bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2025–02–16
nine papers selected by
Xiao Qin, University of Oxford
  1. Lactate controls cancer stemness and plasticity through epigenetic regulation.
  2. Advanced Adenoma and Long-Term Risk of Colorectal Cancer, Cancer-Related Mortality, and Mortality.
  3. Massively parallel in vivo Perturb-seq screening.
  4. Oncofetal reprogramming drives phenotypic plasticity in WNT-dependent colorectal cancer.
  5. Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.
  6. Cellular Systems for Colorectal Stem Cancer Cell Research.
  7. Towards an interpretable deep learning model of cancer.
  8. Scalable co-sequencing of RNA and DNA from individual nuclei.
  9. Deciphering the Role of Cancer Stem Cells: Drivers of Tumor Evolution, Therapeutic Resistance, and Precision Medicine Strategies.
  1. Cell Metab. 2025 Feb 04. pii: S1550-4131(25)00002-6. [Epub ahead of print]
    Lactate controls cancer stemness and plasticity through epigenetic regulation.
    Nguyen T B Nguyen, Sira Gevers, Rutger N U Kok, Lotte M Burgering, Hannah Neikes, Ninouk Akkerman, Max A Betjes, Marlies C Ludikhuize, Can Gulersonmez, Edwin C A Stigter, Yvonne Vercoulen, Jarno Drost, Hans Clevers, Michiel Vermeulen, Jeroen S van Zon, Sander J Tans, Boudewijn M T Burgering, Maria J Rodríguez Colman.
      Tumors arise from uncontrolled cell proliferation driven by mutations in genes that regulate stem cell renewal and differentiation. Intestinal tumors, however, retain some hierarchical organization, maintaining both cancer stem cells (CSCs) and cancer differentiated cells (CDCs). This heterogeneity, coupled with cellular plasticity enabling CDCs to revert to CSCs, contributes to therapy resistance and relapse. Using genetically encoded fluorescent reporters in human tumor organoids, combined with our machine-learning-based cell tracker, CellPhenTracker, we simultaneously traced cell-type specification, metabolic changes, and reconstructed cell lineage trajectories during tumor organoid development. Our findings reveal distinctive metabolic phenotypes in CSCs and CDCs. We find that lactate regulates tumor dynamics, suppressing CSC differentiation and inducing dedifferentiation into a proliferative CSC state. Mechanistically, lactate increases histone acetylation, epigenetically activating MYC. Given that lactate's regulation of MYC depends on the bromodomain-containing protein 4 (BRD4), targeting cancer metabolism and BRD4 inhibitors emerge as a promising strategy to prevent tumor relapse.
    Keywords:  cancer metabolism; cell plasticity; cell types; cell-cell interactions; differentiation; heterogeneity; live imaging; organoids; single-cell tracking; stem cells
    DOI:  https://doi.org/10.1016/j.cmet.2025.01.002
  2. JAMA Netw Open. 2025 Feb 03. 8(2): e2459703
    Advanced Adenoma and Long-Term Risk of Colorectal Cancer, Cancer-Related Mortality, and Mortality.
    Aasma Shaukat, Paolo Goffredo, Jack M Wolf, Kyle Rudser, Timothy R Church.
      
    DOI:  https://doi.org/10.1001/jamanetworkopen.2024.59703
  3. Nat Protoc. 2025 Feb 12.
    Massively parallel in vivo Perturb-seq screening.
    Xinhe Zheng, Patrick C Thompson, Cassandra M White, Xin Jin.
      Advances in genomics have identified thousands of risk genes impacting human health and diseases, but the functions of these genes and their mechanistic contribution to disease are often unclear. Moving beyond identification to actionable biological pathways requires dissecting risk gene function and cell type-specific action in intact tissues. This gap can in part be addressed by in vivo Perturb-seq, a method that combines state-of-the-art gene editing tools for programmable perturbation of genes with high-content, high-resolution single-cell genomic assays as phenotypic readouts. Here we describe a detailed protocol to perform massively parallel in vivo Perturb-seq using several versatile adeno-associated virus (AAV) vectors and provide guidance for conducting successful downstream analyses. Expertise in mouse work, AAV production and single-cell genomics is required. We discuss key parameters for designing in vivo Perturb-seq experiments across diverse biological questions and contexts. We further detail the step-by-step procedure, from designing a perturbation library to producing and administering AAV, highlighting where quality control checks can offer critical go-no-go points for this time- and cost-expensive method. Finally, we discuss data analysis options and available software. In vivo Perturb-seq has the potential to greatly accelerate functional genomics studies in mammalian systems, and this protocol will help others adopt it to answer a broad array of biological questions. From guide RNA design to tissue collection and data collection, this protocol is expected to take 9-15 weeks to complete, followed by data analysis.
