肾脏精准医学项目和损伤近端小管的单细胞生物学。

IF 4.7 2区 医学 Q1 PATHOLOGY
Danielle Janosevic, Thomas De Luca, Michael T Eadon
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引用次数: 0

摘要

单细胞 RNA 测序(scRNA-seq)使我们对健康和疾病中近端小管亚型的了解取得了重大进展。近端小管在整体平衡中发挥着重要功能,但病理或生理干扰会影响其转录组特征和相应的任务。近端肾小管细胞的这些变化通常在 scRNA-seq 图谱中被描述为细胞状态,这是一种病理生理学亚分类,基于细胞对损伤的反应与其原生状态相比在分子和形态上发生的变化。本综述介绍了肾脏精准医学项目(KPMP)scRNA-seq图谱中定义的主要细胞状态。然后,综述将指出 KPMP 与其他开创性工作之间的重叠之处,这些工作可能使用不同的术语或以不同的分辨率对近端小管细胞进行分组,以定义细胞状态亚型。目的是让读者了解这一高度动态和不断发展的领域中重要细胞损伤和再生过程的关键转录组标记。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Kidney Precision Medicine Project and Single-Cell Biology of the Injured Proximal Tubule.

Single-cell RNA sequencing (scRNA-seq) has led to major advances in our understanding of proximal tubule subtypes in health and disease. The proximal tubule serves essential functions in overall homeostasis, but pathologic or physiological perturbations can affect its transcriptomic signature and corresponding tasks. These alterations in proximal tubular cells are often described within a scRNA-seq atlas as cell states, which are pathophysiological subclassifications based on molecular and morphologic changes in a cell's response to that injury compared with its native state. This review describes the major cell states defined in the Kidney Precision Medicine Project's scRNA-seq atlas. The review then identifies the overlap between the Kidney Precision Medicine Project and other seminal works that may use different nomenclature or cluster proximal tubule cells at different resolutions to define cell state subtypes. The goal is for the reader to understand the key transcriptomic markers of important cellular injury and regeneration processes across this highly dynamic and evolving field.

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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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