bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–06–02
four papers selected by
Xiao Qin, University of Oxford
  1. Systematic decoding of cis gene regulation defines context-dependent control of the multi-gene costimulatory receptor locus in human T cells.
  2. Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics.
  3. The art of modeling gene regulatory circuits.
  4. Building and analyzing metacells in single-cell genomics data.
  1. Nat Genet. 2024 May 29.
    Systematic decoding of cis gene regulation defines context-dependent control of the multi-gene costimulatory receptor locus in human T cells.
    Cody T Mowery, Jacob W Freimer, Zeyu Chen, Salvador Casaní-Galdón, Jennifer M Umhoefer, Maya M Arce, Ketrin Gjoni, Bence Daniel, Katalin Sandor, Benjamin G Gowen, Vinh Nguyen, Dimitre R Simeonov, Christian M Garrido, Gemma L Curie, Ralf Schmidt, Zachary Steinhart, Ansuman T Satpathy, Katherine S Pollard, Jacob E Corn, Bradley E Bernstein, Chun Jimmie Ye, Alexander Marson.
      Cis-regulatory elements (CREs) interact with trans regulators to orchestrate gene expression, but how transcriptional regulation is coordinated in multi-gene loci has not been experimentally defined. We sought to characterize the CREs controlling dynamic expression of the adjacent costimulatory genes CD28, CTLA4 and ICOS, encoding regulators of T cell-mediated immunity. Tiling CRISPR interference (CRISPRi) screens in primary human T cells, both conventional and regulatory subsets, uncovered gene-, cell subset- and stimulation-specific CREs. Integration with CRISPR knockout screens and assay for transposase-accessible chromatin with sequencing (ATAC-seq) profiling identified trans regulators influencing chromatin states at specific CRISPRi-responsive elements to control costimulatory gene expression. We then discovered a critical CCCTC-binding factor (CTCF) boundary that reinforces CRE interaction with CTLA4 while also preventing promiscuous activation of CD28. By systematically mapping CREs and associated trans regulators directly in primary human T cell subsets, this work overcomes longstanding experimental limitations to decode context-dependent gene regulatory programs in a complex, multi-gene locus critical to immune homeostasis.
    DOI:  https://doi.org/10.1038/s41588-024-01743-5
  2. Nat Comput Sci. 2024 May;4(5): 316-317
    Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics.
    Zhi Wei.
      
    DOI:  https://doi.org/10.1038/s43588-024-00634-3
  3. NPJ Syst Biol Appl. 2024 May 29. 10(1): 60
    The art of modeling gene regulatory circuits.
    Mariana Gómez-Schiavon, Isabel Montejano-Montelongo, F Sophia Orozco-Ruiz, Cristina Sotomayor-Vivas.
      The amazing complexity of gene regulatory circuits, and biological systems in general, makes mathematical modeling an essential tool to frame and develop our understanding of their properties. Here, we present some fundamental considerations to develop and analyze a model of a gene regulatory circuit of interest, either representing a natural, synthetic, or theoretical system. A mathematical model allows us to effectively evaluate the logical implications of our hypotheses. Using our models to systematically perform in silico experiments, we can then propose specific follow-up assessments of the biological system as well as to reformulate the original assumptions, enriching both our knowledge and our understanding of the system. We want to invite the community working on different aspects of gene regulatory circuits to explore the power and benefits of mathematical modeling in their system.
    DOI:  https://doi.org/10.1038/s41540-024-00380-2
  4. Mol Syst Biol. 2024 May 29.
    Building and analyzing metacells in single-cell genomics data.
    Mariia Bilous, Léonard Hérault, Aurélie Ag Gabriel, Matei Teleman, David Gfeller.
      The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
    Keywords:  Coarse-graining; Metacells; Single-cell Data Analysis; Single-cell Genomics; Tutorial
    DOI:  https://doi.org/10.1038/s44320-024-00045-6
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