多路复用
核酸
检出限
计算生物学
DNA
化学
分子生物学
生物
色谱法
生物化学
遗传学
作者
Saba Safdar,Karen Ven,Julie Van Lent,Benjamin Pavie,Iene Rutten,Annelies Dillen,Sebastian Munck,Jeroen Lammertyn,Dragana Spasić
标识
DOI:10.1016/j.bios.2020.112017
摘要
In disease diagnostics, single- and multiplex nucleic acid (NA) detection, with the potential to discriminate mutated strands, is of paramount importance. Current techniques that rely on target amplification or protein-enzyme based signal amplification are highly relevant, yet still plagued by diverse drawbacks including erroneous target amplification, and the limited stability of protein enzymes. As a solution, we present a multicomponent nucleic acid enzymes (MNAzymes)-based system for singleplex and multiplex detection of NA targets in microwells down to femtomolar (fM) concentrations, without the need for any target amplification or protein enzymes, while operating at room temperature and with single base-pair resolution. After successful validation of the MNAzymes in solution, their performance was further verified on beads in bulk and in femtoliter-sized microwells. The latter is not only a highly simplified system compared to previous microwell-based bioassays but, with the detection limit of 180 fM, it is to-date the most sensitive NAzyme-mediated, bead-based approach, that does not rely on target amplification or any additional signal amplification strategies. Furthermore, we demonstrated, for the first time, multiplexed target detection in microwells, both from buffer and nasopharyngeal swab samples, and presented superior single base-pair resolution of this assay. Because of the design flexibility of MNAzymes and direct demonstration in swab samples, this system holds great promise for multiplexed detection in other clinically relevant matrices without the need for any additional NA or protein components. Moreover, these findings open up the potential for the development of next-generation, protein-free diagnostic tools, including digital assays with single-molecule resolution.
科研通智能强力驱动
Strongly Powered by AbleSci AI