bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2025–01–19
twelve papers selected by
Xiao Qin, University of Oxford
  1. MethylTree: exploring epimutations for accurate and non-invasive lineage tracing.
  2. Recording Lineage History with Cellular Barcodes in the Mammary Epithelium and in Breast Cancer.
  3. High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations.
  4. Learning regulatory grammar.
  5. Massively parallel characterization of transcriptional regulatory elements.
  6. Advances in Molecular Mechanisms and Therapeutic Strategies in Colorectal Cancer: A New Era of Precision Medicine.
  7. Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.
  8. Fate mapping in mouse demonstrates early secretory differentiation directly from Lgr5+ intestinal stem cells.
  9. Multiomics in cancer biomarker discovery and cancer subtyping.
  10. Protocol for the Generation and 3D Culture of Fluorescently Labeled Multicellular Spheroids.
  11. Global Literature Analysis of Tumor Organoid and Tumor-on-Chip Research.
  12. Decoding the functional impact of the cancer genome through protein-protein interactions.
  1. Nat Methods. 2025 Jan 16.
    MethylTree: exploring epimutations for accurate and non-invasive lineage tracing.
      
    DOI:  https://doi.org/10.1038/s41592-024-02568-0
  2. Adv Exp Med Biol. 2025 ;1464 77-94
    Recording Lineage History with Cellular Barcodes in the Mammary Epithelium and in Breast Cancer.
    Candice Merle, Silvia Fre.
      Lineage tracing methods have extensively advanced our understanding of physiological cell behaviour in vivo and in situ and have vastly contributed to decipher the phylogeny and cellular hierarchies during normal and tumour development. In recent years, increasingly complex systems have been developed to track thousands of cells within a given tissue or even entire organisms. Cellular barcoding comprises all techniques designed to genetically label single cells with unique DNA sequences or with a combination of fluorescent proteins, in order to trace their history and lineage production in space and time. We distinguish these two types of cellular barcoding as genetic or optical barcodes. Furthermore, transcribed cellular barcodes can integrate the lineage information with single-cell profiling of each barcoded cell. This enables the potential identification of specific markers or signalling pathways defining distinct stem cell states during development, but also signals promoting tumour growth and metastasis or conferring therapy resistance.In this chapter, we describe recent advances in cellular barcoding technologies and outline experimental and computational challenges. We discuss the biological questions that can be addressed using single-cell dynamic lineage tracing, with a focus on the study of cellular hierarchies in the mammary epithelium and in breast cancer.
    Keywords:  Cellular barcoding; Genetic barcodes; Mouse genetics; Optical barcodes; Single-cell lineage tracing
    DOI:  https://doi.org/10.1007/978-3-031-70875-6_5
  3. Nat Methods. 2025 Jan 16.
    High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations.
    Mengyang Chen, Ruijiang Fu, Yiqian Chen, Li Li, Shou-Wen Wang.
      In vivo lineage tracing holds great potential to reveal fundamental principles of tissue development and homeostasis. However, current lineage tracing in humans relies on extremely rare somatic mutations, which has limited temporal resolution and lineage accuracy. Here, we developed a generic lineage-tracing tool based on frequent epimutations on DNA methylation, enabled by our computational method MethylTree. Using single-cell genome-wide DNA methylation datasets with known lineage and phenotypic labels, MethylTree reconstructed lineage histories at nearly 100% accuracy across different cell types, developmental stages, and species. We demonstrated the epimutation-based single-cell multi-omic lineage tracing in mouse and human blood, where MethylTree recapitulated the differentiation hierarchy in hematopoiesis. Applying MethylTree to human embryos, we revealed early fate commitment at the four-cell stage. In native mouse blood, we identified ~250 clones of hematopoietic stem cells. MethylTree opens the door for high-resolution, noninvasive and multi-omic lineage tracing in humans and beyond.
    DOI:  https://doi.org/10.1038/s41592-024-02567-1
  4. Nat Methods. 2025 Jan;22(1): 7
    Learning regulatory grammar.
    Lei Tang.
      
    DOI:  https://doi.org/10.1038/s41592-024-02588-w
  5. Nature. 2025 Jan 15.
    Massively parallel characterization of transcriptional regulatory elements.
    Vikram Agarwal, Fumitaka Inoue, Max Schubach, Dmitry Penzar, Beth K Martin, Pyaree Mohan Dash, Pia Keukeleire, Zicong Zhang, Ajuni Sohota, Jingjing Zhao, Ilias Georgakopoulos-Soares, William S Noble, Galip Gürkan Yardımcı, Ivan V Kulakovskiy, Martin Kircher, Jay Shendure, Nadav Ahituv.
