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Photolithographic Fabrication of Upconversion Barcodes for Multiplexed Molecular Detection
Advanced Optical Materials ( IF 8.0 ) Pub Date : 2020-09-22 , DOI: 10.1002/adom.202001168
Xiao Zeng 1 , Sudheer Kumar Vanga 2 , Eng Tuan Poh 3 , Yi Shi 2 , Chorng Haur Sow 4 , Andrew Anthony Bettiol 2, 4 , Xiaogang Liu 1
Affiliation  

The demands of clinical diagnostics and genetic analysis for high‐performance multiplex detection systems have driven substantial advancement from planar microarrays to suspension arrays for accurate encoding, inventorying, and detecting extensive databases of biomolecules. Despite the multitude of related works and the commercial availability of nanostructured arrays for biodetection, it remains challenging to develop biodetection modules of massive coding permutation with high production rates and fast reaction kinetics . Herein, the concurrent improvement in both production and coding capacities without compromising detection kinetics is reported using lithography‐based upconversion barcodes as the basis for bioassay detection modules. This method exploits microstructural forms of dense graphical encoding, with barcode production up to 109 per run at extensive color permutations. The upconversion barcoding technique requires a million times lower analyte consumption compared to currently available technologies. Moreover, the use of upconversion barcodes is demonstrated in a DNA hybridization test with high target specificity, low detection limit, and ease of flexible surface bioconjugation, enabling potential development of biomarkers in the analysis of massive proteomics, genomics, and metabolomics data.

中文翻译:

用于多分子检测的上转换条形码的光刻制备。

高性能多重检测系统对临床诊断和遗传分析的需求推动了从平面微阵列到悬浮阵列的大量进步,以进行精确的编码,库存和检测生物分子的广泛数据库。尽管有许多相关的工作和用于生物检测的纳米结构阵列的商业可得性,但是开发具有高生产率和快速反应动力学的大规模编码排列的生物检测模块仍然具有挑战性。在此,据报道,使用基于光刻的上转换条形码作为生物测定检测模块的基础,可以同时提高生产能力和编码能力,同时又不影响检测动力学。该方法利用了密集图形编码的微观结构形式,条形码产生量高达10每次运行9个,进行广泛的颜色排列。与当前可用的技术相比,上转换条形码技术所需的分析物消耗量降低了百万倍。此外,在DNA杂交测试中证明了上转换条形码的使用,具有较高的靶标特异性,较低的检测限和易于进行灵活的表面生物缀合,从而使生物标记物在大规模蛋白质组学,基因组学和代谢组学数据分析中的潜在发展成为可能。
更新日期:2020-11-18
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