bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–07–07
ten papers selected by
Xiao Qin, University of Oxford
  1. Carcinogenesis at single-cell resolution.
  2. Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling.
  3. Revealing gene function with statistical inference at single-cell resolution.
  4. Next-generation patient models for colorectal cancer research.
  5. Exploring the reciprocity between pioneer factors and development.
  6. Patient-derived mini-colons enable long-term modeling of tumor-microenvironment complexity.
  7. Engineered matrices reveal stiffness-mediated chemoresistance in patient-derived pancreatic cancer organoids.
  8. A machine learning tool for spatial multi-omics.
  9. Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalized therapy.
  10. ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists.
  1. Nat Rev Cancer. 2024 Jul 04.
    Carcinogenesis at single-cell resolution.
    Daniela Senft.
      
    DOI:  https://doi.org/10.1038/s41568-024-00722-6
  2. bioRxiv. 2024 Jun 17. pii: 2024.06.13.598858. [Epub ahead of print]
    Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling.
    Matthew J Regner, Susana Garcia-Recio, Aatish Thennavan, Kamila Wisniewska, Raul Mendez-Giraldez, Brooke Felsheim, Philip M Spanheimer, Joel S Parker, Charles M Perou, Hector L Franco.
      Annotation of the cis -regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.
    DOI:  https://doi.org/10.1101/2024.06.13.598858
  3. Nat Rev Genet. 2024 Jul 01.
    Revealing gene function with statistical inference at single-cell resolution.
    Cole Trapnell.
      Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.
    DOI:  https://doi.org/10.1038/s41576-024-00750-w
  4. Nat Biotechnol. 2024 Jul 02.
    Next-generation patient models for colorectal cancer research.
      
    DOI:  https://doi.org/10.1038/s41587-024-02302-3
  5. Development. 2024 Jul 01. pii: dev201921. [Epub ahead of print]151(13):
    Exploring the reciprocity between pioneer factors and development.
    Meghan M Freund, Melissa M Harrison, Eliana F Torres-Zelada.
      Development is regulated by coordinated changes in gene expression. Control of these changes in expression is largely governed by the binding of transcription factors to specific regulatory elements. However, the packaging of DNA into chromatin prevents the binding of many transcription factors. Pioneer factors overcome this barrier owing to unique properties that enable them to bind closed chromatin, promote accessibility and, in so doing, mediate binding of additional factors that activate gene expression. Because of these properties, pioneer factors act at the top of gene-regulatory networks and drive developmental transitions. Despite the ability to bind target motifs in closed chromatin, pioneer factors have cell type-specific chromatin occupancy and activity. Thus, developmental context clearly shapes pioneer-factor function. Here, we discuss this reciprocal interplay between pioneer factors and development: how pioneer factors control changes in cell fate and how cellular environment influences pioneer-factor binding and activity.
    Keywords:  Chromatin; Gene-regulatory network; Pioneer factor
    DOI:  https://doi.org/10.1242/dev.201921
  6. Nat Biotechnol. 2024 Jul 02.
    Patient-derived mini-colons enable long-term modeling of tumor-microenvironment complexity.
    L Francisco Lorenzo-Martín, Nicolas Broguiere, Jakob Langer, Lucie Tillard, Mikhail Nikolaev, George Coukos, Krisztian Homicsko, Matthias P Lutolf.
      Existing organoid models fall short of fully capturing the complexity of cancer because they lack sufficient multicellular diversity, tissue-level organization, biological durability and experimental flexibility. Thus, many multifactorial cancer processes, especially those involving the tumor microenvironment, are difficult to study ex vivo. To overcome these limitations, we herein implemented tissue-engineering and microfabrication technologies to develop topobiologically complex, patient-specific cancer avatars. Focusing on colorectal cancer, we generated miniature tissues consisting of long-lived gut-shaped human colon epithelia ('mini-colons') that stably integrate cancer cells and their native tumor microenvironment in a format optimized for real-time, high-resolution evaluation of cellular dynamics. We demonstrate the potential of this system through several applications: a comprehensive evaluation of drug effectivity, toxicity and resistance in anticancer therapies; the discovery of a mechanism triggered by cancer-associated fibroblasts that drives cancer invasion; and the identification of immunomodulatory interactions among different components of the tumor microenvironment. Similar approaches should be feasible for diverse tumor types.
    DOI:  https://doi.org/10.1038/s41587-024-02301-4
  7. Nat Mater. 2024 Jul 04.
    Engineered matrices reveal stiffness-mediated chemoresistance in patient-derived pancreatic cancer organoids.
    Bauer L LeSavage, Daiyao Zhang, Carla Huerta-López, Aidan E Gilchrist, Brad A Krajina, Kasper Karlsson, Amber R Smith, Kremena Karagyozova, Katarina C Klett, Michelle S Huang, Christopher Long, Gernot Kaber, Christopher M Madl, Paul L Bollyky, Christina Curtis, Calvin J Kuo, Sarah C Heilshorn.
      Pancreatic ductal adenocarcinoma (PDAC) is characterized by its fibrotic and stiff extracellular matrix. However, how the altered cell/extracellular-matrix signalling contributes to the PDAC tumour phenotype has been difficult to dissect. Here we design and engineer matrices that recapitulate the key hallmarks of the PDAC tumour extracellular matrix to address this knowledge gap. We show that patient-derived PDAC organoids from three patients develop resistance to several clinically relevant chemotherapies when cultured within high-stiffness matrices mechanically matched to in vivo tumours. Using genetic barcoding, we find that while matrix-specific clonal selection occurs, cellular heterogeneity is not the main driver of chemoresistance. Instead, matrix-induced chemoresistance occurs within a stiff environment due to the increased expression of drug efflux transporters mediated by CD44 receptor interactions with hyaluronan. Moreover, PDAC chemoresistance is reversible following transfer from high- to low-stiffness matrices, suggesting that targeting the fibrotic extracellular matrix may sensitize chemoresistant tumours. Overall, our findings support the potential of engineered matrices and patient-derived organoids for elucidating extracellular matrix contributions to human disease pathophysiology.
    DOI:  https://doi.org/10.1038/s41563-024-01908-x
  8. Nat Methods. 2024 Jul 05.
    A machine learning tool for spatial multi-omics.
      
