bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2025–11–23
eleven papers selected by
Xiao Qin, University of Oxford



  1. Nat Commun. 2025 Nov 20.
      Understanding how cells differentiate to their final specialized fates is a fundamental problem in biomedical science. Single-cell multi-omic profiling provides an opportunity to identify dynamic molecular changes, but new computational approaches are needed to realize this potential. In particular, previous methods for RNA velocity inference lack support for multi-lineage, multi-sample, and multi-omic single-cell data and cannot be used to identify differential dynamics. To overcome these challenges, we introduce MultiVeloVAE, a probabilistic framework for multi-sample RNA velocity inference that integrates single-cell RNA and multi-omic data. MultiVeloVAE models gene expression and chromatin accessibility on a shared time scale, performs multi-sample inference from datasets with partially overlapping modalities, accounts for lineage bifurcations, and enables statistical testing of velocity parameters among cell types and over time. Using newly generated 10X Multiome datasets from human embryoid bodies and differentiating macrophage cells, we demonstrate that MultiVeloVAE provides novel insights into chromatin accessibility and gene expression dynamics during development.
    DOI:  https://doi.org/10.1038/s41467-025-66287-6
  2. Sci Rep. 2025 Nov 20. 15(1): 40941
      Plasticity, or the ability to rapidly and reversibly change phenotypes, may help explain how a single progenitor cell eventually generates a tumor with many different cell phenotypes. Normal colon plasticity is characterized by a conserved and broadly permissive epigenome, where expression and phenotype are determined by the microenvironment instead of epigenetic remodeling. To determine whether this stem-like plasticity is retained during progression, gene expression was measured with spatial transcriptomics and compared with gene-level DNA methylation in two colorectal cancers (CRCs). Like normal colon, genes that were differentially expressed between regions, subclones, and phenotypes (superficial, invasive, and metastatic) tended to have lower DNA methylation variability. We propose a quantitative signal of plasticity that correlates gene epigenetic variability with gene expression variability. In this framework, negative correlation implies phenotypic plasticity, as more variably expressed genes tend to have less epigenetic variability. We verify the presence of this signal in multiple external single-cell RNA-Seq datasets, in both normal colon and CRC samples. Therefore, the plasticity of normal colon appears to be retained during progression. A CRC progenitor with a preconfigured plastic phenotype is poised for rapid growth because it expresses, as needed, transcripts required for progression with minimal epigenetic remodeling.
    Keywords:  Colorectal cancer; DNA methylation; Phenotypic plasticity; Spatial transcriptomics; Wound healing
    DOI:  https://doi.org/10.1038/s41598-025-24703-3
  3. Trends Cancer. 2025 Nov 18. pii: S2405-8033(25)00276-6. [Epub ahead of print]
      Multi-omics integration is reshaping cancer research by combining histopathology, transcriptomics, and proteomics with spatial and temporal context. Schweizer et al. revealed compartment-specific biology, RNA-protein decoupling, and emergent molecular patterns underpinning malignant transformation in low-grade serous carcinoma, highlighting the potential of integrated multi-omics to uncover novel mechanisms and guide precision oncology.
    Keywords:  compartment biology; multi-omics integration; spatial proteomics; spatial transcriptomics
    DOI:  https://doi.org/10.1016/j.trecan.2025.11.002
  4. Stem Cell Reports. 2025 Nov 20. pii: S2213-6711(25)00320-0. [Epub ahead of print] 102716
      Cell differentiation is regulated by transcription factors (TFs), but specific TFs needed for mammalian differentiation pathways are not fully understood. For example, during spinal motor neuron (MN) differentiation, 1,370 TFs are transcribed, yet only 55 have reported functional relevance. We developed a method combining pluripotent stem cell differentiation, single-cell transcriptomics, and a CRISPR-based TF loss-of-function screen and applied it to MN differentiation. The CRISPR screen identified 245 genes important for mouse MN differentiation, including 116 TFs. This screen uncovered important genes not showing differential transcription and identified a regulatory hub at the MN progenitor (pMN) stage. A secondary human screen of 69 selected candidates revealed a conservation between mouse pMN and human pMN and ventral pMN (vpMN) regulations. The validation of three hits required for efficient human MN differentiation supported the effectiveness of our approach. Collectively, our strategy offers a framework for identifying important TFs in various differentiation pathways.
