bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2026–05–17
fifteen papers selected by
Xiao Qin, University of Oxford
  1. RegVelo: Gene-regulatory-informed dynamics of single cells.
  2. D-SPIN constructs regulatory network models from scRNA-seq that reveal organizing principles of perturbation response.
  3. The emergence of metastasis in colorectal cancer.
  4. Understanding development and cellular plasticity in the pancreas in health and disease.
  5. Harnessing myeloid cell plasticity for cancer therapy.
  6. The pathological landscape of tumor evolution: a paradigm shift from morphology to functional networks.
  7. Spatial Biology Evolution: Past, Present and Future of Mapping Life in Context.
  8. An inflammasome-driven differentiation program in intestinal stem cells protects against Salmonella infection.
  9. AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology.
  10. Decoding and steering spatially resolved cellular dynamics.
  11. New developments and applications of human organoids.
  12. Synthetic Regulatory Genomics.
  13. Automated stem cell-derived organoid platforms for disease modeling.
  14. What's your hypothesis?
  15. Drug-tolerant persister cells in cancer: a scoping review of definitions, models, and molecular mechanisms.
  1. Cell. 2026 May 11. pii: S0092-8674(26)00457-5. [Epub ahead of print]
    RegVelo: Gene-regulatory-informed dynamics of single cells.
    Weixu Wang, Zhiyuan Hu, Philipp Weiler, Sarah Mayes, Marius Lange, Daniel M Fountain, Julianna O Haug, Jingye Wang, Zhengyuan Xue, Tatjana Sauka-Spengler, Fabian J Theis.
      Cell fate transitions are driven by regulatory circuitry, yet RNA velocity models cellular dynamics without explicitly accounting for gene regulatory interactions, limiting mechanistic insight. Conversely, gene regulatory network (GRN) inference methods largely neglect the dynamic nature of biological systems. To overcome this conceptual disconnect, we present RegVelo, a bottom-up, actionable, and interpretable deep learning framework that jointly models splicing kinetics and gene regulatory interactions. Across diverse biological systems, RegVelo provides reliable predictive power for terminal states, gene interactions, and perturbation simulations. By applying RegVelo to zebrafish neural crest development using full-length Smart-seq3 and shared gene expression and chromatin accessibility measurements, we delineate regulatory programs underlying fate specification. Guided by in silico perturbations and validated by CRISPR-Cas9 knockout and single-cell Perturb-seq, we establish tfec as an early driver and elf1 as a regulator of pigment cell fate. RegVelo establishes a quantitative framework for bridging gene regulation and cell fate decisions.
    Keywords:  cell fate decision; deep generative modeling; early drivers; gene regulatory network; in silico perturbation; in vivo Perturb-seq; mechanistic modeling; regulatory dynamics; transcriptional dynamics; zebrafish neural crest
    DOI:  https://doi.org/10.1016/j.cell.2026.04.022
  2. Cell. 2026 May 12. pii: S0092-8674(26)00463-0. [Epub ahead of print]
    D-SPIN constructs regulatory network models from scRNA-seq that reveal organizing principles of perturbation response.
    Jialong Jiang, Sisi Chen, Tiffany Tsou, Christopher S McGinnis, Tahmineh Khazaei, Qin Zhu, Jong H Park, Inna-Marie Strazhnik, Jost Vielmetter, Yingying Gong, John Hanna, Eric D Chow, David A Sivak, Zev J Gartner, Matt Thomson.
      Gene regulatory networks modulate the expression of the genome in response to signals and environmental conditions. Reconstructions of such networks can reveal the control principles cells use to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, dimension-scalable single-cell perturbation integration network (D-SPIN), that infers mechanistically interpretable and generative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of perturbation conditions. The models explain how perturbations modulate cell-state proportions by reconfiguring underlying regulatory interactions. Using large Perturb-seq and drug response datasets, D-SPIN models reveal key regulators of cell fate decisions and the coordination of distant cellular pathways in response to gene knockdowns and drug treatments, elucidate how combinations of immunomodulatory drugs induce combinatorial cell states through additive recruitment of gene expression programs, and simulate shifts in immune cell population structures across unobserved drug dosage combinations. D-SPIN provides a computational framework for revealing principles of cellular information processing and physiological control.
