Pattern recognition-based aptasensor array for discrimination of matrix effect

文献类型： 外文期刊

第一作者： Liu, Hao

作者： Liu, Hao;Mao, Jiangsheng;Yan, Mengmeng;Zhu, Chao;Liu, Hao;Gao, Xibao

作者机构：

关键词： Pattern recognition; Aptamer; Aptasensor array; Matrix effect

期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：5.1； 五年影响因子：4.7 ）

ISSN： 0026-265X

年卷期： 2024 年 207 卷

页码：

收录情况： SCI

摘要： Nucleic acid aptamers have attracted considerable attention in recent years, particularly in the fields of disease diagnosis and treatment and biosensing. However, complex matrix effects have severely hindered their progress towards practical application. In this study, the novel strategy of a pattern recognition-based aptasensor array was introduced for target recognition based on the predictable selectivity capability of aptamers by circumventing the challenge of the interference of the matrix effect. As one proof of concept, we chose lactoferrin as the target protein, three selected aptamers with differential affinity as its recognition probes, eleven matrix samples of milk powder as the pattern discrimination model, and AuNPs color as the response signals. In the AuNPs colorimetric arrays, the lactoferrin-added milk powder matrix induced different degrees of protection on the surface of AuNPs particles due to different degrees of binding of lactoferrin to the three aptamer sequences and therefore exhibited differentiated red-to-blue color change along with salt-induced aggregation behaviors. The difference in color responses exhibited with and without lactoferrin addition was considered as the fingerprint pattern of the lactoferrin in each milk powder matrix, followed by the supervised machine learning analysis of linear discriminant analysis (LDA) for better orthogonality to complete the identification and prediction of the unknown samples. By this strategy, eleven kinds of milk powder at the added concentration of lactoferrin (50 nmol/L) presented a distinct response pattern and have been identified and classified with accuracies of 100 % via LDA patterns (three aptamers x eleven milk powder matrices x five replicates) with three canonical factors (91.2 %, 8.58 %, and 0.22 %). Furthermore, the accuracy of 22 unknown samples was 100 %, indicating the achievement in differentiating between different milk powder matrices and the identification of each matrix. The proposed aptasensor array complements the traditional "lock-and-key" single aptasensor and opens a new direction for developing sensitive sensing array systems circumventing complex matrix effects.

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