bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2026–09–20
fourteen papers selected by
Xiao Qin, University of Oxford
  1. Quantitative imaging of dynamic processes in intestinal organoids.
  2. World models for biomedicine.
  3. Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.
  4. Initial Screening for Colorectal Cancer.
  5. Tumour cells shape their environment to protect them from the immune system.
  6. Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.
  7. Strategies for time-resolved cryogenic electrontomography of transient cellular processes.
  8. What-if effects: A tale of entangled covariates.
  9. A practical guide to studying genome function using single-molecule genomics.
  10. Reimagining research papers as interactive and reliable AI agents.
  11. A high-density CRISPR activation platform for mapping cancer dependencies and resistance pathways ex vivo and in vivo.
  12. Colorectal Cancer Risk among Patients with Serrated Polyps.
  13. The power of theory in the life sciences.
  14. Changing minds: How AI is transforming the life sciences.
  1. Nat Protoc. 2026 Sep 15.
    Quantitative imaging of dynamic processes in intestinal organoids.
    Willem Kasper Spoelstra, Xuan Zheng, Rutger N U Kok, Guizela Huelsz-Prince, Max Betjes, Pascal Ender, Yvonne Goos, Kim A Kuentzelmann, Heidi E Klumpe, Daniel Krueger, Daisong Wang, Hans Clevers, Sander J Tans, Jeroen S van Zon.
      Organoids are powerful models for studying tissue dynamics across multiple cellular generations and offer key insights into organ homeostasis, cellular differentiation and disease. Live imaging of organoids is an essential tool for understanding dynamic processes on the cell level, while still in the tissue context. It allows for simultaneous quantification of gene expression and reconstruction of lineage trees, while tracking cells over space and time. Here, we present protocols for long-term imaging and quantitative analysis of intestinal organoids. Our workflow consists of sample preparation, experimental manipulations (such as drug treatments, laser ablations and fluorescence recovery after photobleaching), long-term live imaging (120 h), in situ fixation and permeabilization, multiplexed antibody staining and single-cell tracking with lineage reconstruction. The workflow is broadly applicable to studying dynamic cellular processes in both 2D and 3D organoids, at timescales from minutes to days. We outline strategies for optimizing organoid health, imaging conditions and experimental interventions to ensure efficient downstream analysis. In addition, we describe how this workflow enables real-time quantification of gene expression using fluorescent reporters and inferring cell type changes by combining multiplexed antibody staining with cell-tracking data. The skills required for following these protocols are organoid culturing and standard live-cell confocal microscopy, and the full experimental protocol takes ~2 weeks. Together, these protocols provide a comprehensive approach for studying cellular behavior, lineage trees, expression dynamics and cell-cell interactions in organoids.
    DOI:  https://doi.org/10.1038/s41596-026-01432-z
  2. Cell. 2026 Sep 17. pii: S0092-8674(26)01005-6. [Epub ahead of print]189(19): 5845-5869
    World models for biomedicine.
    Ayush Noori, Nic Fishman, Ada Fang, Lukas Fesser, Marinka Zitnik.
      Biological systems respond dynamically to changes in their environment, and predicting these responses before complex interventions are applied could change how computational models are used in biology and medicine. In artificial intelligence, world models represent the state of a system and simulate how it evolves under alternative actions. We envision extending this paradigm to biomedicine, with world models that span molecular, cellular, tissue, and clinical scales and simulate biological trajectories under alternative interventions. Building such models will require methods that combine observational data with interventional and longitudinal measurements across modalities. Biomedical world models could open new modes of inquiry by predicting how biological systems respond to novel perturbations, searching for interventions that drive them toward desired states, and planning experimental campaigns over extended time horizons.
    Keywords:  artificial intelligence; biological dynamics; closed-loop discovery; counterfactual prediction; experimental design; multimodal learning; perturbation modeling; simulation; world models
    DOI:  https://doi.org/10.1016/j.cell.2026.08.032
  3. Genome Biol. 2026 Sep 16. pii: 291. [Epub ahead of print]27(1):
    Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.
    Yuhan Sun, Olivia J Cheng, Anjun Ma, Samia Shabnaz, Xuelian Huang, Jing Zhao, Aik Choon Tan, Qin Ma.