    Keywords:  D-SPIN; Perturb-seq; cell-state transition; drug combination; gene regulatory network; immunomodulatory drug; perturbation response; probabilistic graphical model; regulatory network inference; single-cell RNA sequencing
    DOI:  https://doi.org/10.1016/j.cell.2026.04.028
  3. Nat Genet. 2026 May;58(5): 955
    The emergence of metastasis in colorectal cancer.
    Safia Danovi.
      
    DOI:  https://doi.org/10.1038/s41588-026-02622-x
  4. Nat Rev Gastroenterol Hepatol. 2026 May 13.
    Understanding development and cellular plasticity in the pancreas in health and disease.
    Alejo Torres-Cano, Francesca M Spagnoli.
      Pancreatic development is a dynamic process shaped by coordinated interactions between surrounding non-pancreatic tissues and intrinsic transcription factors, integrated through gene regulatory networks. Advances in single-cell genomics and lineage-tracing technologies have substantially expanded our understanding of pancreatic biology, elucidating the ontogeny of pancreatic cells, their diversity and the intercellular interactions that govern them. In this Review, we examine the emergence of pancreatic cell lineages in the embryo through the lens of cellular plasticity, emphasizing how reciprocal interactions with the microenvironment influence lineage decisions. We also draw parallels between developmental and pathological processes, highlighting the dual role of plasticity: enabling lineage specification during development and supporting regenerative responses upon injury or in disease states. A deeper appreciation of the mechanisms underlying pancreatic cell identity and plasticity holds the potential to inform the development of new therapeutic modalities for pancreatic diseases, such as diabetes and pancreatic cancer.
    DOI:  https://doi.org/10.1038/s41575-026-01211-x
  5. Nat Cancer. 2026 May 12.
    Harnessing myeloid cell plasticity for cancer therapy.
    Emilio Sanseviero, Simon T Barry, Dmitry I Gabrilovich.
      Myeloid cells have a multifaceted role in cancer, promoting tumor progression through angiogenesis, metastasis and immune suppression, as well as stimulating antitumor immunity. Traditionally, this dual functionality was attributed to cell heterogeneity, but it appears that myeloid cell plasticity is a more fundamental cancer trait. In this Review, we discuss two types of plasticity that enable myeloid cells to adjust to the tumor environment: differentiation plasticity, characterized by increased myelopoiesis and shifting differentiation patterns, and functional plasticity, marked by the adoption of pathological activation states. We assess the challenges to myeloid cell targeting in cancer and propose adaptable therapeutic strategies to overcome them.
    DOI:  https://doi.org/10.1038/s43018-026-01159-6
  6. Front Oncol. 2026 ;16 1842639
    The pathological landscape of tumor evolution: a paradigm shift from morphology to functional networks.
    Ying Yuan, Yiran Sun, Ying Ma, Hong Liu, Kangrong Sun.
      Tumor pathology is undergoing a profound transformation, shifting from pure morphological description toward multidimensional functional network analysis. This Mini Review focuses on the tumor microenvironment (TME) as a central concept driving tumor initiation, progression, and therapeutic resistance. We first outline the limitations of traditional pathological classification and elaborate on how the dynamic co-evolution of cellular components (such as cancer-associated fibroblasts (CAFs) and immune cells) along with the extracellular matrix (ECM) constitutes a functional unit. Key controversies are discussed, including the translational hurdles of TME-directed therapies and the challenge of spatiotemporally assessing tumor heterogeneity. We further identify critical research gaps, particularly the mechanistic understanding of the tumor-host interface across scales. Finally, we envision that the integration of artificial intelligence-driven spatial pathology, single-cell multi-omics, and in vivo imaging will usher in a new era of "functional pathology," merging morphology, molecular profiling, and dynamic insights.
