无标记有机免疫传感器多参数分析对亚pm水平色氨酸对映体的鉴别。

IF 9.1 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Matteo Genitoni, Pierpaolo Greco, Alessandro Paradisi, Matteo Sensi, Marcello Berto, Mauro Murgia, Michele Di Lauro, Carlo Augusto Bortolotti, Luciano Fadiga, Fabio Biscarini
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引用次数: 0

摘要

电解质门控有机晶体管(EGOTs)将栅极上微小的极化变化呈指数级放大为沟道电流。嫁接在EGOT门电极上的抗体能够特异性识别目标物种,但这种策略本身可能不足以从其拮抗剂中解决目标。在这里,无标记的EGOT免疫传感器被色氨酸(Trp)的抗l-对映体抗体功能化,表现出对Trp手性的敏感性。然而,在1 ~ 10 nm的浓度范围内,传递曲线的相对电流变化并不能明确区分L和D对映体。为了克服这一限制,根据我们最近的EGOT模型,对从整个转移曲线中提取的重归一化参数集应用了多元主成分分析(PCA): L和D对映体通过其主成分的符号整齐地分开。在90%的置信度和预测水平下,对映体识别的开始时间分别是下午1点和10点,至少比以前用EGOT生物传感器报道的对映体识别水平低一个数量级。在最佳拟合剂量曲线上进行的相同分析允许区分L和D对映体,检测到前所未有的100 fm的水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discrimination of Tryptophan Enantiomers at Sub-pm Level by Multiparametric Analysis of a Label-Free Organic Immunosensor.

Electrolyte-gated organic transistors (EGOTs) exponentially amplify minute polarization changes at the gate electrode into the channel current. Antibodies grafted on the EGOT gate electrode enable specific recognition of target species, yet this strategy may not be sufficient per se to resolve the target from its antagonists. Here, a label-free EGOT immunosensor is functionalized with the antibody anti-L-enantiomer of Tryptophan (Trp), exhibiting sensitivity to Trp chirality. Nevertheless, the relative current change in transfer curves does not unambiguously differentiate L from D enantiomers in the concentration range 1 fm to 10 nm. To overcome this limitation, a multivariate principal component analysis (PCA) is applied on the set of renormalized parameters, extracted from the whole transfer curves according to our recent EGOT model: both L and D enantiomers are neatly separated by the sign of their principal components. Enantiomeric discrimination onset is 1 and 10 pm at 90% level of confidence and prediction, respectively, at least one order of magnitude lower than the enantiodiscrimination levels previously reported with EGOT biosensor. The same analysis performed on the best fit dose curves allows to discriminate L and D enantiomers down to the unprecedented level of detection of 100 fm.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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