      The human genome contains millions of candidate cis-regulatory elements (cCREs) with cell-type-specific activities that shape both health and many disease states1. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these cCREs. Here we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of more than 680,000 sequences, representing an extensive set of annotated cCREs among three cell types (HepG2, K562 and WTC11), and found that 41.7% of these sequences were active. By testing sequences in both orientations, we find promoters to have strand-orientation biases and their 200-nucleotide cores to function as non-cell-type-specific 'on switches' that provide similar expression levels to their associated gene. By contrast, enhancers have weaker orientation biases, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict cCRE function and variant effects with high accuracy, delineate regulatory motifs and model their combinatorial effects. Testing a lentiMPRA library encompassing 60,000 cCREs in all three cell types further identified factors that determine cell-type specificity. Collectively, our work provides an extensive catalogue of functional CREs in three widely used cell lines and showcases how large-scale functional measurements can be used to dissect regulatory grammar.
    DOI:  https://doi.org/10.1038/s41586-024-08430-9
  6. Int J Mol Sci. 2025 Jan 02. pii: 346. [Epub ahead of print]26(1):
    Advances in Molecular Mechanisms and Therapeutic Strategies in Colorectal Cancer: A New Era of Precision Medicine.
    Donatella Delle Cave.
      Colorectal cancer (CRC) is one of the leading causes of cancer-related morbidity and mortality worldwide [...].
    DOI:  https://doi.org/10.3390/ijms26010346
  7. Anim Cells Syst (Seoul). 2025 ;29(1): 72-83
    Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.
    Sungwon Jung.
      Dynamic modeling of cellular states has emerged as a pivotal approach for understanding complex biological processes such as cell differentiation, disease progression, and tissue development. This review provides a comprehensive overview of current approaches for modeling cellular state dynamics, focusing on techniques ranging from dynamic or static biomolecular network models to deep learning models. We highlight how these approaches integrated with various omics data such as transcriptomics, and single-cell RNA sequencing could be used to capture and predict cellular behavior and transitions. We also discuss applications of these modeling approaches in predicting gene knockout effects, designing targeted interventions, and simulating organ development. This review emphasizes the importance of selecting appropriate modeling strategies based on scalability and resolution requirements, which vary according to the complexity and size of biological systems under study. By evaluating strengths, limitations, and recent advancements of these methodologies, we aim to guide future research in developing more robust and interpretable models for understanding and manipulating cellular state dynamics in various biological contexts, ultimately advancing therapeutic strategies and precision medicine.
    Keywords:  Cellular state dynamics; cell phenotype modeling; cellular reprogramming; disease progression modeling
    DOI:  https://doi.org/10.1080/19768354.2024.2449518
  8. Dev Cell. 2025 Jan 02. pii: S1534-5807(24)00762-7. [Epub ahead of print]
    Fate mapping in mouse demonstrates early secretory differentiation directly from Lgr5+ intestinal stem cells.
    Isidora Banjac, Martti Maimets, Ingrid H C Tsang, Marius Dioli, Stine Lind Hansen, Kata Krizic, Raul Bardini Bressan, Cecilia Lövkvist, Kim B Jensen.
      The intestinal epithelium has a remarkably high turnover in homeostasis. It remains unresolved how this is orchestrated at the cellular level and how the behavior of stem and progenitor cells ensures tissue maintenance. To address this, we combined quantitative fate mapping in three complementary mouse models with mathematical modeling and single-cell RNA sequencing. Our integrated approach generated a spatially and temporally defined model of crypt maintenance based on two cycling populations: stem cells at the crypt-bottom and transit-amplifying (TA) cells above them. Subsequently, we validated the predictions from the mathematical model, demonstrating that fate decisions between the secretory and absorptive lineages are made within the stem cell compartment, whereas TA cell divisions contribute specifically to the absorptive lineage. These quantitative insights provide further direct evidence for crypt-bottom stem cells as the dominant driver of the intestinal epithelium replenishment.
    Keywords:  fate mapping; intestinal stem cells; mathematical modeling
    DOI:  https://doi.org/10.1016/j.devcel.2024.12.023
  9. Adv Clin Chem. 2025 ;pii: S0065-2423(24)00131-8. [Epub ahead of print]124 161-195
    Multiomics in cancer biomarker discovery and cancer subtyping.
    Seunghwan Choi, Joon-Yong An.