    Keywords:  cancer-associated fibroblasts; spatial pathology; therapeutic resistance; tumor heterogeneity; tumor microenvironment
    DOI:  https://doi.org/10.3389/fonc.2026.1842639
  7. Cells. 2026 Apr 22. pii: 743. [Epub ahead of print]15(9):
    Spatial Biology Evolution: Past, Present and Future of Mapping Life in Context.
    Alexander E Kalyuzhny.
      The life sciences are currently undergoing a serious transition from the reductive biochemical analysis of dissociated tissues to non-destructive "spatial forensics". In addition to discovering new molecules, we are moving towards finding out their precise tissue localization and performing in situ interrogation to uncover a biological logic within preserved cellular "neighborhoods". Our perspective is focused on exploring the spatial imperative, including the structural logic and "neighborhood effects" of the tissue microenvironment, which is a prerequisite to understanding cellular function in normal and in pathological conditions. Beginning with a historical foundation of the origins of histochemistry, dating back to the 19th century with pioneer botanist François-Vincent Raspail, we emphasize the technological metamorphosis, transitioning from classical immunohistochemistry to modern multi- and high-plex spatial multi-omics. A critical evaluation of the current operational landscape has been made, addressing the engineering strategies behind multiplexed immunofluorescence (mIF), the challenges of experimental design in spatial transcriptomics, and the functional symbiosis between targeted and unbiased spatial proteomics. There are many layers of genomic and proteomic information we have to consider in order to unravel the mechanisms underlying body function. If we learn how to combine all this information together, we will be able to better understand how cells communicate with each other and what disrupts their communication, leading to cancer and many other pathologies. It is obvious that by implementing spatial biology tools, it becomes possible to develop new medicines and treat diseases in the most efficient ways. At the same time, we realize that there is an urgent need to learn how to put data pieces together so that they blend seamlessly into a meaningful output, further transitioning spatial biology over time into a routine tool to cure for both common and rare diseases and improve our lives and health.
    Keywords:  IHC; cyclic immunofluorescence (CyCIF); fast fluidic exchange (FFeX); histochemistry; multiplexed immunofluorescence (mIF); sequential immunofluorescence (seqIF); spatial biology history; spatial omics; spatial proteomics (SP); spatial transcriptomics (ST); tumor microenvironment (TME)
    DOI:  https://doi.org/10.3390/cells15090743
  8. Nat Immunol. 2026 May 12.
    An inflammasome-driven differentiation program in intestinal stem cells protects against Salmonella infection.
    S Lebon, A Habshush Menachem, N Davidzohn, A Katz, N Wigoda, T Braun, D Yahalomi, R Rotkopf, V Holiar, T Dadosh, S Levin-Zaidman, N Dezorella, I Goliand, M Kupervaser, S Leebhoff, N Blumberger, D Hoffman, M Grunewald, Y Levin, Y Haberman, M Hofree, M Biton.
      Intestinal stem cells (ISCs) are essential for sustaining epithelial renewal and barrier integrity, yet their role in orchestrating defense against enteric pathogens remains unclear. Here we identify a stem cell-intrinsic immune mechanism whereby Lgr5+ ISCs detect intracellular Salmonella enterica and activate an inflammasome-dependent differentiation program. Using fluorescent-labeled S. enterica, single-cell transcriptomics, fate mapping, organoid models, and genetic perturbations, we show that invaded ISCs undergo rapid reprogramming toward antimicrobial peptide-enriched Paneth cells via apoptosis-associated Speck-like protein containing a CARD (ASC, encoded by Pycard)-mediated inflammasome signaling. This fate switch enhances epithelial antimicrobial capacity and restricts pathogen persistence in the crypt. The response is Salmonella-specific and conserved in human intestinal organoids. Moreover, the invasion-associated transcriptional signature is enriched in ISCs from patients with Crohn's disease. Our findings reveal that ISCs act as active sensors of bacterial invasion and initiate epithelial remodeling through inflammasome signaling, highlighting stem cell plasticity as a frontline innate immune strategy.
    DOI:  https://doi.org/10.1038/s41590-026-02514-6
  9. Cell. 2026 May 08. pii: S0092-8674(26)00458-7. [Epub ahead of print]
    AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology.
