bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2025–08–03
thirteen papers selected by
Xiao Qin, University of Oxford
  1. Learning the language of biological interactions.
  2. Next-Generation Barcoding for Single-Cell Omics.
  3. Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships.
  4. From 2D to 3D and beyond: the evolution and impact of in vitro tumor models in cancer research.
  5. How cancer arises: Genetics releases, plasticity creates, genetics stabilizes.
  6. Plasticity and Functional Heterogeneity of Cancer-Associated Fibroblasts.
  7. The Cancer Genome in the Long Reads Era.
  8. Methods and applications of in vivo CRISPR screening.
  9. Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories.
  10. Coordination of cell subsets in health and cancer.
  11. From genes to patterns: five key dynamical systems concepts to decode developmental regulatory mechanisms.
  12. Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases.
  13. CRISPR-GPT for agentic automation of gene-editing experiments.
  1. Nat Methods. 2025 Jul 31.
    Learning the language of biological interactions.
      
    DOI:  https://doi.org/10.1038/s41592-025-02725-z
  2. Anal Chem. 2025 Jul 29.
    Next-Generation Barcoding for Single-Cell Omics.
    Ting Li, Zhenglong Gu, Guoqiang Zhou.
      The ability to uniquely label and track individual cells at scale has become foundational to single-cell omics. Conventional barcoding strategies, ranging from plate-based combinatorial indexing to droplet microfluidics-based indexing, have enabled the rise of high-throughput single-cell profiling. However, these approaches each face trade-offs in throughput, cost, compatibility with complex biochemical workflows, or accessibility to nonspecialist laboratories. This perspective surveys the principles, benefits, and limitations of current barcoding methods and introduces emerging enzymatic and computational methods that may redefine how we uniquely index the cellular content, opening the door to simpler, more scalable, and more accessible single-cell analysis pipelines.
    DOI:  https://doi.org/10.1021/acs.analchem.5c03587
  3. Nat Biomed Eng. 2025 Jul 28.
    Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships.
    Marco Breinig, Artem Lomakin, Elyas Heidari, Michael Ritter, Gleb Rukhovich, Lio Böse, Luise Butthof, Lena Wendler-Link, Hendrik Wiethoff, Tanja Poth, Felix Sahm, Peter Schirmacher, Oliver Stegle, Moritz Gerstung, Darjus F Tschaharganeh.
      Advancing spatially resolved in vivo functional genomes will link complex genetic alterations prevalent in cancer to critical disease phenotypes within tumour ecosystems. To this end, we developed PERTURB-CAST, a method to streamline the identification of perturbations at the tissue level. By adapting RNA-templated ligation probes, PERTURB-CAST leverages commercial 10X Visium spatial transcriptomics to integrate perturbation mapping with transcriptome-wide phenotyping in the same tissue section using a widely available single-readout platform. In addition, we present CHOCOLAT-G2P, a scalable framework designed to study higher-order combinatorial perturbations that mimic tumour heterogeneity. We apply it to investigate tissue-level phenotypic effects of combinatorial perturbations that induce autochthonous mosaic liver tumours.
    DOI:  https://doi.org/10.1038/s41551-025-01437-1
  4. Nat Methods. 2025 Jul 25.
    From 2D to 3D and beyond: the evolution and impact of in vitro tumor models in cancer research.
    Gat Rauner, Piyush B Gupta, Charlotte Kuperwasser.
      In vitro tumor models are essential tools for cancer research, offering key insights into not only tumor biology but also therapeutic responses. The transition from traditional two-dimensional to three-dimensional organoid systems marks a paradigm shift in cancer modeling. Although two-dimensional models have been instrumental in elucidating fundamental molecular and genetic mechanisms, they fail to accurately replicate the intricate three-dimensional architecture and dynamic microenvironment characteristic of human tumors. Here we outline how advanced organoid technologies now enable more faithful recapitulation of tumor heterogeneity that better mimic native tissue mechanics and biochemistry. We discuss emerging methods, including air-liquid interface cultures, microfluidic tumor-on-a-chip devices and high-content imaging integrated with machine learning, which collectively address longstanding challenges such as matrix variability and the limited incorporation of immune and vascular elements. These innovations promise to enhance reproducibility and scalability while providing unprecedented insights into tumor biology, cancer progression and therapeutic strategies.
