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膜结合随机DNA序列组合阵列识别表面(CARS)的开发。

Development of a membrane-bound random DNA sequence combinatorial array recognition surface (CARS).

作者信息

Bruno John G

机构信息

Operational Technologies Corporation, San Antonio, Texas 78229, USA.

出版信息

J Biomol Tech. 2010 Apr;21(1):35-43.

Abstract

A partially overlapping population of random sequence 60mer DNA molecules consisting of many concatamers of varied lengths was spatially separated in one and two dimensions by electrophoresis in polyacrylamide and transferred to nitrocellulose membranes. The spatially separated library serves as a potential sensor interface on which many different molecular recognition events or target analyte-binding patterns may emerge, thereby theoretically representing a "universal sensor" surface. The separated DNA library has been referred to as a DNA combinatorial array recognition surface or "CARS." After UV baking and various fluorescence staining or fluorescent probe interactions, the one-dimensional (1-D) and 2-D membrane-bound CARS were digitally photographed and subjected to image analysis with National Institutes of Health Image-Java software. Image analysis demonstrated relatively consistent and more similar spatial fluorescence patterns within CARS analyte treatment groups but noteworthy pattern differences before and after analyte addition and between different analyte treatments. Taken together, these data suggest a potential role for CARS as a novel, inexpensive, self-assembling universal molecular recognition surface that could be coupled to sophisticated Bayesian or other pattern recognition algorithms to classify analytes or make specific identifications, much like the senses of smell or taste.

摘要

由许多不同长度的串联体组成的部分重叠的随机序列60聚体DNA分子群体,通过聚丙烯酰胺凝胶电泳在一维和二维空间上进行分离,然后转移到硝酸纤维素膜上。这种空间分离的文库可作为一种潜在的传感器界面,在其上可能出现许多不同的分子识别事件或目标分析物结合模式,因此从理论上讲代表了一个“通用传感器”表面。分离出的DNA文库被称为DNA组合阵列识别表面或“CARS”。经过紫外线烘烤以及各种荧光染色或荧光探针相互作用后,对一维(1-D)和二维膜结合的CARS进行数码拍照,并使用美国国立卫生研究院的Image-Java软件进行图像分析。图像分析表明,在CARS分析物处理组中,空间荧光模式相对一致且更为相似,但在添加分析物前后以及不同分析物处理之间,存在值得注意的模式差异。综合来看,这些数据表明CARS作为一种新型、廉价、自组装的通用分子识别表面具有潜在作用,它可以与复杂的贝叶斯或其他模式识别算法相结合,用于分析物分类或进行特定识别,类似于嗅觉或味觉。

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