five

Optimization of Protocols for Detection of De Novo Protein Synthesis in Whole Blood Samples via Azide–Alkyne Cycloaddition

收藏
Figshare2020-08-04 更新2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/Optimization_of_Protocols_for_Detection_of_De_Novo_Protein_Synthesis_in_Whole_Blood_Samples_via_Azide_Alkyne_Cycloaddition/12833912
下载链接
链接失效反馈
官方服务:
资源简介:
Aberrant protein synthesis and protein expression are a hallmark of many conditions ranging from cancer to Alzheimer’s. Blood-based biomarkers indicative of changes in proteomes have long been held to be potentially useful with respect to disease prognosis and treatment. However, most biomarker efforts have focused on unlabeled plasma proteomics that include nonmyeloid origin proteins with no attempt to dynamically tag acute changes in proteomes. Herein we report a method for evaluating de novo protein synthesis in whole blood liquid biopsies. Using a modification of the “bioorthogonal noncanonical amino acid tagging” (BONCAT) protocol, rodent whole blood samples were incubated with l-azidohomoalanine (AHA) to allow incorporation of this selectively reactive non-natural amino acid within nascent polypeptides. Notably, failure to incubate the blood samples with EDTA prior to implementation of azide–alkyne “click” reactions resulted in the inability to detect probe incorporation. This live-labeling assay was sensitive to inhibition with anisomycin and nascent, tagged polypeptides were localized to a variety of blood cells using FUNCAT. Using labeled rodent blood, these tagged peptides could be consistently identified through standard LC/MS-MS detection of known blood proteins across a variety of experimental conditions. Furthermore, this assay could be expanded to measure de novo protein synthesis in human blood samples. Overall, we present a rapid and convenient de novo protein synthesis assay that can be used with whole blood biopsies that can quantify translational change as well as identify differentially expressed proteins that may be useful for clinical applications.
创建时间:
2020-08-04
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作