摘要
疾病演进是动态的生物过程,其分子网络、细胞行为与微环境的协同变化在三维空间和时间维度上呈现高度异质性。近年来,整合成像技术与高通量测序的空间转录组学实现了原位保留组织结构下的全转录组解析。通过多时间点动态采样构建的时空转录组大数据,结合机器学习算法与生物网络建模,可系统解码疾病演进中“分子-细胞-微环境”的跨尺度互作规律。本文将通过综述时空转录组学的技术原理、数据处理方法和目前在疾病中的应用案例,来揭示时空转录组学在疾病演进动态过程中的作用。
Disease progression is a dynamic biological process characterized by highly heterogeneous changes in molecular networks,cellular behavior,and the microenvironment across three-dimensional space and time dimensions.In recent years,spatial transcriptomics—the integration of imaging techniques with high-throughput sequencing—has enabled the analysis of the entire transcriptome while preserving tissue structure in situ.By constructing spatiotemporal transcriptomics big data through dynamic sampling at multiple time points,combined with machine learning algorithms and biological network modeling,it is possible to systematically decode the cross-scale interaction patterns between“molecules-cells-microenvironment”in disease progression.This paper will review the technical principles,data processing methods,and current application cases of spatiotemporal transcriptomics in diseases to reveal its role in the dynamic process of disease progression.
作者
徐子宁
王子仪
毕凌波
崔勇
盛宇俊
XU Zining;WANG Ziyi;BI Lingbo;CUI Yong;SHENG Yujun(Department of Dermatology,China-Japan Friendship Hospital,Beijing 100029,China;Chinese Institutes for Medical Research,Beijing 100160,China;Graduate School,Capital Medical University,Beijing 100160,China;Graduate School,Peking Union Medical College and Chinese Academy of Medical Sciences,Beijing 100730,China)
出处
《皮肤科学通报》
2025年第4期380-386,共7页
Dermatology Bulletin
关键词
空间转录组学
时间序列分析
疾病进展
Spatial transcriptomics
Time series analysis
Disease progression
