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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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在多重成像数据中的细胞表达的自动分类使用Nimbus.

Josef Lorenz Rumberger1,2,3, Noah F Greenwald4,5, Jolene S Ranek6

  • 1Max-Delbruck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.

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概括

深度学习模型Nimbus从多重成像数据准确地预测细胞标志物阳性. 这种在庞大的Pan-Multiplex数据集上训练的工具,可以在不需要重新训练的情况下增强细胞表型和亚型识别,从而推进空间生物学研究.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 数字病理学数字病理学

背景情况:

  • 多复合成像对于分析健康和疾病中的组织空间地形至关重要.
  • 精确的细胞表型需要列举标记物组合,通常使用无监督的聚类.
  • 现有的方法可能需要对数据集进行特定的再培训,这限制了广泛的适用性.

研究的目的:

  • 开发一种深度学习模型,用于预测多重成像数据中的标志物阳性.
  • 创建一个大规模的数据集 (Pan-Multiplex) 用于培训和验证模型.
  • 通过将模型预测与聚类算法集成,实现强大的细胞亚型识别.

主要方法:

  • 构建Pan-Multiplex (Pan-M) 数据集,包含15种细胞类型的1.97亿个标记物表达注释.
  • 开发了Nimbus,这是一个预训练的深度学习模型,用于分类细胞标记物表达 (正/负).
  • 对Nimbus预测与染色模式的验证和与现有方法的比较.

主要成果:

  • 尼布斯准确地预测了跨多种细胞类型,组织和显微镜平台的标记物阳性,而无需重新训练.
  • 该模型捕获了底层染色模式,并与以前方法的准确性相匹配或超过.
  • 将Nimbus预测与集群算法集成在一起,可稳定地识别细胞亚型.

结论:

  • 尼布斯为分析多重复合成像数据提供了一个强大的,可通用的工具.
  • 开源的Nimbus模型和Pan-M数据集促进了社区驱动的空间生物学进步.
  • 这种方法提高了复杂生物样本中细胞表型和亚型发现的效率和准确性.