    DOI:  https://doi.org/10.1038/s41596-024-01119-3
  4. Nat Genet. 2025 Feb;57(2): 402-412
    Oncofetal reprogramming drives phenotypic plasticity in WNT-dependent colorectal cancer.
    Slim Mzoughi, Megan Schwarz, Xuedi Wang, Deniz Demircioglu, Gulay Ulukaya, Kevin Mohammed, Habiba Zorgati, Denis Torre, Lewis E Tomalin, Federico Di Tullio, Carlos Company, Yuliia Dramaretska, Marc Leushacke, Bruno Giotti, Tamsin Rm Lannagan, Daniel Lozano-Ojalvo, Panagiotis Karras, Peter B Vermeulen, Dan Hasson, Robert Sebra, Alexander M Tsankov, Owen J Sansom, Jean-Christophe Marine, Nick Barker, Gaetano Gargiulo, Ernesto Guccione.
      Targeting cancer stem cells (CSCs) is crucial for effective cancer treatment, yet resistance mechanisms to LGR5+ CSC depletion in WNT-driven colorectal cancer (CRC) remain elusive. In the present study, we revealed that mutant intestinal stem cells (SCs) depart from their canonical identity, traversing a dynamic phenotypic spectrum. This enhanced plasticity is initiated by oncofetal (OnF) reprogramming, driven by YAP and AP-1, with subsequent AP-1 hyperactivation promoting lineage infidelity. The retinoid X receptor serves as a gatekeeper of OnF reprogramming and its deregulation after adenomatous polyposis coli (APC) loss of function establishes an OnF 'memory' sustained by YAP and AP-1. Notably, the clinical significance of OnF and LGR5+ states in isolation is constrained by their functional redundancy. Although the canonical LGR5+ state is sensitive to the FOLFIRI regimen, an active OnF program correlates with resistance, supporting its role in driving drug-tolerant states. Targeting this program in combination with the current standard of care is pivotal for achieving effective and durable CRC treatment.
    DOI:  https://doi.org/10.1038/s41588-024-02058-1
  5. Mol Syst Biol. 2025 Feb 12.
    Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.
    Marco Stock, Corinna Losert, Matteo Zambon, Niclas Popp, Gabriele Lubatti, Eva Hörmanseder, Matthias Heinig, Antonio Scialdone.
      Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for understanding complex cellular regulation. However, the inherent noise and sparsity of scRNA-seq data present significant challenges to accurate GRN inference. This review explores one promising approach that has been proposed to address these challenges: integrating prior knowledge into the inference process to enhance the reliability of the inferred networks. We categorize common types of prior knowledge, such as experimental data and curated databases, and discuss methods for representing priors, particularly through graph structures. In addition, we classify recent GRN inference algorithms based on their ability to incorporate these priors and assess their performance in different contexts. Finally, we propose a standardized benchmarking framework to evaluate algorithms more fairly, ensuring biologically meaningful comparisons. This review provides guidance for researchers selecting GRN inference methods and offers insights for developers looking to improve current approaches and foster innovation in the field.
    Keywords:  Gene Regulatory Network Inference; Graph Learning; Prior Knowledge; Single-cell Multiomics; Single-cell Transcriptomics
    DOI:  https://doi.org/10.1038/s44320-025-00088-3
  6. Cells. 2025 Jan 22. pii: 170. [Epub ahead of print]14(3):
    Cellular Systems for Colorectal Stem Cancer Cell Research.
    Tatyana A Grigoreva, Daria N Kindt, Aleksandra V Sagaidak, Daria S Novikova, Vyacheslav G Tribulovich.
      Oncological diseases consistently occupy leading positions among the most life-threatening diseases, including in highly developed countries. At the same time, the second most common cause of cancer death is colorectal cancer. The current level of research shows that the development of effective therapy, in this case, requires a new grade of understanding processes during the emergence and development of a tumor. In particular, the concept of cancer stem cells that ensure the survival of chemoresistant cells capable of giving rise to new tumors is becoming widespread. To provide adequate conditions that reproduce natural processes typical for tumor development, approaches based on increasingly complex cellular systems are being improved. This review discusses the main strategies that allow for the study of the properties of tumor cells with an emphasis on colorectal cancer stem cells. The features of working with tumor cells and the advantages and disadvantages of 2D and 3D culture systems are considered.
