Luo Man, Cao Yunpeng, Hong Jiayi
School of Health and Nursing, Wuchang University of Technology, Wuhan, 430223 Hubei China.
State Key Laboratory of Plant Diversity and Specialty Crops, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074 Hubei China.
Physiol Mol Biol Plants. 2025 Feb;31(2):199-209. doi: 10.1007/s12298-025-01558-6. Epub 2025 Feb 22.
Single-cell transcriptomics overcomes the limitations of conventional transcriptome methods by isolating and sequencing RNA from individual cells, thus capturing unique expression values for each cell. This technology allows unprecedented precision in observing the stochasticity and heterogeneity of gene expression within cells. However, single-cell RNA sequencing (scRNA-seq) experiments often fail to capture all cells and genes comprehensively, and single-modality data is insufficient to explain cell states and systemic changes. To address this, the integration of multi-source scRNA-seq and single-cell multi-modality data has emerged, enabling the construction of comprehensive cell atlases. These integration methods also facilitate the exploration of causal relationships and gene regulatory mechanisms across different modalities. This review summarizes the fundamental principles, applications, and value of these integration methods in revealing biological changes, and analyzes the advantages, disadvantages, and future directions of current approaches.
单细胞转录组学通过从单个细胞中分离和测序RNA克服了传统转录组方法的局限性，从而为每个细胞捕获独特的表达值。这项技术在观察细胞内基因表达的随机性和异质性方面具有前所未有的精度。然而，单细胞RNA测序（scRNA-seq）实验往往无法全面捕获所有细胞和基因，单模态数据不足以解释细胞状态和系统变化。为了解决这个问题，多源scRNA-seq和单细胞多模态数据的整合应运而生，从而能够构建全面的细胞图谱。这些整合方法还有助于探索不同模态之间的因果关系和基因调控机制。本综述总结了这些整合方法在揭示生物学变化方面的基本原理、应用和价值，并分析了当前方法的优缺点和未来方向。
