Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology (Ministry of Education), Tsinghua University, Beijing 100084, China.
National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
J Phys Chem B. 2022 Oct 6;126(39):7541-7551. doi: 10.1021/acs.jpcb.2c03243. Epub 2022 Sep 21.
Viscoelastic characterization is of great importance for the investigation of biomolecular condensates. Single-particle-tracking-based rotational diffusion analysis of single nanorods is an effective approach for quantitative viscosity measurement. However, in the case of high background and noise with high-speed image acquisition, accurate extraction of diffusivity from the data is a challenging task. Here, we develop a novel frequency-domain-based deep learning (DL) method for single nanorod rotational tracking analysis. We synthesized Brownian rotational time-series data for training, designed a data preprocessing module to reduce the effect of noise, and extracted rotational diffusion coefficient using recurrent neural networks in the frequency domain. Compared with the traditional curve-fitting-based methods, our method shows higher accuracy and a wider detection range for viscosity measurement. We verified our method using experimental data from plasmonic imaging of single gold nanorods (AuNRs) in glycerol solution and PGL droplets. Our method can be potentially applied to the viscosity measurement of different biomolecular condensates and .
粘弹性表征对于生物分子凝聚物的研究非常重要。基于单粒子跟踪的单纳米棒旋转扩散分析是一种定量测量粘度的有效方法。然而，在高速图像采集时存在高背景和噪声的情况下，从数据中准确提取扩散系数是一项具有挑战性的任务。在这里，我们开发了一种基于频域的新型深度学习（DL）方法，用于单纳米棒旋转跟踪分析。我们合成了用于训练的布朗旋转时间序列数据，设计了一个数据预处理模块来降低噪声的影响，并使用频域中的递归神经网络提取旋转扩散系数。与传统的基于曲线拟合的方法相比，我们的方法在粘度测量方面表现出更高的准确性和更宽的检测范围。我们使用等离子体成像实验数据验证了我们的方法，实验对象是甘油溶液和 PGL 液滴中的单个金纳米棒（AuNRs）。我们的方法可潜在应用于不同生物分子凝聚物的粘度测量。
