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相关概念视频

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Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
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科斯莫斯:一个基于形态的实时,使用深度学习进行无标签细胞分类的平台.

Mahyar Salek1, Nianzhen Li2, Hou-Pu Chou2

  • 1Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA. yar@deepcellbio.com.

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概括

单细胞的计算分类和映射 (COSMOS) 使用人工智能和微流体学从明亮场图像中按形态分类细胞. 这个平台可以根据视觉特征有效净化未标记的细胞.

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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.
  • 人工智能的人工智能

背景情况:

  • 了解细胞异质性对于生物和医学研究至关重要.
  • 目前用于细胞表征和分类的方法通常依赖于特定的标签或染料,限制实时分析.
  • 形态分析提供了一种无标签的方法来评估细胞特征.

研究的目的:

  • 开发一个人工智能驱动的平台,使用亮场显微镜实时表征和分类单个细胞.
  • 为了根据形态特征实现无标签的细胞分类.
  • 解决基于深度学习的细胞评估和分类技术差距.

主要方法:

  • 开发单细胞计算分类和映射 (COSMOS) 平台,集成微流体和人工智能.
  • 监督深度学习模型的应用,用于对高分辨率明亮场图像的形态分析.
  • 使用高维嵌入形态向量进行细胞表征和分类,无需生物标志物或染色.

主要成果:

  • 在多个人类细胞系和组织样本上展示了COSMOS的能力.
  • 展示了神经网络嵌入空间的能力,以捕捉细胞的深度视觉特征.
  • 在实时中成功净化了具有所需形态特征的未标记的可活细胞.

结论:

  • 使用人工智能和亮场成像,COSMOS平台有效地根据形态学对单细胞进行特征和分类.
  • 该方法使具有特定视觉特征的细胞能够高效,无标签的净化.
  • 这项技术为基于深度学习的实时细胞分析和分类提供了一个新的解决方案.