    DOI:  https://doi.org/10.1038/s41592-025-02769-1
  5. Proc Natl Acad Sci U S A. 2025 Aug 05. 122(31): e2505377122
    How cancer arises: Genetics releases, plasticity creates, genetics stabilizes.
    Steven A Frank.
      Cancer is the origin of a novel tissue that attracts resources, spreads beyond boundaries, avoids normal controls, and escapes immunity. How does a novel tissue arise? The puzzle is that two seemingly different processes appear to be the primary driving force. On the one hand, overwhelming evidence links (epi)genetic driver mutations to the origin and progression of tumors. Common oncogenic mutations such as KRAS accelerate cell division, and common knockouts of tumor suppressors such as TP53 abrogate cell death or checks on cell division. On the other hand, cancerous tissues create complex traits that require intricate changes in cells and multiple interactions between different cell types. Such novelty often arises by hijacking the developmental plasticity that normally creates the diverse cells and tissues of our bodies from a single original zygotic cell. How can we reconcile the simple genetic changes in carcinogenesis with the complex developmental plasticity that creates novel tissues? This perspective advocates a new model. (Epi)genetic mutations release developmental plasticity. That developmental plasticity creates novel cellular interactions and complex tissues. Initially, novel traits created by developmental plasticity may not be stably heritable, thus subsequent (epi)genetic changes must stabilize the phenotypic novelty. Recent studies show how classic oncogenic and tumor suppressor driver mutations, such as KRAS and TP53, may primarily act in early carcinogenesis as broad releasers of developmental plasticity rather than as stimulators of cell division or knockout of limitations on cellular clonal expansion. In the new model, genetics releases, plasticity creates, and genetics stabilizes.
    Keywords:  cancer evolution; cell state; developmental plasticity; single-cell technology
    DOI:  https://doi.org/10.1073/pnas.2505377122
  6. Cancer Res. 2025 Jul 29.
    Plasticity and Functional Heterogeneity of Cancer-Associated Fibroblasts.
    Jacqueline M Flynn, Nikita Thadani, Erin E Gallagher, Isabella Azzaro, Cameron M Bodnar, Colin P McCarty, Gabriele Romano, Marie R Webster, Claudia Capparelli.
      Tumor heterogeneity and plasticity enable adaptation to metastatic microenvironments and resistance to therapies. Recent progress in single-cell analyses has permitted detailed characterization of the complexity and diversity of the different tumor components in multiple tumor types. Cancer-associated fibroblasts (CAFs) are a central component of the tumor microenvironment (TME) and play critical roles in cancer progression and therapeutic response. The identification of different CAF subtypes and elucidation of their functional plasticity is crucial to identify novel therapeutic approaches to target pro-tumorigenic CAFs and harness tumor suppressive CAFs to enhance the efficacy of cancer treatments. In this review, we discuss how intrinsic and extrinsic factors and the extensive crosstalk between cancer cells and the TME promote CAF heterogeneity and their contributions to cancer progression and therapeutic resistance. Understanding the roles of CAF plasticity and their intercellular interactions may drive the development of effective treatment strategies to improve patient prognosis.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-24-3037
  7. Methods Mol Biol. 2025 ;2955 317-359
    The Cancer Genome in the Long Reads Era.
    Luca Ermini.
      Cancer genomics, driven by advancements in sequencing technologies, is rapidly transforming our understanding of tumor biology. The advent of high-throughput short-read sequencing technologies has enabled a paradigm shift in cancer genomics, providing unprecedented resolution of mutations that drive tumorigenesis. Initially, short-read sequencing dominated the field, revealing the repertoire of mutational signatures and the complexities of tumor heterogeneity. However, the inherent limitations of short-read sequencing, particularly in resolving complex structural variations and repetitive regions, underscored the need for alternative approaches. This chapter delves into the transformative potential of long-read sequencing in overcoming these limitations, ushering in a new era for cancer genomics. By spanning larger genomic regions, long-reads offer a more comprehensive view of structural variations, phasing information, and complex rearrangements, crucial for deciphering the evolutionary trajectories of cancer. Furthermore, the chapter examines the dynamics of somatic evolution, comparing Darwinian and non-Darwinian frameworks, and discusses how these models inform our understanding of cancer progression which have implications for cancer therapies. Finally, a bioinformatics workflow, leveraging long-read sequencing data, is outlined to enable the identification of cancer-associated mutations. Integrating cutting-edge sequencing technologies with advanced computational approaches is essential for accelerating oncological research and improving cancer therapies. Long-read sequencing is poised to unveil the complex genomic architecture of cancers, potentially leading to more precise and effective treatments.