    Eldad D Shulman, Emma M Campagnolo, Roshan Lodha, Youngmin Chung, Amos Stemmer, Thomas Cantore, Beibei Ru, Tian-Gen Chang, Sumona Biswas, Saugato Rahman Dhruba, Sumeet Patiyal, Sushant Patkar, Andrew Wang, Ranjan K Barman, Chuhan Wang, Rohit Paul, Sarath Chandra Kalisetty, Tom Hu, MacLean P Nasrallah, Ellis Patrick, Jean Yang, Yuan Yuan, Karine Sargsyan, Amy Plotkin, Padma Sheila Rajagopal, Stephen-John Sammut, Stanley Lipkowitz, Peng Jiang, Carlos Caldas, Simon R V Knott, Kenneth Aldape, Joo Sang Lee, Danh-Tai Hoang, Eytan Ruppin.
      Spatial transcriptomics (ST) assays are transforming our understanding of tumor heterogeneity, but their high cost limits their application in large-scale biomarker discovery. Here, we present "Path2Space," a deep-learning model that predicts spatial gene expression directly from histopathology slides. Trained on extensive breast cancer ST data, Path2Space robustly predicts the spatial expression of thousands of genes, outperforming 21 established methods. Charting the tumor microenvironment (TME) of 976 breast cancer TCGA (The Cancer Genome Atlas) tumors, it accurately infers cell-type abundances and identifies three spatially defined breast cancer subgroups with distinct survival outcomes. Notably, the derived low-cost spatial TME landscapes enable more accurate predictions of patient response to chemotherapy and trastuzumab compared with costly conventional bulk-sequencing-based biomarkers. Path2Space thus offers a scalable, fast, and cost-effective alternative to molecular assays. It opens avenues for large cohort treatment biomarker discovery and translationally relevant insights into tumor biology, with potential applicability across many cancer indications.
    Keywords:  HER2; artificial intelligence; breast cancer; deep learning; digital pathology; histopathology; spatial biomarkers; spatial transcriptomics; treatment response prediction; tumor microenvironment
    DOI:  https://doi.org/10.1016/j.cell.2026.04.023
  10. Mol Syst Biol. 2026 May 15.
    Decoding and steering spatially resolved cellular dynamics.
    Jiahao Wu, Suoqin Jin.
      
    DOI:  https://doi.org/10.1038/s44320-026-00216-7
  11. Nat Rev Mol Cell Biol. 2026 May 11.
    New developments and applications of human organoids.
    Amanda Andersson-Rolf, Hans Clevers.
      Organoid technology offers unique opportunities for studying human biology and disease in vitro. Organoids are self-organizing 3D structures, derived from pluripotent or tissue-resident stem cells that recapitulate key aspects of primary tissues. Compared with classical cell lines, organoids provide distinct advantages. They can be derived from both healthy tissues and diseased tissues, enabling the investigation of disease mechanisms and the development of personalized therapies, and they better recapitulate the cellular heterogeneity of the native tissue, allowing for better modelling of human (patho)physiology. Although current organoids have provided valuable insights, these insights are inherently reductionist and do not fully capture the complexity of human tissues. The research field is, therefore, moving towards next-generation models that more accurately represent the intricate cellular interactions, tissue architecture and microenvironmental cues that underlie human biology and disease. In this Review, we outline the limitations and challenges of current organoid systems, highlight recent advances aimed at increasing their complexity, and discuss innovations that support their translation into clinical applications. The focus is on human tissue stem cell-derived organoids, with comparisons to pluripotent stem cell-derived organoids where relevant. We conclude by identifying key factors and remaining challenges for developing the next generation of organoids.
    DOI:  https://doi.org/10.1038/s41580-026-00974-0
  12. Annu Rev Genomics Hum Genet. 2026 May 11.
    Synthetic Regulatory Genomics.
    Matthew T Maurano.