    Keywords:  CRISPR screen; development; differentiation; motor neuron; scRNA-seq; spinal cord; transcription factors; zinc finger
    DOI:  https://doi.org/10.1016/j.stemcr.2025.102716
  5. Nucleic Acids Res. 2025 Nov 18. pii: gkaf1126. [Epub ahead of print]
      Analysis and interpretation of omics data largely benefit from the use of prior knowledge. However, this knowledge is fragmented across resources and often is not directly accessible for analytical methods. We developed OmniPath (https://omnipathdb.org/), a database combining diverse molecular knowledge from 168 resources. It covers causal protein-protein, gene regulatory, microRNA, and enzyme-post-translational modification interactions, cell-cell communication, protein complexes, and information about the function, localization, structure, and many other aspects of biomolecules. It prioritizes literature curated data, and complements it with predictions and large scale databases. To enable interactive browsing of this large corpus of knowledge, we developed OmniPath Explorer, which also includes a large language model agent that has direct access to the database. Python and R/Bioconductor client packages and a Cytoscape plugin create easy access to customized prior knowledge for omics analysis environments, such as scverse. OmniPath can be broadly used for the analysis of bulk, single-cell, and spatial multi-omics data, especially for mechanistic and causal modeling.
    DOI:  https://doi.org/10.1093/nar/gkaf1126
  6. Nat Rev Genet. 2025 Nov 18.
      CRISPR-based genome editing technologies, including nuclease-based editing, base editing and prime editing, have revolutionized biological research and modern medicine by enabling precise, programmable modification of the genome and offering new therapeutic strategies for a wide range of genetic diseases. Artificial intelligence (AI), including machine learning and deep learning models, is now further advancing the field by accelerating the optimization of gene editors for diverse targets, guiding the engineering of existing tools and supporting the discovery of novel genome-editing enzymes. In this Review, we summarize key AI methodologies underlying these advances and discuss their recent noteworthy applications to genome editing technologies. We also discuss emerging opportunities, such as AI-powered virtual cell models, which can guide genome editing through target selection or prediction of functional outcomes. Finally, we identify key directions where the integration of AI methods is poised to have a substantial impact going forward.
    DOI:  https://doi.org/10.1038/s41576-025-00907-1
  7. NPJ Precis Oncol. 2025 Nov 18. 9(1): 360
      The incidence of early-onset colorectal cancer (CRC), defined as cases diagnosed in individuals under 50, is rising globally. However, its molecular and immune characteristics remain poorly understood. In this study, we analyze single-cell RNA sequencing data from 168 CRC patients, aged 22 to 91, to investigate differences between early-onset and standard-onset CRC. We find a reduced proportion of tumor-infiltrating myeloid cells, a higher burden of copy number variations, and decreased tumor-immune interactions in early-onset CRC. Additionally, immune signatures unique to early-onset CRC are associated with differential responses to immunotherapy, underscoring the need for tailored therapeutic strategies for this group of patients. These findings provide valuable insights into the molecular and immune landscape of early-onset CRC, emphasizing the importance of developing targeted prevention and treatment strategies.
