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Super-resolution Fluorescence Microscopy

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...

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Updated: Jun 27, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

使用缩小尺寸来可视化高通量显微镜中的表型变化.

Alex X Lu¹, Alan M Moses²

¹Microsoft Research New England, Cambridge, MA, USA. lualex@microsoft.com.

Methods in molecular biology (Clifton, N.J.)

|May 6, 2024

概括

此摘要是机器生成的。

这项研究引入了一种新的计算管道,用于使用深度学习可视化细胞表型变化. 该方法有效地将高维细胞数据映射到缩小的尺寸中,有助于分析蛋白质局部化转移.

关键词:

深度学习 (Deep Learning) 是一种深度学习.图像分析图像分析蛋白质蛋白质蛋白质亚细胞局部化的局部化.

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Published on: August 31, 2012

相关实验视频

Last Updated: Jun 27, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

Published on: August 31, 2012

科学领域:

细胞生物学细胞生物学
生物信息学是一种生物信息学.
计算机成像成像技术

背景情况:

高通量显微镜产生大量的细胞表型数据.
分析复杂的,高维的细胞表型数据,包括蛋白质定位,仍然具有挑战性.
需要公正的方法来可视化和检测细胞表型的微妙变化.

研究的目的:

开发和介绍一个计算管道可用于可视化细胞表型的变化.
为了证明减少维度表示对于分析图像特征空间的实用性.
通过基于深度学习的特征提取来识别蛋白质细胞下定位的变化.

主要方法:

利用深度学习从显微镜图像中提取特征.
开发了用于表型可视化的自由可用的分析管道.
应用了缩小维度的技术来可视化特征空间.
分析了用基尿素处理的酵母模型系统中蛋白质局部化的变化.

主要成果:

在缩小尺寸中成功可视化了高维细胞表型数据.
该管道有效地识别了亚细胞蛋白位址的变化.
证明了该方法在用基尿素处理的酵母GFP采集上的应用.

结论:

缩小维度表示为可视化细胞表型变化提供了一种强大的方法.
开发的管道为分析大规模显微镜数据提供了有价值的工具.
这种方法有助于系统识别对刺激的反应中蛋白质局部化的变化.