      The advent of multiomics has ushered in a new era of cancer research characterized by integrated genomic, transcriptomic and proteomic analysis to unravel the complexities of cancer biology and facilitate the discovery of novel biomarkers. This chapter provides a comprehensive overview of the concept of multiomics, detailing the significant advances in the underlying technologies and their contributions to our understanding of cancer. It delves into the evolution of genomics and transcriptomics, breakthroughs in proteomics, and overarching progress in multiomic methodologies, highlighting their collective impact on cancer biomarker discovery. Furthermore, this chapter explores the computational methods essential for multiomic studies, including clustering techniques for delineating cancer subtypes, strategies for estimating molecular features and activities, and utility of pathway enrichment analyses for interpreting multiomic datasets. Particular focus has been placed on the application of these methods for identifying distinct cancer subtypes, thereby enabling a more personalized approach to cancer treatment. Through a detailed discussion of the scientific principles, technological advancements, and practical applications of multiomics, this chapter aims to underscore the pivotal role of multiomics in advancing cancer research and paving the way for personalized medicine. The insights provided herein not only illuminate the current landscape of cancer biomarker discovery, but also forecast future directions of multiomics research in oncology, advocating for a more integrated and nuanced approach to understanding and combating cancer.
    Keywords:  Cancer heterogeneity; Cancer subtype; Clustering methods; Genomic subtype; Integration methods; Kinase-mediated signaling pathway; Mass spectrometry proteomics; Molecular subtype; Multiomics; Multiomics subtype; Phosphoproteomics; Precision medicine; Proteogenomics; Proteomic subtype; Proteomics
    DOI:  https://doi.org/10.1016/bs.acc.2024.10.004
  10. Methods Mol Biol. 2025 Jan 17.
    Protocol for the Generation and 3D Culture of Fluorescently Labeled Multicellular Spheroids.
    Emily C Liu, Amy L Ryan, Sinem Koc-Günel.
      Spheroid culture systems have been extensively used to model the three-dimensional (3D) behavior of cells in vitro. Traditionally, spheroids consist of a single cell type, limiting their ability to fully recapitulate the complex inter-cellular interactions observed in vivo. Here we describe a protocol for generating cocultured spheroids composed of two distinct cell types, embedded within a 3D extracellular matrix (ECM) to better study cellular interactions. Fluorescent labeling of each cell type enables clear distinction and visualization, facilitating the analysis of cell invasion, proliferation, and behavior within the matrix. This method is particularly suited for studying matrix invasion, an essential process in cancer metastasis, using both fixed and live cell microscopy. The protocol is versatile and can be adapted for various cell types, providing a robust platform for investigating cell-cell interactions in cancer research, tissue remodeling, and drug screening.
    Keywords:  3D spheroids; Extracellular matrix; Fluorescence imaging; Human lung fibroblasts; Invasion; Lymphatic endothelium; Multicellular interactions
    DOI:  https://doi.org/10.1007/7651_2024_592
  11. Cancers (Basel). 2025 Jan 01. pii: 108. [Epub ahead of print]17(1):
    Global Literature Analysis of Tumor Organoid and Tumor-on-Chip Research.
    Jun-Ya Shoji, Richard P Davis, Christine L Mummery, Stefan Krauss.
      Background: Tumor organoid and tumor-on-chip (ToC) platforms replicate aspects of the anatomical and physiological states of tumors. They, therefore, serve as models for investigating tumor microenvironments, metastasis, and immune interactions, especially for precision drug testing. To map the changing research diversity and focus in this field, we performed a quality-controlled text analysis of categorized academic publications and clinical studies. Methods: Previously, we collected metadata of academic publications on organoids or organ-on-chip platforms from PubMed, Web of Science, Scopus, EMBASE, and bioRxiv, published between January 2011 and June 2023. Here, we selected documents from this metadata corpus that were computationally determined as relevant to tumor research and analyzed them using an in-house text analysis algorithm. Additionally, we collected and analyzed metadata from ClinicalTrials.gov of clinical studies related to tumor organoids or ToC as of March 2023. Results and Discussion: From 3551 academic publications and 139 clinical trials, we identified 55 and 24 tumor classes modeled as tumor organoids and ToC models, respectively. The research was particularly active in neural and hepatic/pancreatic tumor organoids, as well as gastrointestinal, neural, and reproductive ToC models. Comparative analysis with cancer statistics showed that lung, lymphatic, and cervical tumors were under-represented in tumor organoid research. Our findings also illustrate varied research topics, including tumor physiology, therapeutic approaches, immune cell involvement, and analytical techniques. Mapping the research geographically highlighted the focus on colorectal cancer research in the Netherlands, though overall the specific research focus of countries did not reflect regional cancer prevalence. These insights not only map the current research landscape but also indicate potential new directions in tumor model research.
    Keywords:  microphysiological systems; text mining; tumor organoid; tumor-on-chip platforms
    DOI:  https://doi.org/10.3390/cancers17010108
  12. Nat Rev Cancer. 2025 Jan 14.
    Decoding the functional impact of the cancer genome through protein-protein interactions.
    Haian Fu, Xiulei Mo, Andrey A Ivanov.
      Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.
    DOI:  https://doi.org/10.1038/s41568-024-00784-6
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