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Protein Discrimination Using a Fluorescence-Based Sensor Array of Thiacarbocyanine-GUMBOS
Author(s) -
Rocío L. Pérez,
Mingyan Cong,
Stephanie R. Vaughan,
Caitlan E. Ayala,
Waduge Indika S. Galpothdeniya,
John K. Mathaga,
Isiah M. Warner
Publication year - 2020
Publication title -
acs sensors
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.055
H-Index - 57
ISSN - 2379-3694
DOI - 10.1021/acssensors.0c00484
Subject(s) - fluorescence , linear discriminant analysis , chemistry , chromatography , analytical chemistry (journal) , computer science , artificial intelligence , physics , quantum mechanics
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.

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