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用有效介质理论解释全息分子结合测定法。

Interpreting holographic molecular binding assays with effective medium theory.

作者信息

Altman Lauren E, Grier David G

机构信息

Department of Physics and Center for Soft Matter Research, New York University, New York, NY 10003, USA.

出版信息

Biomed Opt Express. 2020 Aug 24;11(9):5225-5236. doi: 10.1364/BOE.401103. eCollection 2020 Sep 1.

DOI:10.1364/BOE.401103
PMID:33014610
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7510853/
Abstract

Holographic molecular binding assays use holographic video microscopy to directly detect molecules binding to the surfaces of micrometer-scale colloidal beads by monitoring associated changes in the beads' light-scattering properties. Holograms of individual spheres are analyzed by fitting to a generative model based on the Lorenz-Mie theory of light scattering. Each fit yields an estimate of a probe bead's diameter and refractive index with sufficient precision to watch a population of beads grow as molecules bind. Rather than modeling the molecular-scale coating, however, these fits use effective medium theory, treating the coated sphere as if it were homogeneous. This effective-sphere analysis is rapid and numerically robust and so is useful for practical implementations of label-free immunoassays. Here, we assess how measured effective-sphere properties reflect the actual properties of molecular-scale coatings by modeling coated spheres with the discrete-dipole approximation and analyzing their holograms with the effective-sphere model.

摘要

全息分子结合测定法利用全息视频显微镜,通过监测微米级胶体微珠表面光散射特性的相关变化,直接检测与微珠表面结合的分子。通过基于光散射的洛伦兹 - 米氏理论拟合生成模型,对单个球体的全息图进行分析。每次拟合都能以足够的精度得出探针微珠的直径和折射率估计值,以便观察随着分子结合微珠群体的增长情况。然而,这些拟合并非对分子尺度的涂层进行建模,而是使用有效介质理论,将涂覆球体视为均匀球体来处理。这种有效球体分析快速且数值稳健,因此对于无标记免疫测定的实际应用很有用。在此,我们通过用离散偶极近似法对涂覆球体进行建模,并使用有效球体模型分析其全息图,来评估所测量的有效球体特性如何反映分子尺度涂层的实际特性。

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本文引用的文献

1
CATCH: Characterizing and Tracking Colloids Holographically Using Deep Neural Networks.使用深度神经网络对胶体进行全息特征描述和跟踪
J Phys Chem B. 2020 Mar 5;124(9):1602-1610. doi: 10.1021/acs.jpcb.9b10463. Epub 2020 Feb 25.
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Holographic molecular binding assays.全息分子结合分析。
Sci Rep. 2020 Feb 6;10(1):1932. doi: 10.1038/s41598-020-58833-7.
3
The role of the medium in the effective-sphere interpretation of holographic particle characterization data.介质在全息粒子表征数据有效球解释中的作用。
Soft Matter. 2020 Jan 28;16(4):891-898. doi: 10.1039/c9sm01916b. Epub 2019 Dec 16.
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Optimizing the Synthesis of Monodisperse Colloidal Spheres Using Holographic Particle Characterization.利用全息粒子表征优化单分散胶体球的合成
Langmuir. 2019 May 21;35(20):6602-6609. doi: 10.1021/acs.langmuir.9b00012. Epub 2019 May 8.
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Preparation of Colloidal Organosilica Spheres through Spontaneous Emulsification.通过自发乳化制备胶体有机硅球体。
Langmuir. 2017 Aug 22;33(33):8174-8180. doi: 10.1021/acs.langmuir.7b01398. Epub 2017 Aug 10.
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Holographic characterization of colloidal fractal aggregates.胶态分形聚集体的全息特性。
Soft Matter. 2016 Oct 26;12(42):8774-8780. doi: 10.1039/c6sm01790h.
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Introduction to the Maxwell Garnett approximation: tutorial.麦克斯韦·加尼特近似法介绍:教程
J Opt Soc Am A Opt Image Sci Vis. 2016 Jul 1;33(7):1244-56. doi: 10.1364/JOSAA.33.001244.
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Holographic Characterization of Protein Aggregates.蛋白质聚集体的全息表征
J Pharm Sci. 2016 Mar;105(3):1074-85. doi: 10.1016/j.xphs.2015.12.018. Epub 2016 Feb 2.
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Machine-learning approach to holographic particle characterization.用于全息粒子表征的机器学习方法。
Opt Express. 2014 Nov 3;22(22):26884-90. doi: 10.1364/OE.22.026884.
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Fast feature identification for holographic tracking: the orientation alignment transform.用于全息跟踪的快速特征识别:方向对齐变换。
Opt Express. 2014 Jun 2;22(11):12773-8. doi: 10.1364/OE.22.012773.