基于深度学习的多重免疫光图像的空间免疫造型 区分结核病 疾病状态 多样性 异种小鼠
bioRxiv : the preprint server for biology
|February 9, 2026
概括
无症状结核病 (TB) 肺颗粒瘤表现出明显的空间模式,包括更高的T细胞密度和特定的B细胞定位,使其与活跃疾病区别开来. 这些发现有助于了解结核病感染状态.
科学领域:
- 免疫学 免疫学 免疫学
- 病理学 病理学 病理学
- 计算生物学 计算生物学
背景情况:
- 由Mycobacterium tuberculosis (M. tb) 引起的结核病 (TB) 是一个重要的全球健康问题,临床结果各异.
- 了解M. tb粒瘤的空间组织对于区分无症状感染与活跃疾病至关重要.
- 人类对M. tb的免疫反应是异质的,导致不同的临床表现.
研究的目的:
- 为了识别肺颗粒瘤中的基于细胞的空间特征,这些特征表明无症状的M. tb感染.
- 开发计算模型,用于对颗粒瘤细胞组成和空间关系的自动分析.
- 在小鼠模型中区分与急性,无症状和慢性结核病相关的免疫学特征.
主要方法:
- 使用多重复合免疫光染色,对来自不同疾病状态 (急性,无症状,慢性) 的M. tb感染小鼠的肺部部分进行染色.
- 开发了一种用于细分颗粒瘤,量化细胞类型 (T细胞,B细胞,巨细胞,上皮细胞) 和提取空间免疫细胞数据的自动化模型.
- 采用大型语言模型 (LLM) 来解码复杂的细胞模式并识别不同的免疫学特征.
主要成果:
- 与急性和慢性结核病相比,无症状的M. tb感染颗粒瘤具有较高的CD4+和CD8a+T细胞密度.
- 无症状感染中的颗粒瘤显示B细胞与支气管上皮质的距离增加,T细胞与巨细胞的距离增加.
- 在LLM分析中发现了不同的免疫学特征:无症状的免疫表达平衡,急性结核病的低细胞性,慢性结核病的高细胞性.
结论:
- 肺小粒瘤架构在M. tb感染状态之间有显著差异,无症状感染的特征是特定的空间免疫细胞安排.
- 开发的自动化管道为分析颗粒瘤多重光图像提供了强大的方法.
- 这些发现为更好地了解颗粒瘤变异及其与结核病状况的相关性提供了基础.
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