相关实验视频

Updated: May 13, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

酵母SAM:一种深度学习模型,用于精确细分发芽的酵母细胞

Yonghao Zhao^1,2, Zhouyuan Zhu^1,2, Sen Yang^1,2

¹The Department of Molecular Biology, Cell Biology & Biochemistry, Brown University, Providence, RI 02912, USA.

bioRxiv : the preprint server for biology

|September 26, 2025

概括

研究人员开发了YeastSAM,这是一种用于准确芽酵母细胞细分的深度学习工具. 这种先进的方法改善了定量图像分析,克服了识别分裂酵母细胞的挑战.

科学领域:

显微镜和定量图像分析
细胞生物学和酵母遗传学
生物成像中的深度学习应用.

背景情况:

精确的细胞细分对于定量图像分析至关重要.
由于不对称的分裂,发芽酵母细分很困难,往往导致分裂细胞的错误识别.
现有的方法与芽酵母独特的形态斗争.

研究的目的:

为芽的酵母细胞细分开发一个准确和可访问的深度学习框架.
为了克服在显微镜图像中错误识别分裂酵母细胞的挑战.
为了实现基因表达和酵母体空间调节的先进定量分析.

主要方法:

开发了由μSAM衍生而成的深度学习模型YeastSAM,并为芽酵母进行了优化.
训练和验证模型使用微镜图像的芽酵母.
酵母SAM与单分子RNA和有机体成像技术的整合.

主要成果:

与目前的方法相比,酵母SAM在细分分裂酵母细胞的准确性超过三倍.
该框架成功地减少了由母芽形态学引起的错误.
能够对空间基因表达调节进行精确的定量分析.

结论:

酵母SAM提供了一个高精度,可访问的解决方案,用于芽酵母细胞细分.
该工具使研究人员,即使是那些编程经验有限的研究人员,能够进行复杂的图像分析.
通过改进的定量成像,更深入地了解发芽酵母细胞过程.

更多相关视频

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Published on: May 27, 2020

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

相关概念视频

Yeast Signaling

Yeast Signaling

Yeasts are single-celled organisms, but unlike bacteria, they are eukaryotes (cells with a nucleus). Cell signaling in yeast is similar to signaling in other eukaryotic cells. A ligand, such as a protein or a small molecule released from a yeast cell, attaches to a receptor on the cell surface. The binding stimulates second-messenger kinases to activate or inactivate transcription factors that further regulate gene expression. Many of the yeast intracellular signaling cascades have similar...