High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH.

Journal: Cell
Published Date: Apr 15, 2025

Abstract

Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease.

Authors

  • Yanyu Zhu
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Ashwin Balaji
    Department of Chemistry, Stanford University, Stanford, CA 94305, USA; Biophysics PhD Program, Stanford University, Stanford, CA 94305, USA.
  • Mengting Han
    State Key Laboratory of Natural Medicines, National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University, 210009, China. jinsong.han@cpu.edu.cn.
  • Leonid Andronov
    Department of Chemistry, Stanford University, Stanford, CA 94305, USA.
  • Anish R Roy
    Department of Chemistry, Stanford University, Stanford, CA 94305.
  • Zheng Wei
    Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.
  • Crystal Chen
    Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.
  • Leanne Miles
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Sa Cai
    Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA.
  • Zhengxi Gu
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Ariana Tse
    Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA.
  • Betty Chentzu Yu
    Computational Biology Program, Public Health Sciences Division and Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
  • Takeshi Uenaka
    Institute for Stem Cell Biology & Regenerative Medicine and Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Xueqiu Lin
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Andrew J Spakowitz
    Department of Chemistry, Stanford University, Stanford, CA 94305, USA; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.
  • W E Moerner
    Department of Chemistry, Stanford University, Stanford, CA 94305 wmoerner@stanford.edu.
  • Lei S Qi
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.

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