Scholarlink

<p>Visualizing genome organization and transcriptional dynamics with spatial and temporal precision in living cells is essential for elucidating gene regulation and chromatin-associated disease mechanisms, yet conventional methods confront a fundamental tension between endogenous-sequence targeting and live-cell compatibility. Operator–repressor systems require prior insertion of repetitive arrays at engineered loci, whereas fluorescence in situ hybridization mandates cell fixation and thereby precludes temporal analysis. CRISPR–Cas technologies, originally developed for genome editing, have been re-engineered into a versatile molecular-imaging toolkit capable of interrogating native sequences in living cells. Here, we systematically review CRISPR-based live-cell imaging and sensing platforms, critically evaluating their design principles, mechanistic foundations, and performance limitations. We examine dCas9-based DNA labeling, dCas12a systems for non-repetitive loci, Cas13- and Csm-mediated RNA imaging, novel fluorescent reporters, engineered ribonucleoproteins, and delivery innovations including reagent-based Oligo-LiveFISH. To organize this diverse literature, we distinguish three operationally distinct modalities—live-cell imaging, intracellular sensing, and diagnostic biosensing—and assess each platform through three unifying design trade-offs: sensitivity versus cellular perturbation, multiplexing capacity versus system complexity, and detection threshold versus biological fidelity. Building on this framework, we evaluate the integration of CRISPR imaging with super-resolution microscopy, artificial-intelligence-driven computational analysis, and multimodal spatial omics. Collectively, this synthesis clarifies current capabilities, delineates unresolved constraints, and charts a coherent path toward clinically relevant applications of CRISPR-based live-cell molecular imaging.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Recent Advances in the Development of CRISPR-Based Live-Cell Molecular Imaging and Sensing

  • 期刊论文

  • 发表日期：

    2026年5月13日

查看全文
  • Min Hou,
  • Yulin Li,
  • Xiaobo Wu,
  • Die Long,
  • Di Sun,
  • Peizhen Chen,
  • Haowei Huang

摘要

Visualizing genome organization and transcriptional dynamics with spatial and temporal precision in living cells is essential for elucidating gene regulation and chromatin-associated disease mechanisms, yet conventional methods confront a fundamental tension between endogenous-sequence targeting and live-cell compatibility. Operator–repressor systems require prior insertion of repetitive arrays at engineered loci, whereas fluorescence in situ hybridization mandates cell fixation and thereby precludes temporal analysis. CRISPR–Cas technologies, originally developed for genome editing, have been re-engineered into a versatile molecular-imaging toolkit capable of interrogating native sequences in living cells. Here, we systematically review CRISPR-based live-cell imaging and sensing platforms, critically evaluating their design principles, mechanistic foundations, and performance limitations. We examine dCas9-based DNA labeling, dCas12a systems for non-repetitive loci, Cas13- and Csm-mediated RNA imaging, novel fluorescent reporters, engineered ribonucleoproteins, and delivery innovations including reagent-based Oligo-LiveFISH. To organize this diverse literature, we distinguish three operationally distinct modalities—live-cell imaging, intracellular sensing, and diagnostic biosensing—and assess each platform through three unifying design trade-offs: sensitivity versus cellular perturbation, multiplexing capacity versus system complexity, and detection threshold versus biological fidelity. Building on this framework, we evaluate the integration of CRISPR imaging with super-resolution microscopy, artificial-intelligence-driven computational analysis, and multimodal spatial omics. Collectively, this synthesis clarifies current capabilities, delineates unresolved constraints, and charts a coherent path toward clinically relevant applications of CRISPR-based live-cell molecular imaging.