Live-cell super-resolution imaging unconventional dynamics and assemblies of nuclear pore complexes

Super-resolution microscopy has promoted the development of cell biology, but imaging proteins with low copy numbers in cellular structures remains challenging. The limited number of designated proteins within nuclear pore complexes (NPCs) impedes continuous observation in live cells, although they are often used as a standard for evaluating various SR methods. To address this issue, we tagged POM121 with Halo-SiR and imaged it using structured illumination microscopy with sparse deconvolution (Sparse-SIM). Remarkably, POM121-SiR exhibited more than six-fold fluorescence intensity and four-fold enhanced contrast compared to the same protein labeled with tandem-linked mCherry, while showing negligible photo-bleaching during SR imaging for 200 frames. Using this technique, we discovered various types of NPCs, including ring-like and cluster-like structures, and observed dynamic remodeling along with the sequential appearance of different Nup compositions. Overall, Halo-SiR with Sparse-SIM is a potent tool for extended SR imaging of dynamic structures of NPCs in live cells, and it may also help visualize proteins with limited numbers in general.

Sparse deconvolution method for ultrasound images based on automatic estimation of reference signals

Sparse deconvolution is widely used in the field of non-destructive testing (NDT) for improving the temporal resolution. Generally, the reference signals involved in sparse deconvolution are measured from the reflection echoes of standard plane block, which cannot accurately describe the acoustic properties at different spatial positions. Therefore, the performance of sparse deconvolution will deteriorate, due to the deviations in reference signals. Meanwhile, it is inconvenient for automatic ultrasonic NDT using manual measurement of reference signals. To overcome these disadvantages, a modified sparse deconvolution based on automatic estimation of reference signals is proposed in this paper. By estimating the reference signals, the deviations would be alleviated and the accuracy of sparse deconvolution is therefore improved. Based on the automatic estimation of reference signals, regional sparse deconvolution is achievable by decomposing the whole B-scan image into small regions of interest (ROI), and the image dimensionality is significantly reduced. Since the computation time of proposed method has a power dependence on the signal length, the computation efficiency is therefore improved significantly with this strategy. The performance of proposed method is demonstrated using immersion measurement of scattering targets and steel block with side-drilled holes. The results verify that the proposed method is able to maintain the vertical resolution enhancement and noise-suppression capabilities in different scenarios.