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基于纳米纤维的检测方法增强对 ATTR 淀粉样变的检测。

Enhanced detection of ATTR amyloid using a nanofibril-based assay.

机构信息

Department of Molecular Sciences, Swedish University of Agricultural Sciences (SLU), Uppsala BioCenter, Uppsala, Sweden.

Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden.

出版信息

Amyloid. 2021 Sep;28(3):158-167. doi: 10.1080/13506129.2021.1886072. Epub 2021 Feb 13.

DOI:10.1080/13506129.2021.1886072
PMID:33583280
Abstract

More than 30 proteins and peptides have been found to form amyloid fibrils in human diseases. Fibrils formed by transthyretin (TTR) are associated with ATTR amyloidosis, affecting many vital organs, including the heart and peripheral nervous system. Congo red staining is the gold standard method for detection of amyloid deposits in tissue. However, Congo red staining and amyloid typing methods such as immunofluorescence labelling are limited to relatively large deposits. Detection of small ATTR deposits present at an early stage of the disease could enable timely treatment and prevent severe tissue damage. In this study, we developed an enhanced ATTR amyloid detection method that uses functionalised protein nanofibrils. Using this method, we achieved sensitive detection of monomeric TTR in a microplate immunoassay and immunofluorescence labelling of tissue from two patients containing ATTR aggregates. The system's utility was confirmed on sections from a patient with AA amyloidosis and liver sections from inflamed mouse. These results suggest that the detection system constitutes important new technology for highly sensitive detection of microscopic amounts of ATTR amyloid deposited in tissue.

摘要

已有 30 多种蛋白质和肽被发现可在人类疾病中形成淀粉样纤维。转甲状腺素蛋白(TTR)形成的纤维与 ATTR 淀粉样变性有关,影响包括心脏和外周神经系统在内的许多重要器官。刚果红染色是检测组织中淀粉样沉积物的金标准方法。然而,刚果红染色和免疫荧光标记等淀粉样蛋白分型方法仅限于相对较大的沉积物。检测疾病早期存在的小 ATTR 沉积物可以实现及时治疗并防止严重的组织损伤。在这项研究中,我们开发了一种使用功能化蛋白纳米纤维的增强型 ATTR 淀粉样检测方法。使用该方法,我们在微孔板免疫测定中实现了对单体 TTR 的灵敏检测,并对来自两名含有 ATTR 聚集物的患者的组织进行了免疫荧光标记。该系统在 AA 淀粉样变性患者的切片和炎性小鼠的肝切片上得到了验证。这些结果表明,该检测系统构成了用于高度灵敏检测组织中沉积的微观量 ATTR 淀粉样的重要新技术。

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