      Multi-omics technologies, coupled with AI technologies, have the potential to enable the systematic investigation of complex cancer microbiome biology by uncovering informative patterns and associations across complementary datasets. Here, we review existing and emerging cancer microbiome data, discuss the development, interpretation, and validation of AI models as key considerations for their integration and analysis, and provide practical suggestions for improving the reliability and biological relevance of AI-driven discoveries. We further highlight the opportunities and challenges of translating these discoveries into clinical practice, emphasizing strategies to bridge the gap between research-oriented models and clinical implementations.
    DOI:  https://doi.org/10.1186/s13059-026-04284-8
  4. N Engl J Med. 2026 Sep 17. 395(11): 1131-1133
    Initial Screening for Colorectal Cancer.
    Alejandro Campos, Aasma Shaukat, Douglas J Robertson.
      
    DOI:  https://doi.org/10.1056/NEJMclde2600695
  5. Nature. 2026 Sep 16.
    Tumour cells shape their environment to protect them from the immune system.
      
    Keywords:  Cancer; Immunology
    DOI:  https://doi.org/10.1038/d41586-026-02703-1
  6. Cell. 2026 Sep 17. pii: S0092-8674(26)01008-1. [Epub ahead of print]189(19): 5945-5961.e8
    Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.
    Jesse Zhang, Airol A Ubas, Valentine Svensson, Nicole Thomas, Vishvak Subramanyam, Christopher Carpenter, Aidan Winters, Richard de Borja, Noam Teyssier, Neha Thakar, Umair Khan, Pooja V Akella, Harper J Green, John D Thompson, Vuong Tran, Joseph Pangallo, Efthymia Papalexi, Ajay Sapre, Hoai Nguyen, Oliver Sanderson, Maria Nigos, Olivia Kaplan, Sarah Schroeder, Bryan Hariadi, Simone Marrujo, Crina Curca, Alec Salvino, Guillermo Gallareta Olivares, Ryan Koehler, Alexander Rosenberg, Charles Roco, Chiara Ricci-Tam, Matthew G Jones, Alexander Dobin, Brian S Plosky, Danielle E Lyons, Daniele Merico, Nima Alidoust, Hani Goodarzi, Johnny Yu.
      We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.
    DOI:  https://doi.org/10.1016/j.cell.2026.08.035
  7. Curr Opin Struct Biol. 2026 Sep 16. pii: S0959-440X(26)00167-3. [Epub ahead of print]101 103385
    Strategies for time-resolved cryogenic electrontomography of transient cellular processes.
    Arjen J Jakobi.
      Cryogenic electron tomography provides molecular-scale information from frozen-hydrated cells and tissues, with each tomogram representing a selected cellular volume at the time of vitrification. Time-resolved cryo-ET aims to build structural descriptions of cellular processes by relating vitrified states to defined perturbations or stages of transient cellular events. This requires the relevant state to be generated reproducibly by an experimental perturbation or identified reliably before tomography and sampled in sufficient numbers for comparison. Here, we give an overview of four experimental routes by which temporal information is introduced into cellular cryo-ET experiments, discuss the conditions under which each can provide reliable structural and temporal data, and identify interpretive challenges that are common across all approaches. Time-resolved cryo-ET is a developing area with considerable potential to provide mechanistic insights into transient cellular processes that remain difficult to access by other structural methods.
    DOI:  https://doi.org/10.1016/j.sbi.2026.103385
  8. Patterns (N Y). 2026 Sep 11. 7(9): 101616
    What-if effects: A tale of entangled covariates.
    Zhaowei Yu, Yong Zhang.
      Single-cell epigenomic studies promise to reveal how disease states, donor traits, environmental exposures, and technical factors shape cellular regulatory programs, yet these variables are often highly collinear and difficult to disentangle, particularly in human cohorts. The recent study by Møller and Madsen in Patterns introduces DeepDive, a probabilistic deep-learning framework that disentangles known covariate effects and residual variations in single-nucleus ATAC-seq data, enabling counterfactual "what-if" analyses of chromatin accessibility.
    DOI:  https://doi.org/10.1016/j.patter.2026.101616
  9. Nat Rev Mol Cell Biol. 2026 Sep 14.
    A practical guide to studying genome function using single-molecule genomics.
    Arnaud R Krebs, Nicolas Altemose, L Stirling Churchman, William J Greenleaf, Vijay Ramani, Michael B Stadler, Andrew B Stergachis.
      Single-molecule genomics (SMG) has transformed our ability to study the mechanisms that regulate the genome by enabling profiling of the activity of regulatory factors on individual DNA molecules genome-wide. SMG is able to quantify molecular heterogeneity and the co-occurrence of regulatory events, including epigenetic modifications, transcription factor binding and chromatin organization on single DNA molecules. SMG reveals dynamics of chromatin interactions that cannot be measured by conventional genomics assays. Therefore, SMG offers a unique platform to study how regulatory events combine to control genome activity. In this Expert Recommendation article, we provide a practical guide for adopting SMG and outline best practices.
