Most of lateral flow immunoassay (LFIA) devices rely on gold nanoparticles (GNP) labeled antibodies or other biospecific proteins, to achieve reagent-less color-based detection. GNP size, GNP-protein conjugation level and its stability are crucial points for the development of precise and accurate methods. In addition, the purification of the GNP-protein conjugates from unreacted protein and GNP, is necessary for adequate analytical performance of the assay. To assist the synthesis and production process of GNP and their protein conjugates, we use for the first time a non-destructive, particle separation-multi-detection approach based on miniaturized flow field flow fractionation (HF5). A separation method was developed to baseline size-separate GNP, GNP-protein, protein and GNP including BSA used as a surface coater in less than 30 minutes. Freshly synthesized GNP were first characterized and then conjugated with two different model antibodies: a mouse immunoglobulin (IgG) and a fluorescein-labeled mouse immunoglobulin (FITC-IgG). The IgG-GNP complexes were fractionated using the HF5 apparatus, able to separate IgG-GNP from free proteins by their hydrodynamic size, allowing purification of the conjugation product. Both IgG-GNPs and GNPs were characterized according to their size by the MALS detector, and according to their Surface Plasmon Resonance and spectrum by UV-Vis detection, improving the results obtained via batch characterization. This simple non-invasive approach is very useful for the LFIA development and optimization: the use of HF5-mutidetection offers a unique tool for this purpose facilitating the industrialization of the process and the relate optimization and standardization.
Marassi V., Calabria D., Trozzi I., Zattoni A., Reschiglian P., Roda B. (2021). Comprehensive characterization of gold nanoparticles and their protein conjugates used as a label by hollow fiber flow field flow fractionation with photodiode array and fluorescence detectors and multiangle light scattering. JOURNAL OF CHROMATOGRAPHY A, 1636, 1-8 [10.1016/j.chroma.2020.461739].
Comprehensive characterization of gold nanoparticles and their protein conjugates used as a label by hollow fiber flow field flow fractionation with photodiode array and fluorescence detectors and multiangle light scattering
Marassi V.;Calabria D.;Trozzi I.;Zattoni A.;Reschiglian P.;Roda B.
2021
Abstract
Most of lateral flow immunoassay (LFIA) devices rely on gold nanoparticles (GNP) labeled antibodies or other biospecific proteins, to achieve reagent-less color-based detection. GNP size, GNP-protein conjugation level and its stability are crucial points for the development of precise and accurate methods. In addition, the purification of the GNP-protein conjugates from unreacted protein and GNP, is necessary for adequate analytical performance of the assay. To assist the synthesis and production process of GNP and their protein conjugates, we use for the first time a non-destructive, particle separation-multi-detection approach based on miniaturized flow field flow fractionation (HF5). A separation method was developed to baseline size-separate GNP, GNP-protein, protein and GNP including BSA used as a surface coater in less than 30 minutes. Freshly synthesized GNP were first characterized and then conjugated with two different model antibodies: a mouse immunoglobulin (IgG) and a fluorescein-labeled mouse immunoglobulin (FITC-IgG). The IgG-GNP complexes were fractionated using the HF5 apparatus, able to separate IgG-GNP from free proteins by their hydrodynamic size, allowing purification of the conjugation product. Both IgG-GNPs and GNPs were characterized according to their size by the MALS detector, and according to their Surface Plasmon Resonance and spectrum by UV-Vis detection, improving the results obtained via batch characterization. This simple non-invasive approach is very useful for the LFIA development and optimization: the use of HF5-mutidetection offers a unique tool for this purpose facilitating the industrialization of the process and the relate optimization and standardization.File | Dimensione | Formato | |
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