Scholarlink

<p>Single-molecule localization microscopy enables high-resolution imaging of molecular interactions, but discriminating molecular binding types has traditionally relied on complex strategies, such as multiple dyes, time-division techniques, or kinetic analysis, that are asynchronous, invasive, or time-consuming. Here, we uncover previously overlooked spatiotemporal information embedded within diffraction-limited fluorescence, enabling synchronous classification of individual binding event videos using only a single fluorescent dye. Building on this insight, we propose a Temporal-to-Context Convolutional Neural Network (T2C CNN), which integrates long-term spatial convolutions, shallow cross-connected blocks, and a pooling-free structure to enhance contextual representation while preserving fine-grained temporal features. Applied to DNA-PAINT experiments, T2C CNN achieves up to 94.76% classification accuracy and outperforms state-of-the-art video classification models by 15-25 percentage points. Our approach enables rapid and precise binding-type recognition from fluorescence video data, reducing observation time from minutes to seconds and facilitating high-throughput single-molecule imaging without requiring multiple dye channels or extended kinetic measurements.</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

Discriminating single-molecule binding events from diffraction-limited fluorescence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

查看全文
  • Yueming Yin,
  • Nithin Pathoor,
  • Kamal Kant Sharma,
  • Shiwen Zhu,
  • Iong Ying Loh,
  • Yan Shan Ang,
  • Shao Ren Sim,
  • Lin Yue Lanry Yung,
  • Thorsten Wohland,
  • Lipo Wang

摘要

Single-molecule localization microscopy enables high-resolution imaging of molecular interactions, but discriminating molecular binding types has traditionally relied on complex strategies, such as multiple dyes, time-division techniques, or kinetic analysis, that are asynchronous, invasive, or time-consuming. Here, we uncover previously overlooked spatiotemporal information embedded within diffraction-limited fluorescence, enabling synchronous classification of individual binding event videos using only a single fluorescent dye. Building on this insight, we propose a Temporal-to-Context Convolutional Neural Network (T2C CNN), which integrates long-term spatial convolutions, shallow cross-connected blocks, and a pooling-free structure to enhance contextual representation while preserving fine-grained temporal features. Applied to DNA-PAINT experiments, T2C CNN achieves up to 94.76% classification accuracy and outperforms state-of-the-art video classification models by 15-25 percentage points. Our approach enables rapid and precise binding-type recognition from fluorescence video data, reducing observation time from minutes to seconds and facilitating high-throughput single-molecule imaging without requiring multiple dye channels or extended kinetic measurements.