    DOI:  https://doi.org/10.1038/s41698-025-01129-8
  8. Nat Biotechnol. 2025 Nov 17.
      Metagenomic sequencing and metabolomics of fecal matter have revealed the impact of the gut microbiome on health and disease. In addition to microbiota, feces also contain shed or exfoliated host epithelial, secretory and immune cells, but RNA profiling of these cells is challenging owing to degradation and cross-contamination. Here we introduce exfoliome sequencing (Foli-seq) to profile fecal exfoliated eukaryotic messenger RNAs (feRNAs) originating from the upper and lower gastrointestinal regions and show that this 'fecal exfoliome' harbors stable RNAs that reflect intestinal and immune function. By selectively amplifying targeted transcripts, Foli-seq demonstrates robust, accurate, sensitive and quantitative measurement of feRNAs. In murine colitis models, feRNA reveals temporal processes of epithelial damage, immune response and intestinal recovery specific to different types of gut inflammation. Simultaneous exfoliome and microbiome profiling uncovers a dense host-microbe interaction network. Moreover, we demonstrate stratification of patients with inflammatory bowel disease into subgroups that correlate with disease severity. Fecal Foli-seq is a noninvasive strategy to longitudinally study the gut and profile its health.
    DOI:  https://doi.org/10.1038/s41587-025-02894-4
  9. bioRxiv. 2025 Sep 30. pii: 2025.09.29.679324. [Epub ahead of print]
      During development, cell-cell communication induces a series of cell fate transitions that are maintained by epigenetic gene regulation. Here, we harness endogenous epigenetic silencing machinery to develop synthetic circuits that induce stable gene expression changes. Using synthetic Notch receptors that control the chromatin regulators KRAB and Dnmt3L, we developed input-controlled switches capable of inducing self-sustaining silencing of target loci. We used these modules to construct circuits in which combinatorial inputs specifically direct a choice among multiple alternative cell fates. These epigenetic silencing switches can also be inverted to yield input-induced sustained activation of a target gene. We demonstrate that this epigenetic memory switch can be used to drive morphological fate changes, in response to transient cell signals, that remain stable over many cell divisions, as is observed in development. These synthetic epigenetic circuits represent an important step towards engineering cell populations capable of coordinated multi-cell fate decisions.
    DOI:  https://doi.org/10.1101/2025.09.29.679324
  10. Trends Cancer. 2025 Nov 14. pii: S2405-8033(25)00277-8. [Epub ahead of print]
      Intratumoral heterogeneity in pancreatic cancer poses a significant challenge, contributing to disease aggressiveness and complicating treatment. A recent study by Li et al. reveals that this heterogeneity is maintained by tumor-intrinsic reciprocal signaling between SPP1 and GREM1 in the epithelial and mesenchymal cell populations of pancreatic cancer.
    Keywords:  SPP1–GREM1; epithelial–mesenchymal; pancreatic cancer; tumor heterogeneity
    DOI:  https://doi.org/10.1016/j.trecan.2025.11.003
  11. bioRxiv. 2025 Sep 30. pii: 2025.09.30.679565. [Epub ahead of print]
      Differentially active enhancers are key drivers of cell type specific gene expression. Active enhancers are found in open chromatin, which can be mapped at genome scale across tissue and cell types. Though incompletely understood, the relationship between chromatin accessibility and enhancer activity has been exploited to identify, model, and even design functional enhancers for selected cell types, but to what extent this design strategy can generalize across human cell and tissue types remains unclear. Here, we trained deep neural networks on a large corpus of chromatin accessibility data from hundreds of human biosamples. We used these models to generate an atlas of tens of thousands of synthetic enhancers, targeting hundreds of cell lines, tissues, and differentiation states, aiming to maximize accessibility in target samples and minimize it in all off-target ones. Experimental testing of thousands of designs in a representative subset of ten human cell types and in mouse retina demonstrated their function as specific enhancers, not only in the case of one-versus-all objectives but also when targeting two or three cell types. An explainable AI analysis, enabled by our large-scale enhancer measurements, allowed us to identify similarities and differences between the sequence grammar underlying accessibility and enhancer activity. Our results show that model-guided design of enhancers can help us decipher the cis-regulatory code governing cell type specificity and generate novel tools for selective targeting of human cell states.
    DOI:  https://doi.org/10.1101/2025.09.30.679565