Protein Discrimination Using a Fluorescence-Based Sensor Array of Thiacarbocyanine-GUMBOS
Protein Discrimination Using a Fluorescence-Based Sensor Array of Thiacarbocyanine-GUMBOS
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DOI：
10.1021/acssensors.0c00484
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发表时间：
2020-08-28
期刊：
影响因子：
8.9
通讯作者：
Warner, Isiah M.
中科院分区：
文献类型：
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作者：
Perez, Rocio L.;Cong, Mingyan;Warner, Isiah M.
从复杂样品中灵敏和选择性地检测蛋白质在科学界引起了极大的兴趣。关键蛋白的早期和精确检测在潜在的临床诊断、不同疾病的治疗和蛋白质组学研究中起着重要作用。在这里报道的研究中，六种不同的化合物属于一组统一的材料的基础上的有机盐（GUMBOS）已被合成使用三个thiacarbocyanine（TC）染料和用作荧光传感器。在存在和不存在7种蛋白质的情况下，研究了在磷酸盐缓冲溶液中形成的这些TC基GUMBOS的微米和纳米聚集体的荧光特性。通过线性判别分析（LDA）分析这些基于TC的GUMBOS的荧光响应模式。所构建的LDA模型允许在不同浓度下以100%的准确度区分这七种蛋白质。在两种主要蛋白质的混合物中进一步评估这些基于TC的GUMBOS的传感和辨别能力，即，人血清白蛋白和血红蛋白。通过LDA分析这些混合物的荧光响应模式。该模型允许100%准确度的各种混合物的歧视。此外，制备了加标尿样，并收集这些传感器的响应并通过LDA进行分析。值得注意的是，这七种蛋白质的区分也达到了100%的准确性。
Sensitive and selective detection of proteins from complex samples has gained substantial interest within the scientific community. Early and precise detection of key proteins plays an important role in potential clinical diagnosis, treatment of different diseases, and proteomic research. In the study reported here, six different compounds belonging to a group of uniform materials based on organic salts (GUMBOS) have been synthesized using three thiacarbocyanine (TC) dyes and employed as fluorescent sensors. Fluorescence properties of micro- and nanoaggregates of these TC-based GUMBOS formed in phosphate buffer solutions are studied in the absence and presence of seven proteins. Fluorescence response patterns of these TC-based GUMBOS were analyzed by linear discriminant analysis (LDA). The constructed LDA model allowed discrimination of these seven proteins at various concentrations with 100% accuracy. The sensing and discrimination abilities of these TC-based GUMBOS were further evaluated in mixtures of two major proteins, i.e., human serum albumin and hemoglobin. Fluorescence response patterns of these mixtures were analyzed by LDA. This model allowed discrimination of various mixtures with 100% accuracy. Moreover, spiked urine samples were prepared and the responses of these sensors were collected and analyzed by LDA. Remarkably, discrimination of these seven proteins was also achieved with 100% accuracy.