Department of Chemistry, University of California, Berkeley, CA, 94720, USA.
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, 94720, USA.
Commun Biol. 2023 Mar 28;6(1):336. doi: 10.1038/s42003-023-04729-x.
While critical to biological processes, molecular diffusion is difficult to quantify, and spatial mapping of local diffusivity is even more challenging. Here we report a machine-learning-enabled approach, pixels-to-diffusivity (Pix2D), to directly extract the diffusion coefficient D from single-molecule images, and consequently enable super-resolved D spatial mapping. Working with single-molecule images recorded at a fixed framerate under typical single-molecule localization microscopy (SMLM) conditions, Pix2D exploits the often undesired yet evident motion blur, i.e., the convolution of single-molecule motion trajectory during the frame recording time with the diffraction-limited point spread function (PSF) of the microscope. Whereas the stochastic nature of diffusion imprints diverse diffusion trajectories to different molecules diffusing at the same given D, we construct a convolutional neural network (CNN) model that takes a stack of single-molecule images as the input and evaluates a D-value as the output. We thus validate robust D evaluation and spatial mapping with simulated data, and with experimental data successfully characterize D differences for supported lipid bilayers of different compositions and resolve gel and fluidic phases at the nanoscale.
虽然分子扩散对于生物过程至关重要，但很难对其进行定量，并且对局部扩散率进行空间映射甚至更具挑战性。在这里，我们报告了一种基于机器学习的方法，即像素到扩散率（Pix2D），可以直接从单分子图像中提取扩散系数 D，从而实现超分辨 D 空间映射。在典型的单分子定位显微镜（SMLM）条件下，以固定帧率记录单分子图像，Pix2D 利用了经常被忽视但明显的运动模糊，即单分子在帧记录时间内的运动轨迹与显微镜的衍射极限点扩散函数（PSF）的卷积。虽然扩散的随机性在不同的扩散轨迹上给同一给定 D 下扩散的不同分子留下了印记，但我们构建了一个卷积神经网络（CNN）模型，该模型将单分子图像堆栈作为输入，并将 D 值作为输出进行评估。因此，我们使用模拟数据和实验数据验证了稳健的 D 值评估和空间映射，成功地对不同组成的支撑脂质双层的 D 值差异进行了表征，并在纳米尺度上解析了凝胶相和流态相。