    Keywords:  Bioinformatics; Cancer evolution; Cancer genome; Genomics; Next and third generation sequencing
    DOI:  https://doi.org/10.1007/978-1-0716-4702-8_15
  8. Nat Rev Genet. 2025 Jul 29.
    Methods and applications of in vivo CRISPR screening.
    Antonio J Santinha, Alessio Strano, Randall J Platt.
      A fundamental goal in genetics is to understand the connection between genotype and phenotype in health and disease. Genetic screens in which dozens to thousands of genetic elements are perturbed in a pooled fashion offer the opportunity to generate large-scale, information-rich and unbiased genotype-phenotype maps. Although typically applied in reductionist in vitro settings, methods enabling pooled CRISPR-Cas perturbation screening in vivo are gaining attention as they have the potential to accelerate the discovery and annotation of gene function across cells, tissues, developmental stages, disease states and species. In this Review, we discuss essential criteria for understanding, designing and implementing in vivo screening experiments, with a focus on pooled CRISPR-based screens in mice. We also highlight how the resulting datasets, combined with advances in multi-omics and artificial intelligence, will accelerate progress and enable fundamental discoveries across basic and translational sciences.
    DOI:  https://doi.org/10.1038/s41576-025-00873-8
  9. Cell. 2025 Jul 25. pii: S0092-8674(25)00750-0. [Epub ahead of print]
    Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories.
    Jeanette A I Johnson, Daniel R Bergman, Heber L Rocha, David L Zhou, Eric Cramer, Ian C Mclean, Yoseph W Dance, Max Booth, Zachary Nicholas, Tamara Lopez-Vidal, Atul Deshpande, Randy Heiland, Elmar Bucher, Fatemeh Shojaeian, Matthew Dunworth, André Forjaz, Michael Getz, Inês Godet, Furkan Kurtoglu, Melissa Lyman, John Metzcar, Jacob T Mitchell, Andrew Raddatz, Jacobo Solorzano, Aneequa Sundus, Yafei Wang, David G DeNardo, Andrew J Ewald, Daniele M Gilkes, Luciane T Kagohara, Ashley L Kiemen, Elizabeth D Thompson, Denis Wirtz, Laura D Wood, Pei-Hsun Wu, Neeha Zaidi, Lei Zheng, Jacquelyn W Zimmerman, Jude M Phillip, Elizabeth M Jaffee, Joe W Gray, Lisa M Coussens, Young Hwan Chang, Laura M Heiser, Genevieve L Stein-O'Brien, Elana J Fertig, Paul Macklin.
      Cells interact as dynamically evolving ecosystems. While recent single-cell and spatial multi-omics technologies quantify individual cell characteristics, predicting their evolution requires mathematical modeling. We propose a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This enables systematic integration of biological knowledge and multi-omics data to generate in silico models, enabling virtual "thought experiments" that test and expand our understanding of multicellular systems and generate new testable hypotheses. This paper motivates and describes the grammar, offers a reference implementation, and demonstrates its use in developing both de novo mechanistic models and those informed by multi-omics data. We show its potential through examples in cancer and its broader applicability in simulating brain development. This approach bridges biological, clinical, and systems biology research for mathematical modeling at scale, allowing the community to predict emergent multicellular behavior.
    Keywords:  agent-based modeling; cancer biology; cell behavior hypothesis grammar; cell behaviors; cell interactions; immunology; immunotherapy; mathematical biology; mathematical modeling; modeling language; multi-omics; multicellular systems; multicellular systems biology; physics of multicellular biology; simulation; spatial transcriptomics; tissue dynamics
    DOI:  https://doi.org/10.1016/j.cell.2025.06.048
  10. Nat Cancer. 2025 Jul 30.
    Coordination of cell subsets in health and cancer.
    Tiffanie Chouleur.
      
    DOI:  https://doi.org/10.1038/s43018-025-01028-8
  11. Development. 2025 Jul 15. pii: dev204617. [Epub ahead of print]152(14):
    From genes to patterns: five key dynamical systems concepts to decode developmental regulatory mechanisms.