      The genomics era has yielded high-quality genome assemblies, comprehensive atlases of biochemical signatures of gene regulation, and genetic associations for thousands of common human diseases and traits. These dramatic advances in observational approaches have not been matched by perturbational genetic tools to facilitate direct and systematic hypothesis testing. Enabled by advances in DNA synthesis and assembly, genome engineering tools, and genomic readouts, synthetic regulatory genomics now promises access to a new scale of genomic manipulation to study the function of cohesive genomic units. Synthetic regulatory genomics is distinguished by the breadth of the genetic manipulations and their divergence from the reference sequence. These new tools enable an expanded focus to encompass sufficiency in addition to necessity and to enable a new era of perturbation analysis.
    DOI:  https://doi.org/10.1146/annurev-genom-120324-123022
  13. Trends Biotechnol. 2026 May 14. pii: S0167-7799(26)00148-4. [Epub ahead of print]
    Automated stem cell-derived organoid platforms for disease modeling.
    Xingrui Mou, Nathan Dale, Kiran Ramnarine, Filippo Cipriani.
      Complex diseases arise from genetic, environmental, and lifestyle factors, the combination of which is difficult to model. Conventional animal and 2D cell culture models have limitations in scalability, reproducibility, or human relevance. Human-induced pluripotent stem cells (iPSCs) can be differentiated into 3D organoids that better mimic human biology. However, organoid protocols can be lengthy, variable, and labor-intensive, limiting high-throughput applications. Suspension bioreactors and multilineage differentiation have improved yield and function, but challenges remain in tissue maturity, vascularization, and consistency. Automated high-throughput liquid handling systems are emerging as a solution, enabling large-scale, reproducible production. Here, we discuss how combining iPSC-derived organoids with automation is poised to transform disease modeling and drug development.
    Keywords:  automation and high-throughput systems; complex disease modeling; iPSC-derived organoids
    DOI:  https://doi.org/10.1016/j.tibtech.2026.04.013
  14. Nat Methods. 2026 May;23(5): 865-866
    What's your hypothesis?
      
    DOI:  https://doi.org/10.1038/s41592-026-03104-y
  15. Front Oncol. 2026 ;16 1771061
    Drug-tolerant persister cells in cancer: a scoping review of definitions, models, and molecular mechanisms.
    Jose S Lopez-Gonzalez, Mario Perez-Medina, Miriam Galicia-Velasco, Dolores Aguilar-Cazares.
      Drug-tolerant persistent (DTP) cells have emerged as a reversible, slow-cycling survival state that enables early therapeutic tolerance and underlies the development of stable resistance in all types of cancer. To comprehensively characterize this phenomenon, we conducted a PRISMA-ScR-guided exploratory review across four major databases (PubMed, Scopus, Web of Science, Dimensions), identifying 343 eligible records spanning 2010-2025. In all experimental systems, including 2D cell lines, spheroids, organoids, xenografts, residual disease models, and clinical samples, DTP cells consistently showed survival under high drug concentrations or prolonged exposure, depending on non-genetic adaptive programs, and recovery of proliferative potential and drug sensitivity after treatment cessation. Analysis of the molecular mechanisms revealed a convergence of reversible pathways involving apoptosis escape, quiescence, chromatin remodeling, phenotypic plasticity, metabolic rewiring, downstream survival signaling, and transient programs, such as those of stem cells. These findings support a model in which DTP cells represent an early and plastic node within a broader continuum of resistance, capable of progressing toward genetically fixed resistance through stress-induced mutagenesis. Methodological heterogeneity among studies did not diminish the reproducibility of DTP cells fundamental characteristics but underscored the need for standardized experimental criteria. Notably, the integrated evidence identifies therapeutically exploitable vulnerabilities-epigenetic, metabolic, signaling-based, and plasticity-targeted-that have shown promise in reducing DTP persistence and delaying the development of resistance. This review consolidates current knowledge and provides a mechanistic framework to guide therapeutic strategies that aim to intercept cancer resistance in the earliest and most reversible stages of its development.
    Keywords:  cancer cell plasticity; drug-tolerant persister (DTP) cells; epigenetic reprogramming; metabolic rewiring; non-genetic drug resistance; reversible drug tolerance; therapy-induced adaptation; tumor relapse
    DOI:  https://doi.org/10.3389/fonc.2026.1771061
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