Browsing UMB Open Access Articles by Author "Guo, M."
A Genetically Encoded Biosensor Strategy for Quantifying Non-muscle Myosin II Phosphorylation Dynamics in Living Cells and OrganismsMarkwardt, M.L.; Snell, N.E.; Guo, M. (Elsevier B.V., 2018)Complex cell behaviors require dynamic control over non-muscle myosin II (NMMII) regulatory light chain (RLC) phosphorylation. Here, we report that RLC phosphorylation can be tracked in living cells and organisms using a homotransfer fluorescence resonance energy transfer (FRET) approach. Fluorescent protein-tagged RLCs exhibit FRET in the dephosphorylated conformation, permitting identification and quantification of RLC phosphorylation in living cells. This approach is versatile and can accommodate several different fluorescent protein colors, thus enabling multiplexed imaging with complementary biosensors. In fibroblasts, dynamic myosin phosphorylation was observed at the leading edge of migrating cells and retracting structures where it persistently colocalized with activated myosin light chain kinase. Changes in myosin phosphorylation during C. elegans embryonic development were tracked using polarization inverted selective-plane illumination microscopy (piSPIM), revealing a shift in phosphorylated myosin localization to a longitudinal orientation following the onset of twitching. Quantitative analyses further suggested that RLC phosphorylation dynamics occur independently from changes in protein expression. Copyright 2018 The Author(s)Markwardt et al.
Point supervised extended scenario nuclear analysis framework based on LSTM-CFCNSui, D.; Guo, M.; Zhang, L. (Institute of Electrical and Electronics Engineers Inc., 2020)Cells and cell like particles detection and segmentation are of significant interest to many biological and clinical studies. Traditionally, these tasks are usually performed by visual inspection, which is time consuming, labor intensity and prone to induce subjective bias between different people. These make automatic cell analysis protocols essential for large-scale and objective studies. In recent years, imaging technical has been significantly advanced following the great success by computer vision. In addition, these technologies enable the cross module microscopy analysis, and make the task of cell analysis extremely challenging. Over these decades, computer aided cell detection, counting and segmentation have evolved from earlier filter based methods to the state-of-art deep learning protocols. However, there are still few suitable frameworks that can process multiple source cell images at the same time. In this paper, we seek a different route and propose a novel efficient framework for robust cell analysis based on Long Short Term Memory Channeled Fully Convolution Neural Networks (LSTM-CFCN). The results demonstrates that our framework is able to perform most of cell detection, counting and segmentation tasks from different cell type, and it can also cover most kinds of microscopy images scenarios including dark field, bright field, pathological and electron images. We have perform substantial experiments on several benchmark datasets, the LSTM-CFCN achieves the highest or at least top-2 performance in terms of F1-score, compared with other state-of-the-art methods.