We developed a computational model to simulate contours of entangled lambda DNA. These simulations were used to generate super-resolution DNA images for training a deep neural network (ANNA-PALM) to reconstruct DNA contours from localization images. Our approach enabled reliable contour prediction from microscopy images captured at fast time scale. Analysis of experimental data revealed bright and dark DNA segments, potentially linked to local microviscosity effects imposed by entanglement loci. Our integrated computational modeling and deep learning workflow can provide mapping of topological constraints on polymer motion in diverse materials.
We propose a new method to engineer point spread function (PSF) for 3D single-particle tracking using mica. The imaging system in this method uses a standard wide-field fluorescence microscope; the only difference from the ordinary is using a mica as a substrate to mount the sample instead of the coverslip. This approach would have advantages over easy-toimplement, wide axial range tracking capability, and multiplexity of tracking. We demonstrate 3D single-particle tracking in a homogeneous solution.
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