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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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基于循环的多重光成像的灵活的开源处理工作流.

Guillaume Potier1,2, Aurélie Doméné1,2,3, Perrine Paul-Gilloteaux3,4

  • 1Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, F-44000, France.

F1000Research
|January 22, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种开源软件工作流程,用于预处理复杂化组织成像数据. 该软件解决了空间生物学分析的挑战,改善了细胞群体与空间背景的识别.

关键词:
生物图像分析分析光显微镜的光显微镜.多重复杂的多重复杂.注册注册注册注册注册注册注册注册是什么意思细分化 细分化的细分化信号处理 信号处理 信号处理工作流的工作流.

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科学领域:

  • 空间生物学 空间生物学
  • 生物医学成像学 生物医学成像学
  • 计算病理学计算病理学

背景情况:

  • 多重复合组织成像通过提供空间上下文来补充单细胞分析.
  • 先进的成像技术可以同时测量多个细胞参数.
  • 循环免疫光学和微流体学克服了在记者成像中的光谱重叠问题.

研究的目的:

  • 开发和实施开源软件工作流程,用于预处理多重复杂组织成像数据.
  • 为重要的预处理步骤提供强大的解决方案,包括注册,文物校正和马赛克.
  • 为了促进复杂组织环境中细胞群的空间分析.

主要方法:

  • 作为开源软件 (库,命令行工具,独立应用程序) 实现的新型预处理工作流.
  • 使用广场显微镜和马赛克来获得大视野.
  • 包含用于周期间注册的图像处理,自流光校正和马赛克.

主要成果:

  • 使用PhenoCycler系统示范了工作流程,并提供了较少的数据集用于测试.
  • 开源处理器与商业处理器进行了比较.
  • 开发的软件有效地解决了多重复图像成像数据预处理中的已知问题.

结论:

  • 拟议的开源工作流提供了一个可行的替代方案,用于预处理多重复杂组织成像数据.
  • 该软件增强了空间细胞群的分析,类似于细胞计量,但增加了空间信息.
  • 该工具旨在提高空间生物学数据分析的可访问性和可靠性.