Cao Guangshuo, Chen Dijun
State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China.
Methods Mol Biol. 2025;2883:257-279. doi: 10.1007/978-1-0716-4290-0_11.
Single-cell omics technologies have revolutionized the study of long non-coding RNAs (lncRNAs), offering unprecedented resolution in elucidating their expression dynamics, cell-type specificity, and associated gene regulatory networks (GRNs). Concurrently, the integration of artificial intelligence (AI) methodologies has significantly advanced our understanding of lncRNA functions and its implications in disease pathogenesis. This chapter discusses the progress in single-cell omics data analysis, emphasizing its pivotal role in unraveling the molecular mechanisms underlying cellular heterogeneity and the associated regulatory networks involving lncRNAs. Additionally, we provide a summary of single-cell omics resources and AI models for constructing single-cell gene regulatory networks (scGRNs). Finally, we explore the challenges and prospects of exploring scGRNs in the context of lncRNA biology.
单细胞组学技术彻底改变了对长链非编码RNA（lncRNA）的研究，在阐明其表达动态、细胞类型特异性以及相关基因调控网络（GRN）方面提供了前所未有的分辨率。同时，人工智能（AI）方法的整合显著推进了我们对lncRNA功能及其在疾病发病机制中作用的理解。本章讨论了单细胞组学数据分析的进展，强调了其在揭示细胞异质性背后的分子机制以及涉及lncRNA的相关调控网络方面的关键作用。此外，我们总结了用于构建单细胞基因调控网络（scGRN）的单细胞组学资源和AI模型。最后，我们探讨了在lncRNA生物学背景下探索scGRN所面临的挑战和前景。
