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相关概念视频

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
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Protein Diffusion in the Membrane01:24

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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相关实验视频

Updated: Jul 9, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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深度学习辅助单个粒子跟踪用于扩散和功能之间的自动相关性.

Jacob Kæstel-Hansen1,2,3,4, Marilina de Sautu5,6, Anand Saminathan7,8,9

  • 1Department of Chemistry University of Copenhagen.

bioRxiv : the preprint server for biology
|November 28, 2023
PubMed
概括
此摘要是机器生成的。

新的深度学习工具DeepSPT分析了细胞中的纳米扩散,以揭示分子和亚细胞功能. 这个框架快速而准确地从物体运动中提取生物见解,有助于理解细胞过程.

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A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
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科学领域:

  • 细胞生物学 细胞生物学
  • 生物物理学的生物物理.
  • 计算生物学 计算生物学

背景情况:

  • 亚细胞扩散对于细胞过程和相互作用至关重要.
  • 追踪纳米扩散需要先进的光学显微镜,但提取功能信息是具有挑战性的.
  • 需要对扩散数据进行自动化,不可知性的分析,以获得高效的生物洞察力.

研究的目的:

  • 介绍DeepSPT,这是一个用于分析亚细胞物体扩散的深度学习框架.
  • 为了使2D/3D扩散行为的快速,高效和不可知解释.
  • 为了证明框架在从分子运动中提取功能信息的实用性.

主要方法:

  • 开发一个深度学习框架 (DeepSPT) 来分析时间扩散模式.
  • 应用DeepSPT来跟踪和解释亚细胞物体的运动.
  • 在生物环境中验证DeepSPT的准确性和速度.

主要成果:

  • DeepSPT准确地绘制了早期病毒感染事件的地图,识别了内体细胞器,并分类了氨酸涂层的坑和囊泡 (准确度高达95%).
  • 该框架在几秒钟内处理数据,与需要数周的传统方法相比,这是一个显著的改进.
  • 深度SPT证明,单独的分子运动编码了大量的生物功能.

结论:

  • DeepSPT提供了一个强大的,多功能工具,用于自动分析亚细胞扩散.
  • 该框架显著加速从扩散数据中提取功能生物信息.
  • 分子运动是分子和亚细胞水平功能的关键决定因素.