Issue 27, 2022

Colorimetric visualization of histamine secreted by basophils based on DSP-functionalized gold nanoparticles

Abstract

Histamine released by activated basophils has become an important biomarker and therapeutic target in the development of allergic diseases. To date, several gold nanoparticle (AuNP)-based nanosensors have been reported for histamine detection in foods. However, rapid, highly sensitive and direct detection of histamine in allergic diseases is still lacking due to the complexity of the physical environment. Herein, we developed a novel nanosensor for colorimetric visualization of histamine in activated basophils by simply coupling dithiobis(succinimidylpropionate) (DSP) on the surface of AuNPs (DSP-AuNPs). The DSP moiety serves as a linker and can react with the aliphatic amino group of histamine, and the imidazole ring of histamine can selectively bind with Au by means of p–p conjugation, thus inducing the aggregation of AuNPs. In this study, we experimentally proved that DSP-AuNPs showed good sensitivity and selectivity to histamine among various amino acids, including histidine. Additionally, this nanosensor displayed a rapid response to histamine with a linear range of 0.8–2.5 μM, and the limit of detection (LOD) was 0.014 μM, which is a relatively low LOD in comparison with those of other AuNP-based nanosensors. Finally, DSP-AuNPs are used, for the first time, to successfully detect endogenous histamine changes in activated basophils. Therefore, our work may provide a promising strategy to monitor histamine levels in the basophil activation test.

Graphical abstract: Colorimetric visualization of histamine secreted by basophils based on DSP-functionalized gold nanoparticles

Supplementary files

Article information

Article type
Paper
Submitted
05 Mar 2022
Accepted
08 Jun 2022
First published
30 Jun 2022

Anal. Methods, 2022,14, 2698-2702

Colorimetric visualization of histamine secreted by basophils based on DSP-functionalized gold nanoparticles

X. Ling, Y. Liu, D. Zhu, W. An, J. Geng, L. Li, C. Yu and J. Wei, Anal. Methods, 2022, 14, 2698 DOI: 10.1039/D2AY00379A

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