    DOI:  https://doi.org/10.1038/s41580-026-01017-4
  10. Nature. 2026 Sep 16.
    Reimagining research papers as interactive and reliable AI agents.
    Jiacheng Miao, Joe R Davis, Yaohui Zhang, Jonathan K Pritchard, James Zou.
      Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper's code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent's effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.
    DOI:  https://doi.org/10.1038/s41586-026-11044-y
  11. Sci Adv. 2026 Sep 18. 12(38): eaec0722
    A high-density CRISPR activation platform for mapping cancer dependencies and resistance pathways ex vivo and in vivo.
    Sarah T Diepstraten, Yexuan Deng, Margaret A Potts, Amy Heidersbach, Christina König, Kristel M Dorighi, Lin Tai, Andrew J Kueh, Lauren Whelan, Catherine Chang, Felix Brown, Gemma L Kelly, Jean-Philippe Fortin, Benjamin Haley, John E La Marca, Marco J Herold.
      CRISPR activation (CRISPRa) enables precise up-regulation of gene expression for ex vivo and in vivo applications. However, a lack of scalable, high-coverage tools has limited comprehensive genetic screening in murine models. Here, we introduce Partita, a next-generation mouse whole-genome CRISPRa sgRNA platform, designed for unparalleled efficiency in gene activation studies. Partita uses a high-density targeting strategy, deploying 10 sgRNAs per transcriptional start site, structured into five gene class-specific sublibraries to maximize transcriptional induction. To demonstrate the capabilities of Partita, we performed a series of large-scale screens: an in vitro enrichment/depletion screen, whole-genome CRISPRa screens in a double-hit lymphoma model to uncover resistance factors to proapoptotic drugs (venetoclax, nutlin-3a, and etoposide) and an in vivo screen to identify accelerators of MYC-driven lymphomagenesis. Each experiment revealed both expected and unexpected regulators, with high validation rates. By enabling robust gain-of-function screening, Partita unlocks new avenues for functional genomics and expands the toolkit for discovering key drivers of biological processes across diverse research fields.
    DOI:  https://doi.org/10.1126/sciadv.aec0722
  12. N Engl J Med. 2026 Sep 17. 395(11): 1134-1135
    Colorectal Cancer Risk among Patients with Serrated Polyps.
    EPoS Study Group
      
    DOI:  https://doi.org/10.1056/NEJMc2605026
  13. Elife. 2026 Sep 18. pii: e112987. [Epub ahead of print]15
    The power of theory in the life sciences.
    Jacob L Fine, Finn Creeggan, Joshua Gertsvolf, Purav Gupta, Adam M R Groh.
      The rapid growth of high-throughput biology and genomics over the past two decades has helped catalogue many different aspects of gene function in diverse cell types and conditions. More recently, advances in artificial intelligence and deep learning have shown tremendous promise in making accurate predictions of functional genomics measurements. These advances make it tempting to equate experimental cataloguing and accurate prediction with the growth of our theoretical understanding of biological processes - an equivalence we believe is ultimately misleading.
    Keywords:  cell biology; computational biology; explanatory unification; history of biology; machine learning; none; philosophy of science; scientific practice; single-cell genomics; systems biology; theory
    DOI:  https://doi.org/10.7554/eLife.112987
  14. Cell. 2026 Sep 17. pii: S0092-8674(26)00946-3. [Epub ahead of print]189(19): 5909-5913
    Changing minds: How AI is transforming the life sciences.
    Daphne Koller, Peilong Lu, Laura Cantini, Yuri Pritykin, Jean Yee Hwa Yang, Kanaka Rajan, Martin Steinegger, Mihaela van der Schaar.
      Artificial intelligence (AI) is changing the way biomedical research is conducted by making it possible to integrate diverse types of biological data and tackle questions that were previously difficult to address. At the same time, its growing use also raises important questions about data quality, reliability, interpretability, and how computational predictions should be combined with biological knowledge and experimental validation. In this Voices piece, researchers from diverse disciplines share their perspectives on how AI is advancing their respective fields and highlight both the transformative opportunities and the key challenges that will shape the future of the life sciences.
    DOI:  https://doi.org/10.1016/j.cell.2026.08.019
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