Genetically Engineered Mouse Models for Alzheimer Disease and Frontotemporal Dementia: New Insights from Single-Cell and Spatial Transcriptomics.

IF 4.7 2区 医学 Q1 PATHOLOGY
Yuanpu Chiu, Shangzhou Xia, Haowen Qiao, Zhen Zhao
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引用次数: 0

Abstract

Neurodegenerative diseases, including Alzheimer disease, frontotemporal dementia, Parkinson disease, Huntington disease, and amyotrophic lateral sclerosis, are often casually linked to protein aggregation and inclusion. As the origins of those proteinopathies have been biochemically traced and genetically mapped, genetically engineered animal models carrying the specific mutations or variants are widely used for investigating the etiology of these diseases, as well as for testing potential therapeutics. This article focuses on the mouse models of Alzheimer disease and closely related frontotemporal dementia, particularly the ones that have provided most valuable knowledge, or are in a trajectory of doing so. More importantly, some of the major findings from these models are summarized, based on the recent single-cell transcriptomics, multiomics, and spatial transcriptomics studies. While no model is perfect, it is hoped that the new insights from these models and the practical use of these models will continue to help to establish a path forward.

阿尔茨海默病和额颞叶痴呆的基因工程小鼠模型:来自单细胞和空间转录组学的新见解。
神经退行性疾病,包括阿尔茨海默病、额颞叶痴呆、帕金森氏病、亨廷顿病和肌萎缩侧索硬化症,通常与蛋白质聚集和包涵有关。由于这些蛋白质病变的起源已经被生物化学追踪和遗传图谱,携带特定突变或变异的基因工程动物模型被广泛用于研究这些疾病的病因,以及测试潜在的治疗方法。本文重点关注阿尔茨海默病和与其密切相关的额颞叶痴呆的小鼠模型,特别是那些已经提供最有价值的知识,或正在这样做的轨迹。更重要的是,基于最近的单细胞转录组学、多组学和空间转录组学研究,总结了这些模型的一些主要发现。虽然没有一个模型是完美的,但希望这些模型的新见解和这些模型的实际应用将继续帮助建立一条前进的道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.40
自引率
0.00%
发文量
178
审稿时长
30 days
期刊介绍: The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.
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