    Usha Kadiyala, David Sprinzak, Nicholas A M Monk, Shannon E Taylor, Berta Verd, Katharina F Sonnen, Lauren Moon, Adrienne H K Roeder, Ruben Perez-Carrasco, Pau Formosa-Jordan.
      Developmental biology seeks to unravel the intricate regulatory mechanisms orchestrating the transformation of a single cell into a complex, multicellular organism. Dynamical systems theory provides a powerful quantitative, visual and intuitive framework for understanding this complexity. This Primer examines five core dynamical systems theory concepts and their applications to pattern formation during development: (1) analysis of phase portraits, (2) bistable switches, (3) stochasticity, (4) response to time-dependent signals, and (5) oscillations. We explore how these concepts shed light onto cell fate decision making and provide insights into the dynamic nature of developmental processes driven by signals and gradients, as well as the role of noise in shaping developmental outcomes. Selected examples highlight how integrating dynamical systems with experimental approaches has significantly advanced our understanding of the regulatory logic underlying development across scales, from molecular networks to tissue-level dynamics.
    Keywords:  Developmental dynamics; Dynamical systems; Modelling; Signalling; Waddington landscape
    DOI:  https://doi.org/10.1242/dev.204617
  12. Nat Biotechnol. 2025 Jul 25.
    Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases.
    Sai Zhang, Hantao Shu, Jingtian Zhou, Jasper Rubin-Sigler, Xiaoyu Yang, Yuxi Liu, Johnathan Cooper-Knock, Emma Monte, Chenchen Zhu, Sharon Tu, Han Li, Mingming Tong, Joseph R Ecker, Justin K Ichida, Yin Shen, Jianyang Zeng, Philip S Tsao, Michael P Snyder.
      Polygenic risk scores (PRSs) predict an individual's genetic risk for complex diseases, yet their utility in elucidating disease biology remains limited. We introduce scPRS, a graph neural network-based framework that computes single-cell-resolved PRSs by integrating reference single-cell chromatin accessibility profiles. scPRS outperforms traditional PRS approaches in genetic risk prediction, as demonstrated across multiple diseases including type 2 diabetes, hypertrophic cardiomyopathy, Alzheimer disease and severe COVID-19. Beyond risk prediction, scPRS prioritizes disease-critical cells and, when combined with a layered multiomic analysis, links risk variants to gene regulation in a cell-type-specific manner. Applied to these diseases, scPRS fine-maps causal cell types and cell-type-specific variants and genes, demonstrating its ability to bridge genetic risk with cell-specific biology. scPRS provides a unified framework for genetic risk prediction and mechanistic dissection of complex diseases, laying a methodological foundation for single-cell genetics.
    DOI:  https://doi.org/10.1038/s41587-025-02725-6
  13. Nat Biomed Eng. 2025 Jul 30.
    CRISPR-GPT for agentic automation of gene-editing experiments.
    Yuanhao Qu, Kaixuan Huang, Ming Yin, Kanghong Zhan, Dyllan Liu, Di Yin, Henry C Cousins, William A Johnson, Xiaotong Wang, Mihir Shah, Russ B Altman, Denny Zhou, Mengdi Wang, Le Cong.
      Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, large language models (LLMs) often lack domain-specific knowledge and struggle to accurately solve biological design problems. We present CRISPR-GPT, an LLM agent system to automate and enhance CRISPR-based gene-editing design and data analysis. CRISPR-GPT leverages the reasoning capabilities of LLMs for complex task decomposition, decision-making and interactive human-artificial intelligence (AI) collaboration. This system incorporates domain expertise, retrieval techniques, external tools and a specialized LLM fine tuned with open-forum discussions among scientists. CRISPR-GPT assists users in selecting CRISPR systems, experiment planning, designing guide RNAs, choosing delivery methods, drafting protocols, designing assays and analysing data. We showcase the potential of CRISPR-GPT by knocking out four genes with CRISPR-Cas12a in a human lung adenocarcinoma cell line and epigenetically activating two genes using CRISPR-dCas9 in a human melanoma cell line. CRISPR-GPT enables fully AI-guided gene-editing experiment design and analysis across different modalities, validating its effectiveness as an AI co-pilot in genome engineering.
    DOI:  https://doi.org/10.1038/s41551-025-01463-z
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