    DOI:  https://doi.org/10.1038/s41592-024-02358-8
  9. Sci Adv. 2024 Jul 05. 10(27): eadm9071
    Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalized therapy.
    Daniel Hughes, Alice Evans, Simei Go, Michael Eyres, Liuliu Pan, Somnath Mukherjee, Zahir Soonawalla, Frances Willenbrock, Eric O'Neill.
      Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, a disease with dismal overall survival. Advances in treatment are hindered by a lack of preclinical models. Here, we show how a personalized organotypic "avatar" created from resected tissue allows spatial and temporal reporting on a complete in situ tumor microenvironment and mirrors clinical responses. Our perfusion culture method extends tumor slice viability, maintaining stable tumor content, metabolism, stromal composition, and immune cell populations for 12 days. Using multiplexed immunofluorescence and spatial transcriptomics, we identify immune neighborhoods and potential for immunotherapy. We used avatars to assess the impact of a preclinically validated metabolic therapy and show recovery of stromal and immune phenotypes and tumor redifferentiation. To determine clinical relevance, we monitored avatar response to gemcitabine treatment and identify a patient avatar-predictable response from clinical follow-up. Thus, avatars provide valuable information for syngeneic testing of therapeutics and a truly personalized therapeutic assessment platform for patients.
    DOI:  https://doi.org/10.1126/sciadv.adm9071
  10. Nat Commun. 2024 Jul 03. 15(1): 5600
    ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists.
    Raman Sethi, Kok Siong Ang, Mengwei Li, Yahui Long, Jingjing Ling, Jinmiao Chen.
      ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell's streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application ( https://immunesinglecell.org/ezsc/ ) or a software package with a shinyApp interface ( https://github.com/JinmiaoChenLab/ezSingleCell2 ) for offline analysis.
    DOI:  https://doi.org/10.1038/s41467-024-48188-2
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