr 24. pii: S0959-437X(26)00044-4. [Epub ahead of print]99 102477
    SOX9 as a key regulator of tissue remodelling and epithelial cell fate transitions.
    Maria T Bejar, Maria P Alcolea.
      Epithelial plasticity allows committed cells to bypass rigid differentiation hierarchies, enabling efficient tissue repair through the reactivation of developmental-like programmes. In this review, we focus on the transcription factor SOX9 as a central regulator of epithelial cell fate rewiring. Essential during epithelial development and tissue morphogenesis, SOX9 is dynamically regulated across diverse epithelial tissues following injury, conferring SOX9-expressing cells with an increased 'stemness' and repair/regenerative capacity. Emerging evidence suggests that SOX9 may function as a molecular integrator of microenvironmental inputs during tissue perturbations. However, dysregulation or persistent activation of this programme carries inherent risks of fibrosis and malignancy. Future work aimed at understanding how SOX9 integrates biochemical and mechanical cues will be vital for developing strategies to harness the plastic potential of epithelial cells for regenerative medicine and prevent pathologies associated with this plasticity.
    DOI:  https://doi.org/10.1016/j.gde.2026.102477
  14. Eur J Cancer. 2026 Apr 18. pii: S0959-8049(26)00532-0. [Epub ahead of print]240 116751
    From bench to byte: A UK perspective on data-driven cancer research.
    Sarah K Wooller, Andrew Blake, Martin McCabe, Colin McLean, Gareth Price, Harriet Unsworth, Mieke Van Hemelrijck, Frances M G Pearl.
      Cancer research is undergoing a profound transformation driven by the rapid expansion of clinical, genomic, imaging, and real-world data. As Europe prepares for the implementation of the European Health Data Space (EHDS), the ability of health systems to effectively integrate, govern, and translate these diverse datasets will shape the next era of oncology. However, technological capacity alone is insufficient; sustained impact will depend on building trust, strengthening infrastructure, and supporting the people and cultures that enable data-intensive science. Cancer Research UK (CRUK), the nation's largest cancer charity, invests over £400 million annually in research and has launched a national data strategy to accelerate progress. In partnership with CRUK, we are working to develop a more connected and collaborative cancer data science ecosystem,one that brings together researchers across disciplines, identifies shared challenges, and co-designs practical solutions to overcome them. Through the CRUK Data Science Community and its Data Interest Groups, we highlight common obstacles across health system data, data reuse, public involvement, infrastructure, and training. We also present case studies demonstrating how integrated datasets, AI-enabled analytics, international collaboration, and federated approaches are already reshaping cancer research and clinical practice. By fostering a community-led approach to trustworthy, sustainable and FAIR data access, the UK has an opportunity to unlock the full potential of data-driven research and deliver meaningful benefits for people affected by cancer.
    Keywords:  Cancer Research UK; Cancer data scientist training; Data and sample reuse; Data-driven cancer research; FAIR data; Federated learning; Metadata; OMOP; PPIE; Real World Data
    DOI:  https://doi.org/10.1016/j.ejca.2026.116751
This page is being maintained by Thomas Krichel. It was last updated on 2026‒05‒04 at 19:37.