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.

中文翻译：

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使用基于硫杂甲菁-GUMBOS的基于荧光的传感器阵列进行蛋白质区分。

复杂样品中蛋白质的灵敏和选择性检测已引起科学界的广泛关注。早期和精确检测关键蛋白在潜在的临床诊断，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，使用三种硫代咔菁（TC）染料合成了六种不同的化合物，这些化合物属于基于有机盐（GUMBOS）的一组均匀材料，并用作荧光传感器。在不存在和存在七个蛋白质的情况下，研究了在磷酸盐缓冲液中形成的这些基于TC的GUMBOS的微聚集体和纳米聚集体的荧光特性。这些基于TC的GUMBOS的荧光响应模式通过线性判别分析（LDA）分析。所构建的LDA模型允许以100％的准确度区分不同浓度的这七个蛋白质。这些基于TC的GUMBOS的感知和辨别能力在两种主要蛋白质的混合物中进一步评估，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。该模型允许以100％的准确度区分各种混合物。此外，准备了加标尿液样本，并通过LDA收集并分析了这些传感器的响应。值得注意的是，这七种蛋白质的区分也达到了100％的准确度。通过LDA分析这些混合物的荧光响应模式。该模型允许以100％的准确度区分各种混合物。此外，准备了加标尿液样本，并通过LDA收集并分析了这些传感器的响应。值得注意的是，这七种蛋白质的区分也达到了100％的准确度。通过LDA分析这些混合物的荧光响应模式。该模型允许以100％的准确度区分各种混合物。此外，准备了加标尿液样本，并通过LDA收集并分析了这些传感器的响应。值得注意的是，这七种蛋白质的区分也达到了100％的准确度。