bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2025–02–02
twelve papers selected by
Xiao Qin, University of Oxford
  1. GraphVelo allows for accurate inference of multimodal omics velocities and molecular mechanisms for single cells.
  2. Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns.
  3. Intestinal stem cells in intestinal homeostasis and colorectal tumorigenesis.
  4. Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues.
  5. Inhibition of ERα signaling induces lineage plasticity in vivo.
  6. Randomizing the human genome by engineering recombination between repeat elements.
  7. Enhancer-dependent gene regulation in space, time, and malignancies.
  8. DeST-OT: Alignment of spatiotemporal transcriptomics data.
  9. A Landscape of Cancer Initiation and Cancer Stem Cells.
  10. A genome-wide atlas of human cell morphology.
  11. Comprehensive guide for epigenetics and transcriptomics data quality control.
  12. Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.
  1. Res Sq. 2025 Jan 15. pii: rs.3.rs-5613372. [Epub ahead of print]
    GraphVelo allows for accurate inference of multimodal omics velocities and molecular mechanisms for single cells.
    Jianhua Xing, Yuhao Chen, Yan Zhang, Jiaqi Gan, Ke Ni, Ming Chen, Ivet Bahar.
      RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data. GraphVelo preserves vector magnitude and direction information during transformations across different data representations. Tests on multiple synthetic and experimental scRNA-seq data including viral-host interactome and multi-omics datasets demonstrate that GraphVelo, together with downstream generalized dynamo analyses, extends RNA velocities to multi-modal data and reveals quantitative nonlinear regulation relations between genes, virus and host cells, and different layers of gene regulation.
    DOI:  https://doi.org/10.21203/rs.3.rs-5613372/v1
  2. Nat Commun. 2025 Jan 27. 16(1): 1062
    Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns.
    Hannah M Schlüter, Caroline Uhler.
      Recent barcoding technologies allow reconstructing lineage trees while capturing paired single-cell RNA-sequencing (scRNA-seq) data. Such datasets provide opportunities to compare gene expression memory maintenance through lineage branching and pinpoint critical genes in these processes. Here we develop Permutation, Optimization, and Representation learning based single Cell gene Expression and Lineage ANalysis (PORCELAN) to identify lineage-informative genes or subtrees where lineage and expression are tightly coupled. We validate our method using synthetic data and apply it to recent paired lineage and scRNA-seq data of lung cancer in a mouse model and embryogenesis of mouse and C. elegans. Our method pinpoints subtrees giving rise to metastases or new cell states, and genes identified as most informative about lineage overlap with known pathways involved in lung cancer progression. Furthermore, our method highlights differences in how gene expression memory is maintained through divisions in cancer and embryogenesis, thereby providing a tool for studying cell state memory through divisions across biological systems.
    DOI:  https://doi.org/10.1038/s41467-025-56388-7
  3. Life Med. 2024 Oct;3(5): lnae042
    Intestinal stem cells in intestinal homeostasis and colorectal tumorigenesis.
    Gaoli Shi, Yang Li, Haihong Shen, Qiankun He, Pingping Zhu.
      Colorectal cancer (CRC), one of the most common tumors in the world, is generally proposed to be generated from intestinal stem cells (ISCs). Leucine-rich repeat-containing G protein-coupled receptor 5 (Lgr5)-positive ISCs are located at the bottom of the crypt and harbor self-renewal and differentiation capacities, serving as the resource of all intestinal epithelial cells and CRC cells as well. Here we review recent progress in ISCs both in non-tumoral and tumoral contexts. We summarize the molecular mechanisms of ISC self-renewal, differentiation, and plasticity for intestinal homeostasis and regeneration. We also discuss the function of ISCs in colorectal tumorigenesis as cancer stem cells and summarize fate dynamic, competition, niche regulation, and remote environmental regulation of ISCs for CRC initiation and propagation.
    Keywords:  cancer stem cells; differentiation; intestinal stem cells; niche; self-renewal
    DOI:  https://doi.org/10.1093/lifemedi/lnae042
  4. Nat Methods. 2025 Jan 27.
    Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues.
    Pengfei Guo, Liran Mao, Yufan Chen, Chin Nien Lee, Angelysia Cardilla, Mingyao Li, Marek Bartosovic, Yanxiang Deng.
