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液体活检蛋白质组学与人工智能相结合,可在体内鉴定眼睛衰老和疾病的细胞驱动因素。

Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

机构信息

Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA.

Molecular Surgery Laboratory, Stanford University, Palo Alto, CA 94304, USA; Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA 94304, USA; Department of Biomedicine, Aarhus University, 8000 Aarhus C, Denmark.

出版信息

Cell. 2023 Oct 26;186(22):4868-4884.e12. doi: 10.1016/j.cell.2023.09.012. Epub 2023 Oct 19.

DOI:10.1016/j.cell.2023.09.012
PMID:37863056
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10720485/
Abstract

Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.

摘要

在活体人类中进行单细胞分析对于了解疾病机制至关重要,但在非再生器官(如眼睛和大脑)中并不实际,因为组织活检会造成严重的损伤。我们通过将液体活检的蛋白质组学与所有已知眼部细胞类型的单细胞转录组学相结合来解决这个问题,以追踪在房水中检测到的 5953 种蛋白质的细胞起源。我们鉴定了数百种细胞特异性蛋白标记物,包括单个视网膜细胞类型的标记物。令人惊讶的是,我们的结果表明,视网膜变性发生在帕金森病中,并且驱动糖尿病性视网膜病变的细胞随着疾病阶段而发生变化。最后,我们开发了人工智能(AI)模型来评估个体细胞衰老程度,发现许多与年龄无关的眼部疾病会经历特定于疾病的细胞类型的加速分子衰老。我们的方法可以应用于其他器官系统,有可能改变分子诊断和预后,同时揭示新的细胞疾病和衰老机制。

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