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一个可解释的多实例学习器从空间解析的转录组学解码了细胞招募
Jia Yao1, Ji Seon Shim2, Kelly Wong3
1Department of Epidemiology, Peter O'Donnell School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
Spacer是一种新的深度学习工具,它分析空间转录组学数据,揭示免疫细胞如何被招募到瘤和心脏组织中. 它揭示了细胞定位和参与的机制,推进了空间生物学中的机械学发现.
科学领域:
- *空间生物学和系统免疫学.
- * 计算生物学和生物信息学.
- * 细胞贩运和组织微环境的分子机制.
背景情况:
- *研究细胞在组织微环境中的招募和参与是至关重要的,但与传统方法具有挑战性.
- *对这些过程的全基因组分析需要先进的计算工具.
- *空间解析转录组学 (SRT) 数据为此类调查提供了强大的资源.
研究的目的:
- * 开发一种新的,可解释的深度学习模型来分析SRT数据.
- * 研究瘤和心肌炎中肌和免疫细胞招募的机制.
- * 揭示T细胞局部特征和疾病背景下的差异性反应.
主要方法:
- *开发"Spacer",一个多实例的深度学习器,用于分析SRT数据的转录学和空间模式.
- * 将Spacer应用于17个高清和20个低清SRT数据集.
- *使用免疫组学,空间T细胞受体 (TCR) 测序和单细胞测序验证Spacer的发现.
主要成果:
- *确定了促进T细胞向瘤招募的基因,用于B细胞和巨细胞招募的独特分子程序,以及由粘素介导的T细胞排斥.
- *发现了T细胞内在的特征,控制了局部化,通过空间TCR-seq.验证.
- * 显露的CD4+ T细胞在心肌炎中比CD8+ T细胞更具反应性,尽管数量较少.
结论:
- * Spacer 提供了一个新的空间解析范式,用于研究细胞定位机制 *in situ*.
- *研究从描述性绘图转向空间生物学中的机械学发现.
- *研究结果为瘤和炎症性心脏病中的免疫细胞动态提供了新的见解.
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