      The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner. We applied this technology to mouse embryos and mouse brains, generating detailed multimodal tissue maps that identified more cell types and states compared to unimodal data. This analysis uncovered spatiotemporal relationships among histone modifications, chromatin accessibility, gene expression and protein levels during neuron differentiation, and revealed a radial glia niche with spatially dynamic epigenetic signals. Collectively, the spatial multi-omics approach heralds a new era for characterizing tissue and cellular heterogeneity that single-modality studies alone could not reveal.
    DOI:  https://doi.org/10.1038/s41592-024-02576-0
  5. Nat Cancer. 2025 Jan 24.
    Inhibition of ERα signaling induces lineage plasticity in vivo.
      
    DOI:  https://doi.org/10.1038/s43018-024-00897-9
  6. Science. 2025 Jan 31. 387(6733): eado3979
    Randomizing the human genome by engineering recombination between repeat elements.
    Jonas Koeppel, Raphael Ferreira, Thomas Vanderstichele, Lisa Maria Riedmayr, Elin Madli Peets, Gareth Girling, Juliane Weller, Pierre Murat, Fabio Giuseppe Liberante, Tom Ellis, George McDonald Church, Leopold Parts.
      We lack tools to edit DNA sequences at scales necessary to study 99% of the human genome that is noncoding. To address this gap, we applied CRISPR prime editing to insert recombination handles into repetitive sequences, up to 1697 per cell line, which enables generating large-scale deletions, inversions, translocations, and circular DNA. Recombinase induction produced more than 100 stochastic megabase-sized rearrangements in each cell. We tracked these rearrangements over time to measure selection pressures, finding a preference for shorter variants that avoided essential genes. We characterized 29 clones with multiple rearrangements, finding an impact of deletions on expression of genes in the variant but not on nearby genes. This genome-scrambling strategy enables large deletions, sequence relocations, and the insertion of regulatory elements to explore genome dispensability and organization.
    DOI:  https://doi.org/10.1126/science.ado3979
  7. Int J Cancer. 2025 Jan 31.
    Enhancer-dependent gene regulation in space, time, and malignancies.
    Belinda Blum, Victoria Dachtler, Angelika Feldmann.
      Control of cell-type-specific gene activation requires the coordinated activity of distal regulatory elements, including enhancers, whose inputs must be temporally integrated. Dysregulation of this regulatory capacity, such as aberrant usage of enhancers, can result in malignant transformation of cells. In this review, we provide an overview of our current understanding of enhancer-driven gene regulation and discuss how this activity may be integrated across time, followed by epigenetic and structural alterations of enhancers in cancers.
    Keywords:  cancer epigenetics; distal regulatory elements; enhancer hijacking; genome structure; temporal dynamics
    DOI:  https://doi.org/10.1002/ijc.35350
  8. Cell Syst. 2025 Jan 20. pii: S2405-4712(24)00365-X. [Epub ahead of print]
    DeST-OT: Alignment of spatiotemporal transcriptomics data.
    Peter Halmos, Xinhao Liu, Julian Gold, Feng Chen, Li Ding, Benjamin J Raphael.
      Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression and cell types. SRT has been applied to tissue slices from multiple time points during the development of an organism. We introduce developmental spatiotemporal optimal transport (DeST-OT), a method to align spatiotemporal transcriptomics data using optimal transport (OT). DeST-OT uses semi-relaxed OT to model cellular growth, death, and differentiation processes. We also derive a growth distortion metric and a migration metric to quantify the plausibility of spatiotemporal alignments. DeST-OT outperforms existing methods on the alignment of spatiotemporal transcriptomics data from developing mouse kidney and axolotl brain. DeST-OT estimated growth rates also provide insights into the gene expression programs governing the growth and differentiation of cells over space and time.
    Keywords:  alignment; development; developmental biology; growth rates; optimal transport; semi-relaxed optimal transport; spatially resolved transcriptomics; spatiotemporal; trajectory inference
    DOI:  https://doi.org/10.1016/j.cels.2024.12.001
  9. Cancers (Basel). 2025 Jan 09. pii: 203. [Epub ahead of print]17(2):
    A Landscape of Cancer Initiation and Cancer Stem Cells.
    Masaharu Seno.
