阿尔茨海默病(AD)是最常见的痴呆,以进行性认知障碍和神经变性为特征。广泛的临床和基因组研究揭示了生物标志物,危险因素,通路,以及过去十年AD的目标。然而,AD发生和进展的确切分子基础仍然难以捉摸。新兴的单细胞测序技术可以潜在地提供对疾病的细胞水平见解。在这里,我们系统地回顾了最先进的生物信息学方法来分析单细胞测序数据及其在14个主要方向上的应用。包括1)质量控制和标准化,2)降维和特征提取,3)细胞聚类分析,4)细胞类型的推断和注释,5)差异表达,6)轨迹推断,7)拷贝数变异分析,8)整合单细胞多组学,9)表观基因组分析,10)基因网络推断,11)细胞亚群的优先排序,12)人和小鼠sc-RNA-seq数据的整合分析,13)空间转录组学,和14)比较单细胞AD小鼠模型研究和单细胞人AD研究。我们还解决了使用人类死后和小鼠组织的挑战,并概述了单细胞测序数据分析的未来发展。重要的是,我们已经为每个主要分析方向实施了推荐的工作流程,并将其应用于AD中的大型单核RNA测序(snRNA-seq)数据集.报告关键分析结果,同时通过GitHub与研究社区共享脚本和数据。总之,这篇全面的综述提供了分析单细胞测序数据的各种方法的见解,并为研究设计和各种分析方向提供了具体指南。审查和伴随的软件工具将作为研究AD的细胞和分子机制的宝贵资源,其他疾病,或单细胞水平的生物系统。
Alzheimer\'s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically
review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive
review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The
review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.