    Keywords:  3D cell culture; cancer stem cells (CSCs); colonosphere; organoid; spheroid
    DOI:  https://doi.org/10.3390/cells14030170
  7. NPJ Precis Oncol. 2025 Feb 14. 9(1): 46
    Towards an interpretable deep learning model of cancer.
    Avlant Nilsson, Nikolaos Meimetis, Douglas A Lauffenburger.
      Cancer is a manifestation of dysfunctional cell states. It emerges from an interplay of intrinsic and extrinsic factors that disrupt cellular dynamics, including genetic and epigenetic alterations, as well as the tumor microenvironment. This complexity can make it challenging to infer molecular causes for treating the disease. This may be addressed by system-wide computer models of cells, as they allow rapid generation and testing of hypotheses that would be too slow or impossible to perform in the laboratory and clinic. However, so far, such models have been impeded by both experimental and computational limitations. In this perspective, we argue that they can now be achieved using deep learning algorithms to integrate omics data and prior knowledge of molecular networks. Such models would have many applications in precision oncology, e.g., for identifying drug targets and biomarkers, predicting resistance mechanisms and toxicity effects of drugs, or simulating cell-cell interactions in the microenvironment.
    DOI:  https://doi.org/10.1038/s41698-025-00822-y
  8. Nat Methods. 2025 Feb 12.
    Scalable co-sequencing of RNA and DNA from individual nuclei.
    Timothy R Olsen, Pranay Talla, Romella K Sagatelian, Julia Furnari, Jeffrey N Bruce, Peter Canoll, Shan Zha, Peter A Sims.
      The ideal technology for directly investigating the relationship between genotype and phenotype would analyze both RNA and DNA genome-wide and with single-cell resolution; however, existing tools lack the throughput required for comprehensive analysis of complex tumors and tissues. We introduce a highly scalable method for jointly profiling DNA and expression following nucleosome depletion (DEFND-seq). In DEFND-seq, nuclei are nucleosome-depleted, tagmented and separated into individual droplets for messenger RNA and genomic DNA barcoding. Once nuclei have been depleted of nucleosomes, subsequent steps can be performed using the widely available 10x Genomics droplet microfluidic technology and commercial kits. We demonstrate the production of high-complexity mRNA and gDNA sequencing libraries from thousands of individual nuclei from cell lines, fresh and archived surgical specimens for associating gene expression with both copy number and single-nucleotide variants.
    DOI:  https://doi.org/10.1038/s41592-024-02579-x
  9. Cancers (Basel). 2025 Jan 24. pii: 382. [Epub ahead of print]17(3):
    Deciphering the Role of Cancer Stem Cells: Drivers of Tumor Evolution, Therapeutic Resistance, and Precision Medicine Strategies.
    Mohamed El-Tanani, Syed Arman Rabbani, Shakta Mani Satyam, Imran Rashid Rangraze, Adil Farooq Wali, Yahia El-Tanani, Alaa A A Aljabali.
      Cancer stem cells (CSCs) play a central role in tumor progression, recurrence, and resistance to conventional therapies, making them a critical focus in oncology research. This review provides a comprehensive analysis of CSC biology, emphasizing their self-renewal, differentiation, and dynamic interactions with the tumor microenvironment (TME). Key signaling pathways, including Wnt, Notch, and Hedgehog, are discussed in detail to highlight their potential as therapeutic targets. Current methodologies for isolating CSCs are critically examined, addressing their advantages and limitations in advancing precision medicine. Emerging technologies, such as CRISPR/Cas9 and single-cell sequencing, are explored for their transformative potential in unraveling CSC heterogeneity and informing therapeutic strategies. The review also underscores the pivotal role of the TME in supporting CSC survival, promoting metastasis, and contributing to therapeutic resistance. Challenges arising from CSC-driven tumor heterogeneity and dormancy are analyzed, along with strategies to mitigate these barriers, including novel therapeutics and targeted approaches. Ethical considerations and the integration of artificial intelligence in designing CSC-specific therapies are discussed as essential elements of future research. The manuscript advocates for a multi-disciplinary approach that combines innovative technologies, advanced therapeutics, and collaborative research to address the complexities of CSCs. By bridging existing gaps in knowledge and fostering advancements in personalized medicine, this review aims to guide the development of more effective cancer treatment strategies, ultimately improving patient outcomes.
    Keywords:  artificial intelligence; cancer stem cells; signaling pathways; therapeutic resistance; tumor heterogeneity; tumor microenvironment
    DOI:  https://doi.org/10.3390/cancers17030382
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