      Exposure to radiation and chemicals, oncogenic viruses, microbiomes, and inflammation are the major events of cancer initiation. DNA damage and chromosomal aberrations are classically considered the main causes of cancer. The recent idea of epigenetics is broadening the concept, including the suggestion that oncogenic virus infection disrupts various intracellular signaling cascades. Chronic inflammation was proposed as the origin of cancer in the 19th century, and the molecular level of events has been made clear with scientific development. Much knowledge of cancer initiation has become available for integration into research. Simultaneously, the presence of cancer stem cells has been identified and characterized. However, the point of shift from normal to malignant still appears obscure even when taking cancer stem cells into consideration. From these points of view, the advent of cancer stem cells and cancer initiation are briefly discussed as the points of shift from normal to malignant in this paper.
    Keywords:  cancer initiation; cancer stem cells; chemicals; chronic inflammation; epigenetics; genetic aberration; microbiomes; oncogenic viruses; point of shift from normal to malignant; radiation
    DOI:  https://doi.org/10.3390/cancers17020203
  10. Nat Methods. 2025 Jan 27.
    A genome-wide atlas of human cell morphology.
    Meraj Ramezani, Erin Weisbart, Julia Bauman, Avtar Singh, John Yong, Maria Lozada, Gregory P Way, Sanam L Kavari, Celeste Diaz, Eddy Leardini, Gunjan Jetley, Jenlu Pagnotta, Marzieh Haghighi, Thiago M Batista, Joaquín Pérez-Schindler, Melina Claussnitzer, Shantanu Singh, Beth A Cimini, Paul C Blainey, Anne E Carpenter, Calvin H Jan, James T Neal.
      A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. Here we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-wide genotype-phenotype maps comprising CRISPR-Cas9-based knockouts of >20,000 genes in >30 million cells. Our optical pooled cell profiling platform (PERISCOPE) combines a destainable high-dimensional phenotyping panel (based on Cell Painting) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This perturbation atlas comprises high-dimensional phenotypic profiles of individual cells with sufficient resolution to cluster thousands of human genes, reconstruct known pathways and protein-protein interaction networks, interrogate subcellular processes and identify culture media-specific responses. Using this atlas, we identify the poorly characterized disease-associated TMEM251/LYSET as a Golgi-resident transmembrane protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes. In sum, this perturbation atlas and screening platform represents a rich and accessible resource for connecting genes to cellular functions at scale.
    DOI:  https://doi.org/10.1038/s41592-024-02537-7
  11. STAR Protoc. 2025 Jan 26. pii: S2666-1667(25)00013-9. [Epub ahead of print]6(1): 103607
    Comprehensive guide for epigenetics and transcriptomics data quality control.
    Arianna Comendul, Frederique Ruf-Zamojski, Colby T Ford, Pankaj Agarwal, Elena Zaslavsky, German Nudelman, Manoj Hariharan, Aliza Rubenstein, Hanna Pincas, Venugopalan D Nair, Adam M Michaleas, Philip D Fremont-Smith, Darrell O Ricke, Stuart C Sealfon, Christopher W Woods, Kajal T Claypool, Rafael Jaimes.
      Host response to environmental exposures such as pathogens and chemicals can include modifications to the epigenome and transcriptome. Improved signature discovery, including the identification of the agent and timing of exposure, has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. The interrogation of the epigenome and transcriptome cascade requires analyzing disparate datasets from multiple assay types, often at single-cell resolution, derived from the same biospecimen. However, there remains a paucity of rigorous quality control standards of those datasets that reflect quality assurance of the underlying assay. This guide outlines a comprehensive suite of metrics that can be used to ensure quality from 11 different epigenetics and transcriptomics assays. Recommended mitigative actions to address failed metrics are provided. The workflow presented aims to improve benchwork protocols and dataset quality to enable accurate discovery of exposure signatures.
    Keywords:  Bioinformatics; RNAseq; Sequence analysis
    DOI:  https://doi.org/10.1016/j.xpro.2025.103607
  12. PLoS One. 2025 ;20(1): e0316493
    Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.
    Mwenge Mulenga, Arutchelvan Rajamanikam, Suresh Kumar, Saharuddin Bin Muhammad, Subha Bhassu, Chandramathi Samudid, Aznul Qalid Md Sabri, Manjeevan Seera, Christopher Ifeanyi Eke.
      The emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges. This paper introduces a novel feature engineering method that circumvents these limitations by amalgamating two feature sets derived from input data to generate a new dataset, which is then subjected to feature selection. This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. The proposed method constitutes a significant advancement in the field, providing a robust solution to the intricacies of microbiome data analysis and amplifying the potential of DL methods in disease detection.
    DOI:  https://doi.org/10.1371/journal.pone.0316493
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