空间形态蛋白质特征从组织架构预测疾病状态
Thomas Hu1,2, Efe Ozturk1,2, Mayar Allam1
1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
iScience
|August 18, 2025
概括
图形神经网络管道SNOWFLAKE通过在组织微环境中分析免疫细胞组织和形态来预测疾病状态. 这种方法可以准确地分类感染状态,并揭示与疾病相关的细胞模式.
科学领域:
- 空间系统生物学空间系统生物学
- 计算病理学计算病理学
- 免疫学 免疫学 免疫学
背景情况:
- 了解免疫细胞在组织微环境中的组织对于解释疾病至关重要.
- 空间蛋白质组学数据为细胞相互作用和疾病状态提供了洞察力.
研究的目的:
- 介绍SNOWFLAKE,一种新的图形神经网络管道,用于使用空间蛋白质组学数据预测疾病状态.
- 整合单细胞蛋白表达和形态特征,以便对组织微环境进行增强分析.
主要方法:
- 开发了SNOWFLAKE,这是一个集成单细胞蛋白表达和形态的图形神经网络管道.
- 将SNOWFLAKE应用于儿科COVID-19数据集,用于感染状态分类.
- 将形态纳入图形边缘特征,以识别空间组织的子图.
主要成果:
- 在分类COVID-19感染状态方面,SNOWFLAKE的表现优于传统的机器学习和深度学习方法.
- 在淋巴卵泡中确定了与疾病状况相关的独特的空间组织子图.
- 在单细胞社区内揭示了可解释的细胞图案.
结论:
- 雪花有效地提取有意义的子图嵌入,以了解疾病中的免疫架构变化.
- 这种方法在不同的组织类型中显示出可通用性,包括乳腺癌和三级淋巴体结构.
- 从多重成像数据中,SNOWFLAKE对空间系统生物学和生物标志物发现具有显著的实用性.
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